<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.161128.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Enhancing prostate cancer detection: The role of b-value and apparent diffusion coefficient in DWI</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 1 approved]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Goveas</surname>
                        <given-names>Brian</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Dkhar</surname>
                        <given-names>Winniecia</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-5963-3230</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Kadavigere</surname>
                        <given-names>Rajagopal</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-3486-8740</uri>
                    <xref ref-type="corresp" rid="c2">b</xref>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Nayak</surname>
                        <given-names>Kaushik</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Pradhan</surname>
                        <given-names>Abhimanyu</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-2910-5338</uri>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Venugopal</surname>
                        <given-names>Anand</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Shastry</surname>
                        <given-names>Praveen</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Barnes Abraham</surname>
                        <given-names>Neil</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Validation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India</aff>
                <aff id="a2">
                    <label>2</label>Radiodiagnosis and Imaging, Katsurba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India</aff>
                <aff id="a3">
                    <label>3</label>Radiodiagnosis, Manipal HealthMap, Manipal, Karnataka, 576104, India</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:winniecia.dkhar@manipal.edu">winniecia.dkhar@manipal.edu</email>
                </corresp>
                <corresp id="c2">
                    <label>b</label>
                    <email xlink:href="mailto:rajarad@gmail.com">rajarad@gmail.com</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>4</day>
                <month>2</month>
                <year>2025</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2025</year>
            </pub-date>
            <volume>14</volume>
            <elocation-id>155</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>23</day>
                    <month>1</month>
                    <year>2025</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2025 Goveas B et al.</copyright-statement>
                <copyright-year>2025</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/14-155/pdf"/>
            <abstract>
                <sec>
                    <title>Background</title>
                    <p>Magnetic Resonance Imaging (MRI) is a highly effective tool for the detection of prostate cancer (PCa). Diffusion-weighted MRI (DW-MRI) is a sensitive technique that depends on the b value and apparent diffusion coefficient (ADC) value for the diagnosis of PCa. The main objective of this study was to determine the optimal b-value and apparent diffusion coefficient (ADC) value in DW-MRI for the diagnosis of prostate cancer.</p>
                </sec>
                <sec>
                    <title>Methods</title>
                    <p>A prospective study including 26 male participants were conducted. MRI examinations were performed with T2 fat saturation sequences, and Diffusion weighted imaging (DWI) sequences with b-values (800, 1000, 1500, and 2000 mm
                        <sup>2</sup>/s) were used, and the corresponding ADC maps were calculated. Qualitative and quantitative analyses were conducted.</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>According to the present study, a b-value of 0,1500 mm
                        <sup>2</sup>/s exhibited the highest Signal-to-Noise Ratio (SNR), Signal Intensity Ratio (SIR), and Contrast-to-Noise Ratio (CNR). Area Under the Curve (AUC) for 0,1500 mm
                        <sup>2</sup>/s was 0.80, indicating a high diagnostic accuracy for prostate cancer.</p>
                </sec>
                <sec>
                    <title>Conclusion</title>
                    <p>DWI with a b-value of 1500 mm
                        <sup>2</sup>/s provides good diagnostic accuracy for differential diagnosis of prostate lesions. DWI is a crucial sequence in multiparametric MRI of the prostate and offers detailed information that enhances the accuracy of prostate cancer diagnosis and management.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Prostate Cancer</kwd>
                <kwd>Magnetic Resonance Imaging</kwd>
                <kwd>Diffusion Weighted Imaging</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec5" sec-type="intro">
            <title>Introduction</title>
            <p>MRI is a highly effective tool for the detection, treatment planning, and follow-up of prostate cancer (PCa), but its acceptance is not universal. Diffusion-weighted MRI (DW-MRI) can determine the distribution of water in tissues as well as the extracellular space and cell density within the tissues.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup> Diffusion-weighted imaging is sensitive to the movement of water molecules at the diffusion scale, where it focuses on the Brownian movement of water molecules and has a three-dimensional process that quantifies the diffusion index. This index reflects the apparent mean diffusivity, commonly referred to as the Apparent Diffusion Coefficient (ADC), which estimates the extent of diffusion along three orthogonal directions.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>
                </sup> The sensitivity of this sequences depends upon the factor which is known as b-value. Diffusion weighted imaging (DWI) sequence is sensitive to prostate lesion detection, which can be adjusted by manipulating an extrinsic parameter known as the b-value. Higher b-values produce a stronger diffusion-weighted signal but result in a lower signal-to-noise ratio (SNR), whereas lower b-values are influenced by perfusion, which affects the sensitivity of diffusion sequences.
                <sup>
                    <xref ref-type="bibr" rid="ref3">3</xref>
                </sup> Despite its well-established diagnostic status in oncology, DWI presents challenges in its clinical application for evaluating prostate cancer owing to technical limitations and a lack of standardized protocols. This technique utilizes Echo Planar Imaging (EPI), which allows the rapid acquisition of images without the need for contrast agents.
                <sup>
                    <xref ref-type="bibr" rid="ref4">4</xref>,
                    <xref ref-type="bibr" rid="ref5">5</xref>
                </sup> DWI has been used for prostate imaging in several previous studies; however, its sensitivity and specificity have varied, which is likely the result of variations in technical parameters, such as the selection of b values, strength of the magnetic gradient, and methods for calculating ADC values in the region of interest.
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>,
                    <xref ref-type="bibr" rid="ref7">7</xref>
                </sup> The current literature lacks research on the optimal b-values and ADC values for prostate cancer screening and diagnosis. Establishing a single optimal b-value is crucial for reducing motion artifacts and time. Diffusion-weighted imaging (DWI) is particularly valuable for patients who are unable to undergo contrast-enhanced studies and may enhance the positive predictive value (PPV) for detecting prostate lesions. Thus, further investigations are essential to establish a standardized protocol. The main objective of the present study was to determine the diagnostic accuracy of the optimal b-value and ADC value of DWI for the detection of prostate cancer.</p>
        </sec>
        <sec id="sec6" sec-type="methods">
            <title>Methods</title>
            <p>Ethical approval for this prospective study was obtained from the Institutional Ethics Committee (IEC 126/2023) of Kasturba Hospital, Manipal, India, for data collection, on 4/05/2023. All participants were fully informed about the study&#x2019;s objectives and procedures. Written informed consent was obtained in compliance with the ethical principles set forth in the Declaration of Helsinki. This study was registered in the Clinical Trial Registry of India (CTRI).</p>
            <sec id="sec7">
                <title>Subjects</title>
                <p>The study included 26 male subjects age group&#x2013;35-75 years (mean&#x00b1;SD, 58&#x00b1;2 years) who had a confirmed diagnosis of prostate cancer on ultrasonography and were then recruited for MRI. Subjects who had undergone prostate surgery, chemotherapy, or radiotherapy were excluded.</p>
            </sec>
            <sec id="sec8">
                <title>MRI techniques</title>
                <p>MRI examinations were performed with a Philips Achieva&#x00a9; 1.5Tesla MRI - (Philips, Netherlands) and United Imaging uMR
                    <sup>&#x00ae;</sup>780uCS 3.0 Tesla - Tesla MRI (Shanghai United Imaging, China). A 12-channel pelvic phased-array coil is used. The examination protocol consists of a conventional pulse sequence - Axial T2W (Philips-TR/TE: 2494/100 ms, slice thickness: 3.5 mm, matrix, 224 &#x00d7; 199; NEX, 2; United-TR/TE, 4800/115 ms; slice thickness, 3 mm; matrix, 224 &#x00d7; 199; NEX, 2) for lesion localization and lesion size measurement, and DW sequence (Philips TR/TE: 5193/72 ms, flip angle: 90&#x00b0;, slice thickness: 4 mm, matrix: 108 &#x00d7; 86, signal averages: 1.011, United-TR/TE: 4520/73 ms, flip angle: 90&#x00b0;, slice thickness: 4 mm, matrix: 108 &#x00d7; 86, signal averages: 1.011) with a combination of four b-values (b=0, 800,1000,1500 and 2000 mm
                    <sup>2</sup>/s). The diffusion series was then registered before generating the corresponding ADC maps for each b-value.</p>
            </sec>
            <sec id="sec9">
                <title>Image analysis</title>
                <p>Qualitative and quantitative approaches were integrated in this study to provide a comprehensive assessment of image analysis. Qualitative analysis can provide context and insight, whereas quantitative analysis can offer precise and reproducible measurements. Qualitative image analysis was performed in the T2 FS sequence for localization and measurement of the lesion size in its maximum dimension in centimetres. The two radiologists, each with more than 10 years of experience, were blinded and analysed the DWI images at all different b-values. The image quality was assessed using a 5-point Likert scale, in which 1 represents unacceptable image quality, 2 = suboptimal, 3 = average, 4 = acceptable, and 5 = excellent, based on subjective SNR.
                    <sup>
                        <xref ref-type="bibr" rid="ref8">8</xref>
                    </sup>
                </p>
                <p>Quantitative analysis was conducted by measuring the SI of the lesion, normal glandular tissue, and background noise, by placing the ROI on the acquired images. The SI values were used to compute the signal to intensity ratio (SIR) using the formula SIR= signal intensity of lesion/signal intensity of background noise, and contrast to noise ratio (CNR) using the formula signal intensity of lesion &#x2013; signal intensity of tissue/standard deviation of background noise. The ADC values of the prostate lesions were calculated by drawing three ROIs within the lesion or tumor and three within the normal tissue; these values were averaged for each b-value across the ROIs. Histopathology reports were collected for all patients as part of the investigation.</p>
            </sec>
            <sec id="sec10">
                <title>Statistical analysis</title>
                <p>Data analysis was conducted using Statistical Package for Social Science (SPSS) version 16.0.
                    <sup>
                        <xref ref-type="bibr" rid="ref9">9</xref>
                    </sup> The inter-rate reliability of qualitative items (i.e., image quality) was determined using the kappa statistic.
                    <sup>
                        <xref ref-type="bibr" rid="ref10">10</xref>
                    </sup> Descriptive statistics were analyzed to determine the Mean and Standard Deviation of the ADC values of the prostate lesions. To determine the ADC cut-off value, receiver operating characteristic (ROC) curves were used, and the area under the curve (AUC) was used to calculate the sensitivities, specificities, and positive predictive values (PPV) of the multiple b values to determine the threshold ADC values. Youden&#x2019;s index (J) was used to evaluate the diagnostic performance level (optimal b value) for the detection and differential diagnosis of diseases.</p>
            </sec>
        </sec>
        <sec id="sec11" sec-type="results">
            <title>Results</title>
            <p>A total of 26 subjects were included in this study, 20 of whom had malignant lesions and 6 of which were benign lesions. To evaluate the image quality of DWI with different b values, both subjective and quantitative evaluations were conducted. 
                <xref ref-type="table" rid="T1">
Table 1</xref> illustrates the subjective SNR based on the differences between the signal intensity in the region of interest and background tissue. The Kappa values for b value of 800, 1000, 1500, and 2000 mm
                <sup>2</sup>/s were 0.77, 0.75, 0.64, and 0.64, respectively, which indicates moderate agreement across all b-values. To distinguish lesions from normal tissue, Kappa values varied from 0.78, 0.75, 0.77, and 0.79 for b-values of 800,1000,1500, and 2000, respectively. In the zone of the prostate, b = 1500 mm
                <sup>2</sup>/s exhibited a higher inter reading agreement with a Kappa value of 0.79 compared with other b values. In addition, it was noted that the Geometric Distortion for b values 800 &amp; 2000 mm
                <sup>2</sup>/s had a higher inter reading agreement, with Kappa values of 0.9 and 0.93 as compared to the other b values. A quantitative analysis of the SIR, SNR, and CNR of prostate lesions from the DWI sequence is presented in 
                <xref ref-type="table" rid="T2">
Table 2</xref>. Compared to other b-values, the b-value of 1500 mm
                <sup>2</sup>/s showed excellent signal intensity with minimal noise and excellent contrast differentiation between the lesions and normal tissue. 
                <xref ref-type="table" rid="T3">
Table 3</xref> presents a quantitative analysis of the ADC values for prostatic lesions and normal tissues, including the b-values (800,1000,1500,2000 mm
                <sup>2</sup>/s). The mean ADC values for normal tissues were 1.402&#x00b1;0.20 mm
                <sup>2</sup>/s, 1.606&#x00b1;0.18 mm
                <sup>2</sup>/s, 1.416&#x00b1;0.15 mm
                <sup>2</sup>/s, and 1.25&#x00b1;0.20 mm
                <sup>2</sup>/s for b-values of 800, 1000, 1500, and 2000 mm
                <sup>2</sup>/s, respectively. It was noted that the mean ADC values for benign lesions were 0.708 &#x00d7; 0.149 mm
                <sup>2</sup>/s, 0.839 &#x00d7; 0.15 mm
                <sup>2</sup>/s, 0.665 &#x00d7; 0.15 mm
                <sup>2</sup>/s, and 0.59 &#x00d7; 0.19 mm
                <sup>2</sup>/s for the corresponding b-values. In a similar manner, the mean ADC values of malignant lesions were 0.4949 mm
                <sup>2</sup>/s, 0.014 mm
                <sup>2</sup>/s, 0.52 mm
                <sup>2</sup>/s, and 0.48 mm
                <sup>2</sup>/s, respectively. Therefore, the ADC value at b-1500 mm
                <sup>2</sup>/s was statistically significant for differentiating benign from malignant lesions. In this analysis, it was found that the mean ADC values decreased as the b-value increased, with malignant lesions exhibiting consistently lower ADC values. For a b-value of 800 mm
                <sup>2</sup>/s, the ADC cut-off threshold value was 0.481 &#x00d7; 10
                <sup>-3</sup> mm
                <sup>2</sup>/s which yielded 90.9 sensitivity and 83.3%; for a b-value of 1000 mm
                <sup>2</sup>/s, the ADC cut-off threshold value was 0.510 &#x00d7; 10
                <sup>-3</sup> mm
                <sup>2</sup>/s with 90.9% sensitivity and 79% specificity; for a b-value of 1500 mm
                <sup>2</sup>/s, the ADC cut-off threshold value was 0.389 &#x00d7; 10
                <sup>-3</sup> mm
                <sup>2</sup>/s with 94% sensitivity and 87% specificity; and for a b-value of 2000 mm
                <sup>2</sup>/s the ADC cut-off threshold value was 0.351 &#x00d7; 10
                <sup>-3</sup> mm
                <sup>2</sup>/s with 77% sensitivity and 88% specificity.</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>
Table 1. </label>
                <caption>
                    <title>The Kappa value of the subjective assessment of prostate lesions on a DWI.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">
b value (mm
                                <sup>2</sup>/s)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Criteria 1 Subjective SNR</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Criteria 2 Lesion V/s tissue differentiation</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Criteria 3 Zonal Anatomy</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Criteria 4 Geometric Distortion</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,800</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.78</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.9</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,1000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.81</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,1500</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.79</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.74</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,2000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.79</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.78</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.93</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <table-wrap id="T2" orientation="portrait" position="float">
                <label>
Table 2. </label>
                <caption>
                    <title>Quantitative assessment of Signal Intensity Ratio (SIR), Signal to Noise Ratio (SNR) and Contrast to Noise Ratio (CNR) of the prostate lesion on diffusion weighted images with respect to multiple b-values.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">b value (mm
                                <sup>2</sup>/s)</th>
                            <th align="left" colspan="3" rowspan="1" valign="top">Image Criteria&#x2019;s</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Signal Intensity Ratio</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Signal to Noise Ratio</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Contrast to Noise Ratio</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,800</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.86&#x00b1;0.540</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.31&#x00b1;2.251</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">87.04&#x00b1;70.778</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,1000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.63&#x00b1;0.423</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.24&#x00b1;1.632</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">84.75&#x00b1;91.966</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,1500</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.51&#x00b1;0.630</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.99&#x00b1;2.749</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">117.43&#x00b1;60.019</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0,2000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.46&#x00b1;0.763</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.19&#x00b1;2.891</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">93.05&#x00b1;70.279</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <table-wrap id="T3" orientation="portrait" position="float">
                <label>
Table 3. </label>
                <caption>
                    <title>Mean and Range of ADC values of Benign, Malignant and Normal Prostate tissues at multiple b value in MR Diffusion Weighted Imaging.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">b value (mm
                                <sup>2</sup>/s)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Prostate Tissue</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">ADC Mean &#x00b1; SD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
p-value
</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="3" valign="top">0, 800</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Normal Tissue</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.402 &#x00b1; 0.20</td>
                            <td align="left" colspan="1" rowspan="3" valign="top">&gt;0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Benign</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.708 &#x00b1; 0.14</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Malignant</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.524 &#x00b1; 0.03</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="3" valign="top">0, 1000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Normal Tissue</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.606 &#x00b1; 0.18</td>
                            <td align="left" colspan="1" rowspan="3" valign="top">&gt;0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Benign</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.839 &#x00b1; 0.15</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Malignant</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.79 &#x00b1; 0.014</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="3" valign="top">0, 1500</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Normal Tissue</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.416 &#x00b1; 0.15</td>
                            <td align="left" colspan="1" rowspan="3" valign="top">&lt;0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Benign</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.665 &#x00b1; 0.15</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Malignant</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.52 &#x00b1; 0.16</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="3" valign="top">0, 2000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">Normal Tissue</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.25 &#x00b1; 0.20</td>
                            <td align="left" colspan="1" rowspan="3" valign="top">&gt;0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Benign</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.59 &#x00b1; 0.19</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Malignant</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.48 &#x00b1; 0.09</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
            <p>Based on the Receiver Operating Characteristic (ROC) curve (
                <xref ref-type="fig" rid="f1">
Figure 1</xref>), the area under the curve (AUC) was 0.90, 0.71, 0.80, and 0.64 for b-values of 800, 1000, 1500, and 2000 mm&#x00b2;/s, respectively as shown in 
                <xref ref-type="table" rid="T4">
Table 4</xref>. To distinguish between benign and malignant prostate lesions, the AUC was significantly larger for b-values of 800 and 1500 mm
                <sup>2</sup>/s. Additionally, the prime threshold points and the diagnostic power of each b value were ascertained by analyzing the ADC readings at various cut-off points.</p>
            <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                <label>
Figure 1. </label>
                <caption>
                    <title>Receiver Operating Characteristic (ROC) curves derived from ADC values in differential diagnosis of benign from malignant lesions for b value of (a). 0,800 mm
                        <sup>2</sup>/s; (b). 0, 1000 mm
                        <sup>2</sup>/s; (c). 0, 1500 mm
                        <sup>2</sup>/s; and (d). 0, 2000 mm
                        <sup>2</sup>/s.</title>
                </caption>
                <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/177120/71301b3c-0e66-4905-a8e8-7fb3b3f1ec0a_figure1.gif"/>
            </fig>
            <table-wrap id="T4" orientation="portrait" position="float">
                <label>
Table 4. </label>
                <caption>
                    <title>Cut off Threshold value, Sensitivity, Specificity at multiple b-value for distinguishing between Benign and Malignant Prostate Lesions at different b values.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">
b value (mm
                                <sup>2</sup>/s)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">AUC</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">ADC- Cut Off X 10
                                <sup>&#x2013;3</sup>mm
                                <sup>2</sup>/s Mean SD</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Sensitivity (%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
Specificity (%)</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0, 800</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.90</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.481</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">83.3</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0, 1000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.71</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.510</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">79</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0, 1500</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.80</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.389</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">94</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">87</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">0, 2000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.351</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">88</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
        </sec>
        <sec id="sec12" sec-type="discussion">
            <title>Discussion</title>
            <p>MRI is an extensively accepted diagnostic tool for assessing prostate tissues and anomalies. DWI has a significant potential for assessing the structural properties of tissues and characterizing lesions. The effective diagnosis of a lesion requires its detection, and the b-value in DWI is a crucial factor in determining lesion conspicuity. In the present study, we observed that b-1500 mm
                <sup>2</sup>/s had the highest SNR, SIR, and CNR in comparison to the other b values. Despite the intermediate kappa values across b-values, the high SNR, CNR, and SIR suggest that b-1500 mm
                <sup>2</sup>/s has superior image quality, indicating that the sensitivity of the diffusion weighted imaging is heavily influenced by the b-value. Hence, selecting a smaller b-value results in considerable signal attenuation owing to the high mobility of water molecules. We observed that increasing the b-value decreased the SNR of the image, which is consistent with the findings of previous studies.
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup>
            </p>
            <p>A recent study demonstrated the significance of b-values in the detection of prostate lesions in DWI sequences. We found that b-values of 0 and 1500 mm
                <sup>2</sup>/s yielded optimal image quality. However, in the study reported by Kitajima et al.,
                <sup>
                    <xref ref-type="bibr" rid="ref10">10</xref>
                </sup> noted that b1000 mm
                <sup>2</sup>/s had a better SNR (48.7
                <bold>&#x00b1;</bold>13.5 and 33.2
                <bold>&#x00b1;</bold>7.9 for cancerous and non-cancerous tissue) and CNR of (15.6&#x00b1;8.1) compared to b2000 mm
                <sup>2</sup>/s. In contrast, Nagayama et al.
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>
                </sup> determined that b800 offers a higher SNR and fewer artifacts when compared with higher b-values for the mapping of ADCs.</p>
            <p>In the study by Rezaeian et al.,
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> b 1200 mm
                <sup>2</sup>/s was found to be an effective differential diagnosis technique owing to its low b-value, which allows DW images to show both extravascular molecular diffusion and perfusion characteristics, thus reducing the diagnostic accuracy of ADC values in distinguishing prostate cancer from healthy tissue. In a similar study, Rosenkranz et al.
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup> indicated that prostate cancer diagnosis is most effective, with a b-value between 1500 and 2000 mm
                <sup>2</sup>/s. In contrast, the higher b-values (3000-5000 mm
                <sup>2</sup>/s) demonstrated inferior performance owing to inadequate or excessive signal suppression, resulting in poor anatomical clarity. According to Seung Soo Lee et al.
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup> conducted a study comparing b1000 with b1800 and found that b1800 had improved accuracy and detection rates for lesions. In addition, we observed an increase in the accuracy rate for lesions classified as PI-RADS 4 or 5. As recommended by Chandarana et al.,
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> multiparametric prostate MRI protocols should incorporate DWI sequences with b-values greater than 1000 mm
                <sup>2</sup>/s for the effective differentiation of normal tissue from lesions, both benign and malignant, in which, according to our study, the optimal b-value is 0,1500 mm
                <sup>2</sup>/sec. Based on the quantitative analysis of ADC values for b-values of 800, 1000, 1500, and 2000 mm
                <sup>2</sup>/s obtained in the present study, it was shown that the b1500 ADC value is optimal for the differential diagnosis of prostate lesions (
                <xref ref-type="fig" rid="f2">
Figure 2</xref>). For b value of 0,1500 mm
                <sup>2</sup>/s, the mean ADC values were 1.416 &#x00b1; 0.15 mm
                <sup>2</sup>/s for normal tissue, 0.665 &#x00b1; 0.15 mm
                <sup>2</sup>/s for benign lesions, and 0.52 &#x00b1; 0.16 mm
                <sup>2</sup>/s for malignant lesions, with the threshold cut off ADC value of 0.389 &#x00d7; 10
                <sup>&#x2212;3</sup> mm
                <sup>2</sup>/s, with of 94% sensitivity and 87%specificity. It was also found that for normal tissue, benign and malignant ADC values decreased with increasing b values, which may be due to perfusion or diffusion, as reported by Abbas Rezaeian et al.
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup>
            </p>
            <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                <label>
Figure 2. </label>
                <caption>
                    <title>A 72-year-old male patient with difficulty urinating, the biochemistry revealed a PSA level of 58 mg/dl.</title>
                    <p>Following a biopsy, the patient was diagnosed with prostatic acinar adenocarcinoma with a Gleason score of (4+3) = 7. For further investigation, the patient was referred for MRI Prostate. (a) Axial T2 fat suppression sequence showed a lesion with a length of 20.1mm, clearly delineating its boundaries. (b) The DWI with b value of 1500 mm
                        <sup>2</sup>/s demonstrated values of 0.511&#x00d7;10
                        <sup>-3</sup>mm
                        <sup>2</sup>/s with decreased SNR and good CNR.</p>
                </caption>
                <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/177120/71301b3c-0e66-4905-a8e8-7fb3b3f1ec0a_figure2.gif"/>
            </fig>
            <p>The study by Amol Madanlal et al.
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup> found that the with ADC values for malignant lesions were significantly lower (0.75 &#x00b1; 0.19) compared to benign lesions (1.14 &#x00b1; 0.14), with high sensitivity of 82.98%, specificity of 89.47%, and a positive predictive value of 95.12% in the differentiation between benign and malignant lesions with b value of 1000 mm
                <sup>2</sup>/s. Kazuhiro Kitajima et al
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup> reported an ADC cut-off value of 1.14 &#x00d7; 10
                <sup>&#x2212;3</sup> mm
                <sup>2</sup>/s with a b-value of 1000 mm
                <sup>2</sup>/s, in which malignant tissues exhibited significantly lower ADC values of 0.82 &#x00b1; 0.27 mm
                <sup>2</sup>/s compared to benign tissues with 1.69 &#x00b1; 0.23 mm
                <sup>2</sup>/s. Abbas Rezaeian et al,
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> reported the ADC for the malignant lesions to be 0.87 &#x00b1;0.13 mm
                <sup>2</sup>/s and for benign lesion 1.43&#x00b1;0.12 mm
                <sup>2</sup>/s, with the ADC cut-off value of 0.94 &#x00d7; 10
                <sup>&#x2212;3</sup> mm
                <sup>2</sup>/s at a b-value of 1200 mm
                <sup>2</sup>/s, achieving 90.2% sensitivity, 92.6% specificity, and 91% overall accuracy indicating good diagnostic sequences for differential diagnosis of prostate lesions. Masako Nagayama et al,
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup> reported mean ADC values of 1.00 &#x00b1; 0.22 mm
                <sup>2</sup>/s for malignant lesions and 1.56 &#x00b1; 0.14 mm
                <sup>2</sup>/s for benign lesions, with a threshold cutoff value of 1.35 &#x00d7; 10
                <sup>&#x2212;3</sup> mm
                <sup>2</sup>/s, yielding sensitivity, specificity, and accuracy of 88%, 96%, and 93%, respectively. In addition, significant changes in ADC values may be attributed to necrosis or marked fibrosis, which may affect water diffusion or restriction. According to our analysis, there were slight differences in the threshold cut-off value in all previous studies, which could be attributed to different methods of calculating the qualitative ADC values, small sample sizes, and stages of cancer, in which more in-depth research needs to be conducted.</p>
            <p>We observed that MRI strength did not influence ADC values in the differential diagnosis of prostate cancer. In contrast, a higher magnetic field strength of 3T provided better image quality owing to the improvements in the CNR and SNR. Significant changes in ADC values can occur because of necrosis or marked fibrosis, which affects water diffusion or restriction. The most notable benefit of DWI is that it can be easily integrated into screening protocols for high-risk populations, and when contrast-enhanced imaging is contraindicated, DWI is more reliable than T1- and T2-weighted imaging for the detection of prostate cancer. DWI can quantitatively characterize tumors (ADC value); therefore, it can be used as an alternative to invasive procedures, such as biopsies, which can cause incontinence, erectile dysfunction, infection, and septic shock.</p>
            <p>This study has some limitations, including the small sample size and lack of an endorectal coil, which could have enhanced the image quality and prostate cancer localization using a dedicated coil.</p>
        </sec>
        <sec id="sec13" sec-type="conclusion">
            <title>Conclusion</title>
            <p>Diffusion-weighted sequencing (DWI) in magnetic resonance imaging (MRI) is a valuable tool for both qualitative and quantitative evaluation of prostate pathology. According to our study, the optimal b-value for the detection and differential diagnosis of prostate lesions was 0,1500 mm
                <sup>2</sup>/s. This sequence has the potential to enhance the positive predictive value of prostate cancer screening, and because it requires a short scan time and is highly sensitive, it can be used as a screening tool for high-risk populations. Standardization of b-value will allow for improved inter-study comparisons of the diagnostic accuracy of diffusion-weighted MR prostate imaging. Normalized ADC values can assist in the differential diagnosis of prostate lesions and tumors. Overall, DWI is a crucial sequence in multiparametric MRI (mpMRI) of the prostate, offering detailed information that enhances the accuracy of prostate cancer diagnosis and management.</p>
        </sec>
        <sec id="sec14">
            <title>Ethics and consent</title>
            <p>Ethical approval for this prospective study was obtained from the Institutional Ethics Committee (IEC 126/2023) of Kasturba Hospital, Manipal, India, on 4/05/2023. This study was registered in the Clinical Trial Registry of India (CTRI) and approval was received on the 09
                <sup>th</sup> of June 2023, in which data collection was started on the 15
                <sup>th</sup> of June 2023. All participants were fully informed about the study&#x2019;s objectives and procedures. Written informed consent was obtained in compliance with the ethical principles set forth in the Declaration of Helsinki.</p>
        </sec>
    </body>
    <back>
        <sec id="sec17" sec-type="data-availability">
            <title>Data availability</title>
            <sec id="sec18">
                <title>Underlying data</title>
                <p>Figshare: Annotated data set, 
                    <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.6084/m9.figshare.28219067.v2">https://doi.org/10.6084/m9.figshare.28219067.v2</ext-link>.
                    <sup>
                        <xref ref-type="bibr" rid="ref15">15</xref>
                    </sup>
                </p>
                <p>This project contains the following underlying data:
                    <list list-type="bullet">
                        <list-item>
                            <label>&#x2022;</label>
                            <p>The data consist of the qualitative and quantitative values of DWI</p>
                        </list-item>
                    </list>
                </p>
                <p>Data are available under the terms of the 
                    <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International license</ext-link> (CC-BY 4.0).</p>
            </sec>
        </sec>
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    <sub-article article-type="reviewer-report" id="report365942">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.177120.r365942</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Debnath</surname>
                        <given-names>Manna</given-names>
                    </name>
                    <xref ref-type="aff" rid="r365942a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-2874-5464</uri>
                </contrib>
                <aff id="r365942a1">
                    <label>1</label>Bapubhai Desaibhai Patel Institute of Paramedical Sciences, Charotar University of Science and Technology, Changa, Gujarat, India</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>26</day>
                <month>2</month>
                <year>2025</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2025 Debnath M</copyright-statement>
                <copyright-year>2025</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport365942" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.161128.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>1. The abstract is well written, highlighting this research article's overall summary.</p>
            <p> 2. Is there any Indian study published on prostate cancer detection using DWI? If so, please cite the study in the Introduction section and provide a detailed overview of the Indian perspective.</p>
            <p> 3. Page no. 3, in the MRI techniques section, 2
                <sup>nd</sup> last line, please correct the sentence as with a combination of 
                <bold>five</bold> b-values (b=0, 800,1000,1500 and 2000 mm2/s).</p>
            <p> 4. Page no. 3, in the Image analysis section, 2
                <sup>nd</sup> paragraph, expand SI. Most probably it is Signal Intensity.</p>
            <p> 5. On page 4, in the Results section, first paragraph, the sentence 'The mean ADC values for benign lesions were 0.708 &#x00d7; 0.149 mm&#x00b2;/s, 0.839 &#x00d7; 0.15 mm&#x00b2;/s, 0.665 &#x00d7; 0.15 mm&#x00b2;/s, and 0.59 &#x00d7; 0.19 mm&#x00b2;/s for the corresponding b-values' should be corrected to use the '&#x00b1;' sign instead of '&#x00d7;', as the author has reported mean and standard deviation (SD). Additionally, in the b = 800 mm&#x00b2;/s value, the SD is written as 0.149 in the text but 0.14 in Table 3. Please ensure uniform decimal formatting throughout the paper to avoid reader confusion.</p>
            <p> 6. In the very next line of page 4, in the Results section, the sentence 'In a similar manner, the mean ADC values of malignant lesions were 0.4949 mm&#x00b2;/s, 0.014 mm&#x00b2;/s, 0.52 mm&#x00b2;/s, and 0.48 mm&#x00b2;/s, respectively.' contains discrepancies between the data reported in the text and the values presented in Table 3. Please rectify these inconsistencies to ensure accuracy.</p>
            <p> 7. The discussion and conclusion are well written. However, I suggest adding 2&#x2013;3 lines on future recommendations that could guide further research on prostate imaging using MRI.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Yes</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Yes</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Medical Imaging Technology_MRI</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.</p>
        </body>
    </sub-article>
</article>
