Development of a deep neural network derived from contours defined by consensus-based guidelines for automatic target segmentation in hepatocellular carcinoma radiotherapy: A study protocol

Objectives

To develop a fully-automated target structure contouring system trained on contours delineated according to consensus contouring guidelines. In addition, to evaluate the contouring system’s feasibility and performance during application in normal clinical operations.

Study dataset acquirement

Upper abdominal CT scans with or without contrast enhancement acquired from HCC patients who requested medical second opinion services from Shangfang Health Inc. between 1^st October 2014 and 1^st September 2017 will be retrospectively selected in this study. Patients in the supine position with arms extended overhead, and tumor stage meeting the liver-directed radiotherapy criteria (from the 5th Asia-Pacific primary liver cancer expert meeting) (Park et al., 2016) will be eligible for inclusion.

CT datasets will be stored according to the digital imaging and communications in medicine (DICOM) standards of practice. The number of slices and the slice thickness in each CT series varies from 23 to 191 and 1.0 to 8.0 mm, respectively.

Targets delineation

CT image sets acquired from each patient will be loaded into a radiation treatment planning system (Pinnacle v9.0, Philips Healthcare, Madison, WI) or MIM Maestro software v6.7.5 (MIM Software Inc., OH) for manual delineation of targets. Following nomenclature for structure naming conventions in RT (Santanam et al., 2012), liver tumors will be delineated as GTV. Thirteen abdominal OARs (liver, stomach, duodenum, spleen, left kidney, right kidney, small bowel, colon, gallbladder, esophagus, left lung, right lung and spinal cord) will be named liver, stomach, duodenum, spleen, kidney_L, kidney_R, smallbowel, colon, gallbladder, esophagus, lung_L, lung_R and spinalcord, respectively.

To ensure that a standardized approach to delineate OARs is used across radiation oncologists involved in the study, each participant will be provided with “upper abdominal normal organ contouring guidelines and atlas: A RTOG consensus” (Jabbour et al., 2014). As for the GTV delineation, it will be contoured according to the recommendation previously reported by RTOG - using the union of GTVs across all available phases of multiphasic imaging (Hong et al., 2014).

Delineating OARs using the standard abdominal soft tissue window setting: window width (WW) of 350-400 Hounsfield unit (HU), window level (WL) of 35-50 HU; for GTV, using liver window setting: WW of 150 HU, WL of 50-100 HU.

Two well-trained residents in radiation oncology will be trained by the expert physician (JZ), making sure that they understand all the requirements. Subsequently, they will manually delineate all the targets and check each other’s targets and revise them when necessary. All contours will be reviewed by an attending physician independently to detect any other gross errors (e.g., missing slices) and revise any violations or deviations from the contouring guidelines, any gross errors and violations or deviations detected during the review process will be corrected. After the attending physician’s review and revise, the contours will be reviewed and finally approved by the expert physician. These contours are to be saved as DICOM-RT structure format and transferred to an in-house web based RT-PACS platform for the fully-automated target structure contouring system (deep neural network) development.

Automated target structure contouring system development

In order to develop the auto-contouring system, the following machine learning networks will be used, including supervised and semi-supervised convolutional neural networks, recurrent neural networks, unsupervised clustering, reinforcement learning, etc. The whole technique workflow is shown in Figure 1.

Figure 1. The whole technique workflow of development of the deep neural network.

To consider the 3D structure of the CT image, 5 CT image slices were simultaneously input into the network. To increase model training efficiency, a two phases training process was implemented. In phase 1, we only identify whether this slice has an organ or GTV, which is the classification task. Then a segmentation task follows with adapted weight in last phase. Both phases were trained 200 epochs. A recurrent neural structure, such as LSTM, will be tested in research if available. After initial training, the results will be review by physician, the correction information will be recorded and feed back to automatic contouring network as the input of a reinforcement learning.

Sample size

Approximately 400 retrospective CT data sets from patient cases will be used to establish the automatic contouring system for OARs, and an additional 200 CT data sets will be needed for GTV delineation. Another 50 CT data sets will be used as an external validation data set. The sample size is informed by the existing literature (Chu et al., 2016; Hu et al., 2017; Sun et al., 2017) and by our previous work on knowledge-based radiotherapy treatment planning prediction (Fan et al., 2017).

Data analysis and statistical plan

For external validation patients, all the contours will be manually delineated by expert teams following the same process mentioned previously (in Targets delineation) and used as ground truth. In order to evaluate the performance of auto-segmentation of the deep neural network, segmentation results will be compared with the ground truth for those patients. Analysis and comparison with four methods (HD, MDA, Dice and Jaccard) will be utilized to detect the common areas of disagreement. The explanation of these indies is as follows: HD = Hausdorff Distance, the maximum distance across all points on a surface and their closest point on another surface; MDA = Mean Distance to Agreement, which is similar but using the mean; Dice = 2 * (volume of intersection of A and B) / (volume of A + volume of B); Jaccard = (volume of intersection of A and B) / (volume of union of A and B).

Ethical considerations

This research is not a clinical trial, and doesn’t involve any direct patient contact. The CT images were acquired with permission from Shangfang Health Inc., who own the data. Shangfang Health Inc. has already obtained written informed consent from the patients, who allowed their anonymized CT data to be used for scientific research purposes. Since the data were acquired during standard procedures, with patient consent for use in research, and were anonymized, this study doesn’t need any approval from a Human Research Ethics Committee.

Dissemination

The results of our research will be disseminated through presentations at conferences, such as radiation oncologist and medical physicist education and radiation oncology conferences, regionally and nationally, and through articles published in peer-reviewed journals related to the treatment of cancer using radiation.