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 [version 1; peer review: 1 approved, 1 approved with reservations]

• Published in: F1000 research Vol. 6; p. 1929
• Main Authors: Zhao, Jiandong, Wang, Jiazhou, Cheng, Mingxia
• Format: Journal Article
• Language: English
• Published: 2017
• Subjects: Bioinformatics; Liver Failure & Liver Disease; automatic target segmentation; hepatocellular carcinoma; artificial intelligence; deep neural network; radiation therapy
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.12892.1

Hepatocellular carcinoma (HCC) is a leading cause of cancer death in China and around the world. Tumoricidal doses of modern radiation therapy (RT) can now be safely delivered with excellent local control and minimal toxicity. Delivering adequate doses of radiation to the primary tumor, while preserving adjacent healthy organs, depends on accurate target identification. In recent years, different novel machine learning techniques, including artificial intelligence technology, have been exploited in RT with impressive results in automatic image segmentation. If the machine learning algorithms are trained on delineated contours, according to consensus contouring guidelines, it promises greatly reduced interobserver and intraobserver variability in target delineation, thus substantially improving the quality and efficiency of HCC radiotherapy. This study protocol proposes to develop a fully-automated target structure contouring system, which is based on deep neural networks trained on contours delineated according to consensus contouring guidelines in HCC radiotherapy. In addition, the study will evaluate the contouring system's feasibility and performance during application in normal clinical operations. The study is ongoing (data analysis).