Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. (February 2022)

• Record Type: Journal Article
• Authors: Marin, Thibault; Zhuo, Yue; Lahoud, Rita Maria; Tian, Fei; Ma, Xiaoyue; Xing, Fangxu; Moteabbed, Maryam; Liu, Xiaofeng; Grogg, Kira; Shusharina, Nadya; Woo, Jonghye; Lim, Ruth; Ma, Chao; Chen, Yen-Lin E.; El Fakhri, Georges
• Is Part Of: Radiotherapy and oncology. Volume 167(2022)
• Page Start: 269
• Page End: 276
• Publication Date: 2022-02
• Subjects: Sarcoma -- Radiotherapy planning -- Computer-assisted -- Deep learning; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2021.09.034
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: Deep-learning based GTV delineation modeling reader variability can predict confidence levels in GTV contours. Learning of GTV confidence levels can be performed with a modified U-Net with an ordinal loss combined with a training strategy using discretization of target GTV confidence levels. The agreement between CTV drawn from human and predicted GTV contours is even higher than the agreement between the GTV contours, approaching 90% Dice score. Abstract: Background and purpose: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performed manually which exposes two major issues: cost and reproducibility. Delineation is a time-consuming process that is subject to inter- and intra-observer variability. While methods have been proposed to predict GTV contours, typical approaches ignore variability and therefore fail to utilize the valuable confidence information offered by multiple contours. Materials and methods: In this work we propose an automatic GTV contouring method for soft-tissue sarcomas from X-ray computed tomography (CT) images, using deep learning by integrating inter- and intra-observer variability in the learned model. Sixty-eight patients with soft tissue and bone sarcomas were considered in this evaluation, all underwent pre-operative CT imaging used to perform GTV delineation. Four radiation oncologists and radiologists performed three contouring trials each for all patients. We quantify variability by defining confidence levels based on the frequency of inclusion of a given voxel into the GTV and use a deep convolutional neural network to learn GTV confidence maps. Results: Results were compared to confidence maps from the four readers as well as ground-truth consensus contours established jointly by all readers. The resulting continuous Dice score between predicted and true confidence maps was 87% and the Hausdorff distance was 14 mm. Conclusion: Results demonstrate the ability of the proposed method to predict accurate contours while utilizing variability and as such it can be used to improve clinical workflow.