Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study

Authors:
raw_author_name: Jee Suk Chang
raw_affiliation_string: Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
raw_author_name: Jin Hee Kim
raw_affiliation_string: Department of Radiation Oncology, Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Republic of Korea
raw_author_name: Tae Hyung Kim
raw_affiliation_string: Department of Radiation Oncology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Republic of Korea
raw_author_name: Sungmin Kim
raw_affiliation_string: Department of Radiation Oncology, Dong-A University Hospital, Dong-A University College of Medicine, Busan, Republic of Korea
raw_author_name: Myungsoo Kim
raw_affiliation_string: Department of Radiation Oncology, Incheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
raw_author_name: Mi Young Kim
raw_affiliation_string: Department of Radiation Oncology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
raw_author_name: Bum-Sup Jang
raw_affiliation_string: Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
raw_author_name: Hyebin Lee
raw_affiliation_string: Department of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
raw_author_name: Sun Young Lee
raw_affiliation_string: Department of Radiation Oncology, Chonbuk National University Hospital, Jeonju, Republic of Korea
raw_author_name: Jin Sung Kim
raw_affiliation_string: Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea

Abstract: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potential IOV.In phase 1, two breast cancer cases were randomly selected and distributed to multiple institutions for contouring six clinical target volumes (CTVs) and eight OAR. In Phase 2, auto-contour sets were generated using a previously published DL Breast segmentation model and were made available for all participants. The difference in IOV of submitted contours in phases 1 and 2 was investigated quantitatively using the Dice similarity coefficient (DSC) and Hausdorff distance (HD). The qualitative analysis involved using contour heat maps to visualize the extent and location of these variations and the required modification.Over 800 pairwise comparisons were analysed for each structure in each case. Quantitative phase 2 metrics showed significant improvement in the mean DSC (from 0.69 to 0.77) and HD (from 34.9 to 17.9 mm). Quantitative analysis showed increased interobserver agreement in phase 2, specifically for CTV structures (5-19 %), leading to fewer manual adjustments. Underlying IOV differences causes were reported using a questionnaire and hierarchical clustering analysis based on the volume of CTVs.DL-based auto-contours improved the contour agreement for OARs and CTVs significantly, both qualitatively and quantitatively, suggesting its potential role in minimizing radiation therapy protocol deviation.