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Choi MS, Chang JS, Kim K, Kim JH, Kim TH, Kim S, Cha H, Cho O, Choi JH, Kim M, Kim J, Kim TG, Yeo SG, Chang AR, Ahn SJ, Choi J, Kang KM, Kwon J, Koo T, Kim MY, Choi SH, Jeong BK, Jang BS, Jo IY, Lee H, Kim N, Park HJ, Im JH, Lee SW, Cho Y, Lee SY, Chang JH, Chun J, Lee EM, Kim JS, Shin KH, Kim YB. 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. Breast 2024; 73:103599. [PMID: 37992527 PMCID: PMC10700624 DOI: 10.1016/j.breast.2023.103599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/30/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023] Open
Abstract
PURPOSE 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. METHODS AND MATERIALS 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. RESULTS 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. CONCLUSION 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.
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Affiliation(s)
- Min Seo Choi
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kyubo Kim
- Department of Radiation Oncology, Ewha Womans University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Jin Hee Kim
- Department of Radiation Oncology, Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Tae Hyung Kim
- Department of Radiation Oncology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Republic of Korea
| | - Sungmin Kim
- Department of Radiation Oncology, Dong-A University Hospital, Dong-A University College of Medicine, Busan, Republic of Korea
| | - Hyejung Cha
- Department of Radiation Oncology, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea
| | - Oyeon Cho
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jin Hwa Choi
- Department of Radiation Oncology, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Myungsoo Kim
- Department of Radiation Oncology, Incheon St Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Juree Kim
- Department of Radiation Oncology, Ilsan CHA Medical Center, CHA University School of Medicine, Goyang, Republic of Korea
| | - Tae Gyu Kim
- Department of Radiation Oncology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Seung-Gu Yeo
- Department of Radiation Oncology, Soonchunhyang University College of Medicine, Soonchunhyang University Hospital, Bucheon, Republic of Korea
| | - Ah Ram Chang
- Department of Radiation Oncology, Soonchunhyang University College of Medicine, Seoul, Republic of Korea
| | - Sung-Ja Ahn
- Department of Radiation Oncology, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Jinhyun Choi
- Department of Radiation Oncology, Jeju National University Hospital, Jeju University College of Medicine, Republic of Korea
| | - Ki Mun Kang
- Gyeongsang National University Changwon Hospital, Gyeongsang National University College of Medicine, Jinju, Republic of Korea
| | - Jeanny Kwon
- Department of Radiation Oncology, Chungnam National University School of Medicine, Daejeon, Republic of Korea
| | - Taeryool Koo
- Department of Radiation Oncology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Mi Young Kim
- Department of Radiation Oncology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Seo Hee Choi
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
| | - Bae Kwon Jeong
- Department of Radiation Oncology, Gyeongsang National University Hospital, Gyeongsang National University College of Medicine, Jinju, Republic of Korea
| | - Bum-Sup Jang
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - In Young Jo
- Department of Radiation Oncology, Soonchunhyang University Hospital, Cheonan, Republic of Korea
| | - Hyebin Lee
- Department of Radiation Oncology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hae Jin Park
- Department of Radiation Oncology, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Jung Ho Im
- Department of Radiation Oncology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Sea-Won Lee
- Department of Radiation Oncology, Eunpyeong St. Mary's Hospital, Catholic University of Korea College of Medicine, Seoul, Republic of Korea
| | - Yeona Cho
- Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sun Young Lee
- Department of Radiation Oncology, Chonbuk National University Hospital, Jeonju, Republic of Korea
| | - Ji Hyun Chang
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eung Man Lee
- Department of Radiation Oncology, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Kyung Hwan Shin
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
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Vaassen F, Boukerroui D, Looney P, Canters R, Verhoeven K, Peeters S, Lubken I, Mannens J, Gooding MJ, van Elmpt W. Real-world analysis of manual editing of deep learning contouring in the thorax region. Phys Imaging Radiat Oncol 2022; 22:104-110. [PMID: 35602549 PMCID: PMC9115320 DOI: 10.1016/j.phro.2022.04.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 01/18/2023] Open
Abstract
Background and purpose User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region. Materials and methods A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed. Subsampling was performed for some OARs, using an inter-slice gap of 1-3 slices. Commonly-used whole-organ contouring assessment measures were calculated, and all cases were registered to a common reference shape per OAR to identify regions of manual adjustment. Results were expressed as the median, 10th-90th percentile of adjustment and visualized using 3D renderings. Results Per OAR, the median amount of editing was below 1 mm. However, large adjustments were found in some locations for most OARs. In general, enlarging of the auto-contours was needed. Subsampling DL-contours showed less adjustments were made in the interpolated slices compared to simulated no-subsampling for these OARs. Conclusion The real-world performance of automatic DL-contouring software was evaluated and proven useful in clinical practice. Specific regions-of-adjustment were identified per OAR in the thorax region, and separate models were found to be necessary for specific clinical indications different from training data. This analysis showed the need to perform routine clinical analysis especially when procedures or acquisition protocols change to have the best configuration of the workflow.
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Affiliation(s)
- Femke Vaassen
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | | | - Richard Canters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Karolien Verhoeven
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Stephanie Peeters
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Indra Lubken
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jolein Mannens
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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