Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning

References

• Cardenas CE, Blinde SE, Mohamed ASR, et al. Comprehensive quantitative evaluation of variability in magnetic resonance-guided delineation of oropharyngeal gross tumor volumes and high-risk clinical target volumes: an R-IDEAL stage 0 prospective study. Int J Radiat Oncol Biol Phys. 2022;113(2):426–436. doi:10.1016/j.ijrobp.2022.01.050.
   PubMed Web of Science ®Google Scholar
• Das IJ, Compton JJ, Bajaj A, et al. Intra- and inter-physician variability in target volume delineation in radiation therapy. J Radiat Res. 2021;62(6):1083–1089. doi:10.1093/jrr/rrab080.
   Web of Science ®Google Scholar
• Njeh CF. Tumor delineation: the weakest link in the search for accuracy in radiotherapy. J Med Phys. 2008;33(4):136–140. doi:10.4103/0971-6203.44472.
   PubMedGoogle Scholar
• Offersen BV, Boersma LJ, Kirkove C, et al. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer, version 1.1. Radiother Oncol. 2016;118(1):205–208. doi:10.1016/j.radonc.2015.12.027.
   PubMed Web of Science ®Google Scholar
• Offersen BV, Boersma LJ, Kirkove C, et al. ESTRO consensus guideline on target volume delineation for elective radiation therapy of early stage breast cancer. Radiother Oncol. 2015;114(1):3–10. doi:10.1016/j.radonc.2014.11.030.
   PubMed Web of Science ®Google Scholar
• [Internet]. Nasjonalt handlingsprogram med retningslinjer for diagnostikk, behandling og oppfølging av pasienter med brystkreft. [Norwegian guidelines for diagnostics, treatment and follow-up of patients with breast cancer]. [cited 2023 Apr 28]. Available from: https://nbcg.no/retningslinjer/
   Google Scholar
• Kerr AJ, Dodwell D, McGale P, et al. Adjuvant and neoadjuvant breast cancer treatments: a systematic review of their effects on mortality. Cancer Treat Rev. 2022;105:102375. doi:10.1016/j.ctrv.2022.102375.
   PubMed Web of Science ®Google Scholar
• Falstie-Jensen AM, Kjaersgaard A, Lorenzen EL, et al. Hypothyroidism and the risk of breast cancer recurrence and all-cause mortality - a Danish population-based study. Breast Cancer Res. 2019;21(1):44. doi:10.1186/s13058-019-1122-3.
   PubMedGoogle Scholar
• Taylor C, Correa C, Duane FK, et al. Estimating the risks of breast cancer radiotherapy: evidence from modern radiation doses to the lungs and heart and From previous randomized trials. J Clin Oncol. 2017;35(15):1641–1649. doi:10.1200/JCO.2016.72.0722.
   PubMed Web of Science ®Google Scholar
• Li XA, Tai A, Arthur DW, et al. Variability of target and normal structure delineation for breast cancer radiotherapy: an RTOG Multi-Institutional and Multiobserver Study. Int J Radiat Oncol Biol Phys. 2009;73(3):944–951. doi:10.1016/j.ijrobp.2008.10.034.
   PubMed Web of Science ®Google Scholar
• Samarasinghe G, Jameson M, Vinod S, et al. Deep learning for segmentation in radiation therapy planning: a review. J Med Imaging Radiat Oncol. 2021;65(5):578–595. doi:10.1111/1754-9485.13286.
   PubMed Web of Science ®Google Scholar
• Radici L, Ferrario S, Borca VC, et al. Implementation of a commercial deep learning-based auto segmentation software in radiotherapy: evaluation of effectiveness and impact on workflow. Life (Basel). 2022;12(12):2088. doi:10.3390/life12122088.
   Web of Science ®Google Scholar
• Pera O, Martinez A, Mohler C, et al. Clinical validation of Siemens’ Syngo via automatic contouring system. Adv Radiat Oncol. 2023;8(3):101177. doi:10.1016/j.adro.2023.101177.
   PubMedGoogle Scholar
• Almberg SS, Lervag C, Frengen J, et al. Training, validation, and clinical implementation of a deep-learning segmentation model for radiotherapy of loco-regional breast cancer. Radiother Oncol. 2022;173:62–68. doi:10.1016/j.radonc.2022.05.018.
   PubMed Web of Science ®Google Scholar
• Feng M, Moran JM, Koelling T, et al. Development and validation of a heart atlas to study cardiac exposure to radiation following treatment for breast cancer. Int J Radiat Oncol Biol Phys. 2011;79(1):10–18. doi:10.1016/j.ijrobp.2009.10.058.
   PubMed Web of Science ®Google Scholar
• Sherer MV, Lin D, Elguindi S, et al. Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: a critical review. Radiother Oncol. 2021;160:185–191. doi:10.1016/j.radonc.2021.05.003.
   PubMed Web of Science ®Google Scholar
• Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15(1):29. doi:10.1186/s12880-015-0068-x.
   PubMed Web of Science ®Google Scholar
• Christiaens M, Collette S, Overgaard J, et al. Quality assurance of radiotherapy in the ongoing EORTC 1219-DAHANCA-29 trial for HPV/p16 negative squamous cell carcinoma of the head and neck: results of the benchmark case procedure. Radiother Oncol. 2017;123(3):424–430. doi:10.1016/j.radonc.2017.04.019.
   PubMed Web of Science ®Google Scholar
• Buelens P, Willems S, Vandewinckele L, et al. Clinical evaluation of a deep learning model for segmentation of target volumes in breast cancer radiotherapy. Radiother Oncol. 2022;171:84–90. doi:10.1016/j.radonc.2022.04.015.
   PubMed Web of Science ®Google Scholar
• Wei Z, Ren J, Korreman SS, et al. Towards interactive deep-learning for tumour segmentation in head and neck cancer radiotherapy. Phys Imaging Radiat Oncol. 2023;25:100408. doi:10.1016/j.phro.2022.12.005.
   PubMedGoogle Scholar
• Rodriguez Outeiral R, Bos P, Al-Mamgani A, et al. Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning. Phys Imaging Radiat Oncol. 2021;19:39–44. doi:10.1016/j.phro.2021.06.005.
   PubMedGoogle Scholar
• Boers TGW, Hu Y, Gibson E, et al. Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans. Phys Med Biol. 2020;65(6):065002. doi:10.1088/1361-6560/ab6f99.
   PubMed Web of Science ®Google Scholar
• Rasmussen ME, Nijkamp JA, Eriksen JG, et al. A simple single-cycle interactive strategy to improve deep learning-based segmentation of organs-at-risk in head-and-neck cancer. Phys Imaging Radiat Oncol. 2023;26:100426. doi:10.1016/j.phro.2023.100426.
   PubMedGoogle Scholar
• Ciardo D, Gerardi MA, Vigorito S, et al. Atlas-based segmentation in breast cancer radiotherapy: evaluation of specific and generic-purpose atlases. Breast. 2017;32:44–52. doi:10.1016/j.breast.2016.12.010.
   PubMed Web of Science ®Google Scholar
• Velker VM, Rodrigues GB, Dinniwell R, et al. Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers. Radiat Oncol. 2013;8(1):188. doi:10.1186/1748-717X-8-188.
   PubMedGoogle Scholar
• Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44(2):547–557. doi:10.1002/mp.12045.
   PubMed Web of Science ®Google Scholar
• Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys. 2017;44(12):6377–6389. doi:10.1002/mp.12602.
   PubMed Web of Science ®Google Scholar
• Choi MS, Choi BS, Chung SY, et al. Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer. Radiother Oncol. 2020;153:139–145. doi:10.1016/j.radonc.2020.09.045.
   PubMed Web of Science ®Google Scholar
• Men K, Zhang T, Chen X, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Phys Med. 2018;50:13–19. doi:10.1016/j.ejmp.2018.05.006.
   PubMed Web of Science ®Google Scholar
• Chung SY, Chang JS, Choi MS, et al. Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery. Radiat Oncol. 2021;16(1):44. doi:10.1186/s13014-021-01771-z.
   PubMed Web of Science ®Google Scholar
• Byun HK, Chang JS, Choi MS, et al. Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy. Radiat Oncol. 2021;16(1):203. doi:10.1186/s13014-021-01923-1.
   PubMed Web of Science ®Google Scholar
• Liu Z, Liu F, Chen W, et al. Automatic segmentation of clinical target volume and organs-at-risk for breast conservative radiotherapy using a convolutional neural network. Cancer Manag Res. 2021;13:8209–8217. doi:10.2147/CMAR.S330249.
   PubMed Web of Science ®Google Scholar
• Vaassen F, Hazelaar C, Vaniqui A, et al. Evaluation of measures for assessing time-saving of automatic organ-at-risk segmentation in radiotherapy. Phys Imaging Radiat Oncol. 2020;13:1–6. doi:10.1016/j.phro.2019.12.001.
   PubMedGoogle Scholar
• Vandewinckele L, Claessens M, Dinkla A, et al. Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol. 2020;153:55–66. doi:10.1016/j.radonc.2020.09.008.
   PubMed Web of Science ®Google Scholar
• van der Veen J, Willems S, Deschuymer S, et al. Benefits of deep learning for delineation of organs at risk in head and neck cancer. Radiother Oncol. 2019;138:68–74. doi:10.1016/j.radonc.2019.05.010.
   PubMed Web of Science ®Google Scholar
• Ciardo D, Argenone A, Boboc GI, et al. Variability in axillary lymph node delineation for breast cancer radiotherapy in presence of guidelines on a multi-institutional platform. Acta Oncol. 2017;56(8):1081–1088. doi:10.1080/0284186X.2017.1325004.
   PubMed Web of Science ®Google Scholar
• Ling DC, Moppins BL, Champ CE, et al. Quality of regional nodal irradiation plans in breast cancer patients across a large network-can we translate results from randomized trials Into the clinic? Pract Radiat Oncol. 2021;11(1):e30–e35. doi:10.1016/j.prro.2020.06.007.
   PubMed Web of Science ®Google Scholar
• Wong J, Fong A, McVicar N, et al. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol. 2020;144:152–158. doi:10.1016/j.radonc.2019.10.019.
   PubMed Web of Science ®Google Scholar
• Barragan-Montero A, Bibal A, Dastarac MH, et al. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol. 2022;67(11):11TR01. doi:10.1088/1361-6560/ac678a.
   Web of Science ®Google Scholar
• Batumalai V, Jameson MG, King O, et al. Cautiously optimistic: a survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning. Tech Innov Patient Support Radiat Oncol. 2020;16:58–64. doi:10.1016/j.tipsro.2020.10.003.
   PubMedGoogle Scholar
• Korreman S, Eriksen JG, Grau C. The changing role of radiation oncology professionals in a world of AI - just jobs lost - or a solution to the under-provision of radiotherapy? Clin Transl Radiat Oncol. 2021;26:104–107. doi:10.1016/j.ctro.2020.04.012.
   PubMed Web of Science ®Google Scholar