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Lee BM, Kim JS, Chang Y, Choi SH, Park JW, Byun HK, Kim YB, Lee IJ, Chang JS. Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases. Int J Radiat Oncol Biol Phys 2024; 119:1579-1589. [PMID: 38431232 DOI: 10.1016/j.ijrobp.2024.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments. METHODS AND MATERIALS The auto-contouring system was implemented in 2019. We evaluated data from 2428 patients who underwent adjuvant breast radiation therapy before and after the system's introduction. We collected the treatment's finalized contours, which were reviewed and revised by a multidisciplinary team. After implementation, the treatment contours underwent a finalization process that involved manual review and adjustment of the initial auto-contours. For the preimplementation group (n = 369), auto-contours were generated retrospectively. We compared the auto-contours and final contours using the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95). RESULTS We analyzed 22,215 structures from final and corresponding auto-contours. The final contours were generally larger, encompassing more slices in the superior or inferior directions. Among organs at risk (OAR), the heart, esophagus, spinal cord, and contralateral breast demonstrated significantly increased DSC and decreased HD95 postimplementation (all P < .05), except for the lungs, which presented inaccurate segmentation. Among target volumes, CTVn_L2, L3, L4, and the internal mammary node showed increased DSC and decreased HD95 postimplementation (all P < .05), although the increase was less pronounced than the OAR outcomes. The analysis also covered factors contributing to significant differences, pattern identification, and outlier detection. CONCLUSIONS In our study, the adoption of an auto-contouring system was associated with an increased reliance on automated settings, underscoring its utility and the potential risk of automation bias. Given these findings, we underscore the importance of considering the integration of stringent risk assessments and quality management strategies as a precautionary measure for the optimal use of such systems.
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Affiliation(s)
- Byung Min Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Radiation Oncology, Uijeongbu St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | | | - Seo Hee Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Won Park
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hwa Kyung Byun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Bakx N, Van der Sangen M, Theuws J, Bluemink J, Hurkmans C. Comparison of the use of a clinically implemented deep learning segmentation model with the simulated study setting for breast cancer patients receiving radiotherapy. Acta Oncol 2024; 63:477-481. [PMID: 38899395 PMCID: PMC11332522 DOI: 10.2340/1651-226x.2024.34986] [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: 12/15/2023] [Accepted: 04/24/2024] [Indexed: 06/21/2024]
Abstract
BACKGROUND Deep learning (DL) models for auto-segmentation in radiotherapy have been extensively studied in retrospective and pilot settings. However, these studies might not reflect the clinical setting. This study compares the use of a clinically implemented in-house trained DL segmentation model for breast cancer to a previously performed pilot study to assess possible differences in performance or acceptability. MATERIAL AND METHODS Sixty patients with whole breast radiotherapy, with or without an indication for locoregional radiotherapy were included. Structures were qualitatively scored by radiotherapy technologists and radiation oncologists. Quantitative evaluation was performed using dice-similarity coefficient (DSC), 95th percentile of Hausdorff Distance (95%HD) and surface DSC (sDSC), and time needed for generating, checking, and correcting structures was measured. RESULTS Ninety-three percent of all contours in clinic were scored as clinically acceptable or usable as a starting point, comparable to 92% achieved in the pilot study. Compared to the pilot study, no significant changes in time reduction were achieved for organs at risks (OARs). For target volumes, significantly more time was needed compared to the pilot study for patients including lymph node levels 1-4, although time reduction was still 33% compared to manual segmentation. Almost all contours have better DSC and 95%HD than inter-observer variations. Only CTVn4 scored worse for both metrics, and the thyroid had a higher 95%HD value. INTERPRETATION The use of the DL model in clinical practice is comparable to the pilot study, showing high acceptability rates and time reduction.
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Affiliation(s)
- Nienke Bakx
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands.
| | | | - Jacqueline Theuws
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands
| | - Johanna Bluemink
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, The Netherlands; Technical University Eindhoven, Departments of Applied Physics and Electrical Engineering, Eindhoven, The Netherlands
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Jaikuna T, Osorio EV, Azria D, Chang-Claude J, De Santis MC, Gutiérrez-Enríquez S, van Herk M, Hoskin P, Lambrecht M, Lingard Z, Seibold P, Seoane A, Sperk E, Symonds RP, Talbot CJ, Rancati T, Rattay T, Reyes V, Rosenstein BS, de Ruysscher D, Vega A, Veldeman L, Webb A, West CML, Aznar MC. Contouring variation affects estimates of normal tissue complication probability for breast fibrosis after radiotherapy. Breast 2023; 72:103578. [PMID: 37713940 PMCID: PMC10511799 DOI: 10.1016/j.breast.2023.103578] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/25/2023] [Accepted: 09/08/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Normal tissue complication probability (NTCP) models can be useful to estimate the risk of fibrosis after breast-conserving surgery (BCS) and radiotherapy (RT) to the breast. However, they are subject to uncertainties. We present the impact of contouring variation on the prediction of fibrosis. MATERIALS AND METHODS 280 breast cancer patients treated BCS-RT were included. Nine Clinical Target Volume (CTV) contours were created for each patient: i) CTV_crop (reference), cropped 5 mm from the skin and ii) CTV_skin, uncropped and including the skin, iii) segmenting the 95% isodose (Iso95%) and iv) 3 different auto-contouring atlases generating uncropped and cropped contours (Atlas_skin/Atlas_crop). To illustrate the impact of contour variation on NTCP estimates, we applied two equations predicting fibrosis grade ≥ 2 at 5 years, based on Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models, respectively, to each contour. Differences were evaluated using repeated-measures ANOVA. For completeness, the association between observed fibrosis events and NTCP estimates was also evaluated using logistic regression. RESULTS There were minimal differences between contours when the same contouring approach was followed (cropped and uncropped). CTV_skin and Atlas_skin contours had lower NTCP estimates (-3.92%, IQR 4.00, p < 0.05) compared to CTV_crop. No significant difference was observed for Atlas_crop and Iso95% contours compared to CTV_crop. For the whole cohort, NTCP estimates varied between 5.3% and 49.5% (LKB) or 2.2% and 49.6% (RS) depending on the choice of contours. NTCP estimates for individual patients varied by up to a factor of 4. Estimates from "skin" contours showed higher agreement with observed events. CONCLUSION Contour variations can lead to significantly different NTCP estimates for breast fibrosis, highlighting the importance of standardising breast contours before developing and/or applying NTCP models.
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Affiliation(s)
- Tanwiwat Jaikuna
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom; Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Eliana Vasquez Osorio
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - David Azria
- Department of Radiation Oncology, Montpellier Cancer Institute, Université Montpellier, Inserm, U1194, France
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Germany
| | | | - Sara Gutiérrez-Enríquez
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Hospital Campus, Barcelona, Spain
| | - Marcel van Herk
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Peter Hoskin
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | | | - Zoe Lingard
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Petra Seibold
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alejandro Seoane
- Medical Physics Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Elena Sperk
- Department of Radiation Oncology, Mannheim Cancer Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - R Paul Symonds
- Leicester Cancer Research Centre, University of Leicester, United Kingdom
| | | | - Tiziana Rancati
- Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Tim Rattay
- Leicester Cancer Research Centre, University of Leicester, United Kingdom
| | - Victoria Reyes
- Radiation Oncology Department, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Barry S Rosenstein
- Department of Radiation Oncology, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Dirk de Ruysscher
- Maastricht University Medical Center, Department of Radiation Oncology (Maastro Clinic), GROW School for Oncology and Developmental Biology, Maastricht, the Netherlands
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain; Instituto de Investigación Sanitaria de, Santiago de Compostela, Spain; Biomedical Network on Rare Diseases (CIBERER), Spain
| | - Liv Veldeman
- Ghent University Hospital, Department of Radiation Oncology, Ghent, Belgium
| | - Adam Webb
- Department of Genetics and Genome Biology, University of Leicester, United Kingdom
| | - Catharine M L West
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom
| | - Marianne C Aznar
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Christie NHS Foundation Trust Hospital, Manchester, United Kingdom.
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Skarsø ER, Refsgaard L, Saini A, Sloth Møller D, Lorenzen EL, Maae E, Andersen K, Maraldo MV, Milo ML, Nyeng TB, Vrou Offersen B, Korreman SS. Development of a national deep learning-based auto-segmentation model for the heart on clinical delineations from the DBCG RT nation cohort. Acta Oncol 2023; 62:1201-1207. [PMID: 37712509 DOI: 10.1080/0284186x.2023.2252582] [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: 04/29/2023] [Accepted: 08/16/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND This study aimed at investigating the feasibility of developing a deep learning-based auto-segmentation model for the heart trained on clinical delineations. MATERIAL AND METHODS This study included two different datasets. The first dataset contained clinical heart delineations from the DBCG RT Nation study (1,561 patients). The second dataset was smaller (114 patients), but with corrected heart delineations. Before training the model on the clinical delineations an outlier-detection was performed, to remove cases with gross deviations from the delineation guideline. No outlier detection was performed for the dataset with corrected heart delineations. Both models were trained with a 3D full resolution nnUNet. The models were evaluated with the dice similarity coefficient (DSC), 95% Hausdorff distance (HD95) and Mean Surface Distance (MSD). The difference between the models were tested with the Mann-Whitney U-test. The balance of dataset quantity versus quality was investigated, by stepwise reducing the cohort size for the model trained on clinical delineations. RESULTS During the outlier-detection 137 patients were excluded from the clinical cohort due to non-compliance with delineation guidelines. The model trained on the curated clinical cohort performed with a median DSC of 0.96 (IQR 0.94-0.96), median HD95 of 4.00 mm (IQR 3.00 mm-6.00 mm) and a median MSD of 1.49 mm (IQR 1.12 mm-2.02 mm). The model trained on the dedicated and corrected cohort performed with a median DSC of 0.95 (IQR 0.93-0.96), median HD95 of 5.65 mm (IQR 3.37 mm-8.62 mm) and median MSD of 1.63 mm (IQR 1.35 mm-2.11 mm). The difference between the two models were found non-significant for all metrics (p > 0.05). Reduction of cohort size showed no significant difference for all metrics (p > 0.05). However, with the smallest cohort size, a few outlier structures were found. CONCLUSIONS This study demonstrated a deep learning-based auto-segmentation model trained on curated clinical delineations which performs on par with a model trained on dedicated delineations, making it easier to develop multi-institutional auto-segmentation models.
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Affiliation(s)
- Emma Riis Skarsø
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical medicine, Aarhus University, Aarhus, Denmark
| | - Lasse Refsgaard
- Department of Clinical medicine, Aarhus University, Aarhus, Denmark
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Abhilasha Saini
- Department of Clinical Oncology and Palliative Care, Zealand University Hospital, Næstved, Denmark
| | - Ditte Sloth Møller
- Department of Clinical medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Ebbe Laugaard Lorenzen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Else Maae
- Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Vejle, Denmark
| | - Karen Andersen
- Department of Oncology, Herlev and Gentofte Hospital, Herlev, Denmark
| | - Maja Vestmø Maraldo
- Department of Clinical Oncology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marie Louise Milo
- Department of Oncology, Aalborg University Hospital, Aalborg, Denmark
| | | | - Birgitte Vrou Offersen
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical medicine, Aarhus University, Aarhus, Denmark
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Stine Sofia Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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Bakx N, Rijkaart D, van der Sangen M, Theuws J, van der Toorn PP, Verrijssen AS, van der Leer J, Mutsaers J, van Nunen T, Reinders M, Schuengel I, Smits J, Hagelaar E, van Gruijthuijsen D, Bluemink H, Hurkmans C. Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer. Tech Innov Patient Support Radiat Oncol 2023; 26:100211. [PMID: 37229460 PMCID: PMC10205480 DOI: 10.1016/j.tipsro.2023.100211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/23/2023] [Accepted: 05/09/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively. Methods For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1-4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale. Results Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections. Conclusions A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer.
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Affiliation(s)
- Nienke Bakx
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Dorien Rijkaart
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - Jacqueline Theuws
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - An-Sofie Verrijssen
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Jorien van der Leer
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Joline Mutsaers
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Thérèse van Nunen
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Marjon Reinders
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Inge Schuengel
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Julia Smits
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Els Hagelaar
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | | | - Hanneke Bluemink
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, Eindhoven, the Netherlands
- Technical University Eindhoven, Faculties of Physics and Electrical Engineering, Eindhoven, the Netherlands
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CArdiac and REspiratory adaptive Computed Tomography (CARE-CT): a proof-of-concept digital phantom study. Phys Eng Sci Med 2022; 45:1257-1271. [PMID: 36434201 DOI: 10.1007/s13246-022-01193-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/20/2022] [Indexed: 11/27/2022]
Abstract
Current respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.
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Lin H, Dong L, Jimenez RB. Emerging Technologies in Mitigating the Risks of Cardiac Toxicity From Breast Radiotherapy. Semin Radiat Oncol 2022; 32:270-281. [DOI: 10.1016/j.semradonc.2022.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Almberg SS, Lervåg C, Frengen J, Eidem M, Abramova T, Nordstrand C, Alsaker M, Tøndel H, Raj SX, Wanderås AD. 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] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/07/2022] [Accepted: 05/18/2022] [Indexed: 11/29/2022]
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Zhang S, Lv B, Zheng X, Li Y, Ge W, Zhang L, Mo F, Qiu J. Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy. Front Public Health 2022; 10:860135. [PMID: 35392465 PMCID: PMC8980420 DOI: 10.3389/fpubh.2022.860135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the accuracy of a lung stereotactic body radiotherapy (SBRT) treatment plan with the target of a newly predicted internal target volume (ITVpredict) and the feasibility of its clinical application. ITVpredict was automatically generated by our in-house deep learning model according to the cone-beam CT (CBCT) image database. Method A retrospective study of 45 patients who underwent SBRT was involved, and Mask R-CNN based algorithm model helped to predict the internal target volume (ITV) using the CBCT image database. The geometric accuracy of ITVpredict was verified by the Dice Similarity Coefficient (DSC), 3D Motion Range (R3D), Relative Volume Index (RVI), and Hausdorff Distance (HD). The PTVpredict was generated by ITVpredict, which was registered and then projected on free-breath CT (FBCT) images. The PTVFBCT was margined from the GTV on FBCT images gross tumor volume on free-breath CT (GTVFBCT). Treatment plans with the target of Predict planning target volume on CBCT images (PTVpredict) and planning target volume on free-breath CT (PTVFBCT) were respectively re-established, and the dosimetric parameters included the ratio of the volume of patients receiving at least the prescribed dose to the volume of PTV (R100%), the ratio of the volume of patients receiving at least 50% of the prescribed dose to the volume of PTV in the Radiation Therapy Oncology Group (RTOG) 0813 Trial (R50%), Gradient Index (GI), and the maximum dose 2 cm from the PTV (D2cm), which were evaluated via Plan4DCT, plan which based on PTVpredict (Planpredict), and plan which based on PTVFBCT (PlanFBCT). Result The geometric results showed that there existed a good correlation between ITVpredict and ITV on the 4-dimensional CT [ITV4DCT; DSC= 0.83 ±0.18]. However, the average volume of ITVpredict was 10% less than that of ITV4DCT (p = 0.333). No significant difference in dose coverage was found in V100% for the ITV with 99.98 ± 0.04% in the ITV4DCT vs. 97.56 ± 4.71% in the ITVpredict (p = 0.162). Dosimetry parameters of PTV, including R100%, R50%, GI and D2cm showed no statistically significant difference between each plan (p > 0.05). Conclusion Dosimetric parameters of Planpredict are clinically comparable to those of the original Plan4DCT. This study confirmed that the treatment plan based on ITVpredict produced by our model could automatically meet clinical requirements. Thus, for patients undergoing lung SBRT, the model has great potential for using CBCT images for ITV contouring which can be used in treatment planning.
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The Contribution of Thoracic Radiation Dose Volumes to Subsequent Development of Cardiovascular Disease in Cancer Survivors. J Cardiovasc Nurs 2022; 37:E129-E138. [PMID: 34238842 PMCID: PMC8733046 DOI: 10.1097/jcn.0000000000000834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Although radiation therapy (RT) has been recognized for contributing to cardiovascular disease (CVD), it is unknown whether specific doses received by cardiovascular tissues influence development. OBJECTIVE In this pilot study, we examined the contribution of RT dose distribution on the development of CVD events in patients with cancer within 5 years of RT. METHODS A retrospective case-controlled design was used matching 28 cases receiving thoracic RT who subsequently developed an adverse CVD event with 28 controls based upon age, gender, and cancer type. Dose volume histograms of nongated computed tomography scans received during RT characterized the dose delivered to the heart. Heart chambers were segmented using an atlas approach, and radiomics features for the segmentation as well as planning dose in each chamber were tabulated for analysis. RESULT No significant differences were observed in the RT dose statistics between groups, preexisting CVD, nor significant differences of RT doses delivered to distinct chambers of the heart. Cases were found to have greater CVD risk factors at the time of cancer diagnosis. Morphological significant differences for perimeter on border ( P = .043), equivalent spherical radius ( P = .050), and elongation ( P = .038) were observed, with preexisting CVD having the highest values (ie, larger hearts). CONCLUSION Traditional CVD risk factors were more prevalent in the cases who developed CVD. No differences were observed in doses of RT. Of note, we observed significant differences in heart morphology and mass in known diseased hearts on the pretreatment scans. These new metrics may have implications for the measurement and quantification of CVD.
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Zhou H, Li J, Li A, Qiu X, Shen Z, Ge Y. Diagnostic Application and Systematic Evaluation of Image Registration Software in External Radiotherapy. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2022. [DOI: 10.1166/jmihi.2022.3928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Purpose: Analyze the clinical application of MIM maestro in cancer radiotherapy and evaluate the advantage of the software compare to the clinical applied tools. Materials and Methods: Potentially relevant studies published were identified through a pubmed and web of science
search using words “MIM Maestro,” “Atlas,” “image registration,” “dose accumulation,” “irradiation.” Combinations of words were also searched as were bibliographies of downloaded papers in order to avoid missing relevant publications.
Results: In many patients with cancer radiotherapy, multiple types of images are demanded, MIM Maestro is a multi-modality image information processing system for radiotherapy. Contour atlas and image registration among dose accumulation and individual fractions is beneficial for radiotherapy.
Overall 34 papers were enrolled for analysis. The MIM appears to provide excellent clinical applications such as the function of contour altas, image fusion and registration, dose accumulation in radiotherapy compared to the other software. Conclusions: The regular optimization of radiotherapy
technology and the development of image technology, improve the clinical efficiency. The current paper give a systematic review of MIM Maestro multi-modality image processing software.
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Affiliation(s)
- Han Zhou
- School of Electronic Science and Engineering, Nanjing University, Jiangsu, 210046, China
| | - Jing Li
- Department of Radiation Oncology, Nanjing University, Jinling Hospital, School of Medicine, Nanjing, 210002, China
| | - AoMei Li
- Department of Radiation Oncology, Nanjing University, Jinling Hospital, School of Medicine, Nanjing, 210002, China
| | - XiangNan Qiu
- Department of Radiation Oncology, Nanjing University, Jinling Hospital, School of Medicine, Nanjing, 210002, China
| | - ZeTian Shen
- Department of Radiation Oncology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210013, China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Jiangsu, 210046, China
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12
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Liu Z, Liu F, Chen W, Tao Y, Liu X, Zhang F, Shen J, Guan H, Zhen H, Wang S, Chen Q, Chen Y, Hou X. 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. [PMID: 34754241 PMCID: PMC8572021 DOI: 10.2147/cmar.s330249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 10/04/2021] [Indexed: 12/14/2022] Open
Abstract
Objective Delineation of clinical target volume (CTV) and organs at risk (OARs) is important for radiotherapy but is time-consuming. We trained and evaluated a U-ResNet model to provide fast and consistent auto-segmentation. Methods We collected 160 patients’ CT scans with breast cancer who underwent breast-conserving surgery (BCS) and were treated with radiotherapy. CTV and OARs were delineated manually and were used for model training. The dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (95HD) were used to assess the performance of our model. CTV and OARs were randomly selected as ground truth (GT) masks, and artificial intelligence (AI) masks were generated by the proposed model. Two clinicians randomly compared CTV score differences of the contour. The consistency between two clinicians was tested. Time cost for auto-delineation was evaluated. Results The mean DSC values of the proposed method were 0.94, 0.95, 0.94, 0.96, 0.96 and 0.93 for breast CTV, contralateral breast, heart, right lung, left lung and spinal cord, respectively. The mean 95HD values were 4.31mm, 3.59mm, 4.86mm, 3.18mm, 2.79mm and 4.37mm for the above structures, respectively. The average CTV scores for AI and GT were 2.89 versus 2.92 when evaluated by oncologist A (P=0.612), and 2.75 versus 2.83 by oncologist B (P=0.213), with no statistically significant differences. The consistency between two clinicians was poor (kappa=0.282). The time for auto-segmentation of CTV and OARs was 10.03 s. Conclusion Our proposed model (U-ResNet) can improve the efficiency and accuracy of delineation compared with U-Net, performing equally well with the segmentation generated by oncologists.
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Affiliation(s)
- Zhikai Liu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Fangjie Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, People's Republic of China
| | - Wanqi Chen
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Yinjie Tao
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Xia Liu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Fuquan Zhang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Jing Shen
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Hui Guan
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Hongnan Zhen
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
| | - Shaobin Wang
- MedMind Technology Co., Ltd., Beijing, 100055, People's Republic of China
| | - Qi Chen
- MedMind Technology Co., Ltd., Beijing, 100055, People's Republic of China
| | - Yu Chen
- MedMind Technology Co., Ltd., Beijing, 100055, People's Republic of China
| | - Xiaorong Hou
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China
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13
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Xian L, Li G, Xiao Q, Li Z, Zhang X, Chen L, Hu Z, Bai S. Clinically Oriented Target Contour Evaluation Using Geometric and Dosimetric Indices Based on Simple Geometric Transformations. Technol Cancer Res Treat 2021; 20:15330338211036325. [PMID: 34490802 PMCID: PMC8427914 DOI: 10.1177/15330338211036325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Purpose: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices. Methods: Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression. Results: The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R2: 0.023-0.04, P > 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and Dmean (R2: 0.689-0.988), 80% of which were P < 0.001. For the random errors, the correlations obtained by the all targets were R2 > 0.384, P < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with P < 0.05. Conclusions: Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.
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Affiliation(s)
- Lixun Xian
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,Department of Oncology, Chengdu Second People's Hospital, Chengdu, Sichuan, China.,*Lixun Xian and Guangjun Li are contributed equally to this work
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.,*Lixun Xian and Guangjun Li are contributed equally to this work
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiangbin Zhang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Chen
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhenyao Hu
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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14
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Zhou H, Li Y, Gu Y, Shen Z, Zhu X, Ge Y. A deep learning based automatic segmentation approach for anatomical structures in intensity modulation radiotherapy. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:7506-7524. [PMID: 34814260 DOI: 10.3934/mbe.2021371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To evaluate the automatic segmentation approach for organ at risk (OARs) and compare the parameters of dose volume histogram (DVH) in radiotherapy. METHODOLOGY Thirty-three patients were selected to contour OARs using automatic segmentation approach which based on U-Net, applying them to a number of the nasopharyngeal carcinoma (NPC), breast, and rectal cancer respectively. The automatic contours were transferred to the Pinnacle System to evaluate contour accuracy and compare the DVH parameters. RESULTS The time for manual contour was 56.5 ± 9, 23.12 ± 4.23 and 45.23 ± 2.39min for the OARs of NPC, breast and rectal cancer, and for automatic contour was 1.5 ± 0.23, 1.45 ± 0.78 and 1.8 ± 0.56 min. Automatic contours of Eye with the best Dice-similarity coefficients (DSC) of 0.907 ± 0.02 while with the poorest DSC of 0.459 ± 0.112 of Spinal Cord for NPC; And Lung with the best DSC of 0.944 ± 0.03 while with the poorest DSC of 0.709 ± 0.1 of Spinal Cord for breast; And Bladder with the best DSC of 0.91 ± 0.04 while with the poorest DSC of 0.43 ± 0.1 of Femoral heads for rectal cancer. The contours of Spinal Cord in H & N had poor results due to the division of the medulla oblongata. The contours of Femoral head, which different from what we expect, also due to manual contour result in poor DSC. CONCLUSION The automatic contour approach based deep learning method with sufficient accuracy for research purposes. However, the value of DSC does not fully reflect the accuracy of dose distribution, but can cause dose changes due to the changes in the OARs volume and DSC from the data. Considering the significantly time-saving and good performance in partial OARs, the automatic contouring also plays a supervisory role.
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Affiliation(s)
- Han Zhou
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
- Department of Radiation Oncology The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210002, China
| | - Yikun Li
- Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China
| | - Ying Gu
- Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China
| | - Zetian Shen
- Department of Radiation Oncology The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210002, China
| | - Xixu Zhu
- Department of Radiation Oncology, Jinling Hospital, Nanjing, Jiangsu, 210002, China
| | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
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15
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Spoor DS, Sijtsema NM, van den Bogaard VAB, van der Schaaf A, Brouwer CL, Ta BDP, Vliegenthart R, Kierkels RGJ, Langendijk JA, Maduro JH, Peters FBJ, Crijns APG. Validation of separate multi-atlases for auto segmentation of cardiac substructures in CT-scans acquired in deep inspiration breath hold and free breathing. Radiother Oncol 2021; 163:46-54. [PMID: 34343547 DOI: 10.1016/j.radonc.2021.07.025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 07/23/2021] [Accepted: 07/24/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE Developing NTCP-models for cardiac complications after breast cancer (BC) radiotherapy requires cardiac dose-volume parameters for many patients. These can be obtained by using multi-atlas based automatic segmentation (MABAS) of cardiac structures in planning CT scans. We investigated the relevance of separate multi-atlases for deep inspiration breath hold (DIBH) and free breathing (FB) CT scans. MATERIALS AND METHODS BC patients scanned in DIBH (n = 10) and in FB (n = 20) were selected to create separate multi-atlases consisting of expert panel delineations of the whole heart, atria and ventricles. The accuracy of atlas-generated contours was validated with expert delineations in independent datasets (n = 10 for DIBH and FB) and reported as Dice coefficients, contour distances and dose-volume differences in relation to interobserver variability of manual contours. Dependency of MABAS contouring accuracy on breathing technique was assessed by validation of a FB atlas in DIBH patients and vice versa (cross-validation). RESULTS For all structures the FB and DIBH atlases resulted in Dice coefficients with their respective reference contours ≥ 0.8 and average contour distances ≤ 2 mm smaller than slice thickness of (CTs). No significant differences were found for dose-volume parameters in volumes receiving relevant dose levels (WH, LV and RV). Accuracy of the DIBH atlas was at least similar to, and for the ventricles better than, the interobserver variation in manual delineation. Cross-validation between breathing techniques showed a reduced MABAS performance. CONCLUSION Multi-atlas accuracy was at least similar to interobserver delineation variation. Separate atlases for scans made in DIBH and FB could benefit atlas performance because accuracy depends on breathing technique.
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Affiliation(s)
- Daan S Spoor
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Nanna M Sijtsema
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands.
| | - Veerle A B van den Bogaard
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Arjen van der Schaaf
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Charlotte L Brouwer
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Bastiaan D P Ta
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Roel G J Kierkels
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Johannes A Langendijk
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - John H Maduro
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Femke B J Peters
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
| | - Anne P G Crijns
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands
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16
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Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y, Yan Z, Chen W, Chang L, Li W. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat 2021; 20:15330338211016386. [PMID: 34142614 PMCID: PMC8216350 DOI: 10.1177/15330338211016386] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.
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Affiliation(s)
- Danju Huang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Wang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yu Hou
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Lan Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Zhirui Yan
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenrui Chen
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenhui Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
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17
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Ying Y, Wang H, Chen H, Cheng J, Gu H, Shao Y, Duan Y, Feng A, Feng W, Fu X, Quan H, Xu Z. A novel specific grading standard study of auto-segmentation of organs at risk in thorax: subjective-objective-combined grading standard. Biomed Eng Online 2021; 20:54. [PMID: 34082755 PMCID: PMC8173789 DOI: 10.1186/s12938-021-00890-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 05/24/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To develop a novel subjective-objective-combined (SOC) grading standard for auto-segmentation for each organ at risk (OAR) in the thorax. METHODS A radiation oncologist manually delineated 13 thoracic OARs from computed tomography (CT) images of 40 patients. OAR auto-segmentation accuracy was graded by five geometric objective indexes, including the Dice similarity coefficient (DSC), the difference of the Euclidean distance between centers of mass (ΔCMD), the difference of volume (ΔV), maximum Hausdorff distance (MHD), and average Hausdorff distance (AHD). The grading results were compared with those of the corresponding geometric indexes obtained by geometric objective methods in the other two centers. OAR auto-segmentation accuracy was also graded by our subjective evaluation standard. These grading results were compared with those of DSC. Based on the subjective evaluation standard and the five geometric indexes, the correspondence between the subjective evaluation level and the geometric index range was established for each OAR. RESULTS For ΔCMD, ΔV, and MHD, the grading results of the geometric objective evaluation methods at our center and the other two centers were inconsistent. For DSC and AHD, the grading results of three centers were consistent. Seven OARs' grading results in the subjective evaluation standard were inconsistent with those of DSC. Six OARs' grading results in the subjective evaluation standard were consistent with those of DSC. Finally, we proposed a new evaluation method that combined the subjective evaluation level of those OARs with the range of corresponding DSC to determine the grading standard. If the DSC ranges between the adjacent levels did not overlap, the DSC range was used as the grading standard. Otherwise, the mean value of DSC was used as the grading standard. CONCLUSIONS A novel OAR-specific SOC grading standard in thorax was developed. The SOC grading standard provides a possible alternative for evaluation of the auto-segmentation accuracy for thoracic OARs.
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Affiliation(s)
- Yanchen Ying
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
- Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and Center for Electronic Microscopy and Department of Physics, Wuhan University, Wuhan, 430070, China
| | - Hao Wang
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Jianfan Cheng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hengle Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yanhua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Aihui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Wen Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hong Quan
- Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education and Center for Electronic Microscopy and Department of Physics, Wuhan University, Wuhan, 430070, China
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, 200030, China.
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Choi SH, Chang JS, Byun HK, Son NH, Hong CS, Hong N, Park Ms YI, Kim J, Kim JS, Kim YB. Risk of Hypothyroidism in Women After Radiation Therapy for Breast Cancer. Int J Radiat Oncol Biol Phys 2021; 110:462-472. [PMID: 33412261 DOI: 10.1016/j.ijrobp.2020.12.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 12/19/2020] [Accepted: 12/28/2020] [Indexed: 01/17/2023]
Abstract
PURPOSE To study the hypothyroidism risk after adjuvant radiation therapy (RT) and the association of different RT targets with hypothyroidism risk. METHODS We studied 4073 women treated with adjuvant RT for breast cancer from 2007 to 2016. The primary endpoint was hypothyroidism development after RT. Patients were divided and analyzed into 3 groups: whole breast (WB)-alone (n = 2468), regional node irradiation (RNI)-Lv.4 (n = 215; cranial border at the subclavian artery, according to the European Society for Radiotherapy and Oncology consensus guideline), and RNI-supraclavicular lymph node (SCL) (n = 1390; cranial border at the cricoid cartilage). In general, RNI-Lv.4 was used in the patients with high-risk pN0 and pN1 breast cancer. In auxiliary analysis, the mean thyroid dose was estimated in each group (total n = 600, 200 from each group). All the doses were converted to the equivalent dose in 2 Gy fractions (EQD2) with α/β ratios of 3. RESULTS The median follow-up duration was 84 months (WB-alone, 84 months; RNI-Lv.4, 44 months; RNI-SCL, 91 months). The 3-year hypothyroidism incidence rate differed significantly between the RNI-SCL and WB-alone groups (2.2% vs 0.8%; Bonferroni corrected P [Pc] < .001) but not between the RNI-Lv.4 and WB-alone groups (0.9% vs 0.8%; Pc > .05). The Cox model revealed an adjusted hazard ratio of 2.25 (95% CI, 1.49-3.38) for RNI-SCL vs WB-alone, 1.69 (95% CI, 1.12-2.56) for adjuvant systemic therapies, and 2.07 (95% CI, 1.07-3.99) for age <60 years. In the subgroup analysis, the hypothyroidism risk became more prominent in patients aged <60 years. The mean exposure doses to the thyroid were 0.23 versus 1.93 versus 7.89 Gy (EQD2) for the WB-alone versus RNI-Lv.4 versus RNI-SCL groups (P < .001). No statistically different locoregional recurrence rates were seen between groups (5-year rate: <3%). CONCLUSIONS The risk of hypothyroidism increases after RNI-SCL for breast cancer but not after RNI-Lv 4. These data support routine contouring of the thyroid in the RNI setting, and future studies are required to develop optimal dose-volume constraints.
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Affiliation(s)
- Seo Hee Choi
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Korea.
| | - Hwa Kyung Byun
- Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Nak-Hoon Son
- Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Chae-Seon Hong
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Ye-In Park Ms
- Department of Radiation Oncology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea
| | - Jihun Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Korea
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19
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Chung SY, Chang JS, Choi MS, Chang Y, Choi BS, Chun J, Keum KC, Kim JS, Kim YB. 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; 16:44. [PMID: 33632248 PMCID: PMC7905884 DOI: 10.1186/s13014-021-01771-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 02/11/2021] [Indexed: 02/07/2023] Open
Abstract
Background In breast cancer patients receiving radiotherapy (RT), accurate target delineation and reduction of radiation doses to the nearby normal organs is important. However, manual clinical target volume (CTV) and organs-at-risk (OARs) segmentation for treatment planning increases physicians’ workload and inter-physician variability considerably. In this study, we evaluated the potential benefits of deep learning-based auto-segmented contours by comparing them to manually delineated contours for breast cancer patients. Methods CTVs for bilateral breasts, regional lymph nodes, and OARs (including the heart, lungs, esophagus, spinal cord, and thyroid) were manually delineated on planning computed tomography scans of 111 breast cancer patients who received breast-conserving surgery. Subsequently, a two-stage convolutional neural network algorithm was used. Quantitative metrics, including the Dice similarity coefficient (DSC) and 95% Hausdorff distance, and qualitative scoring by two panels from 10 institutions were used for analysis. Inter-observer variability and delineation time were assessed; furthermore, dose-volume histograms and dosimetric parameters were also analyzed using another set of patient data. Results The correlation between the auto-segmented and manual contours was acceptable for OARs, with a mean DSC higher than 0.80 for all OARs. In addition, the CTVs showed favorable results, with mean DSCs higher than 0.70 for all breast and regional lymph node CTVs. Furthermore, qualitative subjective scoring showed that the results were acceptable for all CTVs and OARs, with a median score of at least 8 (possible range: 0–10) for (1) the differences between manual and auto-segmented contours and (2) the extent to which auto-segmentation would assist physicians in clinical practice. The differences in dosimetric parameters between the auto-segmented and manual contours were minimal. Conclusions The feasibility of deep learning-based auto-segmentation in breast RT planning was demonstrated. Although deep learning-based auto-segmentation cannot be a substitute for radiation oncologists, it is a useful tool with excellent potential in assisting radiation oncologists in the future. Trial registration Retrospectively registered.
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Affiliation(s)
- Seung Yeun Chung
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.,Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Min Seo Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | | | - Byong Su Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ki Chang Keum
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
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20
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Finnegan R, Laugaard Lorenzen E, Dowling J, Thwaites D, Delaney G, Brink C, Holloway L. Validation of a new open-source method for automatic delineation and dose assessment of the heart and LADCA in breast radiotherapy with simultaneous uncertainty estimation. Phys Med Biol 2021; 66:035014. [PMID: 33202389 DOI: 10.1088/1361-6560/abcb1d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Radiotherapy has been shown to increase risks of cardiotoxicities for breast cancer patients. Automated delineation approaches are necessary for consistent and efficient assessment of cardiac doses in large, retrospective datasets, while patient-specific estimation of the uncertainty in these doses provides valuable additional data for modelling and understanding risks. In this work, we aim to validate the consistency of our previously described open-source software model for automatic cardiac delineation in the context of dose assessment, relative to manual contouring. We also extend our software to introduce a novel method to automatically quantify the uncertainty in cardiac doses based on expected inter-observer variability (IOV) in contouring. This method was applied to a cohort of 15 left-sided breast cancer patients treated in Denmark using modern tangential radiotherapy techniques. On each image set, the whole heart and left anterior descending coronary artery (LADCA) were contoured by nine independent experts; the range of doses to these nine volumes provided a reference for the dose uncertainties generated from the automatic method. Local and external atlas sets were used to test the method. Results give confidence in the consistency of automatic segmentations, with mean whole heart dose differences for local and external atlas sets of -0.20 ± 0.17 and -0.10 ± 0.14 Gy, respectively. Automatic estimates of uncertainties in doses are similar to those from IOV for both the whole heart and LADCA. Overall, this study confirms that our automated approach can be used to accurately assess cardiac doses, and the proposed method can provide a useful tool in estimating dose uncertainties.
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Affiliation(s)
- Robert Finnegan
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia. Ingham Institute for Applied Medical Research, Liverpool, Australia
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21
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Finnegan RN, Orlandini L, Liao X, Yin J, Lang J, Dowling J, Fontanarosa D. Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients. PLoS One 2021; 16:e0245364. [PMID: 33444379 PMCID: PMC7808597 DOI: 10.1371/journal.pone.0245364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/23/2020] [Indexed: 12/24/2022] Open
Abstract
Incidental radiation exposure to the heart during lung cancer radiotherapy is associated with radiation-induced heart disease and increased rates of mortality. By considering the respiratory-induced motion of the heart it is possible to create a radiotherapy plan that results in a lower overall cardiac dose. This approach is challenging using current clinical practices: manual contouring of the heart is time consuming, and subject to inter- and intra-observer variability. In this work, we investigate the feasibility of our previously developed, atlas-based, automatic heart segmentation tool to delineate the heart in four-dimensional x-ray computed tomography (4D-CT) images. We used a dataset comprising 19 patients receiving radiotherapy for lung cancer, with 4D-CT imaging acquired at 10 respiratory phases and with a maximum intensity projection image generated from these. For each patient, one of four experienced radiation oncologists contoured the heart on each respiratory phase image and the maximum intensity image. Automatic segmentation of the heart on these same patient image sets was achieved using a leave-one-out approach, where for each patient the remaining 18 were used as an atlas set. The consistency of the automatic segmentation relative to manual contouring was evaluated using the Dice similarity coefficient (DSC) and mean absolute surface-to-surface distance (MASD). The DSC and MASD are comparable to inter-observer variability in clinically acceptable whole heart delineations (average DSC > 0.93 and average MASD < 2.0 mm in all the respiratory phases). The comparison between automatic and manual delineations on the maximum intensity images produced an overall mean DSC of 0.929 and a mean MASD of 2.07 mm. The automatic, atlas-based segmentation tool produces clinically consistent and robust heart delineations and is easy to implement in the routine care of lung cancer patients.
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Affiliation(s)
- Robert Neil Finnegan
- Institute of Medical Physics, School of Physics, University of Sydney, Camperdown, New South Wales, Australia
| | - Lucia Orlandini
- Sichuan Cancer Hospital & Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Xiongfei Liao
- Sichuan Cancer Hospital & Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Jun Yin
- Sichuan Cancer Hospital & Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China (UESTC), Chengdu, China
- * E-mail: (JY); (JL)
| | - Jinyi Lang
- Sichuan Cancer Hospital & Institute, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, China
- School of Medicine, University of Electronic Science and Technology of China (UESTC), Chengdu, China
- * E-mail: (JY); (JL)
| | - Jason Dowling
- Institute of Medical Physics, School of Physics, University of Sydney, Camperdown, New South Wales, Australia
- Australian eHealth Research Centre, CSIRO, Herston, Queensland, Australia
| | - Davide Fontanarosa
- Institute of Health Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Queensland, Australia
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22
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Wang X, Miralbell R, Fargier-Bochaton O, Bulling S, Vallée JP, Dipasquale G. Atlas Sampling for Prone Breast Automatic Segmentation of Organs at Risk: The Importance of Patients' Body Mass Index and Breast Cup Size for an Optimized Contouring of the Heart and the Coronary Vessels. Technol Cancer Res Treat 2020; 19:1533033820920624. [PMID: 32314647 PMCID: PMC7175049 DOI: 10.1177/1533033820920624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Objective: Delineation of organs at risk is a time-consuming task. This study evaluates the benefits of using single-subject atlas-based automatic segmentation of organs at risk in patients with breast cancer treated in prone position, with 2 different criteria for choosing the atlas subject. Together with laterality (left/right), the criteria used were either (1) breast volume or (2) body mass index and breast cup size. Methods: An atlas supporting different selection criteria for automatic segmentation was generated from contours drawn by a senior radiation oncologist (RO_A). Atlas organs at risk included heart, left anterior descending artery, and right coronary artery. Manual contours drawn by RO_A and automatic segmentation contours of organs at risk and breast clinical target volume were created for 27 nonatlas patients. A second radiation oncologist (RO_B) manually contoured (M_B) the breast clinical target volume and the heart. Contouring times were recorded and the reliability of the automatic segmentation was assessed in the context of 3-D planning. Results: Accounting for body mass index and breast cup size improved automatic segmentation results compared to breast volume-based sampling, especially for the heart (mean similarity indexes >0.9 for automatic segmentation organs at risk and clinical target volume after RO_A editing). Mean similarity indexes for the left anterior descending artery and the right coronary artery edited by RO_A expanded by 1 cm were ≥0.8. Using automatic segmentation reduced contouring time by 40%. For each parameter analyzed (eg, D2%), the difference in dose, averaged over all patients, between automatic segmentation structures edited by RO_A and the same structure manually drawn by RO_A was <1.5% of the prescribed dose. The mean heart dose was reliable for the unedited heart segmentation, and for right-sided treatments, automatic segmentation was adequate for treatment planning with 3-D conformal tangential fields. Conclusions: Automatic segmentation for prone breast radiotherapy stratified by body mass index and breast cup size improved segmentation accuracy for the heart and coronary vessels compared to breast volume sampling. A significant reduction in contouring time can be achieved by using automatic segmentation.
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Affiliation(s)
- Xinzhuo Wang
- Division of Radiation Oncology, Tianjin Union Medicine Center, China.,Division of Radiation Oncology, Geneva University Hospital, Switzerland
| | - Raymond Miralbell
- Division of Radiation Oncology, Geneva University Hospital, Switzerland.,Institut Oncològic Teknon, Barcelona, Spain
| | | | | | - Jean Paul Vallée
- Radiology division, Diagnostic department, Geneva University Hospital and University of Geneva, Geneva, Switzerland
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23
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Barrett S, Simpkin AJ, Walls GM, Leech M, Marignol L. Geometric and Dosimetric Evaluation of a Commercially Available Auto-segmentation Tool for Gross Tumour Volume Delineation in Locally Advanced Non-small Cell Lung Cancer: a Feasibility Study. Clin Oncol (R Coll Radiol) 2020; 33:155-162. [PMID: 32798158 DOI: 10.1016/j.clon.2020.07.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 06/24/2020] [Accepted: 07/24/2020] [Indexed: 12/25/2022]
Abstract
AIMS To quantify the reliability of a commercially available auto-segmentation tool in locally advanced non-small cell lung cancer using serial four-dimensional computed tomography (4DCT) scans during conventionally fractionated radiotherapy. MATERIALS AND METHODS Eight patients with serial 4DCT scans (n = 44) acquired over the course of radiotherapy were assessed. Each 4DCT had a physician-defined primary tumour manual contour (MC). An auto-contour (AC) and a user-adjusted auto-contour (UA-AC) were created for each scan. Geometric agreement of the AC and the UA-AC to the MC was assessed using the dice similarity coefficient (DSC), the centre of mass (COM) shift from the MC and the structure volume difference from the MC. Bland Altman analysis was carried out to assess agreement between contouring methods. Dosimetric reliability was assessed by comparison of planning target volume dose coverage on the MC and UA-AC. The time trend analysis of the geometric accuracy measures from the initial planning scan through to the final scan for each patient was evaluated using a Wilcoxon signed ranks test to assess the reliability of the UA-AC over the duration of radiotherapy. RESULTS User adjustment significantly improved all geometric comparison metrics over the AC alone. Improved agreement was observed in smaller tumours not abutting normal soft tissue and median values for geometric comparisons to the MC for DSC, tumour volume difference and COM offset were 0.80 (range 0.49-0.89), 0.8 cm3 (range 0.0-5.9 cm3) and 0.16 cm (range 0.09-0.69 cm), respectively. There were no significant differences in dose metrics measured from the MC and the UA-AC after Bonferroni correction. Variation in geometric agreement between the MC and the UA-AC were observed over the course of radiotherapy with both DSC (P = 0.035) and COM shift from the MC (ns) worsening. The median tumour volume difference from the MC improved at the later time point. CONCLUSIONS These findings suggest that the UA-AC can produce geometrically and dosimetrically acceptable contours for appropriately selected patients with non-small cell lung cancer. Larger studies are required to confirm the findings.
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Affiliation(s)
- S Barrett
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland.
| | - A J Simpkin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland
| | - G M Walls
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - M Leech
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland
| | - L Marignol
- Applied Radiation Therapy Trinity, Discipline of Radiation Therapy, Trinity College Dublin, Dublin, Ireland
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24
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Artificial Intelligence in radiotherapy: state of the art and future directions. Med Oncol 2020; 37:50. [PMID: 32323066 DOI: 10.1007/s12032-020-01374-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/13/2020] [Indexed: 02/06/2023]
Abstract
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients' medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a "black box" in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.
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25
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Finnegan R, Lorenzen E, Dowling J, Holloway L, Thwaites D, Brink C. Localised delineation uncertainty for iterative atlas selection in automatic cardiac segmentation. ACTA ACUST UNITED AC 2020; 65:035011. [DOI: 10.1088/1361-6560/ab652a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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26
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Poortmans PMP, Takanen S, Marta GN, Meattini I, Kaidar-Person O. Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer. Breast 2020; 49:194-200. [PMID: 31931265 PMCID: PMC7375562 DOI: 10.1016/j.breast.2019.11.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/16/2019] [Accepted: 11/20/2019] [Indexed: 01/08/2023] Open
Abstract
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes parameters. Future developments include incorporating dose/outcome data to optimise dose distributions with optimal coverage of the high-risk areas, while at the same time limiting doses to low-risk areas. An infinite gradient of volumes and doses to deliver spatially-adjusted radiation can be generated, allowing to avoid unnecessary radiation to organs at risk. Therefore, data about patient-, tumour-, and treatment-related factors have to be combined with dose distributions and outcome-containing databases.
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Affiliation(s)
| | - Silvia Takanen
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Gustavo Nader Marta
- Department of Radiation Oncology - Hospital Sírio-Libanês, Brazil; Department of Radiology and Oncology - Radiation Oncology, Instituto Do Câncer Do Estado de São Paulo (ICESP), Faculdade de Medicina da Universidade de São Paulo, Brazil
| | - Icro Meattini
- Department of Experimental and Clinical Biomedical Sciences "M. Serio", University of Florence, Florence, Italy; Radiation Oncology Unit, Oncology Department, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Orit Kaidar-Person
- Radiation Oncology Unit, Breast Radiation Unit, Sheba Tel Ha'shomer, Ramat Gan, Israel
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27
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Jung JW, Lee C, Mosher EG, Mille MM, Yeom YS, Jones EC, Choi M, Lee C. Automatic segmentation of cardiac structures for breast cancer radiotherapy. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2019; 12:44-48. [PMID: 33458294 PMCID: PMC7807574 DOI: 10.1016/j.phro.2019.11.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 11/22/2019] [Accepted: 11/22/2019] [Indexed: 12/25/2022]
Abstract
We developed an automatic method to segment cardiac sub-structures for radiotherapy planning CTs. The Dice Similarity Coefficients and Average Surface Distance were up to 97% and < 11 mm, respectively. The whole heart showed the absolute dose difference < 0.3 Gy whereas the coronary arteries showed < 2.3 Gy in breast radiotherapy simulations. No notable improvement in our method beyond 10 atlases and using the manual guide points.
Background and purpose We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy. Material and methods We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method. Results The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance. Conclusion We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.
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Affiliation(s)
- Jae Won Jung
- Department of Physics, East Carolina University, Greenville, NC 27858, USA
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elizabeth G Mosher
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Matthew M Mille
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Yeon Soo Yeom
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Elizabeth C Jones
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20852, USA
| | - Minsoo Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
| | - Choonsik Lee
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA
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Guo B, Shah C, Xia P. Automated planning of whole breast irradiation using hybrid IMRT improves efficiency and quality. J Appl Clin Med Phys 2019; 20:87-96. [PMID: 31743598 PMCID: PMC6909113 DOI: 10.1002/acm2.12767] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 09/05/2019] [Accepted: 10/14/2019] [Indexed: 11/25/2022] Open
Abstract
Purpose To develop an automated workflow for whole breast irradiation treatment planning using hybrid intensity modulated radiation therapy (IMRT) approach and to demonstrate that this workflow can improve planning quality and efficiency when compared to manual planning. Methods The auto planning framework was built based on scripting with MIM and Pinnacle systems. MIM workflows were developed to automatically segment normal structures and targets, identify landmarks for beam placement, select beam energies, and set beam configurations. Pinnacle scripts were generated from the MIM workflow to create hybrid IMRT plans automatically. Each hybrid IMRT plan included two prescriptions: a three‐dimensional (3D) prescription consisted of two open tangent beams, and an IMRT prescription consisted of two step‐and‐shoot IMRT beams. The 3D prescription delivered a full prescription dose to the maximum dose point, and the IMRT prescription was optimized to deliver a uniform dose to the entire breast while sparing dose to the normal structures. For 30 patients, the auto plans were compared with clinically accepted manual plans using the paired sample t‐test. Results The auto planning process took approximately 8 min to complete. The mean dice coefficients between auto‐segmentation and manual contours were 0.98, 0.94 and 0.88 for the lungs, heart, and PTVeval_Breast, respectively. The MUs of the auto plans was on average 13% higher than that of the manual plans. Auto planning improved plan quality significantly: percentage volume receiving 95% of the prescription dose (V95%) of the PTVeval_Breast increased from 91.5% to 93.2% (P = 0.001), V105% of the PTVeval_Breast decreased from 7.2% to 1.2% (P = 0.013), V20Gy of the ipsilateral lung decreased from 13.1% to 10.4% (P = 0.001) and mean heart dose for left‐sided breast patients decreased from 1.2 Gy to 0.9 Gy (P < 0.001). Conclusion An automated treatment planning process can make the planning process efficient with improved plan quality.
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Affiliation(s)
- Bingqi Guo
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Chirag Shah
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ping Xia
- Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
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van den Bogaard VA, van Dijk LV, Vliegenthart R, Sijtsema NM, Langendijk JA, Maduro JH, Crijns AP. Development and evaluation of an auto-segmentation tool for the left anterior descending coronary artery of breast cancer patients based on anatomical landmarks. Radiother Oncol 2019; 136:15-20. [DOI: 10.1016/j.radonc.2019.03.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/12/2019] [Accepted: 03/13/2019] [Indexed: 12/25/2022]
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30
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Kaderka R, Gillespie EF, Mundt RC, Bryant AK, Sanudo-Thomas CB, Harrison AL, Wouters EL, Moiseenko V, Moore KL, Atwood TF, Murphy JD. Geometric and dosimetric evaluation of atlas based auto-segmentation of cardiac structures in breast cancer patients. Radiother Oncol 2019; 131:215-220. [DOI: 10.1016/j.radonc.2018.07.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 06/20/2018] [Accepted: 07/16/2018] [Indexed: 12/25/2022]
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31
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Andrianarison VA, Laouiti M, Fargier-Bochaton O, Dipasquale G, Wang X, Nguyen NP, Miralbell R, Vinh-Hung V. Contouring workload in adjuvant breast cancer radiotherapy. Cancer Radiother 2018; 22:747-753. [PMID: 30322819 DOI: 10.1016/j.canrad.2018.01.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Revised: 01/22/2018] [Accepted: 01/24/2018] [Indexed: 12/20/2022]
Abstract
PURPOSE To measure the impact of contouring on worktime in the adjuvant radiation treatment of breast cancer, and to identify factors that might affect the measurements. MATERIAL AND METHODS The dates and times of contouring clinical target volumes and organs at risk were recorded by a senior and by two junior radiation oncologists. Outcome measurements were contour times and the time from start to approval. The factors evaluated were patient age, type of surgery, radiation targets and setup, operator, planning station, part of the day and day of the week on which the contouring started. The Welch test was used to comparatively assess the measurements. RESULTS Two hundred and three cases were included in the analysis. The mean contour time per patient was 34minutes for a mean of 4.72 structures, with a mean of 7.1minutes per structure. The clinical target volume and organs at risk times did not differ significantly. The mean time from start to approval per patient was 29.4hours. Factors significantly associated with longer contour times were breast-conserving surgery (P=0.026), prone setup (P=0.002), junior operator (P<0.0001), Pinnacle planning station (P=0.026), contouring start in the morning (P=0.001), and contouring start by the end of the week (P<0.0001). Factors significantly associated with time from start to approval were age (P=0.038), junior operator (P<0.0001), planning station (P=0.016), and contouring start by the end of the week (P=0.004). CONCLUSION Contouring is a time-consuming process. Each delineated structure influences worktime, and many factors may be targeted for optimization of the workflow. These preliminary data will serve as basis for future prospective studies to determine how to establish a cost-effective solution.
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Affiliation(s)
- V A Andrianarison
- Radiation Oncology, CHU de Martinique, boulevard Pasteur, 97200 Fort-de-France, Martinique; Joseph-Ravoahangy-Andrianavalona University Hospital, Antananarivo 101, Madagascar
| | - M Laouiti
- Radiation Oncology, hôpital Fribourgeois, 1708 Fribourg, Switzerland
| | - O Fargier-Bochaton
- Radiation Oncology, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - G Dipasquale
- Radiation Oncology, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - X Wang
- Radiation Oncology, Tianjin Union Medical Center, Tianjin 300121, China
| | - N P Nguyen
- Radiation Oncology, Howard University Hospital, Washington DC 20060, United States
| | - R Miralbell
- Radiation Oncology, Geneva University Hospitals, 1205 Geneva, Switzerland
| | - V Vinh-Hung
- Radiation Oncology, CHU de Martinique, boulevard Pasteur, 97200 Fort-de-France, Martinique.
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Yang J, Veeraraghavan H, Armato SG, Farahani K, Kirby JS, Kalpathy‐Kramer J, van Elmpt W, Dekker A, Han X, Feng X, Aljabar P, Oliveira B, van der Heyden B, Zamdborg L, Lam D, Gooding M, Sharp GC. Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med Phys 2018; 45:4568-4581. [PMID: 30144101 PMCID: PMC6714977 DOI: 10.1002/mp.13141] [Citation(s) in RCA: 127] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/15/2018] [Accepted: 08/15/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images. METHODS Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans. Eleven participants completed the offline challenge, and seven completed the online challenge. The OARs were left and right lungs, heart, esophagus, and spinal cord. Clinical contours used for treatment planning were quality checked and edited to adhere to the RTOG 1106 contouring guidelines. Algorithms were evaluated using the Dice coefficient, Hausdorff distance, and mean surface distance. A consolidated score was computed by normalizing the metrics against interrater variability and averaging over all patients and structures. RESULTS The interrater study revealed highest variability in Dice for the esophagus and spinal cord, and in surface distances for lungs and heart. Five out of seven algorithms that participated in the online challenge employed deep-learning methods. Although the top three participants using deep learning produced the best segmentation for all structures, there was no significant difference in the performance among them. The fourth place participant used a multi-atlas-based approach. The highest Dice scores were produced for lungs, with averages ranging from 0.95 to 0.98, while the lowest Dice scores were produced for esophagus, with a range of 0.55-0.72. CONCLUSION The results of the challenge showed that the lungs and heart can be segmented fairly accurately by various algorithms, while deep-learning methods performed better on the esophagus. Our dataset together with the manual contours for all training cases continues to be available publicly as an ongoing benchmarking resource.
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Affiliation(s)
- Jinzhong Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | | | | | - Keyvan Farahani
- Cancer Imaging ProgramNational Cancer InstituteBethesdaMDUSA
| | - Justin S. Kirby
- Cancer Imaging ProgramFrederick National Laboratory for Cancer Research sponsored by the National Cancer InstituteFrederickMDUSA
| | | | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO)GROW ‐ School for Oncology and Developmental BiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (MAASTRO)GROW ‐ School for Oncology and Developmental BiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Xiao Han
- Elekta Inc.Maryland HeightsMOUSA
| | - Xue Feng
- Department of Biomedical EngineeringUniversity of VirginiaCharlottesvilleVAUSA
| | | | - Bruno Oliveira
- Life and Health Sciences Research Institute (ICVS), School of MedicineUniversity of MinhoBragaPortugal
- ICVS/3Bs ‐ PT Government Associaste LaboratoryBraga/GuimaresPortugal
| | - Brent van der Heyden
- Department of Radiation Oncology (MAASTRO)GROW ‐ School for Oncology and Developmental BiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Leonid Zamdborg
- Department of Radiation OncologyBeaumont HealthRoyal OakMIUSA
| | - Dao Lam
- Department of Radiation OncologyWashington University School of Medicine in St. LouisSt. LouisMOUSA
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Men K, Zhang T, Chen X, Chen B, Tang Y, Wang S, Li Y, Dai J. 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. [PMID: 29891089 DOI: 10.1016/j.ejmp.2018.05.006] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Revised: 05/03/2018] [Accepted: 05/04/2018] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data. METHODS DD-ResNet was an end-to-end model enabling fast training and testing. We used big data comprising 800 patients who underwent breast-conserving therapy for evaluation. The CTV were validated by experienced radiation oncologists. We performed a fivefold cross-validation to test the performance of the model. The segmentation accuracy was quantified by the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). The performance of the proposed model was evaluated against two different deep learning models: deep dilated convolutional neural network (DDCNN) and deep deconvolutional neural network (DDNN). RESULTS Mean DSC values of DD-ResNet (0.91 and 0.91) were higher than the other two networks (DDCNN: 0.85 and 0.85; DDNN: 0.88 and 0.87) for both right-sided and left-sided BC. It also has smaller mean HD values of 10.5 mm and 10.7 mm compared with DDCNN (15.1 mm and 15.6 mm) and DDNN (13.5 mm and 14.1 mm). Mean segmentation time was 4 s, 21 s and 15 s per patient with DDCNN, DDNN and DD-ResNet, respectively. The DD-ResNet was also superior with regard to results in the literature. CONCLUSIONS The proposed method could segment the CTV accurately with acceptable time consumption. It was invariant to the body size and shape of patients and could improve the consistency of target delineation and streamline radiotherapy workflows.
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Affiliation(s)
- Kuo Men
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Tao Zhang
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xinyuan Chen
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Bo Chen
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yu Tang
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Shulian Wang
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Yexiong Li
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
| | - Jianrong Dai
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
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Medical physics in radiation Oncology: New challenges, needs and roles. Radiother Oncol 2017; 125:375-378. [PMID: 29150160 DOI: 10.1016/j.radonc.2017.10.035] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 10/30/2017] [Indexed: 12/21/2022]
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Ciardo D, Gerardi MA, Vigorito S, Morra A, Dell'acqua V, Diaz FJ, Cattani F, Zaffino P, Ricotti R, Spadea MF, Riboldi M, Orecchia R, Baroni G, Leonardi MC, Jereczek-Fossa BA. 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] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 11/21/2016] [Accepted: 12/18/2016] [Indexed: 12/22/2022] Open
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Quality assessment of delineation and dose planning of early breast cancer patients included in the randomized Skagen Trial 1. Radiother Oncol 2017; 123:282-287. [PMID: 28351523 DOI: 10.1016/j.radonc.2017.03.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 03/07/2017] [Accepted: 03/08/2017] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND PURPOSE To report on a Quality assessment (QA) of Skagen Trial 1, exploring hypofractionation for breast cancer patients with indication for regional nodal radiotherapy. MATERIAL AND METHODS Deviations from protocol regarding target volume delineations and dose parameters (Dmin, Dmax, D98%, D95% and D2%) from randomly selected dose plans were assessed. Target volume delineation according to ESTRO guidelines was obtained through atlas based automated segmentation and centrally approved as gold standard (GS). Dice similarity scores (DSC) with original delineations were measured. Dose parameters measured in the two delineations were reported to assess their dosimetric outcome. RESULTS Assessment included 88 plans from 12 centres in 4 countries. DSC showed high agreement in contouring, 99% and 96% of the patients had a complete delineation of target volumes and organs at risk. No deviations in the dosimetric outcome were found in 76% of the patients, 82% and 95% of the patients had successful coverage of breast/chestwall and CTVn_L2-4-interpectoral. Dosimetric outcomes of original delineation and GS were comparable. CONCLUSIONS QA showed high protocol compliance and adequate dose coverage in most patients. Inter-observer variability in contouring was low. Dose parameters were in harmony with protocol regardless original or GS segmentation.
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Eldesoky AR, Francolini G, Thomsen MS, Yates ES, Nyeng TB, Kirkove C, Kamby C, Blix ES, Nielsen MH, Taheri-Kadkhoda Z, Berg M, Offersen BV. Dosimetric assessment of an Atlas based automated segmentation for loco-regional radiation therapy of early breast cancer in the Skagen Trial 1: A multi-institutional study. Clin Transl Radiat Oncol 2017; 2:36-40. [PMID: 29657998 PMCID: PMC5893527 DOI: 10.1016/j.ctro.2017.01.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Revised: 01/08/2017] [Accepted: 01/10/2017] [Indexed: 11/29/2022] Open
Abstract
40 dose plans from the Skagen Trial 1 collected from Denmark, Belgium and Norway. Atlas-based automated segmentation of each CT scan was obtained using MIM Maestro™. DSC and difference in volume with manual segmentation were collected. HI, V95 and V90% measured on the two different segmentations were compared. Inter-observer variability was low and dose parameters were comparable.
The effect of Atlas-based automated segmentation (ABAS) on dose volume histogram (DVH) parameters compared to manual segmentation (MS) in loco-regional radiotherapy (RT) of early breast cancer was investigated in patients included in the Skagen Trial 1. This analysis supports implementation of ABAS in clinical practice and multi-institutional trials.
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Affiliation(s)
- Ahmed R Eldesoky
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark.,Department of Clinical Oncology and Nuclear Medicine, Mansoura University, 60 Elgomhoria st, Mansoura, Egypt
| | - Giulio Francolini
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark.,Department of Radiation Oncology, University of Florence, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Firenze, Florence, Italy
| | - Mette S Thomsen
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Esben S Yates
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Tine B Nyeng
- Department of Medical Physics, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
| | - Carine Kirkove
- Department of Radiation Oncology, Catholic University of Louvain, 10 Ave Hippocrate, B-1200 Brussels, Belgium
| | - Claus Kamby
- Department of Oncology, Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Egil S Blix
- Department of Oncology, University Hospital of North Norway, Sykehusvegen 38, 9019 Tromsø, Norway
| | - Mette H Nielsen
- Department of Oncology, Odense University Hospital, Institute of Clinical Research, University of Southern Denmark, Odense, Winslowparken 19, 3, DK-5000 Odense C, Denmark
| | - Zahra Taheri-Kadkhoda
- Department of Oncology, Zealand University Hospital, Sygeshusvej 10, 4000 Roskilde, Denmark
| | - Martin Berg
- Department of Medical Physics, Hospital of Vejle, Kabbeltoft 25, 7100 Vejle, Denmark
| | - Birgitte V Offersen
- Department of Oncology, Aarhus University Hospital, Norrebrogade 44, DK-8000 Aarhus C, Denmark
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