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Li Z, Gan G, Guo J, Zhan W, Chen L. Accurate object localization facilitates automatic esophagus segmentation in deep learning. Radiat Oncol 2024; 19:55. [PMID: 38735947 PMCID: PMC11088757 DOI: 10.1186/s13014-024-02448-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 05/01/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.
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Affiliation(s)
- Zhibin Li
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guanghui Gan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jian Guo
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Wei Zhan
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Long Chen
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
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Lee TH, Ahn SH, Chung K, Park W, Cho WK, Kim N, Kim TG, Kim H. Quantitative assessment of breast volume changes after whole-breast irradiation for breast cancer using breast auto-segmentation. Breast Cancer Res Treat 2024; 203:205-214. [PMID: 37833452 DOI: 10.1007/s10549-023-07146-0] [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: 06/28/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE This study aimed to quantitatively estimate the changes in breast volume associated with radiotherapy in patients undergoing breast-conserving surgery and whole-breast irradiation (WBI). METHODS Pre-WBI simulation computed tomography (CT) scans and post-WBI follow-up chest CT scans from a total of 1,151 breast cancer patients were analyzed using a deep-learning-driven auto-segmentation approach. The CT-based asymmetry index (CTAI) was calculated by dividing the volume of the irradiated breast by the volume of the contralateral breast. Significant breast shrinkage was defined as a CTAI < 0.85. To quantify changes in CTAI over the follow-up period, the CTAI ratio was determined as the post-WBI CTAI divided by the pre-WBI CTAI. A multivariate logistic regression analysis was conducted to identify potential variables associated with post-WBI significant breast shrinkage. RESULTS The median CTAI values for pre- and post-WBI CT scans were 0.973 (interquartile range: 0.887-1.069) and 0.866 (interquartile range: 0.773-0.967), respectively. The difference between them was statistically significant (p < 0.001). Following WBI, there was an increase in the rate of significant breast shrinkage from 16.3 to 44.8%. The CTAI ratio showed a negative association with the time interval (p < 0.001, Pearson r = - 0.310). In the multivariate logistic regression analysis, lower pre-WBI CTAI, younger age, and longer interval between CT scans were found to be significantly associated with a higher occurrence of post-WBI significant breast shrinkage. CONCLUSION Breast volume decreases following WBI, and this decrease is correlated with an increased duration after WBI. These findings highlight the long-term consequences of WBI on breast asymmetry.
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Affiliation(s)
- Tae Hoon Lee
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Sang Hee Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Kwangzoo Chung
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Won Kyung Cho
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Tae Gyu Kim
- Department of Radiation Oncology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
| | - Haeyoung Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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Weisman AJ, Huff DT, Govindan RM, Chen S, Perk TG. Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer. Biomed Phys Eng Express 2023; 9:065021. [PMID: 37725928 DOI: 10.1088/2057-1976/acfb06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/19/2023] [Indexed: 09/21/2023]
Abstract
Objective. Automated organ segmentation on CT images can enable the clinical use of advanced quantitative software devices, but model performance sensitivities must be understood before widespread adoption can occur. The goal of this study was to investigate performance differences between Convolutional Neural Networks (CNNs) trained to segment one (single-class) versus multiple (multi-class) organs, and between CNNs trained on scans from a single manufacturer versus multiple manufacturers.Methods. The multi-class CNN was trained on CT images obtained from 455 whole-body PET/CT scans (413 for training, 42 for testing) taken with Siemens, GE, and Phillips PET/CT scanners where 16 organs were segmented. The multi-class CNN was compared to 16 smaller single-class CNNs trained using the same data, but with segmentations of only one organ per model. In addition, CNNs trained on Siemens-only (N = 186) and GE-only (N = 219) scans (manufacturer-specific) were compared with CNNs trained on data from both Siemens and GE scanners (manufacturer-mixed). Segmentation performance was quantified using five performance metrics, including the Dice Similarity Coefficient (DSC).Results. The multi-class CNN performed well compared to previous studies, even in organs usually considered difficult auto-segmentation targets (e.g., pancreas, bowel). Segmentations from the multi-class CNN were significantly superior to those from smaller single-class CNNs in most organs, and the 16 single-class models took, on average, six times longer to segment all 16 organs compared to the single multi-class model. The manufacturer-mixed approach achieved minimally higher performance over the manufacturer-specific approach.Significance. A CNN trained on contours of multiple organs and CT data from multiple manufacturers yielded high-quality segmentations. Such a model is an essential enabler of image processing in a software device that quantifies and analyzes such data to determine a patient's treatment response. To date, this activity of whole organ segmentation has not been adopted due to the intense manual workload and time required.
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Affiliation(s)
- Amy J Weisman
- AIQ Solutions, Madison, WI, United States of America
| | - Daniel T Huff
- AIQ Solutions, Madison, WI, United States of America
| | | | - Song Chen
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
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Xie X, Song Y, Ye F, Wang S, Yan H, Zhao X, Dai J. Prior information guided auto-segmentation of clinical target volume of tumor bed in postoperative breast cancer radiotherapy. Radiat Oncol 2023; 18:170. [PMID: 37840132 PMCID: PMC10577969 DOI: 10.1186/s13014-023-02355-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/25/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Accurate delineation of clinical target volume of tumor bed (CTV-TB) is important but it is also challenging due to surgical effects and soft tissue contrast. Recently a few auto-segmentation methods were developed to improve the process. However, those methods had comparatively low segmentation accuracy. In this study the prior information was introduced to aid auto-segmentation of CTV-TB based on a deep-learning model. METHODS To aid the delineation of CTV-TB, the tumor contour on preoperative CT was transformed onto postoperative CT via deformable image registration. Both original and transformed tumor contours were used for prior information in training an auto-segmentation model. Then, the CTV-TB contour on postoperative CT was predicted by the model. 110 pairs of preoperative and postoperative CT images were used with a 5-fold cross-validation strategy. The predicted contour was compared with the clinically approved contour for accuracy evaluation using dice similarity coefficient (DSC) and Hausdorff distance. RESULTS The average DSC of the deep-learning model with prior information was improved than the one without prior information (0.808 vs. 0.734, P < 0.05). The average DSC of the deep-learning model with prior information was higher than that of the traditional method (0.808 vs. 0.622, P < 0.05). CONCLUSIONS The introduction of prior information in deep-learning model can improve segmentation accuracy of CTV-TB. The proposed method provided an effective way to automatically delineate CTV-TB in postoperative breast cancer radiotherapy.
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Affiliation(s)
- Xin Xie
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, No 420, Fuma Road, Jinan District, Fuzhou, 350011, China
| | - Yuchun Song
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Feng Ye
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Hui Yan
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Jianrong Dai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No 17, Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Mikalsen SG, Skjøtskift T, Flote VG, Hämäläinen NP, Heydari M, Rydén-Eilertsen K. Extensive clinical testing of Deep Learning Segmentation models for thorax and breast cancer radiotherapy planning. Acta Oncol 2023; 62:1184-1193. [PMID: 37883678 DOI: 10.1080/0284186x.2023.2270152] [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: 10/04/2023] [Indexed: 10/28/2023]
Abstract
BACKGROUND The performance of deep learning segmentation (DLS) models for automatic organ extraction from CT images in the thorax and breast regions was investigated. Furthermore, the readiness and feasibility of integrating DLS into clinical practice were addressed by measuring the potential time savings and dosimetric impact. MATERIAL AND METHODS Thirty patients referred to radiotherapy for breast cancer were prospectively included. A total of 23 clinically relevant left- and right-sided organs were contoured manually on CT images according to ESTRO guidelines. Next, auto-segmentation was executed, and the geometric agreement between the auto-segmented and manually contoured organs was qualitatively assessed applying a scale in the range [0-not acceptable, 3-no corrections]. A quantitative validation was carried out by calculating Dice coefficients (DSC) and the 95% percentile of Hausdorff distances (HD95). The dosimetric impact of optimizing the treatment plans on the uncorrected DLS contours, was investigated from a dose coverage analysis using DVH values of the manually delineated contours as references. RESULTS The qualitative analysis showed that 93% of the DLS generated OAR contours did not need corrections, except for the heart where 67% of the contours needed corrections. The majority of DLS generated CTVs needed corrections, whereas a minority were deemed not acceptable. Still, using the DLS-model for CTV and heart delineation is on average 14 minutes faster. An average DSC=0.91 and H95=9.8 mm were found for the left and right breasts, respectively. Likewise, and average DSC in the range [0.66, 0.76]mm and HD95 in the range [7.04, 12.05]mm were found for the lymph nodes. CONCLUSION The validation showed that the DLS generated OAR contours can be used clinically. Corrections were required to most of the DLS generated CTVs, and therefore warrants more attention before possibly implementing the DLS models clinically.
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Affiliation(s)
| | | | | | | | - Mojgan Heydari
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
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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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Baroudi H, Huy Minh Nguyen CI, Maroongroge S, Smith BD, Niedzielski JS, Shaitelman SF, Melancon A, Shete S, Whitaker TJ, Mitchell MP, Yvonne Arzu I, Duryea J, Hernandez S, El Basha D, Mumme R, Netherton T, Hoffman K, Court L. Automated contouring and statistical process control for plan quality in a breast clinical trial. Phys Imaging Radiat Oncol 2023; 28:100486. [PMID: 37712064 PMCID: PMC10498301 DOI: 10.1016/j.phro.2023.100486] [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] [Received: 05/04/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Background and purpose Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients' computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data's mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial.
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Affiliation(s)
- Hana Baroudi
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Callistus I. Huy Minh Nguyen
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sean Maroongroge
- Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Benjamin D. Smith
- Department of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joshua S. Niedzielski
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Simona F. Shaitelman
- Department of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Adam Melancon
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanjay Shete
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Thomas J. Whitaker
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Melissa P. Mitchell
- Department of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Isidora Yvonne Arzu
- Department of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jack Duryea
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Soleil Hernandez
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Daniel El Basha
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Raymond Mumme
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker Netherton
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karen Hoffman
- Department of Breast Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence Court
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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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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Bakx N, van der Sangen M, Theuws J, Bluemink H, Hurkmans C. Comparison of the output of a deep learning segmentation model for locoregional breast cancer radiotherapy trained on 2 different datasets. Tech Innov Patient Support Radiat Oncol 2023; 26:100209. [PMID: 37213441 PMCID: PMC10199413 DOI: 10.1016/j.tipsro.2023.100209] [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] [Received: 02/14/2023] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023] Open
Abstract
Introduction The development of deep learning (DL) models for auto-segmentation is increasing and more models become commercially available. Mostly, commercial models are trained on external data. To study the effect of using a model trained on external data, compared to the same model trained on in-house collected data, the performance of these two DL models was evaluated. Methods The evaluation was performed using in-house collected data of 30 breast cancer patients. Quantitative analysis was performed using Dice similarity coefficient (DSC), surface DSC (sDSC) and 95th percentile of Hausdorff Distance (95% HD). These values were compared with previously reported inter-observer variations (IOV). Results For a number of structures, statistically significant differences were found between the two models. For organs at risk, mean values for DSC ranged from 0.63 to 0.98 and 0.71 to 0.96 for the in-house and external model, respectively. For target volumes, mean DSC values of 0.57 to 0.94 and 0.33 to 0.92 were found. The difference of 95% HD values ranged 0.08 to 3.23 mm between the two models, except for CTVn4 with 9.95 mm. For the external model, both DSC and 95% HD are outside the range of IOV for CTVn4, whereas this is the case for the DSC found for the thyroid of the in-house model. Conclusions Statistically significant differences were found between both models, which were mostly within published inter-observer variations, showing clinical usefulness of both models. Our findings could encourage discussion and revision of existing guidelines, to further decrease inter-observer, but also inter-institute variability.
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Affiliation(s)
- Nienke Bakx
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | | | - Jacqueline Theuws
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | - Hanneke Bluemink
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
| | - Coen Hurkmans
- Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands
- Technical University Eindhoven, Faculties of Physics and Electrical Engineering, 5600MB Eindhoven, the Netherlands
- Corresponding author at: Catharina Hospital, Department of Radiation Oncology, 5602ZA Eindhoven, the Netherlands.
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Guzene L, Beddok A, Nioche C, Modzelewski R, Loiseau C, Salleron J, Thariat J. Assessing Interobserver Variability in the Delineation of Structures in Radiation Oncology: A Systematic Review. Int J Radiat Oncol Biol Phys 2023; 115:1047-1060. [PMID: 36423741 DOI: 10.1016/j.ijrobp.2022.11.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/04/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE The delineation of target volumes and organs at risk is the main source of uncertainty in radiation therapy. Numerous interobserver variability (IOV) studies have been conducted, often with unclear methodology and nonstandardized reporting. We aimed to identify the parameters chosen in conducting delineation IOV studies and assess their performances and limits. METHODS AND MATERIALS We conducted a systematic literature review to highlight major points of heterogeneity and missing data in IOV studies published between 2018 and 2021. For the main used metrics, we did in silico analyses to assess their limits in specific clinical situations. RESULTS All disease sites were represented in the 66 studies examined. Organs at risk were studied independently of tumor site in 29% of reviewed IOV studies. In 65% of studies, statistical analyses were performed. No gold standard (GS; ie, reference) was defined in 36% of studies. A single expert was considered as the GS in 21% of studies, without testing intraobserver variability. All studies reported both absolute and relative indices, including the Dice similarity coefficient (DSC) in 68% and the Hausdorff distance (HD) in 42%. Limitations were shown in silico for small structures when using the DSC and dependence on irregular shapes when using the HD. Variations in DSC values were large between studies, and their thresholds were inconsistent. Most studies (51%) included 1 to 10 cases. The median number of observers or experts was 7 (range, 2-35). The intraclass correlation coefficient was reported in only 9% of cases. Investigating the feasibility of studying IOV in delineation, a minimum of 8 observers with 3 cases, or 11 observers with 2 cases, was required to demonstrate moderate reproducibility. CONCLUSIONS Implementation of future IOV studies would benefit from a more standardized methodology: clear definitions of the gold standard and metrics and a justification of the tradeoffs made in the choice of the number of observers and number of delineated cases should be provided.
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Affiliation(s)
- Leslie Guzene
- Department of Radiation Oncology, University Hospital of Amiens, Amiens, France
| | - Arnaud Beddok
- Department of Radiation Oncology, Institut Curie, Paris/Saint-Cloud/Orsay, France; Laboratory of Translational Imaging in Oncology (LITO), InsermUMR, Institut Curie, Orsay, France
| | - Christophe Nioche
- Laboratory of Translational Imaging in Oncology (LITO), InsermUMR, Institut Curie, Orsay, France
| | - Romain Modzelewski
- LITIS - EA4108-Quantif, Normastic, University of Rouen, and Nuclear Medicine Department, Henri Becquerel Center, Rouen, France
| | - Cedric Loiseau
- Department of Radiation Oncology, Centre François Baclesse; ARCHADE Research Community Caen, France; Département de Biostatistiques, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France
| | - Julia Salleron
- Département de Biostatistiques, Institut de Cancérologie de Lorraine, Vandœuvre-lès-Nancy, France
| | - Juliette Thariat
- Department of Radiation Oncology, Centre François Baclesse; ARCHADE Research Community Caen, France; Laboratoire de Physique Corpusculaire, Caen, France; Unicaen-Université de Normandie, Caen, France.
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11
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De-Colle C, Kirby A, Russell N, Shaitelman S, Currey A, Donovan E, Hahn E, Han K, Anandadas C, Mahmood F, Lorenzen E, van den Bongard D, Groot Koerkamp M, Houweling A, Nachbar M, Thorwarth D, Zips D. Adaptive radiotherapy for breast cancer. Clin Transl Radiat Oncol 2023; 39:100564. [PMID: 36632056 PMCID: PMC9826896 DOI: 10.1016/j.ctro.2022.100564] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022] Open
Abstract
Research in the field of local and locoregional breast cancer radiotherapy aims to maintain excellent oncological outcomes while reducing treatment-related toxicity. Adaptive radiotherapy (ART) considers variations in target and organs at risk (OARs) anatomy occurring during the treatment course and integrates these in re-optimized treatment plans. Exploiting ART routinely in clinic may result in smaller target volumes and better OAR sparing, which may lead to reduction of acute as well as late toxicities. In this review MR-guided and CT-guided ART for breast cancer patients according to different clinical scenarios (neoadjuvant and adjuvant partial breast irradiation, whole breast, chest wall and regional nodal irradiation) are reviewed and their advantages as well as challenging aspects discussed.
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Affiliation(s)
- C. De-Colle
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - A. Kirby
- Department of Radiotherapy, Royal Marsden NHS Foundation Trust and Institute of Cancer Research, Sutton, United Kingdom
| | - N. Russell
- Department of Radiotherapy, The Netherlands Cancer Institute–Antoni van Leeuwenhoek Hospital, Amsterdam, Netherlands
| | - S.F. Shaitelman
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - A. Currey
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - E. Donovan
- Department of Radiation Oncology, Odette Cancer Centre - Sunnybrook Health Sciences Centre, Toronto, Canada
| | - E. Hahn
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - K. Han
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | - C.N. Anandadas
- Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - F. Mahmood
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | - E.L. Lorenzen
- Department of Oncology, Odense University Hospital, Odense, Denmark
| | | | - M.L. Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - A.C. Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - M. Nachbar
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
| | - D. Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology. University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - D. Zips
- Department of Radiation Oncology, University Hospital and Medical Faculty, Eberhard Karls University Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), partner site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany
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12
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Khalal DM, Azizi H, Maalej N. Automatic segmentation of kidneys in computed tomography images using U-Net. Cancer Radiother 2023; 27:109-114. [PMID: 36739197 DOI: 10.1016/j.canrad.2022.08.004] [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: 04/08/2022] [Revised: 08/03/2022] [Accepted: 08/12/2022] [Indexed: 02/05/2023]
Abstract
PURPOSE Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning. MATERIALS AND METHODS In this contribution, we used the CT scans of 20 patients. Segmentation of the kidneys was performed using the U-Net model. The Dice similarity coefficient (DSC), the Matthews correlation coefficient (MCC), the Hausdorff distance (HD), the sensitivity and the specificity were used to quantitatively evaluate this delineation. RESULTS This model was able to segment the organs with a good accuracy. The obtained values of the used metrics for the kidneys segmentation, were presented. Our results were also compared to those obtained recently by other authors. CONCLUSION Fully automated DL-based segmentation of CT images has the potential to improve both the speed and the accuracy of radiotherapy organs contouring.
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Affiliation(s)
- D M Khalal
- Laboratory of dosing, analysis and characterization in high resolution, Department of Physics, Faculty of Sciences, Ferhat Abbas Sétif 1 University, El Baz campus 19137, Sétif, Algeria.
| | - H Azizi
- Laboratory of dosing, analysis and characterization in high resolution, Department of Physics, Faculty of Sciences, Ferhat Abbas Sétif 1 University, El Baz campus 19137, Sétif, Algeria
| | - N Maalej
- Department of Physics, Khalifa University, Abu Dhabi, United Arab Emirates
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13
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Shen J, Gu P, Wang Y, Wang Z. Advances in automatic delineation of target volume and cardiac substructure in breast cancer radiotherapy (Review). Oncol Lett 2023; 25:110. [PMID: 36817059 PMCID: PMC9932716 DOI: 10.3892/ol.2023.13697] [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: 09/14/2022] [Accepted: 01/06/2023] [Indexed: 02/05/2023] Open
Abstract
Postoperative adjuvant radiotherapy plays an important role in the treatment of patients with breast cancer. With the continuous development of radiotherapeutic technologies, the requirements for radiotherapeutic accuracy are increasingly high. The accuracy of target volume and organ at risk delineation significantly affects the effect of radiotherapy. Automatic delineation software has been continuously developed for the automatic delineation of target areas and organs at risk. Automatic segmentation based on an atlas and deep learning is a hot topic in current clinical research. Automatic delineation can not only reduce the workload and delineation times, but also establish a uniform delineation standard and reduce inter-observer and intra-observer differences. In patients with breast cancer, especially in patients who undergo left breast radiotherapy, the protection of the heart is particularly important. Treating the whole heart as an organ at risk cannot meet the clinical needs, and it is necessary to limit the dose to specific cardiac substructures. The present review discusses the importance of automatic delineation of target volume and cardiac substructure in radiotherapy for patients with breast cancer.
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Affiliation(s)
- Jingjing Shen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Peihua Gu
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Yun Wang
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China
| | - Zhongming Wang
- Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Shanghai 200438, P.R. China,Correspondence to: Dr Zhongming Wang, Department of Oncology and Radiotherapy, Shidong Hospital Affiliated to University of Shanghai for Science and Technology, 999 Shiguang Road, Shanghai 200438, P.R. China, E-mail:
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14
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Mackay K, Bernstein D, Glocker B, Kamnitsas K, Taylor A. A Review of the Metrics Used to Assess Auto-Contouring Systems in Radiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:354-369. [PMID: 36803407 DOI: 10.1016/j.clon.2023.01.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/05/2022] [Accepted: 01/23/2023] [Indexed: 02/01/2023]
Abstract
Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of consensus on how to assess and validate auto-contouring systems currently limits clinical use. This review formally quantifies the assessment metrics used in studies published during one calendar year and assesses the need for standardised practice. A PubMed literature search was undertaken for papers evaluating radiotherapy auto-contouring published during 2021. Papers were assessed for types of metric and the methodology used to generate ground-truth comparators. Our PubMed search identified 212 studies, of which 117 met the criteria for clinical review. Geometric assessment metrics were used in 116 of 117 studies (99.1%). This includes the Dice Similarity Coefficient used in 113 (96.6%) studies. Clinically relevant metrics, such as qualitative, dosimetric and time-saving metrics, were less frequently used in 22 (18.8%), 27 (23.1%) and 18 (15.4%) of 117 studies, respectively. There was heterogeneity within each category of metric. Over 90 different names for geometric measures were used. Methods for qualitative assessment were different in all but two papers. Variation existed in the methods used to generate radiotherapy plans for dosimetric assessment. Consideration of editing time was only given in 11 (9.4%) papers. A single manual contour as a ground-truth comparator was used in 65 (55.6%) studies. Only 31 (26.5%) studies compared auto-contours to usual inter- and/or intra-observer variation. In conclusion, significant variation exists in how research papers currently assess the accuracy of automatically generated contours. Geometric measures are the most popular, however their clinical utility is unknown. There is heterogeneity in the methods used to perform clinical assessment. Considering the different stages of system implementation may provide a framework to decide the most appropriate metrics. This analysis supports the need for a consensus on the clinical implementation of auto-contouring.
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Affiliation(s)
- K Mackay
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK.
| | - D Bernstein
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
| | - B Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - K Kamnitsas
- Department of Computing, Imperial College London, South Kensington Campus, London, UK; Department of Engineering Science, University of Oxford, Oxford, UK
| | - A Taylor
- The Institute of Cancer Research, London, UK; The Royal Marsden Hospital, London, UK
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15
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Chung SY, Chang JS, Kim YB. Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer. Front Oncol 2023; 13:1119008. [PMID: 37188180 PMCID: PMC10175826 DOI: 10.3389/fonc.2023.1119008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
Background and purpose Deep learning-based models have been actively investigated for various aspects of radiotherapy. However, for cervical cancer, only a few studies dealing with the auto-segmentation of organs-at-risk (OARs) and clinical target volumes (CTVs) exist. This study aimed to train a deep learning-based auto-segmentation model for OAR/CTVs for patients with cervical cancer undergoing radiotherapy and to evaluate the model's feasibility and efficacy with not only geometric indices but also comprehensive clinical evaluation. Materials and methods A total of 180 abdominopelvic computed tomography images were included (training set, 165; validation set, 15). Geometric indices such as the Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD) were analyzed. A Turing test was performed and physicians from other institutions were asked to delineate contours with and without using auto-segmented contours to assess inter-physician heterogeneity and contouring time. Results The correlation between the manual and auto-segmented contours was acceptable for the anorectum, bladder, spinal cord, cauda equina, right and left femoral heads, bowel bag, uterocervix, liver, and left and right kidneys (DSC greater than 0.80). The stomach and duodenum showed DSCs of 0.67 and 0.73, respectively. CTVs showed DSCs between 0.75 and 0.80. Turing test results were favorable for most OARs and CTVs. No auto-segmented contours had large, obvious errors. The median overall satisfaction score of the participating physicians was 7 out of 10. Auto-segmentation reduced heterogeneity and shortened contouring time by 30 min among radiation oncologists from different institutions. Most participants favored the auto-contouring system. Conclusion The proposed deep learning-based auto-segmentation model may be an efficient tool for patients with cervical cancer undergoing radiotherapy. Although the current model may not completely replace humans, it can serve as a useful and efficient tool in real-world clinics.
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Affiliation(s)
- Seung Yeun Chung
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea
- *Correspondence: Yong Bae Kim,
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Artificial intelligence and machine learning in cancer imaging. COMMUNICATIONS MEDICINE 2022; 2:133. [PMID: 36310650 PMCID: PMC9613681 DOI: 10.1038/s43856-022-00199-0] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 10/06/2022] [Indexed: 11/16/2022] Open
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.
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Kim JS, Kim JH, Chang JH, Kim DW, Shin KH. Prediction of breast cancer-related lymphedema risk after postoperative radiotherapy via multivariable logistic regression analysis. Front Oncol 2022; 12:1026043. [PMID: 36387231 PMCID: PMC9643832 DOI: 10.3389/fonc.2022.1026043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/04/2022] [Indexed: 08/04/2023] Open
Abstract
PURPOSE We identified novel clinical and dosimetric prognostic factors affecting breast cancer-related lymphedema after postoperative radiotherapy (RT) and developed a multivariable logistic regression model to predict lymphedema in these patients. METHODS AND MATERIALS In total, 580 patients with unilateral breast cancer were retrospectively reviewed. All patients underwent breast surgery and postoperative RT with or without systemic treatment in 2015. Among the 580 patients, 532 with available RT plan data were randomly divided into training (n=372) and test (n=160) cohorts at a 7:3 ratio to generate and validate the lymphedema prediction models, respectively. An area under the curve (AUC) value was estimated to compare models. RESULTS The median follow-up duration was 5.4 years. In total, 104 (17.9%) patients experienced lymphedema with a cumulative incidence as follows: 1 year, 10.5%; 3 years, 16.4%; and 5 years, 17.6%. Multivariate analysis showed that body mass index ≥25 kg/m2 (hazard ratio [HR] 1.845), dissected lymph nodes ≥7 (HR 1.789), and taxane-base chemotherapy (HR 4.200) were significantly associated with increased lymphedema risk. Conversely, receipt of RT at least 1 month after surgery reduced the risk of lymphedema (HR 0.638). A multivariable logistic regression model using the above factors, as well as the minimum dose of axillary level I and supraclavicular lymph node, was created with an AUC of 0.761 and 0.794 in the training and test cohorts, respectively. CONCLUSIONS Our study demonstrated that a shorter interval from surgery to RT and other established clinical factors were associated with increased lymphedema risk. By combining these factors with two dosimetric parameters, we propose a multivariable logistic regression model for breast cancer-related lymphedema prediction after RT.
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Affiliation(s)
- Jae Sik Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Soonchunhyang University Seoul Hospital, Seoul, South Korea
| | - Jin Ho Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea
| | - Ji Hyun Chang
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea
| | - Do Wook Kim
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea
| | - Kyung Hwan Shin
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, South Korea
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Cheng T, Zhang Z, Yang X, Lu S, Qian D, Wang X, Zhu H. Automatic delineation of organ at risk in cervical cancer radiotherapy based on ensemble learning. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2022; 47:1058-1064. [PMID: 36097773 PMCID: PMC10950118 DOI: 10.11817/j.issn.1672-7347.2022.220101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES The automatic delineation of organs at risk (OARs) can help doctors make radiotherapy plans efficiently and accurately, and effectively improve the accuracy of radiotherapy and the therapeutic effect. Therefore, this study aims to propose an automatic delineation method for OARs in cervical cancer scenarios of both after-loading and external irradiation. At the same time, the similarity of OARs structure between different scenes is used to improve the segmentation accuracy of OARs in difficult segmentations. METHODS Our ensemble model adopted the strategy of ensemble learning. The model obtained from the pre-training based on the after-loading and external irradiation was introduced into the integrated model as a feature extraction module. The data in different scenes were trained alternately, and the personalized features of the OARs within the model and the common features of the OARs between scenes were introduced. Computer tomography (CT) images for 84 cases of after-loading and 46 cases of external irradiation were collected as the train data set. Five-fold cross-validation was adopted to split training sets and test sets. The five-fold average dice similarity coefficient (DSC) served as the figure-of-merit in evaluating the segmentation model. RESULTS The DSCs of the OARs (the rectum and bladder in the after-loading images and the bladder in the external irradiation images) were higher than 0.7. Compared with using an independent residual U-net (convolutional networks for biomedical image segmentation) model [residual U-net (Res-Unet)] delineate OARs, the proposed model can effectively improve the segmentation performance of difficult OARs (the sigmoid in the after-loading CT images and the rectum in the external irradiation images), and the DSCs were increased by more than 3%. CONCLUSIONS Comparing to the dedicated models, our ensemble model achieves the comparable result in segmentation of OARs for different treatment options in cervical cancer radiotherapy, which may be shorten time for doctors to sketch OARs and improve doctor's work efficiency.
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Affiliation(s)
- Tingting Cheng
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Changsha 410008.
| | - Zijian Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Changsha 410008.
| | - Xin Yang
- Guangzhou Perception Vision Medical Technologies Limited Company, Guangzhou 510530
| | - Shanfu Lu
- Guangzhou Perception Vision Medical Technologies Limited Company, Guangzhou 510530
| | - Dongdong Qian
- Guangzhou Perception Vision Medical Technologies Limited Company, Guangzhou 510530
| | - Xianliang Wang
- Department of Radiotherapy Center, Sichuan Cancer Hospital, Chengdu 610041, China
| | - Hong Zhu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Changsha 410008
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Im JH, Lee IJ, Choi Y, Sung J, Ha JS, Lee H. Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning. Cancers (Basel) 2022; 14:cancers14153581. [PMID: 35892839 PMCID: PMC9332287 DOI: 10.3390/cancers14153581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/08/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023] Open
Abstract
Objective: This study aimed to investigate the segmentation accuracy of organs at risk (OARs) when denoised computed tomography (CT) images are used as input data for a deep-learning-based auto-segmentation framework. Methods: We used non-contrast enhanced planning CT scans from 40 patients with breast cancer. The heart, lungs, esophagus, spinal cord, and liver were manually delineated by two experienced radiation oncologists in a double-blind manner. The denoised CT images were used as input data for the AccuContourTM segmentation software to increase the signal difference between structures of interest and unwanted noise in non-contrast CT. The accuracy of the segmentation was assessed using the Dice similarity coefficient (DSC), and the results were compared with those of conventional deep-learning-based auto-segmentation without denoising. Results: The average DSC outcomes were higher than 0.80 for all OARs except for the esophagus. AccuContourTM-based and denoising-based auto-segmentation demonstrated comparable performance for the lungs and spinal cord but showed limited performance for the esophagus. Denoising-based auto-segmentation for the liver was minimal but had statistically significantly better DSC than AccuContourTM-based auto-segmentation (p < 0.05). Conclusions: Denoising-based auto-segmentation demonstrated satisfactory performance in automatic liver segmentation from non-contrast enhanced CT scans. Further external validation studies with larger cohorts are needed to verify the usefulness of denoising-based auto-segmentation.
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Affiliation(s)
- Jung Ho Im
- CHA Bundang Medical Center, Department of Radiation Oncology, CHA University School of Medicine, Seongnam 13496, Korea;
| | - Ik Jae Lee
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul 03722, Korea; (I.J.L.); (J.S.)
| | - Yeonho Choi
- Department of Radiation Oncology, Gangnam Severance Hospital, Seoul 06273, Korea; (Y.C.); (J.S.H.)
| | - Jiwon Sung
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul 03722, Korea; (I.J.L.); (J.S.)
| | - Jin Sook Ha
- Department of Radiation Oncology, Gangnam Severance Hospital, Seoul 06273, Korea; (Y.C.); (J.S.H.)
| | - Ho Lee
- Department of Radiation Oncology, Yonsei University College of Medicine, Seoul 03722, Korea; (I.J.L.); (J.S.)
- Correspondence: ; Tel.: +82-2-2228-8109; Fax: +82-2-2227-7823
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Lempart M, Nilsson MP, Scherman J, Gustafsson CJ, Nilsson M, Alkner S, Engleson J, Adrian G, Munck Af Rosenschöld P, Olsson LE. Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network. Radiat Oncol 2022; 17:114. [PMID: 35765038 PMCID: PMC9238000 DOI: 10.1186/s13014-022-02088-1] [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] [Received: 03/07/2022] [Accepted: 06/20/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Delineation of organs at risk (OAR) for anal cancer radiation therapy treatment planning is a manual and time-consuming process. Deep learning-based methods can accelerate and partially automate this task. The aim of this study was to develop and evaluate a deep learning model for automated and improved segmentations of OAR in the pelvic region. METHODS A 3D, deeply supervised U-Net architecture with shuffle attention, referred to as Pelvic U-Net, was trained on 143 computed tomography (CT) volumes, to segment OAR in the pelvic region, such as total bone marrow, rectum, bladder, and bowel structures. Model predictions were evaluated on an independent test dataset (n = 15) using the Dice similarity coefficient (DSC), the 95th percentile of the Hausdorff distance (HD95), and the mean surface distance (MSD). In addition, three experienced radiation oncologists rated model predictions on a scale between 1-4 (excellent, good, acceptable, not acceptable). Model performance was also evaluated with respect to segmentation time, by comparing complete manual delineation time against model prediction time without and with manual correction of the predictions. Furthermore, dosimetric implications to treatment plans were evaluated using different dose-volume histogram (DVH) indices. RESULTS Without any manual corrections, mean DSC values of 97%, 87% and 94% were found for total bone marrow, rectum, and bladder. Mean DSC values for bowel cavity, all bowel, small bowel, and large bowel were 95%, 91%, 87% and 81%, respectively. Total bone marrow, bladder, and bowel cavity segmentations derived from our model were rated excellent (89%, 93%, 42%), good (9%, 5%, 42%), or acceptable (2%, 2%, 16%) on average. For almost all the evaluated DVH indices, no significant difference between model predictions and manual delineations was found. Delineation time per patient could be reduced from 40 to 12 min, including manual corrections of model predictions, and to 4 min without corrections. CONCLUSIONS Our Pelvic U-Net led to credible and clinically applicable OAR segmentations and showed improved performance compared to previous studies. Even though manual adjustments were needed for some predicted structures, segmentation time could be reduced by 70% on average. This allows for an accelerated radiation therapy treatment planning workflow for anal cancer patients.
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Affiliation(s)
- Michael Lempart
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden. .,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden.
| | - Martin P Nilsson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Jonas Scherman
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Mikael Nilsson
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Sara Alkner
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Jens Engleson
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Gabriel Adrian
- Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Clinical Sciences, Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Per Munck Af Rosenschöld
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Lars E Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden.,Department of Translational Medicine, Medical Radiation Physics, Lund University, Malmö, Sweden
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21
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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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22
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Kawula M, Purice D, Li M, Vivar G, Ahmadi SA, Parodi K, Belka C, Landry G, Kurz C. Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer. Radiat Oncol 2022; 17:21. [PMID: 35101068 PMCID: PMC8805311 DOI: 10.1186/s13014-022-01985-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 01/10/2022] [Indexed: 12/19/2022] Open
Abstract
Background The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients. Methods A database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated. Results 3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V\documentclass[12pt]{minimal}
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\begin{document}$$_{60/65/70\,{\mathrm{Gy}}}$$\end{document}60/65/70Gy for the bladder and V\documentclass[12pt]{minimal}
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\begin{document}$$_{98/2\%}$$\end{document}98/2% and V\documentclass[12pt]{minimal}
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\begin{document}$$_{95\%}$$\end{document}95%, for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation. Conclusions The 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.
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23
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Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2022; 3:795-816. [PMID: 36654817 PMCID: PMC9834285 DOI: 10.37349/etat.2022.00113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 12/28/2022] Open
Abstract
The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.
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Affiliation(s)
- Maurizio Cè
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy,Correspondence: Maurizio Cè, Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy.
| | - Elena Caloro
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Maria E. Pellegrino
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Mariachiara Basile
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | - Adriana Sorce
- Postgraduate School in Diagnostic and Interventional Radiology, University of Milan, 20122 Milan, Italy
| | | | - Giancarlo Oliva
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Michaela Cellina
- Department of Radiology, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
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24
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Research on the Characteristics of Food Impaction with Tight Proximal Contacts Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:1000820. [PMID: 34777558 PMCID: PMC8589471 DOI: 10.1155/2021/1000820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/11/2021] [Indexed: 11/17/2022]
Abstract
Objective Based on deep learning, the characteristics of food impaction with tight proximal contacts were studied to guide the subsequent clinical treatment of occlusal adjustment. At the same time, digital model building, software measurement, and statistical correlation analysis were used to explore the cause of tooth impaction and to provide evidence for clinical treatment. Methods Volunteers with (n = 250) and without (n = 250) tooth impaction were recruited, respectively, to conduct a questionnaire survey. Meanwhile, models were made and perfused by skilled clinical physicians for these patients, and characteristics such as adjacent line length, adjacent surface area, tongue abduction gap angle, buccal abduction gap angle, and occlusal abduction gap angle were measured. A normality test, differential analysis, correlation analysis of pathological characteristics of the impaction group, principal component analysis (PCA), and binary logistic regression analysis were performed. Results The adjacent line length, adjacent surface area, tongue abduction gap angle, buccal abduction gap angle, and occlusal abduction gap angle all met normal distribution. There were statistically significant differences in adjacent line length (p < 0.001), adjacent surface area (p < 0.001), and occlusal abduction gap angle (p < 0.001) between the two groups. After dimensionality reduction by PCA on characteristics, adjacent line length, adjacent surface area, buccal abduction gap angle, and occlusal abduction gap angle had a strong correlation with the principal components. Binary logistic regression analysis showed that adjacent line length and adjacent surface area had positive effects on impaction. The buccal abduction gap angle and occlusal abduction gap angle had a significant negative influence on impaction. Conclusion Adjacent line length, adjacent surface area, buccal abduction gap angle, and occlusal abduction gap angle are independent factors influencing food impaction.
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25
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Byun HK, Chang JS, Choi MS, Chun J, Jung J, Jeong C, Kim JS, Chang Y, Chung SY, Lee S, Kim YB. Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy. Radiat Oncol 2021; 16:203. [PMID: 34649569 PMCID: PMC8518257 DOI: 10.1186/s13014-021-01923-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 09/27/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. Methods Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to compare the degree of agreement between the best manual contour (chosen by an independent expert committee) and each autocontour, corrected autocontour, and manual contour. Higher DSCs and lower HDs indicated a better geometric overlap. The amount of time reduction using the autocontouring system was examined. User satisfaction was evaluated using a survey. Results Manual contours, corrected autocontours, and autocontours had a similar accuracy in the average DSC value (0.88 vs. 0.90 vs. 0.90). The accuracy of autocontours ranked the second place, based on DSCs, and the first place, based on HDs among the manual contours. Interphysician variations among the experts were reduced in corrected autocontours, compared to variations in manual contours (DSC: 0.89–0.90 vs. 0.87–0.90; HD: 4.3–5.8 mm vs. 5.3–7.6 mm). Among the manual delineations, the breast contours had the largest variations, which improved most significantly with the autocontouring system. The total mean times for nine OARs were 37 min for manual contours and 6 min for corrected autocontours. The results of the survey revealed good user satisfaction. Conclusions The autocontouring system had a similar performance in OARs as that of the experts’ manual contouring. This system can be valuable in improving the quality of breast radiotherapy and reducing interphysician variability in clinical practice. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01923-1.
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Affiliation(s)
- Hwa Kyung Byun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jee Suk Chang
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
| | - Min Seo Choi
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jaehee Chun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jinhong Jung
- Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
| | - Chiyoung Jeong
- Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | | | - Seung Yeun Chung
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.,Department of Radiation Oncology, Ajou University School of Medicine, Suwon, South Korea
| | - Seungryul Lee
- Yonsei University College of Medicine, Seoul, South Korea
| | - Yong Bae Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
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Robert C, Munoz A, Moreau D, Mazurier J, Sidorski G, Gasnier A, Beldjoudi G, Grégoire V, Deutsch E, Meyer P, Simon L. Clinical implementation of deep-learning based auto-contouring tools-Experience of three French radiotherapy centers. Cancer Radiother 2021; 25:607-616. [PMID: 34389243 DOI: 10.1016/j.canrad.2021.06.023] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/23/2022]
Abstract
Deep-learning (DL)-based auto-contouring solutions have recently been proposed as a convincing alternative to decrease workload of target volumes and organs-at-risk (OAR) delineation in radiotherapy planning and improve inter-observer consistency. However, there is minimal literature of clinical implementations of such algorithms in a clinical routine. In this paper we first present an update of the state-of-the-art of DL-based solutions. We then summarize recent recommendations proposed by the European society for radiotherapy and oncology (ESTRO) to be followed before any clinical implementation of artificial intelligence-based solutions in clinic. The last section describes the methodology carried out by three French radiation oncology departments to deploy CE-marked commercial solutions. Based on the information collected, a majority of OAR are retained by the centers among those proposed by the manufacturers, validating the usefulness of DL-based models to decrease clinicians' workload. Target volumes, with the exception of lymph node areas in breast, head and neck and pelvic regions, whole breast, breast wall, prostate and seminal vesicles, are not available in the three commercial solutions at this time. No implemented workflows are currently available to continuously improve the models, but these can be adapted/retrained in some solutions during the commissioning phase to best fit local practices. In reported experiences, automatic workflows were implemented to limit human interactions and make the workflow more fluid. Recommendations published by the ESTRO group will be of importance for guiding physicists in the clinical implementation of patient specific and regular quality assurances.
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Affiliation(s)
- C Robert
- Department of Radiotherapy, Gustave-Roussy, Villejuif, France.
| | - A Munoz
- Department of Radiotherapy, Centre Léon-Bérard, Lyon, France
| | - D Moreau
- Department of Radiotherapy, Hôpital Européen Georges-Pompidou, Paris, France
| | - J Mazurier
- Department of Radiotherapy, Clinique Pasteur-Oncorad, Toulouse, France
| | - G Sidorski
- Department of Radiotherapy, Clinique Pasteur-Oncorad, Toulouse, France
| | - A Gasnier
- Department of Radiotherapy, Gustave-Roussy, Villejuif, France
| | - G Beldjoudi
- Department of Radiotherapy, Centre Léon-Bérard, Lyon, France
| | - V Grégoire
- Department of Radiotherapy, Centre Léon-Bérard, Lyon, France
| | - E Deutsch
- Department of Radiotherapy, Gustave-Roussy, Villejuif, France
| | - P Meyer
- Service d'Oncologie Radiothérapie, Institut de Cancérologie Strasbourg Europe (Icans), Strasbourg, France
| | - L Simon
- Institut Claudius Regaud (ICR), Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT-O), Toulouse, France
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Liu X, Li KW, Yang R, Geng LS. Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy. Front Oncol 2021; 11:717039. [PMID: 34336704 PMCID: PMC8323481 DOI: 10.3389/fonc.2021.717039] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 06/21/2021] [Indexed: 12/14/2022] Open
Abstract
Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.
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Affiliation(s)
- Xi Liu
- School of Physics, Beihang University, Beijing, China
| | - Kai-Wen Li
- School of Physics, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Li-Sheng Geng
- School of Physics, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing, China
- Beijing Key Laboratory of Advanced Nuclear Materials and Physics, Beihang University, Beijing, China
- School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, China
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Wong J, Huang V, Wells D, Giambattista J, Giambattista J, Kolbeck C, Otto K, Saibishkumar EP, Alexander A. Implementation of deep learning-based auto-segmentation for radiotherapy planning structures: a workflow study at two cancer centers. Radiat Oncol 2021; 16:101. [PMID: 34103062 PMCID: PMC8186196 DOI: 10.1186/s13014-021-01831-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 06/01/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose We recently described the validation of deep learning-based auto-segmented contour (DC) models for organs at risk (OAR) and clinical target volumes (CTV). In this study, we evaluate the performance of implemented DC models in the clinical radiotherapy (RT) planning workflow and report on user experience. Methods and materials DC models were implemented at two cancer centers and used to generate OAR and CTVs for all patients undergoing RT for a central nervous system (CNS), head and neck (H&N), or prostate cancer. Radiation Therapists/Dosimetrists and Radiation Oncologists completed post-contouring surveys rating the degree of edits required for DCs (1 = minimal, 5 = significant) and overall DC satisfaction (1 = poor, 5 = high). Unedited DCs were compared to the edited treatment approved contours using Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD). Results Between September 19, 2019 and March 6, 2020, DCs were generated on approximately 551 eligible cases. 203 surveys were collected on 27 CNS, 54 H&N, and 93 prostate RT plans, resulting in an overall survey compliance rate of 32%. The majority of OAR DCs required minimal edits subjectively (mean editing score ≤ 2) and objectively (mean DSC and 95% HD was ≥ 0.90 and ≤ 2.0 mm). Mean OAR satisfaction score was 4.1 for CNS, 4.4 for H&N, and 4.6 for prostate structures. Overall CTV satisfaction score (n = 25), which encompassed the prostate, seminal vesicles, and neck lymph node volumes, was 4.1. Conclusions Previously validated OAR DC models for CNS, H&N, and prostate RT planning required minimal subjective and objective edits and resulted in a positive user experience, although low survey compliance was a concern. CTV DC model evaluation was even more limited, but high user satisfaction suggests that they may have served as appropriate starting points for patient specific edits. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01831-4.
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Affiliation(s)
- Jordan Wong
- BC Cancer - Vancouver, 600 W 10th Ave, Rm 4550, Vancouver, BC, V5Z 4E6, Canada.
| | - Vicky Huang
- BC Cancer - Fraser Valley, 13750 96th Avenue, Surrey, BC, V3V 1Z2, Canada
| | - Derek Wells
- BC Cancer - Victoria, 2410 Lee Avenue, Victoria, BC, V8R 6V5, Canada
| | - Joshua Giambattista
- Saskatchewan Cancer Agency, 503-1801 Hamilton St, Regina, SK, S4P 4B4, Canada.,Limbus AI Inc, 2076 Athol Street, Regina, SK, S4T 3E5, Canada
| | | | - Carter Kolbeck
- Limbus AI Inc, 2076 Athol Street, Regina, SK, S4T 3E5, Canada
| | - Karl Otto
- Limbus AI Inc, 2076 Athol Street, Regina, SK, S4T 3E5, Canada
| | | | - Abraham Alexander
- BC Cancer - Victoria, 2410 Lee Avenue, Victoria, BC, V8R 6V5, Canada
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