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Hizam DA, Tan LK, Saad M, Muaadz A, Ung NM. Comparison of commercial atlas-based automatic segmentation software for prostate radiotherapy treatment planning. Phys Eng Sci Med 2024:10.1007/s13246-024-01411-2. [PMID: 38647633 DOI: 10.1007/s13246-024-01411-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/20/2024] [Indexed: 04/25/2024]
Abstract
This study aims to assess the accuracy of automatic atlas-based contours for various key anatomical structures in prostate radiotherapy treatment planning. The evaluated structures include the bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. CT images from 20 patients who underwent intensity-modulated radiotherapy were randomly chosen to create an atlas library. Atlas contours of the seven anatomical structures were generated using four software packages: ABAS, Eclipse, MIM, and RayStation. These contours were then compared to manual delineations performed by oncologists, which served as the ground truth. Evaluation metrics such as dice similarity coefficient (DSC), mean distance to agreement (MDA), and volume ratio (VR) were calculated to assess the accuracy of the contours. Additionally, the time taken by each software to generate the atlas contour was recorded. The mean DSC values for the bladder exhibited strong agreement (>0.8) with manual delineations for all software except for Eclipse and RayStation. Similarly, the femoral heads showed significant similarity between the atlas contours and ground truth across all software, with mean DSC values exceeding 0.9 and MDA values close to zero. On the other hand, the penile bulb displayed only moderate agreement with the ground truth, with mean DSC values ranging from 0.5 to 0.7 for all software. A similar trend was observed in the prostate atlas contours, except for MIM, which achieved a mean DSC of over 0.8. For the rectum, both ABAS and MIM atlases demonstrated strong agreement with the ground truth, resulting in mean DSC values of more than 0.8. Overall, MIM and ABAS outperformed Eclipse and RayStation in both DSC and MDA. These results indicate that the atlas-based segmentation employed in this study produces acceptable contours for the anatomical structures of interest in prostate radiotherapy treatment planning.
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Affiliation(s)
- Diyana Afrina Hizam
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Li Kuo Tan
- Department of Biomedical Imaging, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Marniza Saad
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Asyraf Muaadz
- Department of Clinical Oncology, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Ngie Min Ung
- Clinical Oncology Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
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Wen X, Zhao C, Zhao B, Yuan M, Chang J, Liu W, Meng J, Shi L, Yang S, Zeng J, Yang Y. Application of deep learning in radiation therapy for cancer. Cancer Radiother 2024; 28:208-217. [PMID: 38519291 DOI: 10.1016/j.canrad.2023.07.015] [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: 06/26/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 03/24/2024]
Abstract
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. This study summarizes the commonly used and latest deep learning algorithms (including transformer, and diffusion models), introduces the workflow of different radiotherapy, and illustrates the application of different algorithms in different radiotherapy modules, as well as the defects and challenges of deep learning in the field of radiotherapy, so as to provide some help for the development of automatic radiotherapy for cancer.
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Affiliation(s)
- X Wen
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - C Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai, China
| | - B Zhao
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - M Yuan
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - J Chang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - W Liu
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Meng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - L Shi
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - S Yang
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - J Zeng
- Cancer Institute of the Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao University, Qingdao, China; School of Basic Medicine, Qingdao University, Qingdao, China
| | - Y Yang
- Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
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Singh S, Singh BK, Kumar A. Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique. Radiography (Lond) 2024; 30:673-680. [PMID: 38364707 DOI: 10.1016/j.radi.2024.02.001] [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: 06/22/2023] [Revised: 11/25/2023] [Accepted: 02/05/2024] [Indexed: 02/18/2024]
Abstract
INTRODUCTION This paper presents a novel approach to automate the segmentation of Organ-at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined with Ensemble Learning techniques. The study aims to improve the accuracy and efficiency of OAR segmentation, essential for radiotherapy treatment planning. METHODS The dataset comprised computed tomography (CT) scans of 182 patients in DICOM format, obtained from an institutional image bank. Experienced Radiation Oncologists manually segmented seven OARs for each scan. Two models, 3D U-Net and 3D DenseNet-FCN, were trained on reduced CT scans (192 × 192 x 128) due to memory limitations. Ensemble Learning techniques were employed to enhance accuracy and segmentation metrics. Testing was conducted on 78 patients from the institutional dataset and an open-source dataset (TCGA-HNSC and Head-Neck Cetuximab) consisting of 31 patient scans. RESULTS Using the Ensemble Learning technique, the average dice similarity coefficient for OARs ranged from 0.990 to 0.994, indicating high segmentation accuracy. The 95% Hausdorff distance (mm) ranged from 1.3 to 2.1, demonstrating precise segmentation boundaries. CONCLUSION The proposed automated segmentation method achieved efficient and accurate OAR segmentation, surpassing human expert performance in terms of time and accuracy. IMPLICATIONS FOR PRACTICE This approach has implications for improving treatment planning and patient care in radiotherapy. By reducing manual segmentation reliance, the proposed method offers significant time savings and potential improvements in treatment planning efficiency and precision for head and neck cancer patients.
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Affiliation(s)
- S Singh
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India; Department of Radiation Oncology, Division of Medical Physics, Rajiv Gandhi Cancer Institute and Research Center, New Delhi, India.
| | - B K Singh
- Department of Physics, GLA University, Mathura, Uttar Pradesh, India.
| | - A Kumar
- Department of Radiotherapy, S N. Medical College, Agra, Uttar Pradesh, India.
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Chen L, Platzer P, Reschl C, Schafasand M, Nachankar A, Lukas Hajdusich C, Kuess P, Stock M, Habraken S, Carlino A. Validation of a deep-learning segmentation model for adult and pediatric head and neck radiotherapy in different patient positions. Phys Imaging Radiat Oncol 2024; 29:100527. [PMID: 38222671 PMCID: PMC10787237 DOI: 10.1016/j.phro.2023.100527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/15/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024] Open
Abstract
Background and purpose Autocontouring for radiotherapy has the potential to significantly save time and reduce interobserver variability. We aimed to assess the performance of a commercial autocontouring model for head and neck (H&N) patients in eight orientations relevant to particle therapy with fixed beam lines, focusing on validation and implementation for routine clinical use. Materials and methods Autocontouring was performed on sixteen organs at risk (OARs) for 98 adult and pediatric patients with 137 H&N CT scans in eight orientations. A geometric comparison of the autocontours and manual segmentations was performed using the Hausdorff Distance 95th percentile, Dice Similarity Coefficient (DSC) and surface DSC and compared to interobserver variability where available. Additional qualitative scoring and dose-volume-histogram (DVH) parameters analyses were performed for twenty patients in two positions, consisting of scoring on a 0-3 scale based on clinical usability and comparing the mean (Dmean) and near-maximum (D2%) dose, respectively. Results For the geometric analysis, the model performance in head-first-supine straight and hyperextended orientations was in the same range as the interobserver variability. HD95, DSC and surface DSC was heterogeneous in other orientations. No significant geometric differences were found between pediatric and adult autocontours. The qualitative scoring yielded a median score of ≥ 2 for 13/16 OARs while 7/32 DVH parameters were significantly different. Conclusions For head-first-supine straight and hyperextended scans, we found that 13/16 OAR autocontours were suited for use in daily clinical practice and subsequently implemented. Further development is needed for other patient orientations before implementation.
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Affiliation(s)
- Linda Chen
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
- Erasmus MC Cancer Institute, University Medical Center, Department of Radiotherapy, Rotterdam, the Netherlands
- Delft University of Technology, Faculty of Mechanical, Maritime and Materials Engineering, Delft, the Netherlands
- Leiden University Medical Center, Faculty of Medicine, Leiden, the Netherlands
| | - Patricia Platzer
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
- Fachhochschule Wiener Neustadt, Department MedTech, Wiener Neustadt, Austria
| | - Christian Reschl
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
| | - Mansure Schafasand
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
- Karl Landsteiner University of Health Sciences, Department of Oncology, Krems an der Donau, Austria
| | - Ankita Nachankar
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
- ACMIT Gmbh, Department of Medicine, Wiener Neustadt, Austria
| | | | - Peter Kuess
- Medical University of Vienna, Department of Radiation Oncology, Vienna, Austria
| | - Markus Stock
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
- Karl Landsteiner University of Health Sciences, Department of Oncology, Krems an der Donau, Austria
| | - Steven Habraken
- Erasmus MC Cancer Institute, University Medical Center, Department of Radiotherapy, Rotterdam, the Netherlands
- Holland Proton Therapy Center, Department of Medical Physics & Informatics, Delft, the Netherlands
| | - Antonio Carlino
- MedAustron Ion Therapy Center, Department of Medical Physics, Wiener Neustadt, Austria
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Paczona VR, Capala ME, Deák-Karancsi B, Borzási E, Együd Z, Végváry Z, Kelemen G, Kószó R, Ruskó L, Ferenczi L, Verduijn GM, Petit SF, Oláh J, Cserháti A, Wiesinger F, Hideghéty K. Magnetic Resonance Imaging-Based Delineation of Organs at Risk in the Head and Neck Region. Adv Radiat Oncol 2022; 8:101042. [PMID: 36636382 PMCID: PMC9830100 DOI: 10.1016/j.adro.2022.101042] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/24/2022] [Indexed: 01/16/2023] Open
Abstract
Purpose The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)-based delineation of OARs in the head and neck (H&N) region. Methods and Materials After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences. Every contour defined was revised by 4 radiation oncologists and subsequently by 2 independent senior experts (H&N radiation oncologist and radiologist). After revision, the final structures were presented to the consortium partners. Results A definitive consensus was reached after multi-institutional review. On that basis, we provided a detailed anatomic and functional description and specific MRI characteristics of the OARs. Conclusions In the era of precision radiation therapy, the need for well-built, straightforward contouring guidelines is on the rise. Precise, uniform, delineation-based, automated OAR segmentation on MRI may lead to increased accuracy in terms of organ boundaries and analysis of dose-dependent sequelae for an adequate definition of normal tissue complication probability.
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Affiliation(s)
- Viktor R. Paczona
- Department of Oncotherapy, University of Szeged, Szeged, Hungary,Corresponding author: Viktor R. Paczona, MD
| | | | | | - Emőke Borzási
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Zsófia Együd
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Zoltán Végváry
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Gyöngyi Kelemen
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | - Renáta Kószó
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | | | | | | | | | - Judit Oláh
- Department of Oncotherapy, University of Szeged, Szeged, Hungary
| | | | | | - Katalin Hideghéty
- Department of Oncotherapy, University of Szeged, Szeged, Hungary,ELI-HU Non-Profit Ltd, Szeged, Hungary
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D’Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D, Piras A, Di Dio C, Iezzi M, Preziosi F, Menna S, Quaranta F, Boschetti A, Marras M, Miccichè F, Gallus R, Indovina L, Bussu F, Valentini V, Cusumano D, Mattiucci GC. Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159057. [PMID: 35897425 PMCID: PMC9329735 DOI: 10.3390/ijerph19159057] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/12/2022] [Accepted: 07/20/2022] [Indexed: 02/01/2023]
Abstract
Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center. Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT). Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae. Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.
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Affiliation(s)
- Andrea D’Aviero
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Alessia Re
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Francesco Catucci
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Danila Piccari
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
- Correspondence:
| | - Claudio Votta
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
| | - Domenico Piro
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, 90011 Bagheria, Italy;
| | - Carmela Di Dio
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Martina Iezzi
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Francesco Preziosi
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Sebastiano Menna
- Medical Physics, Mater Olbia Hospital, 07026 Sassari, Italy; (S.M.); (F.Q.); (D.C.)
| | | | - Althea Boschetti
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Marco Marras
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
| | - Francesco Miccichè
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
| | - Roberto Gallus
- Otolaryngology, Mater Olbia Hospital, 07026 Sassari, Italy;
| | - Luca Indovina
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
| | - Francesco Bussu
- Otolaryngology, Azienda Ospedaliero Universitaria di Sassari, 07100 Sassari, Italy;
- Dipartimento delle Scienze Mediche, Chirurgiche e Sperimentali, Università di Sassari, 07100 Sassari, Italy
| | - Vincenzo Valentini
- UOC Radioterapia Oncologica, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (F.M.); (L.I.); (V.V.)
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
| | - Davide Cusumano
- Medical Physics, Mater Olbia Hospital, 07026 Sassari, Italy; (S.M.); (F.Q.); (D.C.)
| | - Gian Carlo Mattiucci
- Radiation Oncology, Mater Olbia Hospital, 07026 Olbia, Italy; (A.D.); (A.R.); (F.C.); (C.V.); (D.P.); (C.D.D.); (M.I.); (F.P.); (A.B.); (M.M.); (G.C.M.)
- Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
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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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8
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Rao D, K P, Singh R, J V. Automated segmentation of the larynx on computed tomography images: a review. Biomed Eng Lett 2022; 12:175-183. [PMID: 35529346 PMCID: PMC9046475 DOI: 10.1007/s13534-022-00221-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/03/2022] Open
Abstract
AbstractThe larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement.This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work.We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy.
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Zhang G, Yang Z, Huo B, Chai S, Jiang S. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106419. [PMID: 34563895 DOI: 10.1016/j.cmpb.2021.106419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 09/12/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurately and reliably defining organs at risk (OARs) and tumors are the cornerstone of radiation therapy (RT) treatment planning for lung cancer. Almost all segmentation networks based on deep learning techniques rely on fully annotated data with strong supervision. However, existing public imaging datasets encountered in the RT domain frequently include singly labelled tumors or partially labelled organs because annotating full OARs and tumors in CT images is both rigorous and tedious. To utilize labelled data from different sources, we proposed a dual-path semi-supervised conditional nnU-Net for OARs and tumor segmentation that is trained on a union of partially labelled datasets. METHODS The framework employs the nnU-Net as the base model and introduces a conditioning strategy by incorporating auxiliary information as an additional input layer into the decoder. The conditional nnU-Net efficiently leverages prior conditional information to classify the target class at the pixelwise level. Specifically, we employ the uncertainty-aware mean teacher (UA-MT) framework to assist in OARs segmentation, which can effectively leverage unlabelled data (images from a tumor labelled dataset) by encouraging consistent predictions of the same input under different perturbations. Furthermore, we individually design different combinations of loss functions to optimize the segmentation of OARs (Dice loss and cross-entropy loss) and tumors (Dice loss and focal loss) in a dual path. RESULTS The proposed method is evaluated on two publicly available datasets of the spinal cord, left and right lung, heart, esophagus, and lung tumor, in which satisfactory segmentation performance has been achieved in term of both the region-based Dice similarity coefficient (DSC) and the boundary-based Hausdorff distance (HD). CONCLUSIONS The proposed semi-supervised conditional nnU-Net breaks down the barriers between nonoverlapping labelled datasets and further alleviates the problem of "data hunger" and "data waste" in multi-class segmentation. The method has the potential to help radiologists with RT treatment planning in clinical practice.
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Affiliation(s)
- Guobin Zhang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Zhiyong Yang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China
| | - Bin Huo
- Department of Oncology, Tianjin Medical University Second Hospital, Tianjin, 300211, China
| | - Shude Chai
- Department of Oncology, Tianjin Medical University Second Hospital, Tianjin, 300211, China
| | - Shan Jiang
- School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China.
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10
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Liu Z, Chen W, Guan H, Zhen H, Shen J, Liu X, Liu A, Li R, Geng J, You J, Wang W, Li Z, Zhang Y, Chen Y, Du J, Chen Q, Chen Y, Wang S, Zhang F, Qiu J. An Adversarial Deep-Learning-Based Model for Cervical Cancer CTV Segmentation With Multicenter Blinded Randomized Controlled Validation. Front Oncol 2021; 11:702270. [PMID: 34490103 PMCID: PMC8417437 DOI: 10.3389/fonc.2021.702270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/29/2021] [Indexed: 12/31/2022] Open
Abstract
Purpose To propose a novel deep-learning-based auto-segmentation model for CTV delineation in cervical cancer and to evaluate whether it can perform comparably well to manual delineation by a three-stage multicenter evaluation framework. Methods An adversarial deep-learning-based auto-segmentation model was trained and configured for cervical cancer CTV contouring using CT data from 237 patients. Then CT scans of additional 20 consecutive patients with locally advanced cervical cancer were collected to perform a three-stage multicenter randomized controlled evaluation involving nine oncologists from six medical centers. This evaluation system is a combination of objective performance metrics, radiation oncologist assessment, and finally the head-to-head Turing imitation test. Accuracy and effectiveness were evaluated step by step. The intra-observer consistency of each oncologist was also tested. Results In stage-1 evaluation, the mean DSC and the 95HD value of the proposed model were 0.88 and 3.46 mm, respectively. In stage-2, the oncologist grading evaluation showed the majority of AI contours were comparable to the GT contours. The average CTV scores for AI and GT were 2.68 vs. 2.71 in week 0 (P = .206), and 2.62 vs. 2.63 in week 2 (P = .552), with no significant statistical differences. In stage-3, the Turing imitation test showed that the percentage of AI contours, which were judged to be better than GT contours by ≥5 oncologists, was 60.0% in week 0 and 42.5% in week 2. Most oncologists demonstrated good consistency between the 2 weeks (P > 0.05). Conclusions The tested AI model was demonstrated to be accurate and comparable to the manual CTV segmentation in cervical cancer patients when assessed by our three-stage evaluation framework.
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Affiliation(s)
- Zhikai Liu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanqi Chen
- Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hui Guan
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongnan Zhen
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Shen
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xia Liu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - An Liu
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Richard Li
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Jianhao Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Jing You
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Weihu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Zhouyu Li
- Department of Radiation Oncology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, China
| | - Yongfeng Zhang
- Department of Radiation Oncology, The Fourth Hospital of Jilin University (FAW General Hospital), Jilin, China
| | - Yuanyuan Chen
- Oncology Department, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine, Hebei, China
| | - Junjie Du
- Department of Radiation Oncology, Yangquan First People's Hospital, Shanxi, China
| | - Qi Chen
- Research and Development Department, MedMind Technology Co., Ltd., Beijing, China
| | - Yu Chen
- Research and Development Department, MedMind Technology Co., Ltd., Beijing, China
| | - Shaobin Wang
- Research and Development Department, MedMind Technology Co., Ltd., Beijing, China
| | - Fuquan Zhang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Qiu
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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11
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Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J, Patel Y, Meyer C, Askham H, Romera-Paredes B, Kelly C, Karthikesalingam A, Chu C, Carnell D, Boon C, D'Souza D, Moinuddin SA, Garie B, McQuinlan Y, Ireland S, Hampton K, Fuller K, Montgomery H, Rees G, Suleyman M, Back T, Hughes CO, Ledsam JR, Ronneberger O. Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res 2021; 23:e26151. [PMID: 34255661 PMCID: PMC8314151 DOI: 10.2196/26151] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/10/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
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Affiliation(s)
| | | | | | - Ruheena Mendes
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | | | | | | | | | | | | | | | - Dawn Carnell
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Cheng Boon
- Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Derek D'Souza
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Syed Ali Moinuddin
- University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | | | | | | | | | - Geraint Rees
- University College London, London, United Kingdom
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12
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Qiu B, van der Wel H, Kraeima J, Glas HH, Guo J, Borra RJH, Witjes MJH, van Ooijen PMA. Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review. J Pers Med 2021; 11:629. [PMID: 34357096 PMCID: PMC8307673 DOI: 10.3390/jpm11070629] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/26/2021] [Accepted: 06/28/2021] [Indexed: 01/05/2023] Open
Abstract
Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications.
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Affiliation(s)
- Bingjiang Qiu
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Hylke van der Wel
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Joep Kraeima
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Haye Hendrik Glas
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Jiapan Guo
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Ronald J. H. Borra
- Medical Imaging Center (MIC), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Max Johannes Hendrikus Witjes
- 3D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands; (B.Q.); (H.v.d.W.); (J.K.); (H.H.G.); (M.J.H.W.)
- Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Peter M. A. van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Data Science Center in Health (DASH), University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
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Guo H, Wang J, Xia X, Zhong Y, Peng J, Zhang Z, Hu W. The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer. Radiat Oncol 2021; 16:113. [PMID: 34162410 PMCID: PMC8220801 DOI: 10.1186/s13014-021-01837-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Accepted: 06/10/2021] [Indexed: 12/25/2022] Open
Abstract
Purpose To investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer. Methods and materials Twenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics. Results FD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics. Conclusions Deep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs. Supplementary Information The online version contains supplementary material available at 10.1186/s13014-021-01837-y.
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Affiliation(s)
- Hongbo Guo
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Xiang Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Yang Zhong
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Jiayuan Peng
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Zhen Zhang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. .,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China.
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. .,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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14
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Wong J, Huang V, Giambattista JA, Teke T, Kolbeck C, Giambattista J, Atrchian S. Training and Validation of Deep Learning-Based Auto-Segmentation Models for Lung Stereotactic Ablative Radiotherapy Using Retrospective Radiotherapy Planning Contours. Front Oncol 2021; 11:626499. [PMID: 34164335 PMCID: PMC8215371 DOI: 10.3389/fonc.2021.626499] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 05/14/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Deep learning-based auto-segmented contour (DC) models require high quality data for their development, and previous studies have typically used prospectively produced contours, which can be resource intensive and time consuming to obtain. The aim of this study was to investigate the feasibility of using retrospective peer-reviewed radiotherapy planning contours in the training and evaluation of DC models for lung stereotactic ablative radiotherapy (SABR). METHODS Using commercial deep learning-based auto-segmentation software, DC models for lung SABR organs at risk (OAR) and gross tumor volume (GTV) were trained using a deep convolutional neural network and a median of 105 contours per structure model obtained from 160 publicly available CT scans and 50 peer-reviewed SABR planning 4D-CT scans from center A. DCs were generated for 50 additional planning CT scans from center A and 50 from center B, and compared with the clinical contours (CC) using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance (HD). RESULTS Comparing DCs to CCs, the mean DSC and 95% HD were 0.93 and 2.85mm for aorta, 0.81 and 3.32mm for esophagus, 0.95 and 5.09mm for heart, 0.98 and 2.99mm for bilateral lung, 0.52 and 7.08mm for bilateral brachial plexus, 0.82 and 4.23mm for proximal bronchial tree, 0.90 and 1.62mm for spinal cord, 0.91 and 2.27mm for trachea, and 0.71 and 5.23mm for GTV. DC to CC comparisons of center A and center B were similar for all OAR structures. CONCLUSIONS The DCs developed with retrospective peer-reviewed treatment contours approximated CCs for the majority of OARs, including on an external dataset. DCs for structures with more variability tended to be less accurate and likely require using a larger number of training cases or novel training approaches to improve performance. Developing DC models from existing radiotherapy planning contours appears feasible and warrants further clinical workflow testing.
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Affiliation(s)
- Jordan Wong
- Radiation Oncology, British Columbia Cancer – Vancouver, Vancouver, BC, Canada
| | - Vicky Huang
- Medical Physics, British Columbia Cancer – Fraser Valley, Surrey, BC, Canada
| | - Joshua A. Giambattista
- Radiation Oncology, Saskatchewan Cancer Agency, Regina, SK, Canada
- Limbus AI Inc, Regina, SK, Canada
| | - Tony Teke
- Medical Physics/Radiation Oncology, British Columbia Cancer – Kelowna, Kelowna, BC, Canada
| | | | | | - Siavash Atrchian
- Medical Physics/Radiation Oncology, British Columbia Cancer – Kelowna, Kelowna, BC, Canada
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15
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Kiser KJ, Barman A, Stieb S, Fuller CD, Giancardo L. Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. J Digit Imaging 2021; 34:541-553. [PMID: 34027588 PMCID: PMC8329111 DOI: 10.1007/s10278-021-00460-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 03/28/2021] [Accepted: 05/04/2021] [Indexed: 12/20/2022] Open
Abstract
Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman’s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman’s rank correlation coefficients or Mann–Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ = − 0.48 versus ρ = − 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool’s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.
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Affiliation(s)
- Kendall J. Kiser
- Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO USA
| | - Arko Barman
- Center for Precision Health, UT Health School of Biomedical Informatics, Houston, TX USA
| | - Sonja Stieb
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Luca Giancardo
- Center for Precision Health, UT Health School of Biomedical Informatics, Houston, TX USA
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Zhang X, Chen H, Chen W, Dyer BA, Chen Q, Benedict SH, Rao S, Rong Y. Technical note: Atlas-based Auto-segmentation of masticatory muscles for head and neck cancer radiotherapy. J Appl Clin Med Phys 2020; 21:233-240. [PMID: 32841492 PMCID: PMC7592960 DOI: 10.1002/acm2.13008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/15/2020] [Accepted: 07/15/2020] [Indexed: 02/05/2023] Open
Abstract
PURPOSE The study aimed to use quantitative geometric and dosimetric metrics to assess the accuracy of atlas-based auto-segmentation of masticatory muscles (MMs) compared to manual drawn contours for head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Fifty-eight patients with HNC treated with RT were analyzed. Paired MMs (masseter, temporalis, and medial and lateral pterygoids) were manually delineated on planning computed tomography (CT) images for all patients. Twenty-nine patients were used to generate the MM atlas. Using this atlas, automatic segmentation of the MMs was performed for the remaining 29 patients without manual correction. Auto-segmentation accuracy for MMs was compared using dice similarity coefficients (DSCs), Hausdorff distance (HD), HD95, and variation in the center of mass (∆COM). The dosimetric impact on MMs was calculated (∆dose) using dosimetric parameters (D99%, D95%, D50%, and D1%), and compared with the geometric indices to test correlation. RESULTS DSCmean ranges from 0.79 ± 0.04 to 0.85 ± 0.04, HDmean from 0.43 ± 0.08 to 0.82 ± 0.26 cm, HD95mean from 0.32 ± 0.08 to 0.42 ± 0.16 cm, and ∆COMmean from 0.18 ± 0.11 to 0.33 ± 0.23 cm. The mean MM volume difference was < 15%. The correlation coefficient (r) of geometric and dosimetric indices for the four MMs ranges between -0.456 and 0.300. CONCLUSIONS Atlas-based auto-segmentation for masticatory muscles provides geometrically accurate contours compared to manual drawn contours. Dose obtained from those auto-segmented contours is comparable to that from manual drawn contours. Atlas-based auto-segmentation strategy for MM in HN radiotherapy is readily availalbe for clinical implementation.
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Affiliation(s)
- Xiangguo Zhang
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
- Department of Radiation OncologyThe Affiliated Yuebei People’s Hospital of Shantou University Medical CollegeShaoguanChina
| | - Haihui Chen
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
- Department of Radiation OncologyLiuzhou Worker's HospitalLiuzhouGuangxiChina
| | - Wen Chen
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
- Department of Radiation OncologyXiangya Hospital of Central South UniversityChangshaChina
| | - Brandon A. Dyer
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
- Department of Radiation OncologyUniversity of WashingtonSeattleWAUSA
| | - Quan Chen
- Department of Radiation OncologyUniversity of KentuckyLexingtonKYUSA
| | - Stanley H. Benedict
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
| | - Shyam Rao
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
| | - Yi Rong
- Department of Radiation OncologyUniversity of California Davis Medical CenterSacramentoCAUSA
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Zhang T, Yang Y, Wang J, Men K, Wang X, Deng L, Bi N. Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer. Medicine (Baltimore) 2020; 99:e21800. [PMID: 32846816 PMCID: PMC7447392 DOI: 10.1097/md.0000000000021800] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 07/09/2020] [Accepted: 07/15/2020] [Indexed: 02/06/2023] Open
Abstract
Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional neural network (CNN) has been established for lung cancer patients at our institution. The aim of this study is to compare automatic segmentation based on CNN (AS-CNN) with automatic segmentation based on atlas (AS-Atlas) in terms of the efficiency and accuracy of OARs contouring.The OARs, including the lungs, esophagus, heart, liver, and spinal cord, of 19 non-small cell lung cancer patients were delineated using three methods: AS-CNN, AS-Atlas in the Pinnacle-software, and manual delineation (MD) by a senior radiation oncologist. MD was used as the ground-truth reference, and the segmentation efficiency was evaluated by the time spent per patient. The accuracy was evaluated using the Mean surface distance (MSD) and Dice similarity coefficient (DSC). The paired t-test or Wilcoxon signed-rank test was used to compare these indexes between the 2 automatic segmentation models.In the 19 testing cases, both AS-CNN and AS-Atlas saved substantial time compared with MD. AS-CNN was more efficient than AS-Atlas (1.6 min vs 2.4 min, P < .001). In terms of the accuracy, AS-CNN performed well in the esophagus, with a DSC of 73.2%. AS-CNN was better than AS-Atlas in segmenting the left lung (DSC: 94.8% vs 93.2%, P = .01; MSD: 1.10 cm vs 1.73 cm, P < .001) and heart (DSC: 89.3% vs 85.8%, P = .05; MSD: 1.65 cm vs 3.66 cm, P < .001). Furthermore, AS-CNN exhibited superior performance in segmenting the liver (DSC: 93.7% vs 93.6%, P = .81; MSD: 2.03 cm VS 2.11 cm, P = .66). The results obtained from AS-CNN and AS-Atlas were similar in segmenting the right lung. However, the performance of AS-CNN in the spinal cord was inferior to that of AS-Atlas (DSC: 82.1% vs 86.8%, P = .01; MSD: 0.87 cm vs 0.66 cm, P = .01).Our study demonstrated that AS-CNN significantly reduced the contouring time and outperformed AS-Atlas in most cases. AS-CNN can potentially be used for OARs segmentation in patients with pathological N2 (pN2) non-small cell lung cancer.
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Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B. Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods. Med Phys 2020; 47:e929-e950. [PMID: 32510603 DOI: 10.1002/mp.14320] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 05/27/2020] [Accepted: 05/29/2020] [Indexed: 02/06/2023] Open
Abstract
Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modality - both computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.
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Affiliation(s)
- Tomaž Vrtovec
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Domen Močnik
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Primož Strojan
- Institute of Oncology Ljubljana, Zaloška cesta 2, Ljubljana, SI-1000, Slovenia
| | - Franjo Pernuš
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia
| | - Bulat Ibragimov
- Faculty Electrical Engineering, University of Ljubljana, Tržaška cesta 25, Ljubljana, SI-1000, Slovenia.,Department of Computer Science, University of Copenhagen, Universitetsparken 1, Copenhagen, D-2100, Denmark
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Kim N, Chang JS, Kim YB, Kim JS. Atlas-based auto-segmentation for postoperative radiotherapy planning in endometrial and cervical cancers. Radiat Oncol 2020; 15:106. [PMID: 32404123 PMCID: PMC7218589 DOI: 10.1186/s13014-020-01562-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/05/2020] [Indexed: 12/22/2022] Open
Abstract
Background Since intensity-modulated radiation therapy (IMRT) has become popular for the treatment of gynecologic cancers, the contouring process has become more critical. This study evaluated the feasibility of atlas-based auto-segmentation (ABAS) for contouring in patients with endometrial and cervical cancers. Methods A total of 75 sets of planning CT images from 75 patients were collected. Contours for the pelvic nodal clinical target volume (CTV), femur, and bladder were carefully generated by two skilled radiation oncologists. Of 75 patients, 60 were randomly registered in three different atlas libraries for ABAS in groups of 20, 40, or 60. ABAS was conducted in 15 patients, followed by manual correction (ABASc). The time required to generate all contours was recorded, and the accuracy of segmentation was assessed using Dice’s coefficient (DC) and the Hausdorff distance (HD) and compared to those of manually delineated contours. Results For ABAS-CTV, the best results were achieved with groups of 60 patients (DC, 0.79; HD, 19.7 mm) and the worst results with groups of 20 patients (DC, 0.75; p = 0.012; HD, 21.3 mm; p = 0.002). ABASc-CTV performed better than ABAS-CTV in terms of both HD and DC (ABASc [n = 60]; DC, 0.84; HD, 15.6 mm; all p < 0.017). ABAS required an average of 45.1 s, whereas ABASc required 191.1 s; both methods required less time than the manual methods (p < 0.001). Both ABAS-Femur and simultaneous ABAS-Bilateral-femurs showed satisfactory performance, regardless of the atlas library used (DC > 0.9 and HD ≤10.0 mm), with significant time reduction compared to that needed for manual delineation (p < 0.001). However, ABAS-Bladder did not prove to be feasible, with inferior results regardless of library size (DC < 0.6 and HD > 40 mm). Furthermore, ABASc-Bladder required a longer processing time than manual contouring to achieve the same accuracy. Conclusions ABAS could help physicians to delineate the CTV and organs-at-risk (e.g., femurs) in IMRT planning considering its consistency, efficacy, and accuracy.
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Affiliation(s)
- Nalee Kim
- 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, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, 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
| | - 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
| | - 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.
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