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Marruecos Querol J, Jurado-Bruggeman D, Lopez-Vidal A, Mesía Nin R, Rubió-Casadevall J, Buxó M, Eraso Urien A. Contouring aid tools in radiotherapy. Smoothing: the false friend. Clin Transl Oncol 2024; 26:1956-1967. [PMID: 38493446 DOI: 10.1007/s12094-024-03420-9] [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: 11/27/2023] [Accepted: 02/23/2024] [Indexed: 03/19/2024]
Abstract
OBJECTIVE Contouring accuracy is critical in modern radiotherapy. Several tools are available to assist clinicians in this task. This study aims to evaluate the performance of the smoothing tool in the ARIA system to obtain more consistent volumes. METHODS Eleven different geometric shapes were delineated in ARIA v15.6 (Sphere, Cube, Square Prism, Six-Pointed Star Prism, Arrow Prism, And Cylinder and the respective volumes at 45° of axis deviation (_45)) in 1, 3, 5, 7, and 10 cm side or diameter each. Post-processing drawing tools to smooth those first-generated volumes were applied in different options (2D-ALL vs 3D) and grades (1, 3, 5, 10, 15, and 20). These volumetric transformations were analyzed by comparing different parameters: volume changes, center of mass, and DICE similarity coefficient index. Then we studied how smoothing affected two different volumes in a head and neck cancer patient: a single rounded node and the volume delineating cervical nodal areas. RESULTS No changes in data were found between 2D-ALL or 3D smoothing. Minimum deviations were found (range from 0 to 0.45 cm) in the center of mass. Volumes and the DICE index decreased as the degree of smoothing increased. Some discrepancies were found, especially in figures with cleft and spikes that behave differently. In the clinical case, smoothing should be applied only once throughout the target delineation process, preferably in the largest volume (PTV) to minimize errors. CONCLUSION Smoothing is a good tool to reduce artifacts due to the manual delineation of radiotherapy volumes. The resulting volumes must be always carefully reviewed.
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Affiliation(s)
- Jordi Marruecos Querol
- Radiation Oncology Department, Catalan Institute of Oncology, Girona, Spain.
- Research Group in Radiation Oncology and Medical Physics of Girona, Girona Biomedical Research Institute (IDIBGI), Girona, Spain.
- Department of Radiation Oncology, ICO, Girona, Spain.
| | - Diego Jurado-Bruggeman
- Research Group in Radiation Oncology and Medical Physics of Girona, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
- Medical Physics and Radiation Protection Department, Catalan Institute of Oncology, Girona, Spain
| | - Anna Lopez-Vidal
- Medical Oncology Department, Catalan Institute of Oncology, Girona, Spain
| | - Ricard Mesía Nin
- Medical Oncology Department, Catalan Institute of Oncology, B-ARGO Group, IGTP, Badalona, Spain
| | | | - Maria Buxó
- Girona Biomedical Research Institute (IDIBGI), Girona, Spain
| | - Aranzazu Eraso Urien
- Radiation Oncology Department, Catalan Institute of Oncology, Girona, Spain
- Research Group in Radiation Oncology and Medical Physics of Girona, Girona Biomedical Research Institute (IDIBGI), Girona, Spain
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2
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Zhang S, Li K, Sun Y, Wan Y, Ao Y, Zhong Y, Liang M, Wang L, Chen X, Pei X, Hu Y, Chen D, Li M, Shan H. Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study. Int J Radiat Oncol Biol Phys 2024; 119:1590-1600. [PMID: 38432286 DOI: 10.1016/j.ijrobp.2024.02.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.
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Affiliation(s)
- Shuaitong Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yuchen Sun
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yun Wan
- Department of Radiology, Xinyi City People's Hospital, Xinyi, Guangdong, China
| | - Yong Ao
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Yinghua Zhong
- Department of Radiology, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong, China
| | - Mingzhu Liang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Lizhu Wang
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong, China
| | - Xiaofeng Pei
- Department of Radiation Oncology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China
| | - Yi Hu
- Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China; State Key Laboratory of Oncology in South China, Guangdong Esophageal Cancer Institute, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Man Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
| | - Hong Shan
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China; Department of Interventional Medicine, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong, China.
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3
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Gay SS, Cardenas CE, Nguyen C, Netherton TJ, Yu C, Zhao Y, Skett S, Patel T, Adjogatse D, Guerrero Urbano T, Naidoo K, Beadle BM, Yang J, Aggarwal A, Court LE. Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy. Sci Rep 2023; 13:21797. [PMID: 38066074 PMCID: PMC10709623 DOI: 10.1038/s41598-023-48944-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.
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Affiliation(s)
- Skylar S Gay
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Callistus Nguyen
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Tucker J Netherton
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Cenji Yu
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Yao Zhao
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | | | | | | | | | | | | | - Jinzhong Yang
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | | | - Laurence E Court
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
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Li C, Fu Y, Yi X, Guan X, Liu L, Chen BT. Application of radiomics in adrenal incidentaloma: a literature review. Discov Oncol 2022; 13:112. [PMID: 36305962 PMCID: PMC9616972 DOI: 10.1007/s12672-022-00577-z] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/13/2022] [Indexed: 11/05/2022] Open
Abstract
Assessment of adrenal incidentaloma relies on imaging analysis and evaluation of adrenal function. Radiomics as a tool for quantitative image analysis is useful for evaluation of adrenal incidentaloma. In this review, we examined radiomic literature on adrenal incidentaloma including both adrenal functional assessment and structural differentiation of benign versus malignant adrenal tumors. In this review, we summarized the status of radiomic application on adrenal incidentaloma and suggested potential direction for future research.
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Affiliation(s)
- Cheng Li
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, 410008, Hunan, People's Republic of China
| | - Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, 410008, Hunan, People's Republic of China.
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha , 410008, Hunan, People's Republic of China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
| | - Xiao Guan
- Department of Urological Surgery, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
| | - Longfei Liu
- Department of Urological Surgery, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, People's Republic of China.
| | - Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA
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5
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Koo J, Caudell JJ, Latifi K, Jordan P, Shen S, Adamson PM, Moros EG, Feygelman V. Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy. Radiother Oncol 2022; 174:52-58. [PMID: 35817322 DOI: 10.1016/j.radonc.2022.06.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE To introduce and validate a newly developed deep-learning (DL) auto-segmentation algorithm for head and neck (HN) organs at risk (OARs) and to compare its performance with a published commercial algorithm. METHODS A total of 864 HN cancer cases were available to train and evaluate a prototype algorithm. The algorithm is based on a fully convolutional network with combined U-Net and V-net. A Dice loss plus Cross-Entropy Loss function with Adam optimizer was used in training. For 75 validation cases, OAR sets were generated with three DL-based models (A: the prototype model trained with gold data, B: a commercial software trained with the same data, and C: the same software trained with data from another institution). The auto-segmented structures were evaluated with Dice similarity coefficient (DSC), Hausdorff distance (HD), voxel-penalty metric (VPM) and DSC of area under dose-volume histograms. A subjective qualitative evaluation was performed on 20 random cases. RESULTS Overall trend was for the prototype algorithm to be the closest to the gold data by all five metrics. The average DSC/VPM/HD for algorithms A, B, and C were 0.81/84.1/1.6 mm, 0.74/62.8/3.2 mm, and 0.66/46.8/3.3 mm, respectively. 93% of model A structures were evaluated to be clinically useful. CONCLUSION The superior performance of the prototype was validated, even when trained with the same data. In addition to the challenges of perfecting the algorithms, the auto-segmentation results can differ when the same algorithm is trained at different institutions.
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Affiliation(s)
- Jihye Koo
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA; Department of Physics, University of South Florida, FL, USA.
| | - Jimmy J Caudell
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA.
| | - Kujtim Latifi
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA.
| | | | | | | | - Eduardo G Moros
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA.
| | - Vladimir Feygelman
- Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, USA.
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Li F, Li Y, Wang X, Zhang Y, Liu X, Liu S, Wang W, Wang J, Guo Y, Xu M, Li J. Inter-Observer and Intra-Observer Variability in Gross Tumor Volume Delineation of Primary Esophageal Carcinomas Based on Different Combinations of Diagnostic Multimodal Images. Front Oncol 2022; 12:817413. [PMID: 35433413 PMCID: PMC9010659 DOI: 10.3389/fonc.2022.817413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/28/2022] [Indexed: 12/24/2022] Open
Abstract
Background and Purpose This study aimed to investigate inter-/intra-observer delineation variability in GTVs of primary esophageal carcinomas (ECs) based on planning CT with reference to different combinations of diagnostic multimodal images from endoscopy/EUS, esophagography and FDG-PET/CT. Materials and Methods Fifty patients with pathologically proven thoracic EC who underwent diagnostic multimodal images before concurrent chemoradiotherapy were enrolled. Five radiation oncologist independently delineated the GTVs based on planning CT only (GTVC), CT combined with endoscopy/EUS (GTVCE), CT combined with endoscopy/EUS and esophagography (X-ray) (GTVCEX), and CT combined with endoscopy/EUS, esophagography, and FDG-PET/CT (GTVCEXP). The intra-/inter-observer variability in the volume, longitudinal length, generalized CI (CIgen), and position of the GTVs were assessed. Results The intra-/inter-observer variability in the volume and longitudinal length of the GTVs showed no significant differences (p>0.05). The mean intra-observer CIgen values for all observers was 0.73 ± 0.15. The mean inter-observer CIgen values for the four multimodal image combinations was 0.67 ± 0.11. The inter-observer CIgen for the four combined images was the largest, showing significant differences with those for the other three combinations. The intra-observer CIgen among different observers and inter-observer CIgen among different combinations of multimodal images showed significant differences (p<0.001). The intra-observer CIgen for the senior radiotherapists was larger than that for the junior radiotherapists (p<0.001). Conclusion For radiation oncologists with advanced medical imaging training and clinical experience, using diagnostic multimodal images from endoscopy/EUS, esophagography, and FDG-PET/CT could reduce the intra-/inter-observer variability and increase the accuracy of target delineation in primary esophageal carcinomas.
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Affiliation(s)
- Fengxiang Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- *Correspondence: Fengxiang Li, ; Jianbin Li,
| | - Yankang Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xue Wang
- Department of Radiation Oncology, Linyi Cancer Hospital, Linyi, China
| | - Yingjie Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xijun Liu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shanshan Liu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Wei Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinzhi Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yanluan Guo
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Min Xu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jianbin Li
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
- *Correspondence: Fengxiang Li, ; Jianbin Li,
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Watanabe S, Ogino I, Shigenaga D, Hata M. Impact of Regional Lymph Node Irradiation on Reducing Lymph Node Recurrence in Esophageal Cancer Patients. CANCER DIAGNOSIS & PROGNOSIS 2022; 2:223-231. [PMID: 35399167 PMCID: PMC8962802 DOI: 10.21873/cdp.10098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND/AIM To evaluate the preventive effects of regional lymph node irradiation on lymph node recurrence in esophageal cancer (EC). PATIENTS AND METHODS The study included 289 patients who received definitive radiotherapy for EC. The regional lymph node area of group 1 was determined as the area with the highest probability of lymph node metastasis and group 2 was determined as the area with the next highest probability of lymph node metastasis depending on the primary site of EC. RESULTS The patients in whom group 2 was completely included in the irradiated field had a significantly lower rate of recurrence of regional lymph node metastasis than those in whom group 2 was not or insufficiently included (p=0.0337). There was no significant difference in overall survival (p=0.4627) or disease-specific survival (p=0.6174) between the two groups. CONCLUSION Regional lymph node irradiation did not have survival-prolonging effects but significantly reduced regional lymph node recurrence.
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Affiliation(s)
- Shigenobu Watanabe
- Department of Radiation Oncology, Yokohama City University Medical Center, Yokohama, Japan
| | - Ichiro Ogino
- Department of Radiation Oncology, Yokohama City University Medical Center, Yokohama, Japan
| | - Daisuke Shigenaga
- Department of Radiation Oncology, Yokohama City University Medical Center, Yokohama, Japan
| | - Masaharu Hata
- Department of Radiation Oncology, Graduate School of Medicine, Yokohama City University, Yokohama, Japan
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