Lang Yu , Wenjun Zhang , Jie Zhang , Qi Chen , Lu Bai , Nan Liu , Tingtian Pang , Bo Yang , Jie Qiu
{"title":"基于 CNN 的宫颈癌近距离放射治疗规划剂量预测方法","authors":"Lang Yu , Wenjun Zhang , Jie Zhang , Qi Chen , Lu Bai , Nan Liu , Tingtian Pang , Bo Yang , Jie Qiu","doi":"10.1016/j.jrras.2024.101013","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Brachytherapy (BT) plays a crucial role in cervical cancer treatment. This study aimed to develop a 3D dose prediction model for cervical BT using Convolutional Neural Network (CNN).</p></div><div><h3>Methods</h3><p>In this study, we introduced a dose prediction model guided to generate dose distributions with explicit anatomical mask guidance. The model encompassed 224 clinical cases, including 190 for training-validation and 34 for testing. For performance evaluation, DVH metrics and 3D Gamma analysis were employed. The results were compared with those obtained using a 3D U-net model.</p></div><div><h3>Results</h3><p>DVH metrics for the test set, including HRCTV D90, HRCTV D95, HRCTV D100, bladder D2CC, sigmoid D2CC, rectum D2CC, and intestine D2CC, yielded values of 5.44 ± 0.91, 5.05 ± 0.88, 3.34 ± 0.79, 4.39 ± 1.53, 3.24 ± 1.31, 3.03 ± 1.87, and 2.71 ± 1.79, respectively. The DVH metrics of dose differences between the predicted dose distribution and the ground-truth plan were 0.63 ± 0.63, 0.60 ± 0.61, 0.53 ± 0.61, 1.21 ± 0.85, 0.71 ± 0.61, 1.16 ± 1.09, and 0.86 ± 0.58, respectively. The 3D gamma passing rates for the 3%/3 mm criteria of HRCTV, bladder, sigmoid, rectum, and intestine were 0.95 ± 0.04, 0.99 ± 0.02, 1.00 ± 0.02, 1.00 ± 0.01, and 1.00 ± 0.00, respectively.</p></div><div><h3>Conclusion</h3><p>The 3D BT dose prediction system, based on a 3D anatomical mask-guided deep learning network, could accurately generate 3D dose distributions, offering decision support for automatic clinical BT treatment planning.</p></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1687850724001973/pdfft?md5=1761d6f5a6c5168f15e9dd7be321486c&pid=1-s2.0-S1687850724001973-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A CNN-based dose prediction method for brachytherapy treatment planning of patients with cervical cancer\",\"authors\":\"Lang Yu , Wenjun Zhang , Jie Zhang , Qi Chen , Lu Bai , Nan Liu , Tingtian Pang , Bo Yang , Jie Qiu\",\"doi\":\"10.1016/j.jrras.2024.101013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Brachytherapy (BT) plays a crucial role in cervical cancer treatment. This study aimed to develop a 3D dose prediction model for cervical BT using Convolutional Neural Network (CNN).</p></div><div><h3>Methods</h3><p>In this study, we introduced a dose prediction model guided to generate dose distributions with explicit anatomical mask guidance. The model encompassed 224 clinical cases, including 190 for training-validation and 34 for testing. For performance evaluation, DVH metrics and 3D Gamma analysis were employed. The results were compared with those obtained using a 3D U-net model.</p></div><div><h3>Results</h3><p>DVH metrics for the test set, including HRCTV D90, HRCTV D95, HRCTV D100, bladder D2CC, sigmoid D2CC, rectum D2CC, and intestine D2CC, yielded values of 5.44 ± 0.91, 5.05 ± 0.88, 3.34 ± 0.79, 4.39 ± 1.53, 3.24 ± 1.31, 3.03 ± 1.87, and 2.71 ± 1.79, respectively. The DVH metrics of dose differences between the predicted dose distribution and the ground-truth plan were 0.63 ± 0.63, 0.60 ± 0.61, 0.53 ± 0.61, 1.21 ± 0.85, 0.71 ± 0.61, 1.16 ± 1.09, and 0.86 ± 0.58, respectively. The 3D gamma passing rates for the 3%/3 mm criteria of HRCTV, bladder, sigmoid, rectum, and intestine were 0.95 ± 0.04, 0.99 ± 0.02, 1.00 ± 0.02, 1.00 ± 0.01, and 1.00 ± 0.00, respectively.</p></div><div><h3>Conclusion</h3><p>The 3D BT dose prediction system, based on a 3D anatomical mask-guided deep learning network, could accurately generate 3D dose distributions, offering decision support for automatic clinical BT treatment planning.</p></div>\",\"PeriodicalId\":16920,\"journal\":{\"name\":\"Journal of Radiation Research and Applied Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1687850724001973/pdfft?md5=1761d6f5a6c5168f15e9dd7be321486c&pid=1-s2.0-S1687850724001973-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Radiation Research and Applied Sciences\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1687850724001973\",\"RegionNum\":4,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiation Research and Applied Sciences","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1687850724001973","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A CNN-based dose prediction method for brachytherapy treatment planning of patients with cervical cancer
Purpose
Brachytherapy (BT) plays a crucial role in cervical cancer treatment. This study aimed to develop a 3D dose prediction model for cervical BT using Convolutional Neural Network (CNN).
Methods
In this study, we introduced a dose prediction model guided to generate dose distributions with explicit anatomical mask guidance. The model encompassed 224 clinical cases, including 190 for training-validation and 34 for testing. For performance evaluation, DVH metrics and 3D Gamma analysis were employed. The results were compared with those obtained using a 3D U-net model.
Results
DVH metrics for the test set, including HRCTV D90, HRCTV D95, HRCTV D100, bladder D2CC, sigmoid D2CC, rectum D2CC, and intestine D2CC, yielded values of 5.44 ± 0.91, 5.05 ± 0.88, 3.34 ± 0.79, 4.39 ± 1.53, 3.24 ± 1.31, 3.03 ± 1.87, and 2.71 ± 1.79, respectively. The DVH metrics of dose differences between the predicted dose distribution and the ground-truth plan were 0.63 ± 0.63, 0.60 ± 0.61, 0.53 ± 0.61, 1.21 ± 0.85, 0.71 ± 0.61, 1.16 ± 1.09, and 0.86 ± 0.58, respectively. The 3D gamma passing rates for the 3%/3 mm criteria of HRCTV, bladder, sigmoid, rectum, and intestine were 0.95 ± 0.04, 0.99 ± 0.02, 1.00 ± 0.02, 1.00 ± 0.01, and 1.00 ± 0.00, respectively.
Conclusion
The 3D BT dose prediction system, based on a 3D anatomical mask-guided deep learning network, could accurately generate 3D dose distributions, offering decision support for automatic clinical BT treatment planning.
期刊介绍:
Journal of Radiation Research and Applied Sciences provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and applications of nuclear, radiation and isotopes in biology, medicine, drugs, biochemistry, microbiology, agriculture, entomology, food technology, chemistry, physics, solid states, engineering, environmental and applied sciences.