{"title":"利用深度学习自动分割脑转移瘤：一项多中心、随机交叉、多阅读器评估研究。","authors":"Xiao Luo, Yadi Yang, Shaohan Yin, Hui Li, Ying Shao, Dechun Zheng, Xinchun Li, Jianpeng Li, Weixiong Fan, Jing Li, Xiaohua Ban, Shanshan Lian, Yun Zhang, Qiuxia Yang, Weijing Zhang, Cheng Zhang, Lidi Ma, Yingwei Luo, Fan Zhou, Shiyuan Wang, Cuiping Lin, Jiao Li, Ma Luo, Jianxun He, Guixiao Xu, Yaozong Gao, Dinggang Shen, Ying Sun, Yonggao Mou, Rong Zhang, Chuanmiao Xie","doi":"10.1093/neuonc/noae113","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.</p><p><strong>Methods: </strong>A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at 5 centers. Five radiology residents and 5 attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared.</p><p><strong>Results: </strong>The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (P = .67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (P < .001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs 0.03 [0.03-0.03]; P < .001), but a similar time reduction (reduced median time, 44% [40-47%] vs 40% [37-44%]; P = .92) with BMSS assistance.</p><p><strong>Conclusions: </strong>The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.</p>","PeriodicalId":19377,"journal":{"name":"Neuro-oncology","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.\",\"authors\":\"Xiao Luo, Yadi Yang, Shaohan Yin, Hui Li, Ying Shao, Dechun Zheng, Xinchun Li, Jianpeng Li, Weixiong Fan, Jing Li, Xiaohua Ban, Shanshan Lian, Yun Zhang, Qiuxia Yang, Weijing Zhang, Cheng Zhang, Lidi Ma, Yingwei Luo, Fan Zhou, Shiyuan Wang, Cuiping Lin, Jiao Li, Ma Luo, Jianxun He, Guixiao Xu, Yaozong Gao, Dinggang Shen, Ying Sun, Yonggao Mou, Rong Zhang, Chuanmiao Xie\",\"doi\":\"10.1093/neuonc/noae113\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.</p><p><strong>Methods: </strong>A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at 5 centers. Five radiology residents and 5 attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared.</p><p><strong>Results: </strong>The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (P = .67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (P < .001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs 0.03 [0.03-0.03]; P < .001), but a similar time reduction (reduced median time, 44% [40-47%] vs 40% [37-44%]; P = .92) with BMSS assistance.</p><p><strong>Conclusions: </strong>The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.</p>\",\"PeriodicalId\":19377,\"journal\":{\"name\":\"Neuro-oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuro-oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/neuonc/noae113\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuro-oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/neuonc/noae113","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.
Background: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation.
Methods: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at 5 centers. Five radiology residents and 5 attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared.
Results: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (P = .67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (P < .001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs 0.03 [0.03-0.03]; P < .001), but a similar time reduction (reduced median time, 44% [40-47%] vs 40% [37-44%]; P = .92) with BMSS assistance.
Conclusions: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.
期刊介绍：
Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field.
The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.
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