Yan Li, Zekun Jiang, Jia Tan, Zhihao Wang, Yuehao Ma, Deng Xiong, Yanhui Liu, Kang Li, Su Lui, Min Wu
{"title":"胶质母细胞瘤术后智能管理系统,集成了自动分割、风险分层和复发空间映射。","authors":"Yan Li, Zekun Jiang, Jia Tan, Zhihao Wang, Yuehao Ma, Deng Xiong, Yanhui Liu, Kang Li, Su Lui, Min Wu","doi":"10.1016/j.radonc.2025.111180","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop DeepGBM-Recure, an integrated artificial intelligence (AI) system for optimizing precision radiotherapy and individualized surveillance in glioblastoma (GBM) by automating postoperative risk stratification and spatial targeting of recurrence hotspots.</p><p><strong>Methods: </strong>This DeepGBM-Recure system comprises three synergistic modules: 1) Automated segmentation of peri-cavitary hyperintense regions on postoperative fluid-attenuated inversion recovery (FLAIR) images using a 3D nnU-Net framework; 2) Patient-level early recurrence prediction based on radiomics features and random forest classification; 3) Voxel-wise spatial mapping of high-risk subregions via supervoxel analysis. The system was trained and validated on data from 145 patients across two centers and externally tested on data from 39 patients across another two centers.</p><p><strong>Results: </strong>On the test set, the nnU-Net segmentation model achieved a mean Dice coefficient of 0.85 ± 0.09. The patient-level and voxel-level prediction models achieved area under the ROC curves (AUCs) of 0.76 and 0.80, respectively. Notably, the voxel-level model exhibited strong spatial concordance between predicted high-risk heatmaps and ground-truth recurrence regions. Performance was further supported by calibration curves, decision curve analysis, and clinical application in representative cases, demonstrating favorable predictive accuracy in real-world scenarios.</p><p><strong>Conclusion: </strong>DeepGBM-Recure represents a pioneering integrated solution that combines automated anatomical delineation, individualized risk stratification, and spatial recurrence guidance, offering a clinically applicable tool for precision radiotherapy and individualized surveillance. Prospective multi-center trials with larger cohorts are warranted to validate clinical utility and facilitate integration into real-world decision-making workflows.</p>","PeriodicalId":21041,"journal":{"name":"Radiotherapy and Oncology","volume":" ","pages":"111180"},"PeriodicalIF":5.3000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An intelligent postoperative management system for glioblastoma integrating automated segmentation, risk stratification, and recurrence spatial mapping.\",\"authors\":\"Yan Li, Zekun Jiang, Jia Tan, Zhihao Wang, Yuehao Ma, Deng Xiong, Yanhui Liu, Kang Li, Su Lui, Min Wu\",\"doi\":\"10.1016/j.radonc.2025.111180\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study aimed to develop DeepGBM-Recure, an integrated artificial intelligence (AI) system for optimizing precision radiotherapy and individualized surveillance in glioblastoma (GBM) by automating postoperative risk stratification and spatial targeting of recurrence hotspots.</p><p><strong>Methods: </strong>This DeepGBM-Recure system comprises three synergistic modules: 1) Automated segmentation of peri-cavitary hyperintense regions on postoperative fluid-attenuated inversion recovery (FLAIR) images using a 3D nnU-Net framework; 2) Patient-level early recurrence prediction based on radiomics features and random forest classification; 3) Voxel-wise spatial mapping of high-risk subregions via supervoxel analysis. The system was trained and validated on data from 145 patients across two centers and externally tested on data from 39 patients across another two centers.</p><p><strong>Results: </strong>On the test set, the nnU-Net segmentation model achieved a mean Dice coefficient of 0.85 ± 0.09. The patient-level and voxel-level prediction models achieved area under the ROC curves (AUCs) of 0.76 and 0.80, respectively. Notably, the voxel-level model exhibited strong spatial concordance between predicted high-risk heatmaps and ground-truth recurrence regions. Performance was further supported by calibration curves, decision curve analysis, and clinical application in representative cases, demonstrating favorable predictive accuracy in real-world scenarios.</p><p><strong>Conclusion: </strong>DeepGBM-Recure represents a pioneering integrated solution that combines automated anatomical delineation, individualized risk stratification, and spatial recurrence guidance, offering a clinically applicable tool for precision radiotherapy and individualized surveillance. Prospective multi-center trials with larger cohorts are warranted to validate clinical utility and facilitate integration into real-world decision-making workflows.</p>\",\"PeriodicalId\":21041,\"journal\":{\"name\":\"Radiotherapy and Oncology\",\"volume\":\" \",\"pages\":\"111180\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiotherapy and Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.radonc.2025.111180\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiotherapy and Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.radonc.2025.111180","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
An intelligent postoperative management system for glioblastoma integrating automated segmentation, risk stratification, and recurrence spatial mapping.
Purpose: This study aimed to develop DeepGBM-Recure, an integrated artificial intelligence (AI) system for optimizing precision radiotherapy and individualized surveillance in glioblastoma (GBM) by automating postoperative risk stratification and spatial targeting of recurrence hotspots.
Methods: This DeepGBM-Recure system comprises three synergistic modules: 1) Automated segmentation of peri-cavitary hyperintense regions on postoperative fluid-attenuated inversion recovery (FLAIR) images using a 3D nnU-Net framework; 2) Patient-level early recurrence prediction based on radiomics features and random forest classification; 3) Voxel-wise spatial mapping of high-risk subregions via supervoxel analysis. The system was trained and validated on data from 145 patients across two centers and externally tested on data from 39 patients across another two centers.
Results: On the test set, the nnU-Net segmentation model achieved a mean Dice coefficient of 0.85 ± 0.09. The patient-level and voxel-level prediction models achieved area under the ROC curves (AUCs) of 0.76 and 0.80, respectively. Notably, the voxel-level model exhibited strong spatial concordance between predicted high-risk heatmaps and ground-truth recurrence regions. Performance was further supported by calibration curves, decision curve analysis, and clinical application in representative cases, demonstrating favorable predictive accuracy in real-world scenarios.
Conclusion: DeepGBM-Recure represents a pioneering integrated solution that combines automated anatomical delineation, individualized risk stratification, and spatial recurrence guidance, offering a clinically applicable tool for precision radiotherapy and individualized surveillance. Prospective multi-center trials with larger cohorts are warranted to validate clinical utility and facilitate integration into real-world decision-making workflows.
期刊介绍:
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.