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。

IF 5.3 1区 医学 Q1 ONCOLOGY
Yan Li, Zekun Jiang, Jia Tan, Zhihao Wang, Yuehao Ma, Deng Xiong, Yanhui Liu, Kang Li, Su Lui, Min Wu
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引用次数: 0

摘要

目的:本研究旨在开发一种集成人工智能(AI)系统DeepGBM-Recure,通过自动化术后风险分层和复发热点的空间靶向,优化胶质母细胞瘤(GBM)的精确放疗和个体化监测。方法:该系统包括三个协同模块:1)使用3D nnU-Net框架对术后液体衰减反演恢复(FLAIR)图像的腔周高强度区域进行自动分割;2)基于放射组学特征和随机森林分类的患者级早期复发预测;3)基于超体素分析的高风险子区域体素空间制图。该系统在两个中心的145名患者的数据上进行了培训和验证,并在另外两个中心的39名患者的数据上进行了外部测试。结果:在测试集上,nnU-Net分割模型的平均Dice系数为0.85 ± 0.09。患者水平和体素水平预测模型的ROC曲线下面积(auc)分别为0.76和0.80。值得注意的是,体素级模型在预测的高风险热图和地面真值重现区之间显示出很强的空间一致性。校准曲线、决策曲线分析和代表性病例的临床应用进一步支持了性能,在现实场景中显示了良好的预测准确性。结论:DeepGBM-Recure是一种开创性的综合解决方案,将自动解剖描绘、个体化风险分层和空间复发指导相结合,为精确放疗和个体化监测提供了一种临床适用的工具。具有较大队列的前瞻性多中心试验有必要验证临床效用,并促进与现实世界决策工作流程的整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Radiotherapy and Oncology
Radiotherapy and Oncology 医学-核医学
CiteScore
10.30
自引率
10.50%
发文量
2445
审稿时长
45 days
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.
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