Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.

IF 3.5 2区 医学 Q2 ONCOLOGY
Ali Shahriari, Sasan Ghazanafar Ahari, Ali Mousavi, Mahdie Sadeghi, Marjan Abbasi, Mahsa Hosseinpour, Asal Mir, Dorrin Zohouri Zanganeh, Hossein Gharedaghi, Saba Ezati, Ali Sareminia, Dina Seyedi, Mahla Shokouhfar, Ali Darzi, Alireza Ghaedamini, Sara Zamani, Farbod Khosravi, Mahsa Asadi Anar
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引用次数: 0

Abstract

Background: Machine learning (ML) applied to radiomics has revolutionized neuro-oncological imaging, yet the diagnostic performance of ML models based specifically on ^18F-FDG PET features in glioma remains poorly characterized.

Objective: To systematically evaluate and quantitatively synthesize the diagnostic accuracy of ML models trained on ^18F-FDG PET radiomics for glioma classification.

Methods: We conducted a PRISMA-compliant systematic review and meta-analysis registered on OSF ( https://doi.org/10.17605/OSF.IO/XJG6P ). PubMed, Scopus, and Web of Science were searched up to January 2025. Studies were included if they applied ML algorithms to ^18F-FDG PET radiomic features for glioma classification and reported at least one performance metric. Data extraction included demographics, imaging protocols, feature types, ML models, and validation design. Meta-analysis was performed using random-effects models with pooled estimates of accuracy, sensitivity, specificity, AUC, F1 score, and precision. Heterogeneity was explored via meta-regression and Galbraith plots.

Results: Twelve studies comprising 2,321 patients were included. Pooled diagnostic metrics were: accuracy 92.6% (95% CI: 91.3-93.9%), AUC 0.95 (95% CI: 0.94-0.95), sensitivity 85.4%, specificity 89.7%, F1 score 0.78, and precision 0.90. Heterogeneity was high across all domains (I² >75%). Meta-regression identified ML model type and validation strategy as partial moderators. Models using CNNs or PET/MRI integration achieved superior performance.

Conclusion: ML models based on ^18F-FDG PET radiomics demonstrate strong and balanced diagnostic performance for glioma classification. However, methodological heterogeneity underscores the need for standardized pipelines, external validation, and transparent reporting before clinical integration.

Abstract Image

Abstract Image

Abstract Image

机器学习驱动的放射组学对18个F-FDG PET的胶质瘤诊断:系统回顾和荟萃分析。
背景:机器学习(ML)应用于放射组学已经彻底改变了神经肿瘤学成像,然而基于^18F-FDG PET特征的ML模型在胶质瘤中的诊断性能仍然很差。目的:系统评价和定量综合^18F-FDG PET放射组学训练的ML模型对胶质瘤分类的诊断准确性。方法:我们在OSF (https://doi.org/10.17605/OSF.IO/XJG6P)上注册了一项符合prisma标准的系统评价和荟萃分析。PubMed、Scopus和Web of Science的检索截止到2025年1月。如果研究将ML算法应用于^18F-FDG PET放射学特征进行胶质瘤分类,并报告了至少一项性能指标,则纳入研究。数据提取包括人口统计、成像协议、特征类型、ML模型和验证设计。采用随机效应模型进行meta分析,对准确性、敏感性、特异性、AUC、F1评分和精度进行汇总估计。通过meta回归和Galbraith图探讨异质性。结果:纳入了12项研究,包括2,321例患者。合并诊断指标为:准确率92.6% (95% CI: 91.3-93.9%), AUC 0.95 (95% CI: 0.94-0.95),敏感性85.4%,特异性89.7%,F1评分0.78,精密度0.90。所有领域的异质性都很高(I²>75%)。元回归确定ML模型类型和验证策略为部分调节因子。使用cnn或PET/MRI集成的模型获得了更好的性能。结论:基于^18F-FDG PET放射组学的ML模型在胶质瘤分类中具有强大而平衡的诊断性能。然而,方法的异质性强调了在临床整合之前需要标准化的管道、外部验证和透明的报告。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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