人工智能方法在基于pet的淋巴瘤预后预测成像中的表现：一项系统综述和荟萃分析

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-06-01 DOI:10.1016/j.ejrad.2025.112204

Mohammad Mehdi Mehrabi Nejad , Mohammad Reza Ghanbari Boroujeni , Alireza Hayati , Fatemeh Dashti , Jayaram K. Udupa , Drew A. Torigian

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引用次数: 0

摘要

目的通过系统回顾和荟萃分析，评价人工智能（AI）方法在淋巴瘤预后预测中的预测性能。方法检索spubmed - medline、Scopus和Web of Science，检索截至2024年10月基于pet成像的淋巴瘤AI预测模型的原始研究。符合条件的研究报告的结果包括无进展生存期（PFS）、总生存期（OS）或治疗反应。采用Stata软件进行meta分析、亚组分析、meta回归、敏感性分析和发表偏倚分析。结果纳入75项研究，主要集中于非霍奇金淋巴瘤（NHL, n = 61）。人工智能方法包括深度学习（DL, n = 13）、机器学习（ML, n = 2）、ML/放射组学联合（n = 23）和放射组学（n = 37）。合并分析显示，主要组对PFS （HR: 4.11 [3.20-5.29], AUC: 0.78 [0.68-0.86], C-index: 0.79[0.76-0.83]）和OS （HR: 3.38 [2.29-4.99], AUC: 0.75 [0.66-0.83], C-index: 0.79[0.76-0.81]）具有较强的预测能力，验证组结果一致。对于治疗反应，合并OR为5.36 [1.53-18.78]，AUC为0.85[0.74-0.92]。DL在PFS和治疗反应预测方面优于其他人工智能方法。结论人工智能方法，尤其是深度学习，对基于pet成像的淋巴瘤结果具有很强的预测能力，支持其在精准医疗中的潜在应用。临床整合需要进一步的前瞻性研究。

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Performance of AI methods in PET-based imaging for outcome prediction in lymphoma: A systematic review and meta-analysis

Objectives

To evaluate the predictive performance of artificial intelligence (AI) methods using pre-treatment PET-based imaging for outcome prediction in lymphoma through a systematic review and meta-analysis.

Methods

PubMed-MEDLINE, Scopus, and Web of Science were searched for original studies on AI prediction models using PET-based imaging in lymphoma up to October 2024. Eligible studies reported outcomes including progression-free survival (PFS), overall survival (OS), or treatment response. Meta-analyses, subgroup analyses, meta-regressions, sensitivity analysis, and publication bias analysis were conducted using Stata software.

Results

Seventy-five studies were included, predominantly focusing on non-Hodgkin lymphoma (NHL, n = 61). AI methods included deep learning (DL, n = 13), machine learning (ML, n = 2), combined ML/radiomics (n = 23), and radiomics (n = 37). Pooled analyses showed strong predictive performance for PFS (HR: 4.11 [3.20–5.29], AUC: 0.78 [0.68–0.86], C-index: 0.79 [0.76–0.83]) and OS (HR: 3.38 [2.29–4.99], AUC: 0.75 [0.66–0.83], C-index: 0.79 [0.76–0.81]) in the main groups with consistent results in the validation groups. For treatment response, pooled OR was 5.36 [1.53–18.78], and AUC was 0.85 [0.74–0.92]. DL outperformed other AI methods in PFS and treatment response prediction.

Conclusion

AI methods, particularly DL, show strong predictive performance for lymphoma outcomes using PET-based imaging, supporting their potential utility in precision medicine. Further prospective studies are needed for clinical integration.

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.