基于mri的放射组学预测前列腺癌生化复发:系统回顾和荟萃分析。

IF 2.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Mohsen Salimi, Pouria Vadipour, Shakiba Houshi, Fereshteh Yazdanpanah, Sharareh Seifi
{"title":"基于mri的放射组学预测前列腺癌生化复发:系统回顾和荟萃分析。","authors":"Mohsen Salimi,&nbsp;Pouria Vadipour,&nbsp;Shakiba Houshi,&nbsp;Fereshteh Yazdanpanah,&nbsp;Sharareh Seifi","doi":"10.1007/s00261-025-04892-1","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and purpose</h3><p>Biochemical recurrence (BCR) following prostate cancer (PCa) treatment is a significant indicator of metastasis and mortality. Early prediction of BCR can guide treatment decisions, and optimize patient management strategies. MRI is essential for the diagnosis and surveillance of PCa. This study aimed to assess the accuracy and quality of MRI radiomics-based machine learning (ML) models for predicting post-treatment BCR in PCa.</p><h3>Methods</h3><p>A systematic literature search was conducted across five electronic databases (PubMed, Scopus, Embase, Web of Science, and IEEE) up to December 23, 2024, to identify studies developing ML models based on MRI-derived radiomics features for the prediction of BCR in PCa. Studies were assessed for quality using the QUADAS-2 and METRICS tools. A meta-analysis of radiomics, clinical, and clinical-radiomics models in validation cohorts was performed to pool sensitivity, specificity, and area under the curve (AUC) using a bivariate random-effects model.</p><h3>Results</h3><p>A total of 24 studies were incorporated into the systematic review, with 14 included in the meta-analysis. The pooled AUC, sensitivity, and specificity for radiomics-based ML models were 0.75, 72%, and 78%, respectively. Clinical-radiomics models showed the highest performance with a pooled AUC of 0.88, sensitivity of 85%, and specificity of 79%. QUADAS-2 revealed significant methodological biases, particularly in the index test and flow and timing domains. The mean METRICS score across studies was 65.68%, ranging from 43.8 to 82.2%, showing overall good quality but highlighting methodological gaps in some domains.</p><h3>Conclusion</h3><p>MRI-based radiomics demonstrates potential for predicting BCR in PCa, especially when integrated with clinical variables. However, it is still far from widespread clinical use, necessitating further standardization and key methodological improvements for better generalizability and robustness. Future studies should adopt multi-center designs and conduct thorough external validation to enhance applicability across diverse patient populations.</p></div>","PeriodicalId":7126,"journal":{"name":"Abdominal Radiology","volume":"50 10","pages":"4748 - 4771"},"PeriodicalIF":2.2000,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis\",\"authors\":\"Mohsen Salimi,&nbsp;Pouria Vadipour,&nbsp;Shakiba Houshi,&nbsp;Fereshteh Yazdanpanah,&nbsp;Sharareh Seifi\",\"doi\":\"10.1007/s00261-025-04892-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and purpose</h3><p>Biochemical recurrence (BCR) following prostate cancer (PCa) treatment is a significant indicator of metastasis and mortality. Early prediction of BCR can guide treatment decisions, and optimize patient management strategies. MRI is essential for the diagnosis and surveillance of PCa. This study aimed to assess the accuracy and quality of MRI radiomics-based machine learning (ML) models for predicting post-treatment BCR in PCa.</p><h3>Methods</h3><p>A systematic literature search was conducted across five electronic databases (PubMed, Scopus, Embase, Web of Science, and IEEE) up to December 23, 2024, to identify studies developing ML models based on MRI-derived radiomics features for the prediction of BCR in PCa. Studies were assessed for quality using the QUADAS-2 and METRICS tools. A meta-analysis of radiomics, clinical, and clinical-radiomics models in validation cohorts was performed to pool sensitivity, specificity, and area under the curve (AUC) using a bivariate random-effects model.</p><h3>Results</h3><p>A total of 24 studies were incorporated into the systematic review, with 14 included in the meta-analysis. The pooled AUC, sensitivity, and specificity for radiomics-based ML models were 0.75, 72%, and 78%, respectively. Clinical-radiomics models showed the highest performance with a pooled AUC of 0.88, sensitivity of 85%, and specificity of 79%. QUADAS-2 revealed significant methodological biases, particularly in the index test and flow and timing domains. The mean METRICS score across studies was 65.68%, ranging from 43.8 to 82.2%, showing overall good quality but highlighting methodological gaps in some domains.</p><h3>Conclusion</h3><p>MRI-based radiomics demonstrates potential for predicting BCR in PCa, especially when integrated with clinical variables. However, it is still far from widespread clinical use, necessitating further standardization and key methodological improvements for better generalizability and robustness. Future studies should adopt multi-center designs and conduct thorough external validation to enhance applicability across diverse patient populations.</p></div>\",\"PeriodicalId\":7126,\"journal\":{\"name\":\"Abdominal Radiology\",\"volume\":\"50 10\",\"pages\":\"4748 - 4771\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Abdominal Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00261-025-04892-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Abdominal Radiology","FirstCategoryId":"3","ListUrlMain":"https://link.springer.com/article/10.1007/s00261-025-04892-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

背景与目的:前列腺癌(PCa)治疗后生化复发(BCR)是判断前列腺癌转移和死亡率的重要指标。BCR的早期预测可以指导治疗决策,优化患者管理策略。MRI对前列腺癌的诊断和监测至关重要。本研究旨在评估基于MRI放射组学的机器学习(ML)模型预测PCa治疗后BCR的准确性和质量。方法:系统检索截至2024年12月23日的五个电子数据库(PubMed, Scopus, Embase, Web of Science和IEEE)的文献,以确定基于mri衍生放射组学特征的ML模型的研究,用于预测PCa的BCR。使用QUADAS-2和METRICS工具评估研究的质量。使用双变量随机效应模型对验证队列中的放射组学、临床和临床-放射组学模型进行荟萃分析,以汇集敏感性、特异性和曲线下面积(AUC)。结果:共有24项研究纳入系统评价,其中14项纳入meta分析。基于放射组学的ML模型的总AUC、敏感性和特异性分别为0.75、72%和78%。临床放射组学模型表现出最高的性能,合并AUC为0.88,敏感性为85%,特异性为79%。QUADAS-2显示了显著的方法偏差,特别是在指数测试、流量和时间域。所有研究的METRICS平均得分为65.68%,范围从43.8到82.2%,总体质量良好,但突出了一些领域的方法学差距。结论:基于mri的放射组学显示了预测前列腺癌BCR的潜力,特别是当与临床变量相结合时。然而,它离广泛的临床应用还很远,需要进一步的标准化和关键的方法改进,以更好的通用性和稳健性。未来的研究应采用多中心设计,并进行彻底的外部验证,以提高在不同患者群体中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis

Background and purpose

Biochemical recurrence (BCR) following prostate cancer (PCa) treatment is a significant indicator of metastasis and mortality. Early prediction of BCR can guide treatment decisions, and optimize patient management strategies. MRI is essential for the diagnosis and surveillance of PCa. This study aimed to assess the accuracy and quality of MRI radiomics-based machine learning (ML) models for predicting post-treatment BCR in PCa.

Methods

A systematic literature search was conducted across five electronic databases (PubMed, Scopus, Embase, Web of Science, and IEEE) up to December 23, 2024, to identify studies developing ML models based on MRI-derived radiomics features for the prediction of BCR in PCa. Studies were assessed for quality using the QUADAS-2 and METRICS tools. A meta-analysis of radiomics, clinical, and clinical-radiomics models in validation cohorts was performed to pool sensitivity, specificity, and area under the curve (AUC) using a bivariate random-effects model.

Results

A total of 24 studies were incorporated into the systematic review, with 14 included in the meta-analysis. The pooled AUC, sensitivity, and specificity for radiomics-based ML models were 0.75, 72%, and 78%, respectively. Clinical-radiomics models showed the highest performance with a pooled AUC of 0.88, sensitivity of 85%, and specificity of 79%. QUADAS-2 revealed significant methodological biases, particularly in the index test and flow and timing domains. The mean METRICS score across studies was 65.68%, ranging from 43.8 to 82.2%, showing overall good quality but highlighting methodological gaps in some domains.

Conclusion

MRI-based radiomics demonstrates potential for predicting BCR in PCa, especially when integrated with clinical variables. However, it is still far from widespread clinical use, necessitating further standardization and key methodological improvements for better generalizability and robustness. Future studies should adopt multi-center designs and conduct thorough external validation to enhance applicability across diverse patient populations.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信