双参数磁共振成像放射组学在临床显著性前列腺癌中的诊断价值及外部验证。

IF 1.7 3区 医学 Q4 ANDROLOGY
Translational andrology and urology Pub Date : 2025-08-30 Epub Date: 2025-08-25 DOI:10.21037/tau-2025-209
Hui Xing, Yibanu Abudureheman, Xueru Ai, Yunling Wang, Jingxu Xu, Chencui Huang, Gulimire Kelimu, Ting Li
{"title":"双参数磁共振成像放射组学在临床显著性前列腺癌中的诊断价值及外部验证。","authors":"Hui Xing, Yibanu Abudureheman, Xueru Ai, Yunling Wang, Jingxu Xu, Chencui Huang, Gulimire Kelimu, Ting Li","doi":"10.21037/tau-2025-209","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Given the significant economic burden of prostate cancer (PCa), its diagnostic methods need to be improved. The limitations of the subjective Prostate Imaging Reporting and Data System (PI-RADS) underscore the need for generalizable radiomics in clinically significant PCa (csPCa). This study aimed to build a machine-learning model based on biparametric magnetic resonance imaging (bpMRI) to diagnose csPCa.</p><p><strong>Methods: </strong>Prognostic model development: This study retrospectively included the data of 445 patients from two centers, of whom 206 had csPCa and 239 had clinically non-significant PCa (ncsPCa). The training set comprised 120 csPCa patients and 141 ncsPCa patients. The test set comprised 52 csPCa patients and 61 ncsPCa patients. The external validation comprised 34 csPCa patients and 37 ncsPCa patients.</p><p><strong>Results: </strong>Features were extracted from T2-weighted imaging (T2WI) sequences and apparent diffusion coefficient (ADC) maps based on bpMRI radiomics. From 3662 radiomics features, 10 stable radiomics features were selected for model construction based on intraclass correlation coefficients (ICCs). Three diagnostic models for csPCa were constructed. The area under the curve (AUC) values for the PI-RADS-scoring model, which was based on visual assessments by radiologists, were 0.8271, 0.7905, and 0.8331 in the training, test, and external validation sets, respectively; while those for the clinical scoring model were 0.9236, 0.8846, and 0.8378, respectively; and those for the radiomics model were 0.9790, 0.9584, and 0.9523, respectively. There were significant differences between the radiomics model and the PI-RADS-scoring model (P<i><</i>0.001) in both the training and test sets. The P value for the radiomics model and clinical scoring model in the training set was <0.001, while that in the validation set was 0.056. Overall, the AUC values for the three models indicated that the diagnostic performance of the bpMRI radiomics model, which was based on T2WI sequences and ADC images, for csPCa was better than that of both the PI-RADS-scoring and clinical scoring models.</p><p><strong>Conclusions: </strong>The radiomics model can reliably detect and classify csPCa, and is a very powerful non-invasive auxiliary tool that could be used as an alternative method for diagnosing csPCa in personalized medicine.</p>","PeriodicalId":23270,"journal":{"name":"Translational andrology and urology","volume":"14 8","pages":"2269-2278"},"PeriodicalIF":1.7000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12433168/pdf/","citationCount":"0","resultStr":"{\"title\":\"Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.\",\"authors\":\"Hui Xing, Yibanu Abudureheman, Xueru Ai, Yunling Wang, Jingxu Xu, Chencui Huang, Gulimire Kelimu, Ting Li\",\"doi\":\"10.21037/tau-2025-209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Given the significant economic burden of prostate cancer (PCa), its diagnostic methods need to be improved. The limitations of the subjective Prostate Imaging Reporting and Data System (PI-RADS) underscore the need for generalizable radiomics in clinically significant PCa (csPCa). This study aimed to build a machine-learning model based on biparametric magnetic resonance imaging (bpMRI) to diagnose csPCa.</p><p><strong>Methods: </strong>Prognostic model development: This study retrospectively included the data of 445 patients from two centers, of whom 206 had csPCa and 239 had clinically non-significant PCa (ncsPCa). The training set comprised 120 csPCa patients and 141 ncsPCa patients. The test set comprised 52 csPCa patients and 61 ncsPCa patients. The external validation comprised 34 csPCa patients and 37 ncsPCa patients.</p><p><strong>Results: </strong>Features were extracted from T2-weighted imaging (T2WI) sequences and apparent diffusion coefficient (ADC) maps based on bpMRI radiomics. From 3662 radiomics features, 10 stable radiomics features were selected for model construction based on intraclass correlation coefficients (ICCs). Three diagnostic models for csPCa were constructed. The area under the curve (AUC) values for the PI-RADS-scoring model, which was based on visual assessments by radiologists, were 0.8271, 0.7905, and 0.8331 in the training, test, and external validation sets, respectively; while those for the clinical scoring model were 0.9236, 0.8846, and 0.8378, respectively; and those for the radiomics model were 0.9790, 0.9584, and 0.9523, respectively. There were significant differences between the radiomics model and the PI-RADS-scoring model (P<i><</i>0.001) in both the training and test sets. The P value for the radiomics model and clinical scoring model in the training set was <0.001, while that in the validation set was 0.056. Overall, the AUC values for the three models indicated that the diagnostic performance of the bpMRI radiomics model, which was based on T2WI sequences and ADC images, for csPCa was better than that of both the PI-RADS-scoring and clinical scoring models.</p><p><strong>Conclusions: </strong>The radiomics model can reliably detect and classify csPCa, and is a very powerful non-invasive auxiliary tool that could be used as an alternative method for diagnosing csPCa in personalized medicine.</p>\",\"PeriodicalId\":23270,\"journal\":{\"name\":\"Translational andrology and urology\",\"volume\":\"14 8\",\"pages\":\"2269-2278\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12433168/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational andrology and urology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/tau-2025-209\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/8/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"ANDROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational andrology and urology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tau-2025-209","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/8/25 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ANDROLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:鉴于前列腺癌(PCa)的巨大经济负担,其诊断方法有待改进。主观前列腺影像报告和数据系统(PI-RADS)的局限性强调了在临床显著性前列腺癌(csPCa)中推广放射组学的必要性。本研究旨在建立基于双参数磁共振成像(bpMRI)的机器学习模型来诊断csPCa。方法:预后模型建立:本研究回顾性纳入了来自两个中心的445例患者的数据,其中206例为csPCa, 239例为临床无显著性PCa (ncsPCa)。训练集包括120例csPCa患者和141例ncsPCa患者。测试集包括52例csPCa患者和61例ncsPCa患者。外部验证包括34例csPCa患者和37例ncsPCa患者。结果:基于bpMRI放射组学,从T2WI序列和表观扩散系数(ADC)图中提取特征。从3662个放射组学特征中,选择10个稳定的放射组学特征进行基于类内相关系数(ICCs)的模型构建。建立了三种csPCa诊断模型。基于放射科医师视觉评价的pi - rads评分模型的曲线下面积(AUC)值在训练集、测试集和外部验证集分别为0.8271、0.7905和0.8331;临床评分模型分别为0.9236、0.8846、0.8378;放射组学模型分别为0.9790、0.9584和0.9523。放射组学模型与pi - rads评分模型在训练集和测试集上均存在显著差异(P0.001)。结论:放射组学模型可以可靠地检测和分类csPCa,是一种非常强大的无创辅助工具,可作为个性化医疗中csPCa诊断的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.

Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.

Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.

Diagnostic value and external validation of biparametric magnetic resonance imaging radiomics in clinically significant prostate cancer.

Background: Given the significant economic burden of prostate cancer (PCa), its diagnostic methods need to be improved. The limitations of the subjective Prostate Imaging Reporting and Data System (PI-RADS) underscore the need for generalizable radiomics in clinically significant PCa (csPCa). This study aimed to build a machine-learning model based on biparametric magnetic resonance imaging (bpMRI) to diagnose csPCa.

Methods: Prognostic model development: This study retrospectively included the data of 445 patients from two centers, of whom 206 had csPCa and 239 had clinically non-significant PCa (ncsPCa). The training set comprised 120 csPCa patients and 141 ncsPCa patients. The test set comprised 52 csPCa patients and 61 ncsPCa patients. The external validation comprised 34 csPCa patients and 37 ncsPCa patients.

Results: Features were extracted from T2-weighted imaging (T2WI) sequences and apparent diffusion coefficient (ADC) maps based on bpMRI radiomics. From 3662 radiomics features, 10 stable radiomics features were selected for model construction based on intraclass correlation coefficients (ICCs). Three diagnostic models for csPCa were constructed. The area under the curve (AUC) values for the PI-RADS-scoring model, which was based on visual assessments by radiologists, were 0.8271, 0.7905, and 0.8331 in the training, test, and external validation sets, respectively; while those for the clinical scoring model were 0.9236, 0.8846, and 0.8378, respectively; and those for the radiomics model were 0.9790, 0.9584, and 0.9523, respectively. There were significant differences between the radiomics model and the PI-RADS-scoring model (P<0.001) in both the training and test sets. The P value for the radiomics model and clinical scoring model in the training set was <0.001, while that in the validation set was 0.056. Overall, the AUC values for the three models indicated that the diagnostic performance of the bpMRI radiomics model, which was based on T2WI sequences and ADC images, for csPCa was better than that of both the PI-RADS-scoring and clinical scoring models.

Conclusions: The radiomics model can reliably detect and classify csPCa, and is a very powerful non-invasive auxiliary tool that could be used as an alternative method for diagnosing csPCa in personalized medicine.

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
CiteScore
4.10
自引率
5.00%
发文量
80
期刊介绍: ranslational Andrology and Urology (Print ISSN 2223-4683; Online ISSN 2223-4691; Transl Androl Urol; TAU) is an open access, peer-reviewed, bi-monthly journal (quarterly published from Mar.2012 - Dec. 2014). The main focus of the journal is to describe new findings in the field of translational research of Andrology and Urology, provides current and practical information on basic research and clinical investigations of Andrology and Urology. Specific areas of interest include, but not limited to, molecular study, pathology, biology and technical advances related to andrology and urology. Topics cover range from evaluation, prevention, diagnosis, therapy, prognosis, rehabilitation and future challenges to urology and andrology. Contributions pertinent to urology and andrology are also included from related fields such as public health, basic sciences, education, sociology, and nursing.
×
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