分子影像学杂志2026，Vol.49Issue(8)：990-997,8.DOI:10.12122/j.issn.1674-4500.2026.08.02
双参数MRI影像组学联合PI-RADS v2.1评分可有效提高前列腺特异性抗原灰区前列腺癌的诊断价值
Biparametric MRI radiomics combined with PI-RADS v2.1 score can effectively improve the diagnosis value of prostate cancer in the PSA gray zone
摘要
Abstract
Objective To investigate the value of radiomics models based on T2-weighted imaging(T2WI)and diffusion-weighted imaging(DWI)sequences combined with the Prostate Imaging Reporting and Data System version 2.1(PI-RADS v2.1)score in the diagnosis of prostate cancer(PCa)in the prostate-specific antigen(PSA)gray zone(4-10 ng/mL).Methods A retrospective analysis was conducted on the clinical and imaging data of 119 patients with PSA in the gray zone admitted to the First Affiliated Hospital of Bengbu Medical University from January 2020 to September 2025.All patients underwent pathological biopsy,including 35 cases of PCa and 84 cases of benign prostatic hyperplasia(BPH),and all received multiparametric magnetic resonance imaging(mpMRI)prior to surgery.The patients were randomly divided into a training set(95 cases,28 PCa and 67 BPH)and a test set(24 cases,7 PCa and 17 BPH)at an 8∶2 ratio.Regions of interest(ROIs)were manually delineated on T2WI and DWI images.After feature extraction,Min-max normalization was applied,and Pearson correlation along with the Relief algorithm were used for feature dimensionality reduction and selection.The synthetic minority over-sampling technique(SMOTE)was used to balance the data.In the training set,T2WI-and DWI-based radiomics models were built using support vector machine(SVM)and naive Bayes classifiers with 5-fold cross-validation.The features from the two sequences were fused to construct a combined radiomics model.Independent clinical predictors,including the PI-RADS v2.1 score,were screened by logistic regression to build a clinical model,which was ultimately integrated into a combined model.The diagnostic performance was assessed by ROC curve,nomogram,and decision curve analysis.Results In the test set,the AUC of the T2WI and DWI radiomics models were 0.790 and 0.807,with accuracies of 0.792 and 0.667,respectively.The biparametric MRI-based fusion radiomics model showed better diagnostic performance,with an AUC of 0.852(95%CI:0.775-0.910),but the difference was not statistically significant(P>0.05).When the fusion model was further integrated with the PI-RADS v2.1 score,the combined model achieved an AUC of 0.870,significantly outperforming the PI-RADS v2.1 score alone(0.781,P<0.01).Conclusion Radiomics features derived from bpMRI can effectively quantify tumor heterogeneity.Their integration with the PI-RADS v2.1 score may provide more reliable decision support for the accurate and non-invasive diagnosis of prostate cancer in the PSA gray zone,potentially reducing unnecessary biopsies.关键词
前列腺癌/前列腺特异抗原/前列腺影像报告和数据系统版本2.1/双参数磁共振成像/影像组学Key words
prostate cancer/prostate-specific antigen/Prostate Imaging Reporting and Data System Version 2.1/biparametric magnetic resonance imaging/radiomics引用本文复制引用
周甜甜,周静,杨雨琼,李伟,马宜传,徐加利..双参数MRI影像组学联合PI-RADS v2.1评分可有效提高前列腺特异性抗原灰区前列腺癌的诊断价值[J].分子影像学杂志,2026,49(8):990-997,8.基金项目
安徽省教育厅重点项目(2025AHGXZK31539) （2025AHGXZK31539）
