Abstract：
OBJECTIVE: To determine the role of dynamic contrast-enhanced (DCE) MRI-radiomics in predicting the International Society of Urological Pathology Grade Group (ISUP-GG) in therapy-naïve prostate cancer (PCa) patients.
METHODS: In this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardisation initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling.
RESULTS: 73 index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). 16 features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%.
CONCLUSIONS: DCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.
METHODS: In this ethics review board-approved retrospective study on two prospective clinical trials between 2017 and 2020, 73 men with suspected/confirmed PCa were included. All participants underwent multiparametric MRI. On MRI, dominant lesions (per PI-RADS) were identified. DCE-MRI radiomic features were extracted from the segmented volumes following the image biomarker standardisation initiative (IBSI) guidelines through 14 time points. Histopathology evaluation on the cognitive-fusion targeted biopsies was set as the reference standard. Univariate regression was done to evaluate potential predictors across all calculated features. Random forest imputation was used for multivariate modelling.
RESULTS: 73 index lesions were reviewed. Histopathology revealed 28, 16, 13 and 16 lesions with ISUP-GG-Negative/1/2, ISUP-GG-3, ISUP-GG-4 and ISUP-GG-5, respectively. From the extracted features, total lesion enhancement (TLE), minimum enhancement intensity and Grey-Level Run Length Matrix (GLRLM) were the most significantly different parameters among ISUP-GGs (Neg/1/2 vs 3/4 vs 5). 16 features with significant cross-sectional associations with ISUP-GGs entered the multivariate analysis. The final DCE partitioning model used only four features (lesion sphericity, TLE, GLRLM and Grey-Level Zone Length Matrix). For the binarized diagnosis (ISUP-GG≤2 vs ISUP-GG>2), the accuracy reached 81%.
CONCLUSIONS: DCE-MRI radiomics might be used as a non-invasive tool for aiding pathological grade group prediction in therapy-naïve PCa patients, potentially adding complementary information to PI-RADS for supporting tailored diagnostic pathways and treatment planning.
摘要：
目的：确定动态对比增强（DCE）MRI影像组学在预测初治前列腺癌（PCa）患者国际泌尿外科病理学会分级组（ISUP-GG）中的作用。
方法：在这项伦理审查委员会批准的2017年至2020年两项前瞻性临床试验的回顾性研究中，包括73名疑似/确诊PCa的男性。所有参与者均接受多参数MRI检查。核磁共振成像,确定了显性病变（根据PI-RADS）。根据图像生物标志物标准化倡议（IBSI）指南，通过14个时间点从分割的体积中提取DCE-MRI影像组学特征。将针对认知融合的活检的组织病理学评估设定为参考标准。进行单变量回归以评估所有计算特征的潜在预测因子。随机森林插补用于多变量建模。
结果：回顾了73个指标病变。组织病理学显示28、16、13和16个病灶分别为ISUP-GG-阴性/1/2，ISUP-GG-3，ISUP-GG-4和ISUP-GG-5。从提取的特征中，总病变增强（TLE），最小增强强度和灰度游程长度矩阵（GLRLM）是ISUP-GS中最显着的不同参数（Neg/1/2vs3/4vs5）。与ISUP-GS具有显著横截面关联的16个特征进入多变量分析。最终的DCE划分模型只使用了四个特征(病变球形，TLE,GLRLM和灰度区域长度矩阵)。对于二值化诊断（ISUP-GG≤2vsISUP-GG>2），准确率达到81%。
结论：DCE-MRI影像组学可用作非侵入性工具，用于辅助治疗初治PCa患者的病理分级组预测，可能为PI-RADS添加补充信息，以支持量身定制的诊断途径和治疗计划。
方法：在这项伦理审查委员会批准的2017年至2020年两项前瞻性临床试验的回顾性研究中，包括73名疑似/确诊PCa的男性。所有参与者均接受多参数MRI检查。核磁共振成像,确定了显性病变（根据PI-RADS）。根据图像生物标志物标准化倡议（IBSI）指南，通过14个时间点从分割的体积中提取DCE-MRI影像组学特征。将针对认知融合的活检的组织病理学评估设定为参考标准。进行单变量回归以评估所有计算特征的潜在预测因子。随机森林插补用于多变量建模。
结果：回顾了73个指标病变。组织病理学显示28、16、13和16个病灶分别为ISUP-GG-阴性/1/2，ISUP-GG-3，ISUP-GG-4和ISUP-GG-5。从提取的特征中，总病变增强（TLE），最小增强强度和灰度游程长度矩阵（GLRLM）是ISUP-GS中最显着的不同参数（Neg/1/2vs3/4vs5）。与ISUP-GS具有显著横截面关联的16个特征进入多变量分析。最终的DCE划分模型只使用了四个特征(病变球形，TLE,GLRLM和灰度区域长度矩阵)。对于二值化诊断（ISUP-GG≤2vsISUP-GG>2），准确率达到81%。
结论：DCE-MRI影像组学可用作非侵入性工具，用于辅助治疗初治PCa患者的病理分级组预测，可能为PI-RADS添加补充信息，以支持量身定制的诊断途径和治疗计划。
