Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Sci Rep. 2023 Mar 24;13(1):4846. doi: 10.1038/s41598-023-31869-1.
To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.
为了开发基于 MRI 的放射组学模型，以预测前列腺特异性抗原（PSA）水平在 4-10ng/ml 的男性中的前列腺癌（PCa），比较放射组学模型和 PI-RADS v2.1 的性能，并进一步验证放射组学模型对不同 PI-RADS v2.1 评分的病变的预测能力。将 171 名 PSA 水平在 4-10ng/ml 的患者分为训练组（n=119）和测试组（n=52）。由两名放射科医生评估 PI-RADS v2.1 评分。对 T 加权成像、扩散加权成像和表观扩散系数序列上的所有感兴趣容积进行分割，从中提取定量放射组学特征。采用多变量逻辑回归分析建立预测 PCa 的放射组学模型。使用受试者工作特征曲线分析评估诊断性能。在预测 PCa 方面，放射组学模型表现最佳，优于初级放射科医生在训练组（曲线下面积（AUC）：0.803）、测试组（AUC：0.797）和整个队列（AUC：0.801）的 PI-RADS v2.1 评分（P<0.05）。对于 PI-RADS v2.1 评分 3（AUC=0.854，敏感性=84.62%，特异性=84.34%）和 PI-RADS v2.1 评分 4-5（AUC=0.967，敏感性=98.11%，特异性=86.36%）的病变，放射组学模型表现良好，由初级放射科医生分配。放射组学模型在预测 PSA 水平在 4-10ng/ml 的男性中的 PCa 方面，定量优于 PI-RADS v2.1。该模型可以帮助提高初级放射科医生的诊断性能，为泌尿科医生管理不同 PI-RADS v2.1 评分的病变提供更好的决策依据。
