Zhang Chi, Wang Zewen, Shang Peicheng, Zhou Yibin, Zhu Jin, Xu Lijun, Chen Zeyu, Yu Mengqi, Zang Yachen
Department of Urology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Department of Urology, Changzhou Maternal and Child Health Care Hospital, Changzhou, China.
Sci Rep. 2025 Jul 2;15(1):22816. doi: 10.1038/s41598-025-05718-2.
This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor abnormal protein (TAP) and clinical characteristics for diagnosing prostate cancer. A cohort of 109 patients who underwent both mpMRI and TAP assessments prior to prostate biopsy were enrolled. Radiomic features were meticulously extracted from T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC) maps. Feature selection was performed using t-tests and the Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by model construction using the random forest algorithm. To further enhance the model's accuracy and predictive performance, this study incorporated clinical factors including age, serum prostate-specific antigen (PSA) levels, and prostate volume. By integrating these clinical indicators with radiomic features, a more comprehensive and precise predictive model was developed. Finally, the model's performance was quantified by calculating accuracy, sensitivity, specificity, precision, recall, F1 score, and the area under the curve (AUC). From mpMRI sequences of T2WI, dADC(b = 100/1000 s/mm), and dADC(b = 100/2000 s/mm), 8, 10, and 13 radiomic features were identified as significantly correlated with prostate cancer, respectively. Random forest models constructed based on these three sets of radiomic features achieved AUCs of 0.83, 0.86, and 0.87, respectively. When integrating all three sets of data to formulate a random forest model, an AUC of 0.84 was obtained. Additionally, a random forest model constructed on TAP and clinical characteristics achieved an AUC of 0.85. Notably, combining mpMRI radiomic features with TAP and clinical characteristics, or integrating dADC (b = 100/2000 s/mm²) sequence with TAP and clinical characteristics to construct random forest models, improved the AUCs to 0.91 and 0.92, respectively. The proposed model, which integrates radiomic features, TAP and clinical characteristics using machine learning, demonstrated high predictive efficiency in diagnosing prostate cancer.
本研究旨在探讨多参数磁共振成像（mpMRI）影像组学特征与肿瘤异常蛋白（TAP）及临床特征相结合在前列腺癌诊断中的价值。纳入了109例在前列腺活检前接受mpMRI和TAP评估的患者。从T2加权成像（T2WI）和表观扩散系数（ADC）图中精心提取影像组学特征。使用t检验和最小绝对收缩与选择算子（LASSO）回归进行特征选择，随后使用随机森林算法构建模型。为进一步提高模型的准确性和预测性能，本研究纳入了年龄、血清前列腺特异性抗原（PSA）水平和前列腺体积等临床因素。通过将这些临床指标与影像组学特征相结合，开发了一个更全面、精确的预测模型。最后，通过计算准确率、灵敏度、特异性、精确率、召回率、F1分数和曲线下面积（AUC）对模型性能进行量化。在T2WI、dADC（b = 100/1000 s/mm）和dADC（b = 100/2000 s/mm）的mpMRI序列中，分别有8个、10个和13个影像组学特征被确定与前列腺癌显著相关。基于这三组影像组学特征构建的随机森林模型的AUC分别为0.83、0.86和0.87。当整合所有三组数据来构建随机森林模型时，AUC为0.84。此外，基于TAP和临床特征构建的随机森林模型的AUC为0.85。值得注意的是，将mpMRI影像组学特征与TAP和临床特征相结合，或将dADC（b = 100/2000 s/mm²）序列与TAP和临床特征相结合来构建随机森林模型，AUC分别提高到了0.91和0.92。所提出的使用机器学习整合影像组学特征、TAP和临床特征的模型在前列腺癌诊断中显示出较高的预测效率。
