In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.
近年来多项研究表明，低侵袭性（分级组≤2）与高侵袭性（分级组≥3）前列腺癌具有不同的预后和死亡率。因此，本研究旨在开发并外部验证一种基于双参数磁共振成像的影像组学模型，以实现对低侵袭性与高侵袭性前列腺癌的无创分类。本研究回顾性纳入来自四个医疗中心的283例患者，从表观扩散系数图和T2加权序列中提取影像特征。采用交叉验证策略，通过其中两个中心的数据评估多种分类器的稳健性，随后使用另外两个中心的数据对最优分类器进行外部验证。通过沙普利加性解释值和偏依赖图对最终影像组学特征进行解释。最优组合为采用十个特征训练的朴素贝叶斯分类器，其在构建集和外部验证集中分别获得0.75和0.73的受试者工作特征曲线下面积，展现出良好性能。本研究结果表明，该影像组学模型有助于区分低侵袭性与高侵袭性前列腺癌。这种无创检测方法若经进一步验证并整合至能够自动检测前列腺癌的临床决策支持系统，可为临床医生管理疑似前列腺癌患者提供辅助。
肿瘤(癌症)患者之家