Nicoletti Giulia, Mazzetti Simone, Maimone Giovanni, Cignini Valentina, Cuocolo Renato, Faletti Riccardo, Gatti Marco, Imbriaco Massimo, Longo Nicola, Ponsiglione Andrea, Russo Filippo, Serafini Alessandro, Stanzione Arnaldo, Regge Daniele, Giannini Valentina
Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy.
Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy.
Cancers (Basel). 2024 Jan 1;16(1):203. doi: 10.3390/cancers16010203.
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.
在过去几年中，多项研究表明，低侵袭性（分级组（GG）≤2）和高侵袭性（GG≥3）前列腺癌（PCa）具有不同的预后和死亡率。因此，本研究的目的是开发并外部验证一种基于双参数磁共振成像（bpMRI）对低侵袭性和高侵袭性PCa进行无创分类的放射组学模型。为此，从四个中心回顾性纳入了283例患者。从表观扩散系数（ADC）图和T2加权（T2w）序列中提取特征。采用交叉验证（CV）策略，使用四个中心中的两个中心来评估几种分类器的稳健性。然后，使用另外两个中心对最佳分类器进行外部验证。通过Shapley加法解释（SHAP）值和部分依赖图（PDP）对最终的放射组学特征进行了解释。最佳组合是使用十个特征训练的朴素贝叶斯分类器，取得了不错的结果，即在构建集和外部验证集中，受试者操作特征（ROC）曲线下面积（AUC）分别为0.75和0.73。我们的研究结果表明，我们的放射组学模型有助于区分低侵袭性和高侵袭性PCa。这种无创方法如果进一步得到验证并集成到能够自动检测PCa的临床决策支持系统中，可能会帮助临床医生管理疑似PCa的男性患者。
