Liu Yun-Fan, Shu Xin, Qiao Xiao-Feng, Ai Guang-Yong, Liu Li, Liao Jun, Qian Shuang, He Xiao-Jing
Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Big Data and Software Engineering College, Chongqing University, Chongqing, China.
Front Oncol. 2022 Jun 20;12:911426. doi: 10.3389/fonc.2022.911426. eCollection 2022.
To develop and validate a noninvasive radiomic-based machine learning (ML) model to identify P504s/P63 status and further achieve the diagnosis of prostate cancer (PCa).
A retrospective dataset of patients with preoperative prostate MRI examination and P504s/P63 pathological immunohistochemical results between June 2016 and February 2021 was conducted. As indicated by P504s/P63 expression, the patients were divided into label 0 (atypical prostatic hyperplasia), label 1 (benign prostatic hyperplasia, BPH) and label 2 (PCa) groups. This study employed T2WI, DWI and ADC sequences to assess prostate diseases and manually segmented regions of interest (ROIs) with Artificial Intelligence Kit software for radiomics feature acquisition. Feature dimensionality reduction and selection were performed by using a mutual information algorithm. Based on screened features, P504s/P63 prediction models were established by random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), adaptive boosting (AdaBoost) and k-nearest neighbor (KNN) algorithms. The performance was evaluated by the area under the ROC curve (AUC) and accuracy.
A total of 315 patients were enrolled. Among the 851 radiomic features, the 32 top features were derived from T2WI, in which the gray-level run length matrix (GLRLM) and gray-level cooccurrence matrix (GLCM) features accounted for the largest proportion. Among the five models, the RF algorithm performed best in general evaluations (microaverage AUC=0.920, macroaverage AUC=0.870) and provided the most accurate result in further sublabel prediction (the accuracies of label 0, 1, and 2 were 0.831, 0.831, and 0.932, respectively). In comparative sequence analyses, T2WI was the best single-sequence candidate (microaverage AUC=0.94 and macroaverage AUC=0.78). The merged datasets of T2WI, DWI, and ADC yielded optimal AUCs (microaverage AUC=0.930 and macroaverage AUC=0.900).
The radiomic-based RF classifier has the potential to be used to evaluate the presurgical P504s/P63 status and further diagnose PCa noninvasively and accurately.
开发并验证一种基于放射组学的非侵入性机器学习（ML）模型，以识别P504s/P63状态，进而实现前列腺癌（PCa）的诊断。
对2016年6月至2021年2月期间进行术前前列腺MRI检查及P504s/P63病理免疫组化结果的患者进行回顾性数据集研究。根据P504s/P63表达情况，将患者分为标签0（非典型前列腺增生）、标签1（良性前列腺增生，BPH）和标签2（PCa）组。本研究采用T2WI、DWI和ADC序列评估前列腺疾病，并使用人工智能套件软件手动分割感兴趣区域（ROIs）以获取放射组学特征。通过互信息算法进行特征降维和选择。基于筛选出的特征，采用随机森林（RF）、梯度提升决策树（GBDT）、逻辑回归（LR）、自适应提升（AdaBoost）和k近邻（KNN）算法建立P504s/P63预测模型。通过ROC曲线下面积（AUC）和准确率评估模型性能。
共纳入315例患者。在851个放射组学特征中，32个顶级特征来自T2WI，其中灰度游程长度矩阵（GLRLM）和灰度共生矩阵（GLCM）特征占比最大。在五个模型中，RF算法在总体评估中表现最佳（微平均AUC = 0.920，宏平均AUC = 0.870），并在进一步的子标签预测中提供了最准确的结果（标签0、1和2的准确率分别为0.831、0.831和0.932）。在比较序列分析中，T2WI是最佳的单序列候选者（微平均AUC = 0.94，宏平均AUC = 0.78）。T2WI、DWI和ADC的合并数据集产生了最佳的AUC（微平均AUC = 0.930，宏平均AUC = 0.900）。
基于放射组学的RF分类器有潜力用于术前评估P504s/P63状态，并进一步无创、准确地诊断PCa。
