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Abstract：
OBJECTIVE: The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.
METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.
RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.
CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.
METHODS: A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.
RESULTS: The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.
CONCLUSIONS: MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.
摘要：
目的：Gleason评分（GS）和阳性针头是前列腺癌（PCa）的关键侵袭性指标。本研究旨在探讨磁共振成像（MRI）影像组学模型在预测PCa系统活检的GS和阳性针中的有用性。
方法：回顾性收集来自2个中心的218例经病理证实的PCa患者。选择小视场高分辨率T2加权成像和对比后延迟序列来提取影像组学特征。然后，方差分析和递归特征消除被用来去除冗余特征。基于MRI和各种分类器构建了预测GS和阳性针头的影像组学模型，包括支持向量机，线性判别分析，逻辑回归(LR)，和LR使用最小绝对收缩和选择运算符。用受试者工作特性的曲线下面积（AUC）评估模型。
结果：选择11个特征作为GS预测的主要特征子集，而这5个特征被选择用于阳性针头预测。选择LR作为分类器来构建影像组学模型。对于GS预测，在培训中，影像组学模型的AUC分别为0.811、0.814和0.717，内部验证,和外部验证集，分别。对于阳性针头预测，训练中的AUC分别为0.806、0.811和0.791，内部验证,和外部验证集，分别。
结论：MRI影像组学模型适用于预测PCa系统活检的GS和阳性针头。该模型可用于使用非侵入性识别侵袭性PCa，可重复,和准确的诊断方法。
方法：回顾性收集来自2个中心的218例经病理证实的PCa患者。选择小视场高分辨率T2加权成像和对比后延迟序列来提取影像组学特征。然后，方差分析和递归特征消除被用来去除冗余特征。基于MRI和各种分类器构建了预测GS和阳性针头的影像组学模型，包括支持向量机，线性判别分析，逻辑回归(LR)，和LR使用最小绝对收缩和选择运算符。用受试者工作特性的曲线下面积（AUC）评估模型。
结果：选择11个特征作为GS预测的主要特征子集，而这5个特征被选择用于阳性针头预测。选择LR作为分类器来构建影像组学模型。对于GS预测，在培训中，影像组学模型的AUC分别为0.811、0.814和0.717，内部验证,和外部验证集，分别。对于阳性针头预测，训练中的AUC分别为0.806、0.811和0.791，内部验证,和外部验证集，分别。
结论：MRI影像组学模型适用于预测PCa系统活检的GS和阳性针头。该模型可用于使用非侵入性识别侵袭性PCa，可重复,和准确的诊断方法。
