文章：

回顾性定量T2成像技术提升临床显著性外周带前列腺癌的检出率

Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer

原文发布日期：24 January 2025

DOI: 10.3390/cancers17030381

类型: Article

开放获取: 是

英文摘要：

Background: Multiparametric MRI (mpMRI) as a non-invasive imaging tool is important in prostate cancer (PCa) detection and localization. Combined with radiomics analysis, features extracted from mpMRI have been utilized to predict PCa aggressiveness. T2 mapping provides quantitative information in PCa diagnoses but is not routinely available in clinical practice. Previous work from our group developed a deep learning-based method to estimate T2 maps from clinically acquired T1- and T2-weighted images. This study aims to evaluate the added value of the estimated T2 map by combining it with conventional T2-weighted images for detecting clinically significant PCa (csPCa). Methods: An amount of 76 peripheral zone prostate lesions, including clinically significant and insignificant cases, were retrospectively analyzed. Radiomic features were extracted from conventional T2-weighted images and deep learning-estimated T2 maps, followed by feature selection and model development using five-fold cross-validation. Logistic regression and Gaussian Process classifiers were employed to develop the prediction models, with performance evaluated by area under the curve (AUC) and accuracy metrics. Results: The model incorporating features from both T2-weighted images and estimated T2 maps achieved an AUC of 0.803, significantly outperforming the model based solely on T2-weighted image features (AUC of 0.700,p= 0.048). Conclusions: Radiomics features extracted from deep learning-estimated T2 maps provide additional quantitative information that improves the prediction of peripheral zone csPCa aggressiveness, potentially enhancing risk stratification in non-invasive PCa diagnostics.

摘要翻译： 

背景：多参数磁共振成像（mpMRI）作为一种非侵入性成像工具，在前列腺癌（PCa）的检测与定位中具有重要作用。结合影像组学分析，从mpMRI中提取的特征已被用于预测前列腺癌的侵袭性。T2 mapping可为前列腺癌诊断提供定量信息，但尚未在临床实践中常规应用。本课题组先前开发了一种基于深度学习的方法，可从临床获取的T1加权和T2加权图像中估算T2 mapping。本研究旨在评估将估算T2 mapping与传统T2加权图像相结合，在检测临床显著性前列腺癌（csPCa）方面的附加价值。 方法：回顾性分析76例前列腺外周带病灶（包括临床显著性和非显著性病例）。从传统T2加权图像和深度学习估算的T2 mapping中提取影像组学特征，随后通过五折交叉验证进行特征选择和模型构建。采用逻辑回归和高斯过程分类器建立预测模型，并通过曲线下面积（AUC）和准确率指标评估模型性能。 结果：融合T2加权图像特征与估算T2 mapping特征的模型获得0.803的AUC值，显著优于仅基于T2加权图像特征的模型（AUC=0.700，p=0.048）。 结论：从深度学习估算的T2 mapping中提取的影像组学特征可提供额外的定量信息，能够提升外周带临床显著性前列腺癌侵袭性的预测效能，有望增强非侵入性前列腺癌诊断中的风险分层能力。

原文链接：

Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer