文章：

前列腺多参数磁共振成像可疑病灶的临床-基因组风险组分类

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI

原文发布日期：31 October 2023

DOI: 10.3390/cancers15215240

类型: Article

开放获取: 是

英文摘要：

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients’ management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients’ risk using combined clinical-genomic classification.

摘要翻译： 

多参数磁共振成像在前列腺癌患者临床管理决策中的应用近期有所增加。活检后，临床医生可采用美国国家综合癌症网络风险分层方案及商业化基因组分类器（如Decipher）进行风险评估。本研究基于已建立的三级临床-基因组分类系统，构建了基于影像组学的预测模型，旨在活检前识别低风险病灶/患者。从T2加权和弥散加权成像中阳性活检区域及正常表现组织中提取影像组学特征。仅使用活检前可获得的临床信息，评估了五种预测低风险病灶/患者的模型，其构建基础分别为：1.临床变量；2.病灶影像组学特征；3.病灶与正常组织影像组学；4.临床变量与病灶影像组学；5.临床变量、病灶及正常组织影像组学特征。研究分析了78名患者的83次多参数磁共振检查数据。模型1与模型2表现相近（受试者工作特征曲线下面积分别为0.835和0.838），但在直肠指诊阴性患者的亚组分析中，影像组学显著提升了基于病灶的模型性能。引入正常组织影像组学特征后，所有模型的预测性能均获得显著提升。患者层面的模型亦呈现相似规律。据我们所知，本研究首次证明基于机器学习的影像组学模型能够通过临床-基因组联合分类系统预测患者风险。

原文链接：

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI