基于磁共振成像放射组学的机器学习预测可疑 PI-RADS 3 病变中的临床显著前列腺癌。

Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions.

机构信息

Department of Radiology, Weill Cornell Medicine, New York, New York, USA.

Department of Urology, Weill Cornell Medicine, New York, New York, USA.

出版信息

J Magn Reson Imaging. 2021 Nov;54(5):1466-1473. doi: 10.1002/jmri.27692. Epub 2021 May 10.

DOI:10.1002/jmri.27692

PMID:33970516

Abstract

BACKGROUND

While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions typically warrant prostate biopsy and PI-RADS 1 and 2 lesions may be safely observed, PI-RADS 3 lesions are equivocal.

PURPOSE

To construct and cross-validate a machine learning model based on radiomics features from T -weighted imaging (T WI) of PI-RADS 3 lesions to identify clinically significant prostate cancer (csPCa), that is, pathological Grade Group ≥ 2.

STUDY TYPE

Single-center retrospective study.

POPULATION

A total of 240 patients were included (training cohort, n = 188, age range 43-82 years; test cohort, n = 52, age range 41-79 years). Eligibility criteria were 1) magnetic resonance imaging (MRI)-targeted biopsy between 2015 and 2020; 2) PI-RADS 3 index lesion identified on multiparametric MRI; (3) biopsy performed within 1 year of MRI. The percentages of csPCa lesions were 10.6% and 15.4% in the training and test cohorts, respectively.

FIELD STRENGTH/SEQUENCE: A 3 T; T WI turbo-spin echo, diffusion-weighted spin-echo echo planar imaging, dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging.

ASSESSMENT

Multislice volumes-of-interest (VOIs) were drawn in the PI-RADS 3 index lesions on T WI. A total of 107 radiomics features (first-order histogram and second-order texture) were extracted from the segmented lesions.

STATISTICAL TESTS

A random forest classifier using the radiomics features as input was trained and validated for prediction of csPCa. The performance of the machine learning classifier, prostate specific antigen (PSA) density, and prostate volume for csPCa prediction was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

The trained random forest classifier constructed from the T WI radiomics features good and statistically significant area-under-the-curves (AUCs) of 0.76 (P = 0.022) for prediction of csPCa in the test set. Prostate volume and PSA density showed moderate and nonsignificant performance (AUC 0.62, P = 0.275 and 0.61, P = 0.348, respectively) for csPCa prediction in the test set.

CONCLUSION

The machine learning classifier based on T WI radiomic features demonstrated good performance for prediction of csPCa in PI-RADS 3 lesions.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: 2.

摘要

背景

前列腺影像报告和数据系统（PI-RADS）4 级和 5 级病变通常需要前列腺活检，PI-RADS 1 级和 2 级病变可以安全观察，而 PI-RADS 3 级病变则存在不确定性。

目的

构建并交叉验证基于 PI-RADS 3 级病变 T 加权成像（TWI）的放射组学特征的机器学习模型，以识别临床上有意义的前列腺癌（csPCa），即病理分级组≥2。

研究类型

单中心回顾性研究。

人群

共纳入 240 名患者（训练队列，n=188 例，年龄 43-82 岁；测试队列，n=52 例，年龄 41-79 岁）。入选标准为：1）2015 年至 2020 年进行 MRI 靶向活检；2）在多参数 MRI 上识别 PI-RADS 3 级索引病变；3）MRI 后 1 年内进行活检。训练队列和测试队列中 csPCa 病变的百分比分别为 10.6%和 15.4%。

磁场强度/序列：3T；TWI 涡轮自旋回波，扩散加权自旋回波回波平面成像，动态对比增强 MRI 伴时间分辨 T1 加权成像。

评估

在 TWI 上的 PI-RADS 3 级索引病变中绘制多个切片的感兴趣区（VOI）。从分割病变中提取了总共 107 个放射组学特征（一阶直方图和二阶纹理）。

统计检验

使用放射组学特征作为输入的随机森林分类器进行训练和验证，以预测 csPCa。使用受试者工作特征（ROC）分析评估机器学习分类器、前列腺特异性抗原（PSA）密度和前列腺体积对 csPCa 预测的性能。

结果

从 TWI 放射组学特征中训练的随机森林分类器在测试集上对 csPCa 的预测具有良好且具有统计学意义的曲线下面积（AUC）为 0.76（P=0.022）。在测试集上，前列腺体积和 PSA 密度对 csPCa 的预测具有中等且无统计学意义的性能（AUC 0.62，P=0.275 和 0.61，P=0.348）。