使用深度学习对简化前列腺MRI进行前列腺癌风险分层和扫描定制

Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate MRI.

作者信息

Johnson Patricia M, Dutt Tarun, Ginocchio Luke A, Saimbhi Amanpreet Singh, Umapathy Lavanya, Block Kai Tobias, Sodickson Daniel K, Chopra Sumit, Tong Angela, Chandarana Hersh

机构信息

Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

出版信息

J Magn Reson Imaging. 2025 Sep;62(3):858-866. doi: 10.1002/jmri.29798. Epub 2025 Apr 22.

DOI:10.1002/jmri.29798

PMID:40259798

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12335925/

Abstract

BACKGROUND

MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization.

PURPOSE

To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial.

STUDY TYPE

Retrospective and prospective.

POPULATION

The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated.

FIELD STRENGTH/SEQUENCE: 3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI).

ASSESSMENT

The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner.

STATISTICAL TESTS

AUROC with 95% confidence intervals (CI) was used to evaluate model performance.

RESULTS

In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations.

DATA CONCLUSION

The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows.

EVIDENCE LEVEL

TECHNICAL EFFICACY

Stage 2.

摘要

背景

磁共振成像（MRI）在前列腺癌（PCa）的检测和管理中起着关键作用。双参数MRI（bpMRI）为多参数MRI（mpMRI）提供了一种更快、无需造影剂的替代方法。对所有患者常规使用mpMRI可能没有必要，基于个体风险的定制成像方法（bpMRI或mpMRI）可能会优化资源利用。

目的

开发并评估一种深度学习（DL）模型，用于使用bpMRI对临床显著性前列腺癌（csPCa）进行分类，并评估其通过仅在有益时推荐mpMRI的附加序列来优化MRI检查方案选择的潜力。

研究类型

回顾性和前瞻性。

研究对象

DL模型在26,129例前列腺MRI研究中进行训练和验证。对151例患者（平均年龄65±8岁）的回顾性队列进行评估，这些患者通过活检、前列腺切除术或长期随访进行了真实情况验证，同时对142例未接受过治疗的患者（平均年龄65±9岁）的前瞻性队列进行评估，这些患者接受了bpMRI检查。

场强/序列：3T，快速自旋回波T2加权成像（T2WI）和单次激发回波平面扩散加权成像（DWI）。

评估

基于3D ResNet - 50架构的DL模型使用前列腺影像报告和数据系统（PI-RADS）≥3和 Gleason评分≥7作为结果指标对csPCa进行分类。该模型在由三位放射科医生达成共识标记的前瞻性队列以及基于活检或长期随访进行真实情况验证的回顾性队列上进行评估。在自动化MRI工作流程上测试实时推理，直接在扫描仪处提供分类结果。

统计检验

使用95%置信区间（CI）的曲线下面积（AUROC）来评估模型性能。

结果

在前瞻性队列中，该模型对PI-RADS≥3分类的AUC为0.83（95%CI：0.77 - 0.89），灵敏度为93%，特异度为54%。在回顾性队列中，该模型对Gleason≥7分类的AUC为0.86（95%CI：0.80 - 0.91），灵敏度为93%，特异度为62%。实时实施显示检查方案推荐的处理延迟为14 - 16秒。