文章：

一种整合临床与磁共振成像特征的深度学习模型可提升疑似前列腺癌男性的风险分层准确性并减少不必要的活检。

A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer

原文发布日期：7 July 2025

DOI: 10.3390/cancers17132257

类型: Article

开放获取: 是

英文摘要：

Background:Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction.Methods:We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI.Results:Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%.Conclusions:Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions.

摘要翻译： 

背景：对疑似临床显著性前列腺癌（csPCa）进行准确的前期风险分层可减少不必要的前列腺活检。利用深度学习整合临床与磁共振成像（MRI）变量有望提升预测效能。 方法：我们回顾性分析了2019年4月至2024年9月期间接受MRI和活检的538例男性患者。采用五折交叉验证训练全连接神经网络。模型1纳入临床特征（年龄、前列腺特异性抗原[PSA]、PSA密度、直肠指检、家族史、既往阴性活检史及当前治疗情况）；模型2采用MRI衍生的前列腺影像报告与数据系统（PI-RADS）分类；模型3综合前述所有变量及MRI测定的病灶大小、位置与前列腺体积。 结果：模型3获得最高的受试者工作特征曲线下面积（AUC=0.822），其次为模型2（AUC=0.778）和模型1（AUC=0.716）。模型1、2、3检测csPCa的灵敏度分别为87.4%、91.6%和86.8%。尽管模型3灵敏度略低于模型2，但其特异度更高，与模型1和2相比分别减少43.4%和21.2%的活检，有效降低假阳性率。决策曲线分析显示，在风险阈值≤20%时模型2净获益最高，而阈值＞20%时模型3更具优势。 结论：模型3优化了csPCa风险分层，尤其适用于规避活检的临床场景；模型2则在规避癌症风险的情境中更为有效。这些模型为个体化、情境敏感性的活检决策提供了支持。

原文链接：

A Deep Learning Model Integrating Clinical and MRI Features Improves Risk Stratification and Reduces Unnecessary Biopsies in Men with Suspected Prostate Cancer