文章：

基于七项临床变量构建前列腺活检中显著前列腺癌预测模型：机器学习是否优于逻辑回归？

Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression?

原文发布日期：25 March 2025

DOI: 10.3390/cancers17071101

类型: Article

开放获取: 是

英文摘要：

Objective: This study compares machine learning (ML) and logistic regression (LR) algorithms in developing a predictive model for sPCa using the seven predictive variables from the Barcelona (BCN-MRI) predictive model.Method: A cohort of 5005 men suspected of having PCa who underwent MRI and targeted and/or systematic biopsies was used for training, testing, and validation. A feedforward neural network (FNN)-based SimpleNet model (GMV) and a logistic regression-based model (BCN) were developed. The models were evaluated for discrimination ability, precision–recall, net benefit, and clinical utility. Both models demonstrated strong predictive performance.Results: The GMV model achieved an area under the curve of 0.88 in training and 0.85 in test cohorts (95% CI: 0.83–0.90), while the BCN model reached 0.85 and 0.84 (95% CI: 0.82–0.87), respectively (p> 0.05). The GMV model exhibited higher recall, making it more suitable for clinical scenarios prioritizing sensitivity, whereas the BCN model demonstrated higher precision and specificity, optimizing the reduction of unnecessary biopsies. Both models provided similar clinical benefit over biopsying all men, reducing unnecessary procedures by 27.5–29% and 27–27.5% of prostate biopsies at 95% sensitivity, respectively (p> 0.05).Conclusions: Our findings suggest that both ML and LR models offer high accuracy in sPCa detection, with ML exhibiting superior recall and LR optimizing specificity. These results highlight the need for model selection based on clinical priorities.

摘要翻译： 

目的：本研究旨在比较机器学习（ML）与逻辑回归（LR）算法，利用巴塞罗那（BCN-MRI）预测模型中的七个预测变量，构建具有临床意义的前列腺癌（sPCa）预测模型。 方法：研究纳入5005例疑似前列腺癌并接受磁共振成像及靶向和/或系统活检的男性患者队列，用于模型训练、测试与验证。构建了基于前馈神经网络（FNN）的SimpleNet模型（GMV）和基于逻辑回归的模型（BCN），并从区分能力、精确率-召回率、净收益及临床效用等方面对模型进行评估。两种模型均展现出较强的预测性能。 结果：GMV模型在训练队列和测试队列中曲线下面积分别为0.88和0.85（95% CI：0.83–0.90），而BCN模型分别为0.85和0.84（95% CI：0.82–0.87）（p>0.05）。GMV模型具有更高的召回率，更适用于临床中优先考虑敏感性的场景；而BCN模型则表现出更高的精确率和特异性，能更有效地减少不必要的活检。在95%敏感性阈值下，两种模型相较于对所有患者进行活检均显示出相似的临床获益，分别可减少27.5–29%和27–27.5%不必要的前列腺活检（p>0.05）。 结论：研究结果表明，ML与LR模型在前列腺癌检测中均具有较高准确性，其中ML模型在召回率方面表现更优，而LR模型在特异性优化方面更具优势。这些结果提示，临床实践中需根据优先目标进行模型选择。

原文链接：

Developing a Predictive Model for Significant Prostate Cancer Detection in Prostatic Biopsies from Seven Clinical Variables: Is Machine Learning Superior to Logistic Regression?