A machine learning approach to predict progression on active surveillance for prostate cancer.
A machine learning approach to predict progression on active surveillance for prostate cancer.
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DOI：
10.1016/j.urolonc.2021.08.007
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发表时间：
2022-04
期刊：
影响因子：
--
通讯作者：
Feldman AS
中科院分区：
文献类型：
--
作者：
Nayan M;Salari K;Bozzo A;Ganglberger W;Lu G;Carvalho F;Gusev A;Schneider A;Westover BM;Feldman AS
前列腺癌主动监测（AS）进展的稳健预测可以允许风险适应性方案。到目前为止，预测AS进展的模型总是使用传统的统计方法。我们试图评估机器学习（ML）方法是否可以改善对AS进展的预测。我们对1997年至2016年间诊断为极低或低风险前列腺癌并在我们机构接受AS治疗的患者进行了回顾性队列研究。在训练集中，我们训练了传统的逻辑回归（T-LR）分类器和备用ML分类器（支持向量机，随机森林，全连接人工神经网络和ML-LR）来预测等级进展。我们在测试集中评估了模型性能。主要性能指标是F1评分。我们的队列包括790例患者。中位随访时间为6.29年，234例发生了等级进展。按降序排列，F1评分为：支持向量机0.586（95% CI 0.579 - 0.591），ML-LR 0.522（95% CI 0.513 - 0.526），人工神经网络0.392（95% CI 0.379 - 0.396），随机森林0.376（95% CI 0.364 - 0.380）和T-LR 0.182（95% CI 0.151 - 0.185）。所有替代ML模型的F1评分均显著高于T-LR模型（所有p <0.001）。在我们的研究中，ML方法在预测前列腺癌AS进展方面显著优于T-LR。虽然我们的特定模型需要进一步验证，但我们预计ML方法将有助于产生强大的预测模型，从而促进前列腺癌AS的个体化风险分层。
Robust prediction of progression on active surveillance (AS) for prostate cancer can allow for risk-adapted protocols. To date, models predicting progression on AS have invariably used traditional statistical approaches. We sought to evaluate whether a machine learning (ML) approach could improve prediction of progression on AS. We performed a retrospective cohort study of patients diagnosed with very-low or low-risk prostate cancer between 1997 and 2016 and managed with AS at our institution. In the training set, we trained a traditional logistic regression (T-LR) classifier, and alternate ML classifiers (support vector machine, random forest, a fully connected artificial neural network, and ML-LR) to predict grade-progression. We evaluated model performance in the test set. The primary performance metric was the F1 score. Our cohort included 790 patients. With a median follow-up of 6.29 years, 234 developed grade-progression. In descending order, the F1 scores were: support vector machine 0.586 (95% CI 0.579 – 0.591), ML-LR 0.522 (95% CI 0.513 – 0.526), artificial neural network 0.392 (95% CI 0.379 – 0.396), random forest 0.376 (95% CI 0.364 – 0.380), and T-LR 0.182 (95% CI 0.151 – 0.185). All alternate ML models had a significantly higher F1 score than the T-LR model (all p <0.001). In our study, ML methods significantly outperformed T-LR in predicting progression on AS for prostate cancer. While our specific models require further validation, we anticipate that a ML approach will help produce robust prediction models that will facilitate individualized risk-stratification in prostate cancer AS.
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DOI：
10.1200/jco.2015.62.5764
发表时间：
2015-10-20
期刊：
Journal of clinical oncology : official journal of the American Society of Clinical Oncology
影响因子：
--
作者：
Tosoian JJ;Mamawala M;Epstein JI;Landis P;Wolf S;Trock BJ;Carter HB
通讯作者：
Carter HB
影响因子：
1.9
作者：
Morash, Chris;Tey, Rovena;Evans, Andrew
通讯作者：
Evans, Andrew
影响因子：
6.6
作者：
Sanda, Martin G.;Cadeddu, Jeffrey A.;Treadwell, Jonathan R.
通讯作者：
Treadwell, Jonathan R.
影响因子：
4.5
作者：
Pinsky, Paul F.;Miller, Eric;Andriole, Gerald
通讯作者：
Andriole, Gerald
使用机器学习来鉴定外周动脉疾病和未来死亡率风险。
影响因子：
4.3
作者：
Ross, Elsie Gyang;Shah, Nigam H.;Dalman, Ronald L.;Nead, Kevin T.;Cooke, John P.;Leeper, Nicholas J.
通讯作者：
Leeper, Nicholas J.