Wang Guanjin, Teoh Jeremy Yuen-Chun, Choi Kup-Sze
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1-4. doi: 10.1109/EMBC.2018.8513365.
An early diagnosis of prostate cancer (PC) is key for the successful treatment. Although invasive prostate biopsies can provide a definitive diagnosis, the number of biopsies should be reduced to avoid side effects and risks especially for the men with the low risk of cancer. Therefore, an accurate model is in need to predict PC with the aim of reducing unnecessary biopsies. In this study, we developed predictive models using four machine learning methods including Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Artificial Neural Network (ANN) and Random Forest (RF) to detect PC cases using available prebiopsy information. The models were constructed and evaluated on a cohort of 1625 Chinese men with prostate biopsies from Hong Kong hospital. All the models have the excellent performances in detecting significant PC cases, with ANN achieving the highest accuracy of 0.9527 and the AUC value of 0.9755. RF outperformed the other three methods in classifying benign, significant and insignificant PC cases, with an accuracy of 0.9741 and a F1 score of 0.8290.
前列腺癌（PC）的早期诊断是成功治疗的关键。尽管侵入性前列腺活检可以提供明确的诊断，但活检次数应减少，以避免副作用和风险，尤其是对于癌症风险较低的男性。因此，需要一个准确的模型来预测前列腺癌，以减少不必要的活检。在本研究中，我们使用四种机器学习方法，包括支持向量机（SVM）、最小二乘支持向量机（LS-SVM）、人工神经网络（ANN）和随机森林（RF），开发了预测模型，以利用活检前可用信息检测前列腺癌病例。这些模型是在一组来自香港医院的1625名接受前列腺活检的中国男性队列中构建和评估的。所有模型在检测显著前列腺癌病例方面都有出色表现，其中人工神经网络的准确率最高，为0.9527，AUC值为0.9755。在对良性、显著和非显著前列腺癌病例进行分类时，随机森林的表现优于其他三种方法，准确率为0.9741，F1分数为0.8290。
