Nematollahi Hamide, Moslehi Masoud, Aminolroayaei Fahimeh, Maleki Maryam, Shahbazi-Gahrouei Daryoush
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran.
Diagnostics (Basel). 2023 Feb 20;13(4):806. doi: 10.3390/diagnostics13040806.
Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis is of particular importance to controlling and preventing the disease from spreading to other tissues. Artificial intelligence and machine learning have effectively detected and graded several cancers, in particular prostate cancer. The purpose of this review is to show the diagnostic performance (accuracy and area under the curve) of supervised machine learning algorithms in detecting prostate cancer using multiparametric MRI. A comparison was made between the performances of different supervised machine-learning methods. This review study was performed on the recent literature sourced from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up to the end of January 2023. The findings of this review reveal that supervised machine learning techniques have good performance with high accuracy and area under the curve for prostate cancer diagnosis and prediction using multiparametric MR imaging. Among supervised machine learning methods, deep learning, random forest, and logistic regression algorithms appear to have the best performance.
前列腺癌是男性癌症相关死亡的第二大主要原因。其早期正确诊断对于控制和预防疾病扩散到其他组织尤为重要。人工智能和机器学习已有效检测并对多种癌症进行分级，尤其是前列腺癌。本综述的目的是展示监督式机器学习算法在使用多参数磁共振成像检测前列腺癌时的诊断性能（准确性和曲线下面积）。对不同监督式机器学习方法的性能进行了比较。本综述研究基于截至2023年1月底从谷歌学术、PubMed、Scopus和科学网等科学引文网站获取的近期文献进行。本综述的结果表明，监督式机器学习技术在使用多参数磁共振成像进行前列腺癌诊断和预测时具有良好性能，准确性高且曲线下面积大。在监督式机器学习方法中，深度学习、随机森林和逻辑回归算法似乎表现最佳。
