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A Voting Ensemble Method to Assist the Diagnosis of Prostate Cancer Using Multiparametric MRI

机译：一种有助于使用MultiParametric MRI诊断前列腺癌的投票

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Prostate cancer is the second most commonly occurring cancer in men. Diagnosis through Magnetic Resonance Imaging (MRI) is limited, yet current practice holds a relatively low specificity. This paper extends a previous SPIE ProstateX challenge study in three ways (1) to include healthy tissue analysis, creating a solution suitable for clinical practice, which has been requested and validated by collaborating clinicians; (2) by using a voting ensemble method to assist prostate cancer diagnosis through a supervised SVM approach; and (3) using the unsupervised GTM to provide interpretability to understand the supervised SVM classification results. Pairwise classifiers of clinically significant lesion, non-significant lesion, and healthy tissue, were developed. Results showed that when combining multiparametric MRI and patient level metadata, classification of significant lesions against healthy tissue attained an AUC of 0.869 (10-fold cross-validation).
机译：前列腺癌是男性中最常见的癌症。通过磁共振成像（MRI）的诊断是有限的，但目前的实践具有相对较低的特异性。本文以三种方式（1）延伸了先前的SPIE Prostatex挑战研究，以包括健康组织分析，创造适合临床实践的解决方案，通过合作临床医生要求和验证; （2）通过使用投票合奏方法通过监督的SVM方法辅助前列腺癌诊断; （3）使用无监督的GTM提供可解释性，了解监督的SVM分类结果。开发了临床显着病变，非显着病变和健康组织的成对分类剂。结果表明，当组合多体MRI和患者水平元数据时，对健康组织的显着病变的分类达到0.869（交叉验证10倍）的AUC。

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