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Development of a Computer Aided Diagnosis Model for Prostate Cancer Classification on Multi-Parametric MRI

机译：基于多参数MRI的前列腺癌分类计算机辅助诊断模型的开发

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Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score >4+3) vs. low-grade cancer (Gleason score ＜4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist's ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life.
机译：多参数MRI（mp-MRI）正在成为当代前列腺癌筛查和诊断的标准，并已显示出可帮助医生进行癌症检测。与传统的系统活检相比，它具有许多优势，传统活检显示在疾病的所有阶段都具有很高的临床假阴性率，高达23％。然而，mp-MRI的解释相对复杂，并且在病灶定位和分级中存在观察者间差异。已经开发了计算机辅助诊断（CAD）系统作为解决方案，因为它们具有执行确定性定量图像分析的能力。我们测量了这种系统的准确性，该系统使用准确地共同注册的整个山地数字化组织学进行了验证。我们在基于投资回报率的四部分实验中训练了逻辑线性分类器（LOGLC），支持向量机（SVC），k最近邻（KNN）和随机森林分类器（RFC），以针对：1）癌症与非癌症，2 ）高等级（格里森评分> 4 + 3）与低等级癌症（格里森评分＜4 + 3），3）高等级与其他组织成分的比较和4）高等级与良性组织的比较，方法是选择使用前向特征选择中的1-10个特征，具有最高AUC的分类器。 CAD模型能够对恶性组织与良性组织进行分类，并能以高准确度检测出高级别的癌症。一旦得到充分验证，这项工作将成为增强放射科医生检测恶性肿瘤能力的工具的基础，从而有可能改善前列腺癌患者的活检指导，治疗选择和局部治疗，从而最大程度地提高治愈的可能性并提高生活质量。

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