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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI
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Differentiating Cancerous and Non-cancerous Prostate Tissue Using Multi-scale Texture Analysis on MRI

机译：使用MRI多尺度纹理分析区分癌性和非癌性前列腺组织

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Prostate cancer (PCa) diagnosis is established by pathological examination via biopsies, which are associated with significant complications and false negatives. Using MRIs to identify locations with high probability of containing cancer could instead be used to guide the biopsy procedure. The present investigation aims to identify target regions within different prostatic zones on MRI with high probability of being cancerous for assisting in the decision of where and how to perform biopsy. Our approach involved extracting multi-scale texture features for capturing local patterns to distinguish cancer and healthy tissue in different T2W-MRI prostate zones. Three different classification models were fed by the proposed strategy, namely support vector machine (SVM), Adaboost, and Random Forest. SVM with a linear kernel showed the best classification performance, with AUC scores of 0.91 in the anterior fibromuscular stroma area, 0.85 in the peripheral zone, and 0.87 when classification is performed independently of the prostate zone. The proposed method demonstrated that discriminant multi-scale texture features can accurately identify regions of prostate cancer in a zone-specific fashion, via MRI.
机译：前列腺癌（PCa）的诊断是通过活检进行病理检查来确定的，该活检与明显的并发症和假阴性有关。取而代之，使用MRI来确定极有可能包含癌症的位置可以用来指导活检过程。本研究旨在确定MRI上不同前列腺区域内的目标区域，这些区域极有可能发生癌变，从而有助于决定在哪里以及如何进行活检。我们的方法涉及提取多尺度纹理特征以捕获局部模式，以区分不同T2W-MRI前列腺区域中的癌症和健康组织。所提出的策略提供了三种不同的分类模型，即支持向量机（SVM），Adaboost和随机森林。具有线性核的SVM表现出最好的分类性能，前纤维肌间质区域的AUC评分为0.91，周围区域的AUC评分为0.85，而独立于前列腺区域进行分类时的AUC评分为0.87。所提出的方法证明了可辨别的多尺度纹理特征可以通过MRI以区域特定的方式准确地识别前列腺癌的区域。

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