首页> 外国专利> COMPUTERIZED ANALYSIS OF COMPUTED TOMOGRAPHY (CT) IMAGERY TO QUANTIFY TUMOR INFILTRATING LYMPHOCYTES (TILS) IN NON-SMALL CELL LUNG CANCER (NSCLC)

COMPUTERIZED ANALYSIS OF COMPUTED TOMOGRAPHY (CT) IMAGERY TO QUANTIFY TUMOR INFILTRATING LYMPHOCYTES (TILS) IN NON-SMALL CELL LUNG CANCER (NSCLC)

机译:计算机断层扫描(CT)影像定量分析非小细胞肺癌(NSCLC)肿瘤浸润淋巴细胞(TILS)的计算机分析

摘要

Methods, apparatus, and other embodiments predict tumor infiltrating lymphocyte (TIL) density from pre-surgical computed tomography images of a region of tissue demonstrating non-small cell lung cancer (NSCLC). One example apparatus includes a set of circuits that includes an image acquisition circuit that accesses a radiological image of a region of tissue demonstrating cancerous pathology, where the radiological image has a plurality of pixels, and where the radiological image includes an annotated region of interest (ROI), a feature extraction circuit that extracts a set of radiomic features from the ROI, where the set of radiomic features includes at least two texture features and at least one shape feature, and a classification circuit that comprises a machine learning classifier that classifies the ROI as high tumor infiltrating lymphocyte (TIL) density, or low TIL density, based, at least in part, on the set of radiomic features.
机译:方法,设备和其他实施例根据证明非小细胞肺癌(NSCLC)的组织区域的术前计算机断层摄影图像来预测肿瘤浸润淋巴细胞(TIL)的密度。一个示例装置包括一组电路,该电路包括图像获取电路,该图像获取电路访问表现出癌性病理的组织区域的放射线图像,其中放射线图像具有多个像素,并且其中放射线图像包括注释的感兴趣区域( ROI),从ROI中提取一组放射学特征的特征提取电路,其中该组放射学特征包括至少两个纹理特征和至少一个形状特征,以及一个分类电路,该分类电路包括对机器学习分类器进行分类的机器学习分类器ROI是高肿瘤浸润淋巴细胞(TIL)密度或低TIL密度,至少部分基于放射特征集。

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