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Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features

机译:基于全肿瘤计算断层扫描纹理特征的机器学习,肾细胞癌而无可见脂肪的肾血管脂肪瘤的分化

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摘要

Background Morphological findings showed poor accuracy in differentiating angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). Purpose To determine the performance of a machine learning classifier in differentiating AMLwvf from different subtypes of RCC based on whole-tumor slices of CT images. Material and Methods In this retrospective study, 171 pathologically proven renal masses were collected from a single institution. Texture features were extracted from whole-tumor images in three phases including the pre-contrast (PCP), corticomedullary (CMP), and nephrographic (NP) phases. A support vector machine with the recursive feature elimination method based on fivefold cross-validation (SVM-RFECV) with the synthetic minority oversampling technique (SMOTE) was utilized to establish classifiers for differentiating AMLwvf from all subtypes of RCC (all-RCC), clear cell RCC (ccRCC), and non-ccRCC. The performances of the classifiers based on three-phase and single-phase images were compared with each other and morphological interpretations. Results A machine learning classifier achieved the best performance in differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC. The performance of the best machine learning classifier for differentiating AMLwvf from all-RCC (area under the curve [AUC] = 0.96) and ccRCC (AUC = 0.97) was higher than that for differentiating AMLwvf from non-ccRCC (AUC = 0.89); morphological interpretations achieved lower performance for differentiating AMLwvf from all-RCC (AUC = 0.67), ccRCC (AUC = 0.68), and non-ccRCC (AUC = 0.64). Conclusion Machine learning can be a useful non-invasive technique for differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC, and it can be more accurate than morphological interpretation by radiologists.
机译:背景技术形态学发现表明,在没有来自肾细胞癌(RCC)的情况下区分血管益鳞片瘤的准确性差异差。目的是,基于全肿瘤切片的CT图像,确定机器学习分类器的性能。本回顾性研究中的材料和方法,从单一机构收集了171种病理证明肾肿块。在三个阶段中从全肿瘤图像中提取纹理特征,包括预造影术(PCP),皮质体(CMP)和肾图钉(NP)阶段。具有基于五倍交叉验证(SVM-RFECV)的带有递归特征消除方法的支持向量机,用于建立用于区分AMLWVF的分类器,从所有RCC(全RCC),清晰细胞RCC(CCRCC)和非CCRCC。基于三相和单相图像的分类器的性能与彼此和形态解释进行了比较。结果机器学习分类器实现了区分AMLWVF的最佳性能,来自All-RCC,CCRCC和非CCRCC。用于区分AMLWVF的最佳机器学习分类器的性能来自ALL-RCC(曲线[AUC] = 0.96)和CCRCC(AUC = 0.97)的差异,从非CCRCC(AUC = 0.89)区分AMLWVF;形态解释达到差异从All-RCC(AUC = 0.67),CCRCC(AUC = 0.68)和非CCRCC(AUC = 0.64)的较低性能。结论机器学习可以是用于区分AMLWVF的有用的非侵入性技术,来自所有RCC,CCRCC和非CCRCC,它可以比放射科医师的形态解释更准确。

著录项

  • 来源
    《Acta Radiologica》 |2019年第11期|共10页
  • 作者单位

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Radiol 23 Beijie Haibang St;

    Shenzhen Univ Shenzhen Peoples Hosp 2 Hlth Sci Ctr Dept Radiol Affiliated Hosp 1 Shenzhen;

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Pathol Jiangmen Peoples R;

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Pathol Jiangmen Peoples R;

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Radiol 23 Beijie Haibang St;

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Radiol 23 Beijie Haibang St;

    Sun Yat Sen Univ Affiliated Jiangmen Hosp Jiangmen Cent Hosp Dept Radiol 23 Beijie Haibang St;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    Renal cell carcinoma; angiomyolipoma; computed tomography (CT); machine learning;

    机译:肾细胞癌;血管肌脂瘤;计算机断层扫描(CT);机器学习;

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