南方医科大学学报

南方医科大学学报 ›› 2022, Vol. 42 ›› Issue (8): 1174-1181.doi: 10.12122/j.issn.1673-4254.2022.08.09

• • 上一篇    下一篇

肾细胞癌与乏脂肪肾血管平滑肌脂肪瘤的鉴别分类模型：基于随机投影的多分类器分层融合框架

莫天澜，吴煜良，杨蕊梦，甄 鑫   

  1. 南方医科大学附属东莞医院肿瘤科放疗中心，广东 东莞 523059；华南理工大学医学院广州第一人民医院放射科，广东 广州 510180；南方医科大学生物医学工程学院，广东 广州 510515
  • 出版日期:2022-08-20 发布日期:2022-09-05

A discrimination model for differentiation of renal cell carcinoma from renal angiomyolipoma without visible fat: based on hierarchical fusion framework of multi-classifier

MO Tianlan, WU Yuliang, YANG Ruimeng, ZHEN Xin   

  1. Radiotherapy Center of Department of Radiology, Affiliated Dongguan Hospital of Southern Medical University, Dongguan 523059, China; Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China; School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
  • Online:2022-08-20 Published:2022-09-05

RichHTML

43

PDF

295

摘要： 目的 研究基于随机投影的多分类器分层融合的分类模型对良性肾小肿块乏脂肪肾血管平滑肌脂肪瘤（<4 cm）（AMLwvf）和恶性肾小肿块肾细胞癌（RCC）的鉴别能力。方法 回顾性收集163例经病理证实存在肾小肿块的患者，其中118例为肾细胞癌，45例为乏脂肪肾血管平滑肌脂肪瘤，对平扫CT图像中病灶面积最大的代表性切片进行目标感兴趣区域（ROI）勾画，利用放射组学特征构建一个层次型的融合框架。在投影域水平上对同质分类器进行融合，然后在分类器水平上对融合结果进行进一步融合，最终得到基于随机投影的多分类器分层融合的AMLwvf和RCC鉴别分类模型。采用五折交叉验证方法和特异性（SPE）、灵敏度（SEN）、准确率（ACC）、ROC曲线下面积（AUC）评价AMLwvf与RCC鉴别分类模型的性能。将本研究所提模型与使用单一基分类器算法以及几种传统的集成模型对AMLwvf和RCC的鉴别分类能力进行定量比较，验证本研究所提鉴别模型的可行性和有效性。结果 投影数设置为10时，本文提出的分层融合鉴别模型在所有指标上获得最好的结果。基于投影数为10的前提，五折交叉验证结果显示本研究所提出的基于多分类器分层融合的AMLwvf和RCC鉴别分类模型的SPE、SEN、ACC、AUC分别为：0.853、0.693、0.809、0.870。结论 基于随机投影的多分类器集成分类系统构建的AMLwvf和RCC鉴别模型可以很好地对 AMLwvf 和 RCC 进行鉴别分类。同时与基于单一分类器算法以及其他多分类器集成系统构建的AMLwvf和RCC的鉴别模型相比，本文所提出鉴别模型在AMLwvf和RCC的鉴别分类任务中具有较大优势。

关键词: 多分类器；分层融合框架；随机投影；肾细胞癌；乏脂肪肾血管平滑肌脂肪瘤

Abstract: Objective To investigate the capabilities of classification models based on hierarchical fusion framework of multi-classifier using a random projection strategy for differentiation of renal cell carcinoma (RCC) from small renal angiomyolipoma (<4 cm) without visible fat (AMLwvf). Methods We retrospectively collected the clinical data from 163 patients with pathologically proven small renal mass, including 118 with RCC and 45 with AMLwvf. Target region of interest (ROI) delineation was performed on an unenhanced phase (UP) CT image slice displaying the largest lesion area. The radiomics features were used to establish a hierarchical fusion method. On the projection-based level, the homogeneous classifiers were fused, and the fusion results were further fused at the classifier-based level to construct a multi-classifier fusion system based on random projection for differentiation of AMLwvf and RCC. The discriminative capability of this model was quantitatively evaluated using 5-fold cross validation and 4 evaluation indexes [specificity, sensitivity, accuracy and area under ROC curve (AUC)]. We quantitatively compared this multi-classifier fusion framework against different classification models using a single classifier and several multi-classifier ensemble models. Results When the projection number was set at 10, the proposed hierarchical fusion differentiation framework achieved the best results on all the evaluation measurements. At the optimal projection number of 10, the specificity, sensitivity, average accuracy and AUC of the multi-classifier ensemble classification system for differentiation between AMLwvf and RCC were 0.853, 0.693, 0.809 and 0.870, respectively. Conclusion The proposed model constructed based on a multi-classifier fusion system using random projection shows better performance to differentiate RCC from AMLwvf than the AMLwvf and RCC discrimination models based on a single classification algorithm and the currently available benchmark ensemble methods.

Key words: multi-classifier; hierarchical fusion frame-work; random projection; renal cell carcinoma; renal angiomyolipoma without visible fat