Suppr超能文献

一种基于CT特征的非侵入性评分系统,用于鉴别透明细胞肾细胞癌(ccRCC)与无可见脂肪的肾血管平滑肌脂肪瘤(RAML-wvf)。

A Non-Invasive Scoring System to Differential Diagnosis of Clear Cell Renal Cell Carcinoma (ccRCC) From Renal Angiomyolipoma Without Visible Fat (RAML-wvf) Based on CT Features.

作者信息

Wang Xiao-Jie, Qu Bai-Qiang, Zhou Jia-Ping, Zhou Qiao-Mei, Lu Yuan-Fei, Pan Yao, Xu Jian-Xia, Miu You-You, Wang Hong-Qing, Yu Ri-Sheng

机构信息

Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Department of Radiology, Wenling Hospital of Traditional Chinese Medicine, Taizhou, China.

出版信息

Front Oncol. 2021 Apr 23;11:633034. doi: 10.3389/fonc.2021.633034. eCollection 2021.

Abstract

BACKGROUND

Renal angiomyolipoma without visible fat (RAML-wvf) and clear cell renal cell carcinoma (ccRCC) have many overlapping features on imaging, which poses a challenge to radiologists. This study aimed to create a scoring system to distinguish ccRCC from RAML-wvf using computed tomography imaging.

METHODS

A total of 202 patients from 2011 to 2019 that were confirmed by pathology with ccRCC (n=123) or RAML (n=79) were retrospectively analyzed by dividing them randomly into a training cohort (n=142) and a validation cohort (n=60). A model was established using logistic regression and weighted to be a scoring system. ROC, AUC, cut-off point, and calibration analyses were performed. The scoring system was divided into three ranges for convenience in clinical evaluations, and the diagnostic probability of ccRCC was calculated.

RESULTS

Four independent risk factors are included in the system: 1) presence of a pseudocapsule, 2) a heterogeneous tumor parenchyma in pre-enhancement scanning, 3) a non-high CT attenuation in pre-enhancement scanning, and 4) a heterogeneous enhancement in CMP. The prediction accuracy had an ROC of 0.978 (95% CI, 0.956-0.999; P=0.011), similar to the primary model (ROC, 0.977; 95% CI, 0.954-1.000; P=0.012). A sensitivity of 91.4% and a specificity of 93.9% were achieved using 4.5 points as the cutoff value. Validation showed a good result (ROC, 0.922; 95% CI, 0.854-0.991, P=0.035). The number of patients with ccRCC in the three ranges (0 to <2 points; 2-4 points; >4 to ≤11 points) significantly increased with increasing scores.

CONCLUSION

This scoring system is convenient for distinguishing between ccRCC and RAML-wvf using four computed tomography features.

摘要

背景

无可见脂肪的肾血管平滑肌脂肪瘤(RAML-wvf)与透明细胞肾细胞癌(ccRCC)在影像学上有许多重叠特征,这给放射科医生带来了挑战。本研究旨在创建一种评分系统,利用计算机断层扫描成像将ccRCC与RAML-wvf区分开来。

方法

回顾性分析2011年至2019年共202例经病理确诊为ccRCC(n=123)或RAML(n=79)的患者,将他们随机分为训练队列(n=142)和验证队列(n=60)。使用逻辑回归建立模型并加权成为一个评分系统。进行ROC、AUC、截断点和校准分析。为方便临床评估,将评分系统分为三个范围,并计算ccRCC的诊断概率。

结果

该系统包括四个独立危险因素:1)假包膜的存在;2)增强前扫描中肿瘤实质不均匀;3)增强前扫描中CT衰减不高;4)对比增强扫描(CMP)中强化不均匀。预测准确性的ROC为0.978(95%CI,0.956-0.999;P=0.011),与原始模型相似(ROC,0.977;95%CI,0.954-1.000;P=0.012)。以4.5分为截断值时,灵敏度为91.4%,特异度为93.9%。验证显示结果良好(ROC,0.922;95%CI,0.854-0.991,P=0.035)。在三个范围(0至<2分;2-4分;>4至≤11分)中,ccRCC患者数量随分数增加而显著增加。

结论

该评分系统利用四个计算机断层扫描特征便于区分ccRCC和RAML-wvf。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/402d/8103199/b9b563c63cee/fonc-11-633034-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验