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利用中心性指数预测高核级透明细胞肾细胞癌的新型术前列线图

New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma.

作者信息

Feng Zhan, Lou Shuangshuang, Zhang Lixia, Zhang Liang, Lan Wenting, Wang Minhong, Shen Qijun, Hu Zhengyu, Chen Feng

机构信息

Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, People's Republic of China.

Department of Radiology, Zhejiang Cancer Hospital, Hangzhou 310003, People's Republic of China.

出版信息

Cancer Manag Res. 2019 Dec 31;11:10921-10928. doi: 10.2147/CMAR.S229571. eCollection 2019.

Abstract

OBJECTIVE

Nuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI.

MATERIALS AND METHODS

The data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CI-only model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients.

RESULTS

The fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5-80%. And the calibration curve showed good calibration in the training set and validation set.

CONCLUSION

This study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist's evaluation patients' prognostic.

摘要

目的

核分级是透明细胞肾细胞癌(ccRCC)的独立预后因素。临床需要一种非侵入性的术前预测ccRCC的世界卫生组织/国际泌尿病理学会(WHO/ISUP)分级模型。解剖复杂性评分系统可涵盖多种图像模态。中心性指数(CI)是常用于肾肿瘤的定量解剖学评分。本研究的目的是基于CI开发一种简单模型来预测WHO/ISUP分级。

材料与方法

本研究的数据来自五家医院的248例ccRCC患者。我们使用167例患者的训练数据开发了三种预测模型:仅CI模型、有价值的临床参数模型以及CI与有价值临床参数的融合模型。我们通过区分度、临床实用性和校准对这三种模型进行比较和评估,然后在一组来自81例患者的验证数据中对它们进行测试。

结果

由CI和肿瘤大小(有价值的临床参数)组成的融合模型的曲线下面积(AUC)为0.82。在验证集中,AUC为0.85。决策曲线表明该模型在5 - 80%的阈值概率之间具有良好的净效益。校准曲线在训练集和验证集中均显示出良好的校准。

结论

本研究证实CI与ccRCC的WHO/ISUP分级相关,并且包含肿瘤大小的二元模型可能有助于泌尿外科医生评估患者预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af8a/6997223/04b8a1fbc886/CMAR-11-10921-g0001.jpg

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