Suppr超能文献

基于多期CT图像放射组学特征预测膀胱癌复发风险的可行性研究

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images.

作者信息

Qian Jing, Yang Ling, Hu Su, Gu Siqian, Ye Juan, Li Zhenkai, Du Hongdi, Shen Hailin

机构信息

Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.

Department of Radiology, Suzhou Kowloon Hospital Shanghai Jiao Tong University School of Medicine, Suzhou, China.

出版信息

Front Oncol. 2022 Jun 2;12:899897. doi: 10.3389/fonc.2022.899897. eCollection 2022.

Abstract

BACKGROUND

Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.

OBJECTIVE

To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.

METHODS

Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.

RESULTS

Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors-number of tumors, tumor grade, and Rad-score-the AUCs of the training group and validation group were 0.813 (95% CI 0.740-0.886) and 0.838 (95% CI 0.733-0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.

CONCLUSION

Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.

摘要

背景

预测膀胱癌的复发风险对于膀胱癌患者的个体化临床治疗至关重要。

目的

探索基于多期CT图像并结合临床风险因素的影像组学,并进一步构建影像组学-临床模型以预测膀胱癌术后2年内的复发风险。

方法

回顾性纳入2016年1月至2019年12月在苏州大学附属第一医院接受手术治疗的膀胱癌患者,并进行随访以记录疾病复发情况。共纳入183例患者,按7:3的比例随机分为训练组和验证组。采用逻辑回归算法构建平扫、皮质髓质期、肾实质期这三个基本模型以及皮质髓质期+肾实质期和平扫+皮质髓质期+肾实质期这两个联合模型,选择性能较高的模型并计算每位患者的Rad评分(影像组学评分)。依次通过Cox单因素和多因素比例风险模型筛选临床风险因素和Rad评分以获得独立风险因素,进而构建影像组学-临床模型,并评估其性能。

结果

纳入的183例患者中,128例患者组成训练组,55例患者组成验证组。由肿瘤数量、肿瘤分级和Rad评分这三个独立风险因素构建的影像组学-临床模型,训练组和验证组的曲线下面积(AUC)分别为0.813(95%可信区间0.740-0.886)和0.838(95%可信区间0.733-0.943)。在验证组中,诊断准确性、敏感性和特异性分别为0.727、0.739和0.719。

结论

结合基于多期CT图像的影像组学和临床风险因素构建的影像组学-临床模型,在预测膀胱癌术后2年内的复发风险方面具有良好性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e6d5/9201948/f34112ca4ddc/fonc-12-899897-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验