医学
列线图
置信区间
危险系数
一致性
比例危险模型
无线电技术
磁共振成像
核医学
队列
黑色素瘤
内科学
弗雷明翰风险评分
肿瘤科
放射科
疾病
癌症研究
作者
Yaping Su,Xiaolin Xu,Fang Wang,Panli Zuo,Qinghua Chen,Wenbin Wei,Junfang Xian
出处
期刊:Journal of Computer Assisted Tomography
[Ovid Technologies (Wolters Kluwer)]
日期:2022-10-11
卷期号:47 (1): 151-159
被引量:1
标识
DOI:10.1097/rct.0000000000001384
摘要
Objective The aim of this study was to develop a pretreatment magnetic resonance imaging (MRI)–based radiomics model for disease-free survival (DFS) prediction in patients with uveal melanoma (UM). Methods We randomly assigned 85 patients with UM into 2 cohorts: training (n = 60) and validation (n = 25). The radiomics model was built from significant features that were selected from the training cohort by applying a least absolute shrinkage and selection operator to pretreatment MRI scans. Least absolute shrinkage and selection operator regression and the Cox proportional hazard model were used to construct a radiomics score (rad-score). Patients were divided into a low- or a high-risk group based on the median of the rad-score. The Kaplan-Meier analysis was used to evaluate the association between the rad-score and DFS. A nomogram incorporating the rad-score and MRI features was plotted to individually estimate DFS. The model's discrimination power was assessed using the concordance index. Results The radiomics model with 15 optimal radiomics features based on MRI performed well in stratifying patients into the high- or a low-risk group of DFS in both the training and validation cohorts (log-rank test, P = 0.009 and P = 0.02, respectively). Age, basal diameter, and height were selected as significant clinical and MRI features. The nomogram showed good predictive performance with concordance indices of 0.741 (95% confidence interval, 0.637–0.845) and 0.912 (95% confidence interval, 0.847–0.977) in the training and validation cohorts, respectively. Calibration curves demonstrated good agreement. Conclusion The developed clinical-radiomics model may be a powerful predictor of the DFS of patients with UM, thereby providing evidence for preoperative risk stratification.
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