当前位置： SCI文献检索 > EUROPEAN RADIOLOGY期刊下所有文献 > MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.

Abstract：

OBJECTIVES:Accurately predicting the WHO classification of thymomas is urgently needed to optimize individualized therapeutic strategies. We aimed to develop and validate a combined radiomics nomogram for personalized prediction of histologic subtypes in patients with thymomas. METHODS:A total of 182 thymoma patients were divided into training (n = 128) and test (n = 54) cohorts. Radiomics features were extracted from T2-weighted, T2-weighted fat suppression, and diffusion-weighted images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was used to develop a combined radiomics nomogram that incorporated clinical, conventional MR imaging variables, apparent diffusion coefficient (ADC) value, and radiomics signature. The efficacy of clinical, conventional MR imaging, or ADC model was also evaluated respectively. The performances of different models were compared by receiver operating characteristic analysis and Delong test. The discrimination, calibration, and clinical usefulness of the combined radiomics nomogram were assessed. RESULTS:The radiomics signature, consisting of 14 features, achieved favorable predictive efficacy in differentiating low-risk from high-risk thymomas, outperforming clinical, conventional MR imaging, and ADC models. The combined radiomics nomogram incorporating tumor shape, ADC value, and radiomics signature yielded the best performance (training cohort: area under the curve [AUC] = 0.946, test cohort: AUC = 0.878). The calibration curve and decision curve analysis indicated the clinical utility of the combined radiomics nomogram. CONCLUSIONS:The radiomics signature is a useful tool that can be used to predict histologic subtypes of thymomas. The combined radiomics nomogram improved the individualized subtype prediction in patients with thymomas. KEY POINTS:• Fourteen robust features were selected to develop a radiomics signature for preoperative prediction of thymoma subtype. • MRI-based radiomics signature can differentiate low-risk thymomas from high-risk thymomas with favorable predictive efficacy compared with clinical, conventional MR imaging, and ADC models. • Combined radiomics nomogram based on tumor shape, ADC value, and radiomics signature could improve the individualized subtype prediction in patients with thymomas.

journal_name

Eur Radiol

journal_title

European radiology

authors

Xiao G,Hu YC,Ren JL,Qin P,Han JC,Qu XY,Rong WC,Yan WQ,Tian Q,Han Y,Wang WP,Wang SM,Ma J,Wang W,Cui GB

doi

10.1007/s00330-020-07074-3

subject

Has Abstract

pub_date

2021-01-01 00:00:00

pages

447-457

issue

eissn

0938-7994

issn

1432-1084

pii

10.1007/s00330-020-07074-3

journal_volume

pub_type

杂志文章

在线工具

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.