Computed Tomography Radiomics-based Combined Model for Predicting Thymoma Risk Subgroups: A Multicenter Retrospective Study

IF 3.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Academic Radiology Pub Date : 2025-06-01 Epub Date: 2025-02-18 DOI:10.1016/j.acra.2025.01.010

Yifei Liu , Chao Luo , Yongshun Wu , Shumin Zhou , Guangying Ruan , Haojiang Li , Wanyuan Chen , Yi Lin , Lizhi Liu , Tingting Quan , Xiaodong He

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Abstract

Rationale and Objectives

Accurately distinguishing histological subtypes and risk categorization of thymomas is difficult. To differentiate the histologic risk categories of thymomas, we developed a combined radiomics model based on non-enhanced and contrast-enhanced computed tomography (CT) radiomics, clinical, and semantic features.

Materials and Methods

In total, 360 patients with pathologically-confirmed thymomas who underwent CT examinations were retrospectively recruited from three centers. Patients were classified using improved pathological classification criteria as low-risk (LRT: types A and AB) or high-risk (HRT: types B1, B2, and B3). The training and external validation sets comprised 274 (from centers 1 and 2) and 86 (center 3) patients, respectively. A clinical-semantic model was built using clinical and semantic variables. Radiomics features were filtered using intraclass correlation coefficients, correlation analysis, and univariate logistic regression. An optimal radiomics model (Rad_score) was constructed using the AutoML algorithm, while a combined model was constructed by integrating Rad_score with clinical and semantic features. The predictive and clinical performances of the models were evaluated using receiver operating characteristic/calibration curve analyses and decision-curve analysis, respectively.

Results

Radiomics and combined models (area under curve: training set, 0.867 and 0.884; external validation set, 0.792 and 0.766, respectively) exhibited performance superior to the clinical-semantic model. The combined model had higher accuracy than the radiomics model (0.79 vs. 0.78, p<0.001) in the entire cohort. The original_firstorder_median of venous phase had the highest relative importance among features in the radiomics model.

Conclusion

Radiomics and combined radiomics models may serve as noninvasive discrimination tools to differentiate thymoma risk classifications.

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基于计算机断层放射学的预测胸腺瘤危险亚群的联合模型：一项多中心回顾性研究。

理由和目的：准确区分胸腺瘤的组织学亚型和风险分类是困难的。为了区分胸腺瘤的组织学风险类别，我们基于非增强和增强计算机断层扫描（CT）放射组学、临床和语义特征建立了一个联合放射组学模型。材料和方法：回顾性从三个中心招募360例经病理证实的胸腺瘤患者，并行CT检查。采用改进的病理分类标准将患者分为低危（LRT: A型和AB型）或高危（HRT: B1、B2和B3型）。训练集和外部验证集分别由274名（来自中心1和2）和86名（中心3）患者组成。采用临床变量和语义变量建立临床-语义模型。使用类内相关系数、相关分析和单变量逻辑回归对放射组学特征进行过滤。采用AutoML算法构建最优放射组学模型（Rad_score），将Rad_score与临床特征和语义特征相结合构建组合模型。分别采用受试者工作特征/校准曲线分析和决策曲线分析评估模型的预测和临床性能。结果：放射组学和组合模型(曲线下面积：训练集，0.867和0.884；外部验证集（分别为0.792和0.766）表现出优于临床语义模型的性能。联合模型的准确率高于放射组学模型（0.79 vs. 0.78）。结论：放射组学和联合放射组学模型可作为胸腺瘤风险分类的无创鉴别工具。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.