Development and validation of a MRI-based combined radiomics nomogram for differentiation in chondrosarcoma

作者：Xiaofen Li, Min Lan, Xiaolian Wang, Jingkun Zhang, Lianggeng Gong, Fengxiang Liao, Huashan Lin, Shixiang Dai, Bing Fan, Wentao Dong · 发表于：Frontiers in Oncology · 年份：2023 · DOI：10.3389/fonc.2023.1090229 · 被引用次数：13 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Musculoskeletal synovial abnormalities and treatments、Sarcoma Diagnosis and Treatment

Objective: This study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma. Methods: A total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness. Results: Using multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive per...