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

Xiaofen Li; Min Lan; Xiaolian Wang; Jingkun Zhang; Lianggeng Gong; Fengxiang Liao; Huashan Lin; Shixiang Dai; Bing Fan; Wentao Dong

doi:10.3389/fonc.2023.1090229

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

Front Oncol. 2023 Feb 28:13:1090229. doi: 10.3389/fonc.2023.1090229. eCollection 2023.

Authors

Xiaofen Li^{1

2}, Min Lan³, Xiaolian Wang², Jingkun Zhang⁴, Lianggeng Gong⁵, Fengxiang Liao⁶, Huashan Lin⁷, Shixiang Dai², Bing Fan², Wentao Dong²

Affiliations

¹ Medical College of Nanchang University, Nanchang, China.
² Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
³ Department of Orthopedics, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
⁴ Department of Radiology, The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, China.
⁵ Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China.
⁶ Department of Nuclear Medicine, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
⁷ Department of Pharmaceutical Diagnosis, General Electric Healthcare, Changsha, China.

Abstract

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 performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value.

Conclusion: The MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

Keywords: T1-weighted image; chondrosarcoma; magnetic resonance imaging; nomogram; prediction tool; radiomics.