Prediction of response to preoperative neoadjuvant chemotherapy in extremity high-grade osteosarcoma using X-ray and multiparametric MRI radiomics

Article type: Research Article

Authors: Luo, Zhendong^{a; 1} | Li, Jing^{b; 1} | Liao, YuTing^c | Huang, Wenxiao^a | Li, Yulin^d | Shen, Xinping^{a; *}

Affiliations: [a] Department of Radiology, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China | [b] Department of Radiology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China | [c] Philips Healthcare China, Guangzhou, China | [d] Department of Radiology, Peking Universtiy Shenzhen Hospital, Shenzhen, China

Correspondence: [*] Corresponding author: Xinping Shen, Department of Radiology, The University of Hong Kong –Shenzhen Hospital, No.1, Haiyuan Road, Futian District, Shenzhen 518000, China. E-mail: [email protected].

Note: [1] Zhendong Luo and Jing Li contributed equally to this work.

Abstract: PURPOSE:This study aims to evaluate the value of applying X-ray and magnetic resonance imaging (MRI) models based on radiomics feature to predict response of extremity high-grade osteosarcoma to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS:A retrospective dataset was assembled involving 102 consecutive patients (training dataset, n = 72; validation dataset, n = 30) diagnosed with extremity high-grade osteosarcoma. The clinical features of age, gender, pathological type, lesion location, bone destruction type, size, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were evaluated. Imaging features were extracted from X-ray and multi-parametric MRI (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) data. Features were selected using a two-stage process comprising minimal-redundancy-maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression (LR) modelling was then applied to establish models based on clinical, X-ray, and multi-parametric MRI data, as well as combinations of these datasets. Each model was evaluated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS:AUCs of 5 models using clinical, X-ray radiomics, MRI radiomics, X-ray plus MRI radiomics, and combination of all were 0.760 (95% CI: 0.583–0.937), 0.706 (95% CI: 0.506–0.905), 0.751 (95% CI: 0.572–0.930), 0.796 (95% CI: 0.629–0.963), 0.828 (95% CI: 0.676–0.980), respectively. The DeLong test showed no significant difference between any pair of models (p > 0.05). The combined model yielded higher performance than the clinical and radiomics models as demonstrated by net reclassification improvement (NRI) and integrated difference improvement (IDI) values, respectively. This combined model was also found to be clinically useful in the decision curve analysis (DCA). CONCLUSION:Modelling based on combination of clinical and radiomics data improves the ability to predict pathological responses to NAC in extremity high-grade osteosarcoma compared to the models based on either clinical or radiomics data.

Keywords: Radiomics, prognostic indicator, X-ray, Magnetic resonance imaging, Osteosarcoma, chemotherapy response

DOI: 10.3233/XST-221352

Journal: Journal of X-Ray Science and Technology, vol. 31, no. 3, pp. 611-626, 2023