Affordable Access

Access to the full text

Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma

Authors
  • Luo, Zhendong1
  • Li, Jing2
  • Liao, YuTing3
  • Liu, RengYi4
  • Shen, Xinping1
  • Chen, Weiguo4
  • 1 Department of Radiology, The University of Hong Kong - Shenzhen Hospital, Shenzhen , (China)
  • 2 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)
  • 3 Department of Pharmaceuticals Diagnosis, GE Healthcare, Shanghai , (China)
  • 4 Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou , (China)
Type
Published Article
Journal
Frontiers in Oncology
Publisher
Frontiers Media SA
Publication Date
Feb 22, 2022
Volume
12
Identifiers
DOI: 10.3389/fonc.2022.802234
Source
Frontiers
Keywords
Disciplines
  • Oncology
  • Original Research
License
Green

Abstract

Purpose To establish and verify a predictive model involving multiparameter MRI and clinical manifestations for predicting synchronous lung metastases (SLM) in osteosarcoma. Materials and Methods Seventy-eight consecutive patients with osteosarcoma (training dataset, n = 54; validation dataset, n = 24) were enrolled in our study. MRI features were extracted from the T1‐weighted image (T1WI), T2‐weighted image (T2WI), and contrast-enhanced T1-weighted image (CE-T1WI) of each patient. Least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression were performed to select key features and build radiomics models in conjunction with logistic regression (LR) and support vector machine (SVM) classifiers. Eight individual models based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, T1WI+T2WI+CE-T1WI, and clinical features, as well as two combined models, were built. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were employed to assess the different models. Results Tumor size was the most significant univariate clinical indicator (1). The AUC values of the LR predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.686, 0.85, 0.87, 0.879, 0.736, 0.85, and 0.914, respectively (2). The AUC values of the SVM predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI +CE-T1WI, T2WI +CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.629, 0.829, 0.771, 0.879, 0.643, 0.829, and 0.929, respectively (3). The AUC values of the clinical, combined 1 (clinical and LR-radiomics) and combined 2 (clinical and SVM-radiomics) predictive models were 0.779, 0.957, and 0.943, respectively. Conclusion The combined model exhibited good performance in predicting osteosarcoma SLM and may be helpful in clinical decision-making.

Report this publication

Statistics

Seen <100 times