Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: A retrospective multicenter study

Eur J Radiol. 2020 Aug:129:109066. doi: 10.1016/j.ejrad.2020.109066. Epub 2020 May 17.

Abstract

Purpose: To develop and externally validate an MR-based radiomics nomogram from retrospective multicenter datasets for pretreatment prediction of early relapse (≤ 1 year) in osteosarcoma after surgical resection.

Methods: This multicenter study retrospectively enrolled 93 patients (training cohort: 62 patients from four hospitals; validation cohort: 31 patients from two hospitals) with clinicopathologically confirmed osteosarcoma who received neoadjuvant chemotherapy and surgical resection at six hospitals between January 2009 and October 2017. Radiomics features were extracted from contrast-enhanced fat-suppressed T1-weighted (CE FS T1-w) images. Least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature construction. The radiomics nomogram that incorporated the radiomics signature and subjective MRI-assessed candidate predictors was developed to predict early relapse with a multivariate logistic regression model in the training cohort and validated in the external validation cohort. The performance of the nomogram was assessed by its discrimination, calibration, and clinical usefulness.

Results: The radiomics signature comprised six selected features and achieved favorable prediction efficacy. The radiomics nomogram incorporating the radiomics signature and subjective MRI-assessed candidate predictors (joint invasion and perivascular involvement) from the multicenter datasets achieved better discrimination in the training cohort (C-index：0.907, 95 % CI: 0.838-0.977) and external validation cohort (C-index: 0.811, 95 % CI: 0.653-0.970), and good calibration. Decision curve analysis suggested that the combined nomogram was clinically useful.

Conclusion: The proposed MRI-based radiomics nomogram could provide a non-invasive tool to predict early relapse of osteosarcoma, which has the potential to improve personalized pretreatment management of osteosarcoma.

Keywords: Early relapse; Magnetic resonance imaging; Nomogram; Osteosarcoma; Radiomics.

Publication types

  • Multicenter Study
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Bone Neoplasms / diagnostic imaging*
  • Bone Neoplasms / pathology
  • Bone Neoplasms / surgery
  • Bone and Bones / diagnostic imaging
  • Bone and Bones / pathology
  • Bone and Bones / surgery
  • Child
  • Child, Preschool
  • Cohort Studies
  • Datasets as Topic
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Logistic Models
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Neoplasm Recurrence, Local / diagnosis*
  • Nomograms*
  • Osteosarcoma / diagnostic imaging*
  • Osteosarcoma / pathology
  • Osteosarcoma / surgery
  • Predictive Value of Tests
  • Reproducibility of Results
  • Retrospective Studies
  • Young Adult