MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy

Jan C. Peeken, Rebecca Asadpour, Katja Specht, Eleanor Y. Chen, Olena Klymenko, Victor Akinkuoroye, Daniel S. Hippe, Matthew B. Spraker, Stephanie K. Schaub, Hendrik Dapper, Carolin Knebel, Nina A. Mayr, Alexandra S. Gersing, Henry C. Woodruff, Philippe Lambin, Matthew J. Nyflot, Stephanie E. Combs

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Purpose: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment (“delta-radiomics”) may be able to predict the pathological complete response (pCR). Methods: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. Results: The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. Conclusion: This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.

Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalRadiotherapy and Oncology
Volume164
DOIs
StatePublished - Nov 2021

Keywords

  • Delta radiomics
  • MRI
  • Machine learning
  • Neoadjuvant radiotherapy
  • Response prediction
  • Soft-tissue sarcoma

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