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

J.C. Peeken*, R. Asadpour, K. Specht, E.Y. Chen, O. Klymenko, V. Akinkuoroye, D.S. Hippe, M.B. Spraker, S.K. Schaub, H. Dapper, C. Knebel, N.A. Mayr, A.S. Gersing, H.C. Woodruff, P. Lambin, M.J. Nyflot, S.E. Combs

*Corresponding author for this work

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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-radio mics") 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. (c) 2021 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 164 (2021) 73-82
Original languageEnglish
Pages (from-to)73-82
Number of pages10
JournalRadiotherapy and Oncology
Volume164
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • Soft-tissue sarcoma
  • Delta radiomics
  • Neoadjuvant radiotherapy
  • Machine learning
  • Response prediction
  • MRI
  • EUROPEAN ORGANIZATION
  • FEATURE-SELECTION
  • CANCER
  • CHEMOTHERAPY
  • EXTREMITY
  • SURVIVAL
  • NECROSIS
  • FEATURES
  • MODEL

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