Jan C Peeken1, Rebecca Asadpour2, Katja Specht3, Eleanor Y Chen4, Olena Klymenko2, Victor Akinkuoroye2, Daniel S Hippe5, Matthew B Spraker6, Stephanie K Schaub7, Hendrik Dapper2, Carolin Knebel8, Nina A Mayr7, Alexandra S Gersing9, Henry C Woodruff10, Philippe Lambin10, Matthew J Nyflot11, Stephanie E Combs12. 1. Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany; Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands. Electronic address: jan.peeken@tum.de. 2. Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany. 3. Institute of Pathology, Technical University of Munich, Germany. 4. Department of Laboratory Medicine and Pathology, University of Washington, Seattle, United States. 5. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, United States. 6. Department of Radiation Oncology, Washington University in St. Louis, United States. 7. Department of Radiation Oncology, University of Washington, Seattle, United States. 8. Department of Orthopedics and Sports Orthopedics, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany. 9. Department of Radiology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany. 10. Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, The Netherlands; Department of Radiology and Nuclear Imaging, GROW- School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands. 11. Department of Radiation Oncology, University of Washington, Seattle, United States; Department of Radiology, University of Washington, Seattle, United States. 12. Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich (TUM), Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum, München, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Germany.
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.
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.