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Peeken, J.C. ; Bernhofer, M.* ; Spraker, M.B.* ; Pfeiffer, D.* ; Devecka, M.* ; Thamer, A.* ; Shouman, M.A.* ; Ott, A.* ; Nüsslin, F.* ; Mayr, N.A.* ; Rost, B.* ; Nyflot, M.J.* ; Combs, S.E.

CT-based radiomic features predict tumor grading and have prognostic value in patients with soft tissue sarcomas treated with neoadjuvant radiation therapy.

Radiother. Oncol. 135, 187-196 (2019)
Postprint DOI Order publishers version
Open Access Green
Purpose: In soft tissue sarcoma (STS) patients systemic progression and survival remain comparably low despite low local recurrence rates. In this work, we investigated whether quantitative imaging features (“radiomics”) of radiotherapy planning CT-scans carry a prognostic value for pre-therapeutic risk assessment. Methods: CT-scans, tumor grade, and clinical information were collected from three independent retrospective cohorts of 83 (TUM), 87 (UW) and 51 (McGill) STS patients, respectively. After manual segmentation and preprocessing, 1358 radiomic features were extracted. Feature reduction and machine learning modeling for the prediction of grading, overall survival (OS), distant (DPFS) and local (LPFS) progression free survival were performed followed by external validation. Results: Radiomic models were able to differentiate grade 3 from non-grade 3 STS (area under the receiver operator characteristic curve (AUC): 0.64). The Radiomic models were able to predict OS (C-index: 0.73), DPFS (C-index: 0.68) and LPFS (C-index: 0.77) in the validation cohort. A combined clinical-radiomics model showed the best prediction for OS (C-index: 0.76). The radiomic scores were significantly associated in univariate and multivariate cox regression and allowed for significant risk stratification for all three endpoints. Conclusion: This is the first report demonstrating a prognostic potential and tumor grading differentiation by CT-based radiomics.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Soft Tissue Sarcoma ; Radiomics ; Neoadjuvant Radiotherapy ; Biomarker ; Machine Learning ; Tumor Grading
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Quellenangaben Volume: 135, Issue: , Pages: 187-196 Article Number: , Supplement: ,
Publisher Elsevier
Reviewing status Peer reviewed