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Peeken, J.C. ; Etzel, L.* ; Tomov, T.* ; Münch, S.* ; Schüttrumpf, L.* ; Shaktour, J.H.* ; Kiechle, J.* ; Knebel, C.* ; Schaub, S.K.* ; Mayr, N.A.* ; Woodruff, H.C.* ; Lambin, P.* ; Gersing, A.S.* ; Bernhardt, D.* ; Nyflot, M.J.* ; Menze, B.* ; Combs, S.E. ; Navarro, F.*

Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas.

Radiother. Oncol. 197:110338 (2024)
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BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Deep Learning ; Mri ; Radiology ; Radiomics ; Radiotherapy ; Soft Tissue Sarcoma ; Tumor Volume; Radiation-therapy; Preoperative Radiotherapy
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 0167-8140
e-ISSN 1879-0887
Journal Radiotherapy and Oncology
Quellenangaben Volume: 197, Issue: , Pages: , Article Number: 110338 Supplement: ,
Publisher Elsevier
Publishing Place Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)
POF-Topic(s) 30203 - Molecular Targets and Therapies
Research field(s) Radiation Sciences
PSP Element(s) G-501300-001
DOI 10.1016/j.radonc.2024.110338
WOS ID 001246128800001
Scopus ID 85193950652
PubMed ID 38782301
Erfassungsdatum 2024-07-16
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