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Buchner, J.A.* ; Kofler, F. ; Mayinger, M.* ; Christ, S.M.* ; Brunner, T.B.* ; Wittig, A.* ; Menze, B.* ; Zimmer, C.* ; Meyer, B.* ; Guckenberger, M.* ; Andratschke, N.* ; El Shafie, R.A.* ; Debus, J.* ; Rogers, S.* ; Riesterer, O.* ; Schulze, K.* ; Feldmann, H.J.* ; Blanck, O.* ; Zamboglou, C.* ; Ferentinos, K.* ; Bilger-Zähringer, A.* ; Grosu, A.L.* ; Wolff, R.* ; Piraud, M. ; Eitz, K.A. ; Combs, S.E. ; Bernhardt, D.* ; Rueckert, D.* ; Wiestler, B.* ; Peeken, J.C.

Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.

Neuro. Oncol. 26, 1638-1650 (2024)
Verlagsversion Postprint DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
BACKGROUND: Surgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk. METHODS: Data were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of Brain Metastases (AURORA) retrospective study (training cohort: 253 patients from two centers; external test cohort: 99 patients from five centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set. RESULTS: The best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (p < 0.001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively. CONCLUSIONS: A combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Artificial Intelligence ; Brain Metastases ; Local Failure Prediction ; Machine Learning ; Radiomics; Radiosurgery; Diagnosis; Impact
ISSN (print) / ISBN 1522-8517
e-ISSN 1523-5866
Zeitschrift Neuro-Oncology
Quellenangaben Band: 26, Heft: 9, Seiten: 1638-1650 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Journals Dept, 2001 Evans Rd, Cary, Nc 27513 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Radiation Medicine (IRM)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen Deutsche Forschungsgemeinschaft