P05.11 Combined FET PET/MRI radiomics for the differentiation of radiation injury from recurrent brain metastasis. (19th September 2018)

• Record Type: Journal Article
• Authors: Lohmann, P; Kocher, M; Ceccon, G; Bauer, E K; Stoffels, G; Viswanathan, S; Ruge, M I; Neumaier, B; Shah, N J; Fink, G R; Langen, K; Galldiks, N
• Is Part Of: Neuro-oncology. Volume 20(2018)Supplement 3
• Page Start: iii304
• Page End: iii304
• Publication Date: 2018-09-19
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noy139.337
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: The aim of this study was to investigate the potential of combined radiomics textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[ 18 F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of recurrent brain metastasis from radiation injury. Material and Methods: Fifty-two patients with newly diagnosed or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly radiosurgery, 84% of patients) of brain metastases were additionally investigated using FET PET. Based on histology (n=19) or clinicoradiological follow-up (n=33), local recurrent brain metastases were diagnosed in 21 patients (40%) and radiation injury in 31 patients (60%). Forty-two textural features were calculated on both unfiltered and filtered CE-MRI and summed FET PET images (20–40 min p.i). After feature selection, logistic regression models using a maximum of five features to avoid overfitting were calculated for each imaging modality separately and for the combined FET PET/MRI features. The resulting models were validated using cross-validation. Diagnostic accuracies were calculated for each imaging modality separately as well as for the combined model. Results: For differentiation between radiation injury and brain metastasis recurrence, textural features extracted from CE-MRI had a diagnostic accuracy of 81%. FET PET textural features revealed a slightly higher diagnostic accuracy of 83%. However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%). Conclusion: Our findings suggest that combined FET PET/CE-MRI radiomics using textural feature analysis offers a great potential to contribute significantly to the management of patients with brain metastases.