Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Abstract

Background: The aim of this study was to investigate the potential of combined textural feature analysis of contrast-enhanced MRI (CE-MRI) and static O-(2-[ 18 F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation between local recurrent brain metastasis and radiation injury since CE-MRI often remains inconclusive. Methods: Fifty-two patients with new or progressive contrast-enhancing brain lesions on MRI after radiotherapy (predominantly stereotactic radiosurgery) 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.), using the software LIFEx. 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 the differentiation between radiation injury and recurrence of brain metastasis, textural features extracted from CE-MRI had a diagnostic accuracy of 81% (sensitivity, 67%; specificity, 90%). FET PET textural features revealed a slightly higher diagnostic accuracy of 83% (sensitivity, 88%; specificity, 75%). However, the highest diagnostic accuracy was obtained when combining CE-MRI and FET PET features (accuracy, 89%; sensitivity, 85%; specificity, 96%). Conclusions: 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.

PET imaging in patients with brain metastasis—report of the RANO/PET group

“…92 Combined FET PET and MRI radiomics using textural features achieved a diagnostic specificity of more than 90%. 93 • Amino acid PET is useful in distinguishing posttherapeutic reactive changes following radiotherapy from recurrent BM. Present studies consistently show high diagnostic accuracy (evidence level 2).…”

Treatment Monitoring of Immunotherapy and Targeted Therapy Using 18F-FET PET in Patients with Melanoma and Lung Cancer Brain Metastases: Initial Experiences

“…These studies using various radiolabeled amino acids including 18 F-FET consistently revealed that the sensitivity and specificity for the differentiation of treatment-related changes from BM relapse is in the range of 80-90% (24,(33)(34)(35)(36)(37)(38)(39)(40). Additonally, parameters obtained from dynamic 18 F-FET PET acquisition seem to further increase the diagnostic performance compared to static parameters alone (24,34,38).…”

Clinical assessment of a biophysical model for distinguishing tumor progression from radiation necrosis

“…12,31,32 The emerging field of radiomics and machine learning has shown recent promise for differentiating radiation necrosis from tumor progression. 33,34 One recent study by Peng et al found that 51 radiomic features extracted using an in-house software could be used in 66 patients with 77 confirmed lesions, to differentiate tumor progression from radiation necrosis with a sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81. 34 Interestingly, our radiomics regression model showed comparable predictive performance.…”