Elena K. Bauer

Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy.

Background The aim of this study was to investigate the potential of dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (18F-FET) PET for differentiating local recurrent brain metastasis from radiation injury after radiotherapy since contrast-enhanced MRI often remains inconclusive. Methods Sixty-two patients (mean age, 55 ± 11 y) with single or multiple contrast-enhancing brain lesions (n = 76) on MRI after radiotherapy of brain metastases (predominantly stereotactic radiosurgery) were investigated with dynamic 18F-FET PET. Maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) of 18F-FET uptake were determined (20-40 min postinjection) as well as tracer uptake kinetics (ie, time-to-peak and slope of time-activity curves). Diagnoses were confirmed histologically (34%; 26 lesions in 25 patients) or by clinical follow-up (66%; 50 lesions in 37 patients). Diagnostic accuracies of PET parameters for the correct identification of recurrent brain metastasis were evaluated by receiver-operating-characteristic analyses or the chi-square test. Results TBRs were significantly higher in recurrent metastases (n = 36) than in radiation injuries (n = 40) (TBRmax 3.3 ± 1.0 vs 2.2 ± 0.4, P < .001; TBRmean 2.2 ± 0.4 vs 1.7 ± 0.3, P < .001). The highest accuracy (88%) for diagnosing local recurrent metastasis could be obtained with TBRs in combination with the slope of time-activity curves (P < .001). Conclusions The results of this study confirm previous preliminary observations that the combined evaluation of the TBRs of 18F-FET uptake and the slope of time-activity curves can differentiate local brain metastasis recurrence from radiation-induced changes with high accuracy. 18F-FET PET may thus contribute significantly to the management of patients with brain metastases.

Predicting IDH genotype in gliomas using FET PET radiomics

Mutations in the isocitrate dehydrogenase (IDH mut) gene have gained paramount importance for the prognosis of glioma patients. To date, reliable techniques for a preoperative evaluation of IDH genotype remain scarce. Therefore, we investigated the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET radiomics using textural features combined with static and dynamic parameters of FET uptake for noninvasive prediction of IDH genotype. Prior to surgery, 84 patients with newly diagnosed and untreated gliomas underwent FET PET using a standard scanner (15 of 56 patients with IDH mut) or a dedicated high-resolution hybrid PET/MR scanner (11 of 28 patients with IDH mut). Static, dynamic and textural parameters of FET uptake in the tumor area were evaluated. Diagnostic accuracy of the parameters was evaluated using the neuropathological result as reference. Additionally, FET PET and textural parameters were combined to further increase the diagnostic accuracy. The resulting models were validated using cross-validation. Independent of scanner type, the combination of standard PET parameters with textural features increased significantly diagnostic accuracy. The highest diagnostic accuracy of 93% for prediction of IDH genotype was achieved with the hybrid PET/MR scanner. Our findings suggest that the combination of conventional FET PET parameters with textural features provides important diagnostic information for the non-invasive prediction of the IDH genotype.

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

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-[18F]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.