PT  - JOURNAL ARTICLE
AU  - Fanny Orlhac
AU  - Anne-Capucine Rollet
AU  - Charles Bouveyron
AU  - Jacques Darcourt
AU  - Nicholas Ayache
AU  - Olivier Humbert
TI  - Identification of a radiomic signature to distinguish recurrence from radiation-induced necrosis in treated glioblastomas using machine learning methods on dual-point 18F-FDOPA PET images<strong/>
DP  - 2019 May 01
TA  - Journal of Nuclear Medicine
PG  - 57--57
VI  - 60
IP  - supplement 1
4099  - http://jnm.snmjournals.org/content/60/supplement_1/57.short
4100  - http://jnm.snmjournals.org/content/60/supplement_1/57.full
SO  - J Nucl Med2019 May 01; 60
AB  - 57Objectives: In glioblastomas, the differentiation between recurrence and radiation-induced necrosis after initial treatment is often difficult on MRI. 18F-FDOPA PET improves differential diagnosis but is not perfectly accurate. We studied the relevance of radiomic features based on dual-point 18F-FDOPA PET images to distinguish between recurrence and radiation-induced necrosis. Methods: After an initial standard treatment (STUPP protocol ± 2nd line bevacizumab therapy), 78 patients with a glioblastoma and with a clinical suspicion of recurrence were retrospectively included in this study. The final diagnosis was based on pathological data or, if not available, on a 6-month clinical/imaging patient follow up. Two static PET-CT scans were performed 20 and 90 min after the injection of 2 MBq/kg of 18F-FDOPA (mCT-Siemens: OSEM 5 iterations, 24 subsets), called respectively PET-20 and PET-90. The PET images were automatically registered using a rigid registration based on CT images and we created a subtraction image (PET-90 minus PET-20 = PET-sub). Based on PET-20 images, we segmented the contralateral striatum (VOI-S) using a threshold equal to 50% of SUVmax. We used the same value of threshold to segment the suspicious lesion (VOI-L). For each patient, we copied the VOIs on the PET-90 and PET-sub images. For each VOI and image, we computed 43 radiomic features using LIFEx software, including SUVmax, SUVmean, Metabolic Volume (MV) and TLG, as well as histogram, shape and texture indices (resampling step: bin width of 0.1 SUV). We evaluated the performance to differentiate recurrence and radiation-induced necrosis using the high dimensional discriminant analysis (HDDA) based on all features and after a first step of variable selection. The performance was evaluated using the Youden index (Y=sensitivity + specificity -1). Within the leave-one-out cross-validation, the selection step consists in choosing, based on the (N-1) learning patients, the number of top features incrementally (5, 10, 15, 20, 25 or 30), ranked by the p-value of Wilcoxon test. We determined the best number of features that maximizes Y, and tested this combination on the Nth patient. We applied this methodology based on VOI-L feature values and on the ratio between VOI-L and VOI-S feature values for PET-20, PET-90 and PET-sub, and on the difference of VOI-L feature values between PET-90 and PET-20. We compared the results with the visual assessment of PET-20 images using the conventional “Lizzaraga scale”. Results: 68 patients had tumor recurrence and 10 had radiation necrosis. The visual interpretation led to Y equal to 0.27 (Se=97%, Sp=30%). Using radiomic features, Y ranged between -0.26 and 0.45. The best performance was obtained for VOI-L radiomic features extracted from PET-sub images with a Y of 0.45 (Se=65%, Sp=80%), for an average selection of 20±3 features. The study of the correlation of the most frequently selected features highlights 3 types of information: features highly correlated to MV (segmented based on PET-20), features associated with Entropy and features linked to Homogeneity. By comparison, the best performance based on PET-20 images was obtained with the ratio of radiomic features between VOI-L and VOI-S (Y=0.25). The combination of SUVmax and MV based on VOI-L from PET-20 led to a Y of -0.12. Conclusions: In glioblastomas, we demonstrated that, thanks to a machine learning approach designed for low-sample size/high-dimensional data, it is possible to distinguish recurrence and radiation necrosis with better performance than visual assessment. The best finding was obtained based on parametric images resulting from the evolution of 18F-FDOPA uptake between 20 and 90 min post-injection. Our results should be validated on an independent cohort, but confirm that modern machine learning methods applied to medical images can improve patients’ management.