Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy

Objective Normal interictal [18F]FDG-PET can be predicted from the corresponding T1w MRI with Generative Adversarial Networks (GANs). A technique we call SIPCOM (Subtraction Interictal PET Co-registered to MRI) can then be used to compare epilepsy patients' predicted and clinical PET. We assessed the ability of SIPCOM to identify the Resection Zone (RZ) in patients with drug-resistant epilepsy (DRE) with reference to visual and statistical parametric mapping (SPM) analysis. Methods Patients with complete presurgical work-up and subsequent SEEG and cortectomy were included. RZ localisation, the reference region, was assigned to one of eighteen anatomical brain regions. SIPCOM was implemented using healthy controls to train a GAN. To compare, the clinical PET coregistered to MRI was visually assessed by two trained readers, and a standard SPM analysis was performed. Results Twenty patients aged 17-50 (32 ± 7.8) years were included, 14 (70%) with temporal lobe epilepsy (TLE). Eight (40%) were MRI-negative. After surgery, 14 patients (70%) had a good outcome (Engel I-II). RZ localisation rate was 60% with SIPCOM vs 35% using SPM (P = 0.015) and vs 85% using visual analysis (P = 0.54). Results were similar for Engel I-II patients, the RZ localisation rate was 64% with SIPCOM vs 36% with SPM. With SIPCOM localisation was correct in 67% in MRI-positive vs 50% in MRI-negative patients, and 64% in TLE vs 43% in extra-TLE. The average number of false-positive clusters was 2.2 ± 1.3 using SIPCOM vs 2.3 ± 3.1 using SPM. All RZs localized with SPM were correctly localized with SIPCOM. In one case, PET and MRI were visually reported as negative, but both SIPCOM and SPM localized the RZ. Significance SIPCOM performed better than the reference computer-assisted method (SPM) for RZ detection in a group of operated DRE patients. SIPCOM's impact on epilepsy management needs to be prospectively validated. Key points AI can predict PET from T1w magnetic resonance imaging [MRI] with good accuracy. Subtraction of the clinical from the predicted PET (SIPCOM) in analogy to SISCOM. SIPCOM performed better than SPM to localize the resection zone in [18F]FDG-PET. SIPCOM performance advantages were relatively similar in each sub-group (MRI positive/negative patients, temporal lobe epilepsy [TLE] vs extra-TLE patients). Visual analysis performed better than SIPCOM except in one case

Semi-quantitative analysis of visually normal 123I-FP-CIT across three large databases revealed no difference between control and patients

Abstract Background To show the equivalence between the specific binding ratios (SBR) of visually normal 123I-FP-CIT SPECT scans from patients to those from healthy volunteers (Hv) or patients without dopaminergic degeneration to allow their use as a reference database. Methods The SBR values of visually normal SPECT scans from 3 groups were studied: (1) suspected Parkinsonism and no diagnostic follow-up (ScanOnlyDB: n = 764, NM/CT 670 CZT, GE Healthcare), (2) no degenerative dopaminergic pathology after a 5-year follow-up (NoDG5YearsDB: n = 237, Symbia T2, Siemens Medical Solutions), and 3) Hv (HvDB: n = 118, commercial GE database). A general linear model (GLM) was constructed with caudate, putamen, and striatum SBR as the dependent variables, and age and gender as the independent variables. Following post-reconstruction harmonization of the data, DB were combined in pairs, ScanOnlyDB&NoDG5yearsDG and ScanOnlyDB&HvDB before performing GLM analysis. Additionally, ScanOnlyDB GLM estimates were compared to those published from Siemens commercial DB (SiemensDB) and ENC-DAT. Results The dispersion parameters, R 2 and the SBR coefficients of variation, did not differ between databases. For all volumes of interest and all databases, SBR decreased significantly with age (e.g., decrease per decade for the striatum: − 4.94% for ScanOnlyDB, − 4.65% for NoDG5YearsDB, − 5.69% for HvDB). There was a significant covariance between SBR and gender for ScanOnlyDB (P < 10–5) and NoDG5YearsDB (P < 10–2). The age-gender interaction was significant only for ScanOnlyDB (P < 10–2), and the p-value decreased to 10–6 after combining ScanOnlyDB with NoDG5YearsDB. ScanOnlyDB GLM estimates were not significantly different from those from SiemensDB or ENC-DAT except for age-gender interaction. Conclusion SBR values distribution from visually normal scans were not different from the existing reference database, enabling this method to create a reference database by expert nuclear physicians. In addition, it showed a rarely described age-gender interaction related to its size. The proposed post-reconstruction harmonization method can also facilitate the use of semi-quantitative analysis

Additional file 4 of Semi-quantitative analysis of visually normal 123I-FP-CIT across three large databases revealed no difference between control and patients

Additional file 4: Statistical analysis

Additional file 5 of Semi-quantitative analysis of visually normal 123I-FP-CIT across three large databases revealed no difference between control and patients

Additional file 5: SBR harmonization

Additional file 1 of Semi-quantitative analysis of visually normal 123I-FP-CIT across three large databases revealed no difference between control and patients

Additional file 1: ScanOnlyDB and NoDG5YearsDB diagnoses information and patient preparation

Additional file 3 of Semi-quantitative analysis of visually normal 123I-FP-CIT across three large databases revealed no difference between control and patients

Additional file 3: Description of NoDG5Years and Hv databases

Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment

International audience(1) Background: As outcome of patients with metastatic melanoma treated with anti-PD1 immunotherapy can vary in success, predictors are needed. We aimed to predict at the patients’ levels, overall survival (OS) and progression-free survival (PFS) after one year of immunotherapy, based on their pre-treatment 18F-FDG PET; (2) Methods: Fifty-six metastatic melanoma patients—without prior systemic treatment—were retrospectively included. Forty-five 18F-FDG PET-based radiomic features were computed and the top five features associated with the patient’s outcome were selected. The analyzed machine learning classifiers were random forest (RF), neural network, naive Bayes, logistic regression and support vector machine. The receiver operating characteristic curve was used to compare model performances, which were validated by cross-validation; (3) Results: The RF model obtained the best performance after validation to predict OS and PFS and presented AUC, sensitivities and specificities (IC95%) of 0.87 ± 0.1, 0.79 ± 0.11 and 0.95 ± 0.06 for OS and 0.9 ± 0.07, 0.88 ± 0.09 and 0.91 ± 0.08 for PFS, respectively. (4) Conclusion: A RF classifier, based on pretreatment 18F-FDG PET radiomic features may be useful for predicting the survival status for melanoma patients, after one year of a first line systemic treatment by immunotherapy