Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms.Methods: This retrospective study included 140 patients who underwent [¹⁸F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning.Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort.Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

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.

Our aim was to analyse whether biomarkers extracted from baseline ¹⁸F-FDG PET before anti-PD1 treatment contribute to prognostic survival information for early risk stratification in metastatic melanoma. Fifty-six patients, without prior systemic treatment, BRAF wild type, explored using ¹⁸F-FDG PET were included retrospectively. Our primary endpoint was overall survival (OS). Total metabolic tumoral volume (MTV) and forty-one IBSI compliant parameters were extracted from PET. Parameters associated with outcome were evaluated by a cox regression model and when significant helped build a prognostic score. Median follow-up was 22.1 months and 21 patients died. Total MTV and long zone emphasis (LZE) correlated with shorter OS and served to define three risk categories for the prognostic score. For low, intermediate and high risk groups, survival rates were respectively 91.1% (IC 95 80–1), 56.1% (IC 95 37.1–85) and 19% (IC 95 0.06–60.2) and hazard ratios were respectively 0.11 (IC 95 0.025–0.46), P = 0.0028, 1.2 (IC 95 0.48–2.8), P = 0.74 and 5.9 (IC 95 2.5–14), P < 0.0001. To conclude, a prognostic score based on total MTV and LZE separated metastatic melanoma patients in 3 categories with dramatically different outcomes. Innovative therapies should be tested in the group with the lowest prognosis score for future clinical trials.

(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.

Introduction: [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization.Methods: We created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [¹⁸F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics.Results: Compared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence.Conclusion: P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.

Objective: To evaluate the clinical impact of the tract embolization technique using gelatin sponge slurry after percutaneous CT-guided lung biopsy. Methods: We retrospectively compared coaxial needle CT-guided lung biopsies performed without embolization (100 patients) and with the tract embolization technique using a mixture of iodine and gelatin sponge slurry (105 patients) between June 2012 and July 2020. Uni- and multivariate analyses were performed between groups to determine risk factors of pneumothorax. Results: Patients with gelatin sponge slurry tract embolization had statistically lower rates of pneumothorax ((17.1% vs 39%, p < 0.001). In univariate analysis, tract embolization (OR = 0.32, CI = 0.17–0.61 p2 cm (OR = 0.33 CI = 0.14–0.8 p = 0.013) had a protective effect on pneumothorax. The puncture path lengths > 2–20 mm and >20 mm were risk factors for pneumothorax (OR = 3.35 IC = 1.44–8.21 p = 0.006 and OR = 4.36 CI = 1.98–10.29 p<0.001, respectively). In multivariate regression analysis, tract embolization had a protective effect of pneumothorax (OR = 0.25, CI = 0.12–0.51, p < 0.001). The puncture path lengths > 2–20 mm and >20 mm were risk factors for pneumothorax (p = 0.030 and p = 0.002, respectively). Conclusions: The tract embolization technique using iodinated gelatin sponge slurry is safe and considerably reduces pneumothorax after percutaneous CT-guided lung biopsy. Our results suggest that it could be use in clinical routine. Advances in knowledge: The systemic use of gelatin sponge slurry is safe and reduces considerably the rate of pneumothorax upon needle removal when CT-guided core biopsies are performed using large 16–18G coaxial needles.