Objectives: F-DOPA PET is used in glioma follow-up after radiotherapy to discriminate treatment-related changes (TRC) from tumour progression (TP). We compared the performances of a combined PET and MRI analysis with F-DOPA current standard of Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation interpretation. Methods : We included 76 consecutive patients showing at least one gadolinium-enhancing lesion on T1-w MRI sequence (T1G). Two nuclear medicine physicians blindly analysed PET/MRI images. In addition to the conventional PET analysis, they looked for F-DOPA uptake(s) outside T1G-enhancing areas (T1G-/PET), in the white matter (WM/PET), for T1G-enhancing lesion(s) without sufficiently concordant F-DOPA uptake (T1G+/PET), and for F-DOPA uptake(s) away from haemorrhagic changes as shown with a Susceptibility Weighted Imaging sequence (SWI/PET). We measured lesions' F-DOPA uptake using healthy brain background (TBR) and striatum (T/S) as references, and lesions' perfusion with arterial spin labelling cerebral blood flow maps (rCBF). Scores were determined by logistic regression. Results: 53 and 23 patients were diagnosed with TP and TRC, respectively. The accuracies were 74% for T/S, 76% for TBR, and 84% for rCBF, with best cut-off values of 1.3, 3.7 and 1.25, respectively. For hybrid variables, best accuracies were obtained with conventional analysis (82%), T1G+/PET (82%) and SWI/PET (81%). T1G+/PET, SWI/PET and rCBF ≥ 1.25 were selected to construct a 3-point score. It outperformed conventional analysis and rCBF with an AUC of 0.94 and an accuracy of 87%. Conclusions : Our scoring approach combining F-DOPA PET and MRI provided better accuracy than conventional PET analyses for distinguishing TP from TRC in our patients after radiation therapy. Response to Reviewers:Once again, we would like to thank the reviewers and the editorial team for their work.As requested, the manuscript was fully checked with the Grammarly software and modified accordingly.
Purpose: Since 2010, PET/MR has been increasingly used for clinical routine in nuclear medicine departments. One advantage of PET/MR over PET/CT is the lower ionising radiation dose delivered to patients. However, data on the radiation dose delivered to staff operating PET/MR compared to new generation PET/CT is still lacking. Our aim was to compare the radiation dose to nuclear medicine technologists performing routine PET/MR and PET/CT in the same department. Methods: We retrospectively measured during 13 months, the daily radiation dose received by PET technologists by collecting individual dosimetry measurements (from electronic personal dosimeters). Data were analysed taking into account the total number of studies performed of each PET modality (PET/MR with Signa 3T, General Electric Healthcare vs. PET/CT with Biograph mCT flow, Siemens), the type of exploration (brain vs. whole body PET), the 18F activity injected per day and per patient as well as the time spent in contact with patients after tracer injection. Results: Our results show a significantly higher technologist staff whole-body exposure for PET/MR compared to PET/CT of 10.3±3.5 nSv versus 4.7±1.2 nSv per 18F injected MBq, respectively (p<0.05). This difference was related to prolonged contact with injected patients during patient positioning with PET/MR device and MR coils placement, especially in whole-body studies. Conclusions: For an equal injected activity, PET technologist radiation exposure for PET/MR was two-fold that of PET/CT. To minimize radiation dose to staff, efforts should be made to optimize patient positioning, even in departments with extensive PET/CT experience.
We previously showed that the injected activity could be reduced to 1 MBq/kg without significantly degrading image quality for the exploration of neurocognitive disorders in 18F-FDG-PET/MRI. We now hypothesized that injected activity could be reduced ten-fold. We simulated a 18F-FDG-PET/MRI ultra-low-dose protocol (0.2 MBq/Kg, PETULD) and compared it to our reference protocol (2 MBq/Kg, PETSTD) in 50 patients with cognitive impairment. We tested the reproducibility between PETULD and PETSTD using SUVratios measurements. We also assessed the impact of PETULD for between-group comparisons and for visual analysis performed by three physicians. The intra-operator agreement between visual assessment of PETSTD and PETULD in patients with severe anomalies was substantial to almost perfect (kappa > 0.79). For patients with normal metabolism or moderate hypometabolism however, it was only moderate to substantial (kappa > 0.53). SUV ratios were strongly reproducible (SUVratio difference ± SD = 0.09 ± 0.08). Between-group comparisons yielded very similar results using either PETULD or PETSTD. 18F-FDG activity may be reduced to 0.2 MBq/Kg without compromising quantitative measurements. The visual interpretation was reproducible between ultra-low-dose and standard protocol for patients with severe hypometabolism, but less so for those with moderate hypometabolism. These results suggest that a low-dose protocol (1 MBq/Kg) should be preferred in the context of neurodegenerative disease diagnosis.
Purpose: To assess the feasibility of a three-dimensional deep convolutional neural network (3D-CNN) for the general triage of whole-body FDG PET in daily clinical practice. Methods: An institutional clinical data warehouse working environment was devoted to this PET imaging purpose. Dedicated request procedures and data processing workflows were specifically developed within this infrastructure and applied retrospectively to a monocentric dataset as a proof of concept. A custom-made 3D-CNN was first trained and tested on an “unambiguous” well-balanced data sample, which included strictly normal and highly pathological scans. For the training phase, 90% of the data sample was used (learning set: 80%; validation set: 20%, 5-fold cross validation) and the remaining 10% constituted the test set. Finally, the model was applied to a “real-life” test set which included any scans taken. Text mining of the PET reports systematically combined with visual rechecking by an experienced reader served as the standard-of-truth for PET labeling. Results: From 8125 scans, 4963 PETs had processable cross-matched medical reports. For the “unambiguous” dataset (1084 PETs), the 3D-CNN’s overall results for sensitivity, specificity, positive and negative predictive values and likelihood ratios were 84%, 98%, 98%, 85%, 42.0 and 0.16, respectively (F1 score of 90%). When applied to the “real-life” dataset (4963 PETs), the sensitivity, NPV, LR+, LR− and F1 score substantially decreased (61%, 40%, 2.97, 0.49 and 73%, respectively), whereas the specificity and PPV remained high (79% and 90%). Conclusion: An AI-based triage of whole-body FDG PET is promising. Further studies are needed to overcome the challenges presented by the imperfection of real-life PET data.
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