Search

When this article was written, it coincided with the 11th anniversary of the installation of our PET/MR device in Munich. In fact, this was the first fully integrated device to be in clinical use. During this time, we have observed many interesting behaviors, to put it kindly. However, it is more critical that in this process, our understanding of the system also improved - including the advantages and limitations from a technical, logistical, and medical perspective. The last decade of PET/MRI research has certainly been characterized by most sites looking for a "key application." There were many ideas in this context and before and after the devices became available, some of which were based on the earlier work with integrating data from single devices. These involved validating classical PET methods with MRI (eg, perfusion or oncology diagnostics). More important, however, were the scenarios where intermodal synergies could be expected. In this review, we look back on this decade-long journey, at the challenges overcome and those still to come.

Purpose To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. Methods Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1–3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student’s t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). Results All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. Conclusion All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

Background The clinical indication to revascularization of coronary chronic total occlusions (CTO) is set on viability assessment through well-established imaging methods. Precise characterization of the myocardium allows a better interventional risk stratification before revascularization. Hybrid positron emission tomography/magnetic resonance (PET/MR) scanners allow the combination of image based biomarkers through simultaneous acquisition and may potentially improve the prediction of therapy response. This study aims to evaluate the accuracy in recovery prediction after CTO revascularization by combining information of myocardial viability and wall motion abnormalities (WMA) from PET/MR exams. Methods Viability was assessed with PET/MR imaging in 22 patients before percutaneous revascularization of a CTO. A cardiac MR imaging was performed at follow-up after 6 months. Segmental WMA was assessed on the AHA 17-segments model from cine images at baseline and follow-up with a 5-point scale. Recovery of WMA was predicted using six different models combining parameters from fluorodeoxyglucose (FDG) uptake, transmural extent of the scar with late gadolinium enhancement image (LGE), T1 mapping, and WMA at baseline. From 374 myocardial segments, we considered only 82 that were completely characterized and presented at least hypokinetic WMA at baseline. 46 of them were supplied by the treated CTO artery (CTO-subtended), wherefrom 16 segments recovered the contractility. A Random Forest classifier was applied for recovery prediction. Training and testing data were selected through stratified random sampling (50 iterations) with replacement from the two sets: CTO-subtended and non-CTO-subtended segments. To compensate for class imbalance and limited sample size, SMOTE oversampling was applied to the training data. Prediction assessment was based on balanced accuracy (bAcc), sensitivity, specificity, ROC AUC, and Precision-Recall AUC (PR AUC), while Wilcoxon test with a Bonferroni's correction was used for comparison between the proposed models and LGE+FDG, which has shown the best performance in previous studies [1,2]. Results The best model for the prediction of recovery after revascularisation was the combination of LGE+FDG+WMA+T1mapping (bAcc 0.62±0.11, sensitivity 0.72±0.18, specificity 0.70±0.19, ROC AUC 0.67±0.12, PR AUC 0.60±0.12), followed by LGE+FDG+WMA, LGE+FDG+T1mapping, LGE+FDG, LGE, and FDG, respectively. The first model statistically significantly (p-value Conclusion This study documents the benefits of a hybrid PET/MR multiparametric assessment beyond the standard analysis based on FDG uptake and LGE for the prediction of contractility recovery after revascularization of CTO of the coronary arteries. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764458 PETMR protocolROC AUC comparsion

Purpose In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers. Methods In 173 subjects imaged with 68Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework. Results In the development set, including 18F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1–87.8] for identification of suspicious uptake sites, 77% (CI: 70.0–83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage. Conclusion The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden.