Our previous study has demonstrated the feasibility of noninvasive imaging of fibroblast activation protein (FAP)-expression after myocardial infarction (MI) in MI-territory in a rat model with 68Ga-FAPI-04-PET. In the current extended clinical case, we sought to delineate cardiac uptake of 68Ga-FAPI-04 in a patient after MI with clinical indication for the evidence of fibroblast activation. Carcinoma patients without cardiac disease underwent 68Ga-FAPI-04-PET/CT as control. The patient with one-vessel disease underwent dynamic 68Ga-FAPI-04-cardiac-PET/CMR for 60 minutes. Correlation of cardiac 68Ga-FAPI-04 uptake with clinical findings, ECG, echocardiography, coronary-arteriography and enhanced cardiac-MRI with T1 MOLLI and ECV mapping were performed. No uptake was found in normal myocardium and in mature scar. A focal intense 68Ga-FAPI-04 uptake with continuous wash-out in the infarct territory of coronary occlusion correlating with T1 and ECV mapping was observed. The uptake of 68Ga-FAPI-04 extends beyond the actual infarcted area and overestimates the infarct size as confirmed by follow-up CMR.

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.

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Here, we present a case with a pacemaker due to an atrioventricular (AV) block 2 Mobitz type, in whom a gating failure resulted in a relevant underestimation of cardiac function in myocardial perfusion scintigraphy. A set of quality control steps for gating errors is proposed.