Search

Contains fulltext : 297141.pdf (Publisher’s version ) (Closed access)

Imaging water pathways in the human body provides an excellent way of measuring accurately the blood flow directed to different organs. This makes it a powerful diagnostic tool for a wide range of diseases that are related to perfusion and oxygenation. Although water PET has a long history, its true potential has not made it into regular clinical practice. The article highlights the potential of water PET in molecular imaging and suggests its prospective role in becoming an essential tool for the 21st century precision medicine in different domains ranging from preclinical to clinical research and practice. The recent technical advances in high-sensitivity PET imaging can play a key accelerating role in empowering this technique, though there are still several challenges to overcome.

Background: Early detection of recurrent or progressive HCC remains the strongest prognostic factor for survival. Dual tracer PET/CT imaging with [11C]CH and [18F]FDG can further increase detection rates as both tracers entail different metabolic pathways involved in HCC development. We investigated dual-tracer PET/CT in clinical decision making in patients suspected of recurrent or progressive HCC. All HCC patients who underwent both [11C]CH and [18F]FDG PET/CT in our institute from February 2018 to December 2021 were included. Both tracer PET/CT were within 4 weeks of each other with at least 6-month follow-up. Patients underwent dual tracer PET/CT because of unexplained and suspicious CT/MRI or sudden rise of serum tumour markers. A detected lesion was considered critical when the finding had prognostic consequences leading to treatment changes. Results: Nineteen patients who underwent [11C]CH and [18F]FDG PET/CT were included of which all but six patients were previously treated for HCC. Dual-tracer critical finding detection rate was 95%, with [18F]FDG 68%, and [11C]CH 84%. Intrahepatic HCC recurrence finding rate was 65% for both tracers. [18F]FDG found more ablation site recurrences (4/5) compared to [11C]CH (2/5). Only [11C]CH found two needle tract metastases. Both tracers found 75% of the positive lymph nodes. Two new primary tumours were found, one by [18F]FDG and both by [11C]CH. Conclusions: Our study favours a dual-tracer approach in HCC staging in high-risk patients or when conventional imaging is non-conclusive.

Contains fulltext : 283301.pdf (Publisher’s version ) (Open Access), OBJECTIVES: To determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI). MATERIALS AND METHODS: This study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning-based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis. RESULTS: In the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66-0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52-0.73]). CONCLUSIONS: A DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%. KEY POINTS: * Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa. * Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction. * Applying deep learning to an auto-fixed VOI radiomics approach can

Contains fulltext : 239809.pdf (Publisher’s version ) (Open Access)

Post-transplant lymphoproliferative disorders (PTLDs) are a spectrum of hematological malignancies occurring after solid organ and hematopoietic stem cell transplantation. [F]FDG PET/CT is routinely performed at PTLD diagnosis, allowing for both staging of the disease and quantification of volumetric parameters, such as whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). In this retrospective study, we aimed to determine the prognostic value of MTV and TLG in PTLD patients, together with other variables of interest, such as the International Prognostic Index (IPI), organ transplant type, EBV tumor status, time after transplant, albumin levels and PTLD morphology. A total of 88 patients were included. The 1-, 3-, 5- year overall survival rates were 67%, 58% and 43% respectively. Multivariable analysis indicated that a high IPI (HR: 1.56, 95% CI: 1.13-2.16) and an EBV-negative tumor (HR: 2.71, 95% CI: 1.38-5.32) were associated with poor overall survival. Patients with a kidney transplant had a longer overall survival than any other organ recipients (HR: 0.38 95% CI: 0.16-0.89). IPI was found to be the best predicting parameter of overall survival in our cohort. Whole-body MTV, TLG, time after transplant, hypoalbuminemia and PTLD morphology were not associated with overall survival. [F]FDG PET/CT whole-body volumetric quantitative parameters were not predictive of overall survival in PTLD. In our cohort, high IPI and an EBV-negative tumor were found to predictors of worse overall survival while kidney transplant patients had a longer overall survival compared to other organ transplant recipients.

Post-transplant lymphoproliferative disorder (PTLD) is a complication of organ transplantation classified according to the WHO as nondestructive, polymorphic, monomorphic, and classic Hodgkin Lymphoma subtypes. In this retrospective study, we investigated the potential of semi-quantitative 2-[F]fluoro-2-deoxy-D-glucose ([F]FDG) PET/computed tomography (CT)-based parameters to differentiate between the PTLD morphological subtypes. 96 patients with histopathologically confirmed PTLD and baseline [F]FDG PET/CT between 2009 and 2019 were included. Extracted semi-quantitative measurements included: Maximum, peak, and mean standardized uptake value (SUV, SUV, and SUV). Median SUVs were highest for monomorphic PTLD followed by polymorphic and nondestructive subtypes. The median SUV at the biopsy site was significantly higher in monomorphic PTLD (17.8, interquartile range (IQR):16) than in polymorphic subtypes (9.8, IQR:13.4) and nondestructive (4.1, IQR:6.1) ( = 0.04 and ≤ 0.01, respectively). An SUV ≥ 24.8 was always indicative of a monomorphic PTLD in our dataset. Nevertheless, there was a considerable overlap in SUV across the different morphologies. The median SUV at the biopsy site was significantly higher in monomorphic PTLD than polymorphic and nondestructive subtypes. However, due to significant SUV overlap across the different subtypes, these values may only serve as an indication of PTLD morphology, and SUV-based parameters cannot replace histopathological classification.

Aim/Introduction: Chronic stress is associated with aderegulation of the hypothalamus-pituitary adrenal (HPA)axis and can result in behavioral abnormalities, such asdepressive behavior. Adrenalectomy (ADX) inhibits theproduction and release of corticosterone, impairingthe response towards stressors, and thus may preventstress-induced depressive behavior. We aim to determinebehavioral and neuroinflammatory effects of HPA-axisdisruption via bilateral adrenalectomy (ADX) in animalssubmitted to repeated social defeat (RSD). Materials and Methods: 8-weeks old male Wistar rats were dividedinto four groups: ADX+RSD; ADX+Control; Sham+RSD;Sham+Control. Seven days after ADX surgery, animals weresubmitted to a 5 days RSD or Control protocols. One- andtwo days after the last RSD trial, animals went through anopen field and social interaction test, respectively. 14 daysafter RSD, animals went through a 30 minutes [11C]-PBR28scan for microglia activation. Multifactorial ANOVA wasperformed for statistical assessment. Results: There was asignificant effect of RSD (p=0.038) and ADX (p=0.049) onthe social interaction of the animals, as well as an interactionbetween surgery and RSD (p=0.042). Post-hoc analysisshowed a significantly lower social interaction of Sham+RSDwhen compared with the other groups (vs. Control+ADX,p=0.032; Control+Sham, p=0.023; ADX+RSD, p=0.035).Open field analysis showed no anxiety-like behavior norlocomotion effect of RSD or ADX (p>0.05). Microglialactivation assessed through [11C]-PBR28 also showed noeffect of RSD or ADX (p>0.05). Conclusion: HPA-axis signalingdisruption is able to counterbalance the impairment on thesocial behavior of these animals. In contrast, no effects ofADX or RSD were observed in other behavioral paradigms.Neuroinflammation was also not observed two weeks afterthe RSD, suggesting that the inflammatory response ofmicroglial cells is too mild to be detected by PET or thatneuroinflammation was only transient and already resolvedat

Contains fulltext : 219672.pdf (Publisher’s version ) (Open Access), OBJECTIVES: To create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa). METHODS: This study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3-5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets. RESULTS: The best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980-0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920-0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution. CONCLUSIONS: The phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa. KEY POINTS: * T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly i