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Purpose: To assess in healthy volunteers the whole-body distribution and dosimetry of [11C]metoclopramide, a new positron emission tomography (PET) tracer to measure P-glycoprotein activity at the blood-brain barrier. Procedures: Ten healthy volunteers (five women, five men) were intravenously injected with 387 ± 49 MBq of [11C]metoclopramide after low dose CT scans and were then imaged by whole-body PET scans from head to upper thigh over approximately 70 min. Ten source organs (brain, thyroid gland, right lung, myocardium, liver, gall bladder, left kidney, red bone marrow, muscle and the contents of the urinary bladder) were manually delineated on whole-body images. Absorbed doses were calculated with QDOSE (ABX-CRO) using the integrated IDAC-Dose 2.1 module. Results: The majority of the administered dose of [11C]metoclopramide was taken up into the liver followed by urinary excretion and, to a smaller extent, biliary excretion of radioactivity. The mean effective dose of [11C]metoclopramide was 1.69 ± 0.26 μSv/MBq for female subjects and 1.55 ± 0.07 μSv/MBq for male subjects. The two organs receiving the highest radiation doses were the urinary bladder (10.81 ± 0.23 μGy/MBq and 8.78 ± 0.89 μGy/MBq) and the liver (6.80 ± 0.78 μGy/MBq and 4.91 ± 0.74 μGy/MBq) for female and male subjects, respectively. Conclusions: [11C]Metoclopramide showed predominantly renal excretion, and is safe and well tolerated in healthy adults. The effective dose of [11C]metoclopramide was comparable to other 11C-labeled PET tracers. [ABSTRACT FROM AUTHOR]

Purpose: Medullary thyroid carcinoma (MTC) is characterized by a high rate of metastasis. In this study we evaluated the ability of [18F]DOPA PET/ceCT to stage MTC in patients with suspicious thyroid nodules and pathologically elevated serum calcitonin (Ctn) levels prior to total thyroidectomy and lymph node (LN) dissection.Methods: A group of 32 patients with sonographically suspicious thyroid nodules and pathologically elevated basal Ctn (bCtn) and stimulated Ctn (sCtn) levels underwent DOPA PET/ceCT prior to surgery. Postoperative histology served as the standard of reference for ultrasonography and DOPA PET/ceCT region-based LN staging. Univariate and multivariate regression analyses as well as receiver operating characteristic analysis were used to evaluate the correlations between preoperative and histological parameters and postoperative tumour persistence or relapse.Results: Primary MTC was histologically verified in all patients. Of the 32 patients, 28 showed increased DOPA decarboxylase activity in the primary tumour (sensitivity 88%, mean SUVmax 10.5). Undetected tumours were exclusively staged pT1a. The sensitivities of DOPA PET in the detection of central and lateral metastatic neck LN were 53% and 73%, in contrast to 20% and 39%, respectively, for neck ultrasonography. Preoperative bCtn and carcinoembryonic antigen levels as well as cN1b status and the number of involved neck regions on DOPA PET/ceCT were predictive of postoperative tumour persistence/relapse in the univariate regression analysis (P < 0.05). Only DOPA PET/ceCT cN1b status remained significant in the multivariate analysis (P = 0.016, relative risk 4.02).Conclusion: This study revealed that DOPA PET/ceCT has high sensitivity in the detection of primary MTC and superior sensitivity in the detection of LN metastases compared to ultrasonography. DOPA PET/ceCT identification of N1b status predicts postoperative tumour persistence. Thus, implementation of a DOPA-guided LN dissection might improve surgical success. [ABSTRACT FROM AUTHOR]

Purpose: To determine the diagnostic performance of three-dimensional (3D) texture analysis (TA) of contrast-enhanced computed tomography (CE-CT) images for treatment response assessment in patients with Hodgkin lymphoma (HL), compared with F-18-fludeoxyglucose (FDG) positron emission tomography/CT. Methods: 3D TA of 48 lymph nodes in 29 patients was performed on venous-phase CE-CT images before and after chemotherapy. All lymph nodes showed pathologically elevated FDG uptake at baseline. A stepwise logistic regression with forward selection was performed to identify classic CT parameters and texture features (TF) that enable the separation of complete response (CR) and persistent disease. Results: The TF fraction of image in runs, calculated for the 45° direction, was able to correctly identify CR with an accuracy of 75%, a sensitivity of 79.3%, and a specificity of 68.4%. Classical CT features achieved an accuracy of 75%, a sensitivity of 86.2%, and a specificity of 57.9%, whereas the combination of TF and CT imaging achieved an accuracy of 83.3%, a sensitivity of 86.2%, and a specificity of 78.9%. Conclusions: 3D TA of CE-CT images is potentially useful to identify nodal residual disease in HL, with a performance comparable to that of classical CT parameters. Best results are achieved when TA and classical CT features are combined. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype. [ABSTRACT FROM AUTHOR]

Abstract: Aim:: In advanced seminoma the management of residuals after completion of chemotherapy is controversial. Some centres routinely perform surgery for lesions ≥3cm diameter, others recommend surgery solely if the residual fail to shrink or show even growth. This study prospectively investigates whether FDG PET can improve the prediction of viable tumour in post-chemotherapy seminoma residuals. Materials and methods:: After an expansion of a previous study population, 54 patients from eight centres with metastatic seminoma and a CT-documented mass after chemotherapy were included in the study. Six patients were excluded from evaluation because of protocol violations. After PET, the patients underwent either surgery or were followed clinically. On follow-up the lesions were considered to be non-viable when there was unequivocal shrinking, or when the lesion remained morphologically stable for at least 24 months. Any lesion growth was assumed to be malignant. PET results were compared to CT discrimination (< or ≥3cm) of the residual masses. Results:: Fifty-two PET scans were evaluable. After adequate chemotherapy, there were 74 CT-documented residual masses ranging in size from 1 to 11cm (median, 2.2cm). Their dignities were confirmed histologically in 13 lesions, or by follow-up CT in 61 lesions. Four of forty-seven lesions <3cm and 11/27 lesions ≥3cm were viable. PET was true positive in one lesion <3cm and in 11 lesions ≥3cm, false negative in three lesions <3cm, and true negative in 59 lesions (43 lesions <3cm). No PET scan was false positive. In detecting viability the sensitivity and specificity was 73% (95% CI, 44–88), and 73% (59–83), respectively, for CT (< or ≥3cm); and 80% (51–95), and 100% (93–100), respectively, for PET (specificity, P < 0.001). Conclusion:: In post-chemotherapy seminoma residuals, a positive PET is highly predictive for the presence of viable tumour. The specificity of PET is significantly higher than that of CT when using a ≥3cm cut-off. A negative PET scan is excellent for the exclusion of disease in lesions ≥3cm, with a somewhat higher sensitivity than CT (n.s.). PET can contribute to the management of residual seminoma lesions, especially in terms of avoiding unnecessary additional treatment for patients with lesions ≥3cm. [Copyright &y& Elsevier]

• Assessment of inter-organ metabolic connectivity. • Automated and manual organ delineation. • Validation of metabolic connectivity approach. • Liver and kidney strongest connectivity with brain. The nervous and circulatory system interconnects the various organs of the human body, building hierarchically organized subsystems, enabling fine-tuned, metabolically expensive brain-body and inter-organ crosstalk to appropriately adapt to internal and external demands. A deviation or failure in the function of a single organ or subsystem could trigger unforeseen biases or dysfunctions of the entire network, leading to maladaptive physiological or psychological responses. Therefore, quantifying these networks in healthy individuals and patients may help further our understanding of complex disorders involving body-brain crosstalk. Here we present a generalized framework to automatically estimate metabolic inter-organ connectivity utilizing whole-body functional positron emission tomography (fPET). The developed framework was applied to 16 healthy subjects (mean age ± SD, 25 ± 6 years; 13 female) that underwent one dynamic 18F-FDG PET/CT scan. Multiple procedures of organ segmentation (manual, automatic, circular volumes) and connectivity estimation (polynomial fitting, spatiotemporal filtering, covariance matrices) were compared to provide an optimized thorough overview of the workflow. The proposed approach was able to estimate the metabolic connectivity patterns within brain regions and organs as well as their interactions. Automated organ delineation, but not simplified circular volumes, showed high agreement with manual delineation. Polynomial fitting yielded similar connectivity as spatiotemporal filtering at the individual subject level. Furthermore, connectivity measures and group-level covariance matrices did not match. The strongest brain-body connectivity was observed for the liver and kidneys. The proposed framework offers novel opportunities towards analyzing metabolic function from a systemic, hierarchical perspective in a multitude of physiological pathological states. [ABSTRACT FROM AUTHOR]