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Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume (MTV) and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). A moderate association of CNN probabilities with MTV (r = 0.57) and Dmaxbulk (r = 0.52) was observed in the external dataset. Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (

Purpose Visual reading of 18F-florbetapir positron emission tomography (PET) scans is used in the diagnostic process of patients with cognitive disorders for assessment of amyloid-ß (Aß) depositions. However, this can be time-consuming, and difficult in case of borderline amyloid pathology. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop, train, validate and test a convolutional neural network (CNN) for discriminating between Aß negative and positive 18F-florbetapir PET scans in patients with subjective cognitive decline (SCD). Methods 18F-florbetapir PET images were acquired and visually assessed. The SCD cohort consisted of 133 patients from the SCIENCe cohort and 22 patients from the ADNI database. From the SCIENCe cohort, standardized uptake value ratio (SUVR) images were computed. From the ADNI database, SUVR images were extracted. 2D CNNs (axial, coronal and sagittal) were built to capture features of the scans. The SCIENCe scans were randomly divided into training and validation set (5-fold cross-validation), and the ADNI scans were used as test set. Performance was evaluated based on average accuracy, sensitivity and specificity from the cross-validation. Next, the best performing CNN was evaluated on the test set. Results The sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity. Conclusion The 2D-CNN algorithm can classify Aß negative and positive 18F-florbetapir PET scans with high performance in SCD patients.

Purpose Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [18F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. Methods In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [18F]DCFPyL PET-CT. Primary tumors were delineated using 50–70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. Results The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p p p p Conclusion Machine learning-based analysis of quantitative [18F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

Purpose The simplified reference tissue model (SRTM) is commonly applied for the quantification of brain positron emission tomography (PET) studies, particularly because it avoids arterial cannulation. SRTM requires a validated reference region which is obtained by baseline-blocking or displacement studies. Once a reference region is validated, the use should be verified for each new subject. This verification normally requires volume of distribution (VT) of a reference region. However, performing dynamic scanning and arterial sampling is not always possible, specifically in elderly subjects and in advanced disease stages. The aim of this study was to investigate the use of non-invasive standardised uptake value (SUV) approaches, in comparison to VT, as a verification of the previously validated grey matter cerebellum reference region for [18F]flortaucipir and [18F]florbetapir PET imaging in Alzheimer’s disease (AD) patients and controls. Procedures Dynamic 130-min [18F]flortaucipir PET scans obtained from nineteen subjects (10 AD patients) and 90-min [18F]florbetapir dynamic scans obtained from fourteen subjects (8 AD patients) were included. Regional VT’s were estimated for both tracers and were considered the standard verification of the previously validated reference region. Non-invasive SUVs corrected for body weight (SUVBW), lean body mass (SUL), and body surface area (SUVBSA) were obtained by using later time intervals of the dynamic scans. Simulations were also performed to assess the effect of flow and specific binding (BPND) on the SUVs. Results A low SUV corresponded well with a low VT for both [18F]flortaucipir and [18F]florbetapir. Simulation confirmed that SUVs were only slightly affected by flow changes and that increases in SUV were predominantly determined by the presence of specific binding. Conclusions In situations where dynamic scanning and arterial sampling is not possible, a low SUV(80–100 min) for [18F]flortaucipir and a low SUV(50–70 min) for [18F]florbetapir may be used as indication for absence of specific binding in the grey matter cerebellum reference region.

Purpose The simplified reference tissue model (SRTM) is commonly applied for the quantification of brain positron emission tomography (PET) studies, particularly because it avoids arterial cannulation. SRTM requires a validated reference region which is obtained by baseline-blocking or displacement studies. Once a reference region is validated, the use should be verified for each new subject. This verification normally requires volume of distribution (VT) of a reference region. However, performing dynamic scanning and arterial sampling is not always possible, specifically in elderly subjects and in advanced disease stages. The aim of this study was to investigate the use of non-invasive standardised uptake value (SUV) approaches, in comparison to VT, as a verification of the previously validated grey matter cerebellum reference region for [18F]flortaucipir and [18F]florbetapir PET imaging in Alzheimer’s disease (AD) patients and controls. Procedures Dynamic 130-min [18F]flortaucipir PET scans obtained from nineteen subjects (10 AD patients) and 90-min [18F]florbetapir dynamic scans obtained from fourteen subjects (8 AD patients) were included. Regional VT’s were estimated for both tracers and were considered the standard verification of the previously validated reference region. Non-invasive SUVs corrected for body weight (SUVBW), lean body mass (SUL), and body surface area (SUVBSA) were obtained by using later time intervals of the dynamic scans. Simulations were also performed to assess the effect of flow and specific binding (BPND) on the SUVs. Results A low SUV corresponded well with a low VT for both [18F]flortaucipir and [18F]florbetapir. Simulation confirmed that SUVs were only slightly affected by flow changes and that increases in SUV were predominantly determined by the presence of specific binding. Conclusions In situations where dynamic scanning and arterial sampling is not possible, a low SUV(80–100 min) for [18F]flortaucipir and a low SUV(50–70 min) for [18F]florbetapir may be used as indication for absence of specific binding in the grey matter cerebellum reference region. Supplementary Information The online version contains supplementary material available at 10.1007/s11307-020-01572-y.