Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET.

BACKGROUND: Investigate the potential benefits of sequential deployment of two deep learning (DL) algorithms namely DL-Enhancement (DLE) and DL-based time-of-flight (ToF) (DLT). DLE aims to enhance the rapidly reconstructed ordered-subset-expectation-maximisation algorithm (OSEM) images towards block-sequential-regularised-expectation-maximisation (BSREM) images, whereas DLT aims to improve the quality of BSREM images reconstructed without ToF. As the algorithms differ in their purpose, sequential application may allow benefits from each to be combined. 20 FDG PET-CT scans were performed on a Discovery 710 (D710) and 20 on Discovery MI (DMI; both GE HealthCare). PET data was reconstructed using five combinations of algorithms:1. ToF-BSREM, 2. ToF-OSEM + DLE, 3. OSEM + DLE + DLT, 4. ToF-OSEM + DLE + DLT, 5. ToF-BSREM + DLT. To assess image noise, 30 mm-diameter spherical VOIs were drawn in both lung and liver to measure standard deviation of voxels within the volume. In a blind clinical reading, two experienced readers rated the images on a five-point Likert scale based on lesion detectability, diagnostic confidence, and image quality. RESULTS: Applying DLE + DLT reduced noise whilst improving lesion detectability, diagnostic confidence, and image reconstruction time. ToF-OSEM + DLE + DLT reconstructions demonstrated an increase in lesion SUVmax of 28 ± 14% (average ± standard deviation) and 11 ± 5% for data acquired on the D710 and DMI, respectively. The same reconstruction scored highest in clinical readings for both lesion detectability and diagnostic confidence for D710. CONCLUSIONS: The combination of DLE and DLT increased diagnostic confidence and lesion detectability compared to ToF-BSREM images. As DLE + DLT used input OSEM images, and because DL inferencing was fast, there was a significant decrease in overall reconstruction time. This could have applications to total body PET.

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). METHODS: A total of 273 [18F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality. RESULTS: The non-ToF, DL-ToF low, medium, and high methods resulted in - 28 ± 18, - 28 ± 19, - 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUVmax for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUVmean differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUVmean differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0-5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence. CONCLUSION: Deep learning-based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners.

Management of children and adults with all stages of nodular lymphocyte predominant Hodgkin lymphoma - All StAGEs: A consensus-based position paper from the Hodgkin lymphoma subgroup of the UK National Cancer Research Institute.

A consensus statement for the management for patients of all ages with all stages of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) - All StAGEs - is proposed by representatives of the UK National Cancer Research Institute (NCRI) Hodgkin lymphoma study group and the Children's Cancer & Leukaemia Group. Based on current practices and published evidence, a consensus has been reached regarding diagnosis, staging and risk-ik7 stratified management which includes active surveillance, low- and standard-dose immunochemotherapy and radiotherapy.

Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise.

PURPOSE: To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. METHODS: List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and »-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). RESULTS: OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. CONCLUSION: Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

Discordant nodal staging identifies intermediate-risk group for overall survival in patients with cT3 oesophageal adenocarcinoma.

OBJECTIVES: Oesophageal adenocarcinoma has a poor prognosis and relies on multi-modality assessment for accurate nodal staging. The aim of the study was to determine the prognostic significance of nodal concordance between PET/CT and EUS in oesophageal adenocarcinoma. METHODS: Consecutive patients with oesophageal adenocarcinoma staged between 2010 and 2016 were included. Groups comprising concordant node-negative (C-ve), discordant (DC), and concordant node-positive (C+ve) patients were analysed. Survival analysis using log-rank tests and Cox proportional hazards model was performed. The primary outcome was overall survival. A p value < 0.05 was considered statistically significant. RESULTS: In total, 310 patients (median age = 66.0; interquartile range 59.5-72.5, males = 264) were included. The median overall survival was 23.0 months (95% confidence intervals (CI) 18.73-27.29). There was a significant difference in overall survival between concordance groups (X2 = 44.91, df = 2, p < 0.001). The hazard ratios for overall survival of DC and C+ve patients compared with those of C-ve patients with cT3 tumours were 1.21 (95% CI 0.81-1.79) and 1.79 (95% CI 1.23-2.61), respectively. On multivariable analysis, nodal concordance was significantly and independently associated with overall survival (HR 1.44, 95% CI 1.12-1.83, p = 0.004) and performed better than age at diagnosis (HR 1.02, 95% CI 1.003-1.034, p = 0.016) and current cN-staging methods (HR 1.20, 95% CI 0.978-1.48, p = 0.080). CONCLUSIONS: Patients with discordant nodal staging on PET/CT and EUS represent an intermediate-risk group for overall survival. This finding was consistent in patients with cT3 tumours. These findings will assist optimum treatment decisions based upon perceived prognosis for each patient. KEY POINTS: • Clinicians are commonly faced with results of discordant nodal staging in oesophageal adenocarcinoma. • There is a significant difference in overall survival between patients with negative, discordant, and positive lymph node staging. • Patients with discordant lymph node staging between imaging modalities represent an intermediate-risk group for overall survival.

Determinants of physiological uptake of 18F-fluorodeoxyglucose in palatine tonsils.

To determine the extent of physiological variation of uptake of F-flurodeoxyglucose (FDG) within palatine tonsils. To define normal limits for side-to-side variation and characterize factors affecting tonsillar uptake of FDG.Over a period of 16 weeks 299 adult patients at low risk for head and neck pathology, attending our center for FDG positron emission tomography/computed tomography (PET/CT) scans were identified. The maximum standardized uptake value (SUVmax) was recorded for each palatine tonsil. For each patient age, gender, smoking status, scan indication and prior tonsillectomy status as well as weather conditions were noted.There was a wide variation in palatine tonsil FDG uptake with SUVmax values between 1.3 and 11.4 recorded. There was a strong left to right correlation for tonsillar FDG uptake within each patient (P < .01). The right palatine tonsil showed increased FDG uptake (4.63) compared to the left (4.47) (P < .01). In multivariate analysis, gender, scan indication, and prevailing weather had no significant impact of tonsillar FDG uptake. Lower tonsillar uptake was seen in patients with a prior history of tonsillectomy (4.13) than those without this history (4.64) (P < .01). Decreasing tonsillar FDG uptake was seen with advancing age (P < .01). Significantly lower uptake was seen in current smokers (SUVmax 4.2) than nonsmokers (SUV 4.9) (P = .03).Uptake of FDG in palatine tonsils is variable but shows a strong side-to-side correlation. We suggest the left/ right SUVmax ratio as a guide to normality with a first to 99th percentiles of (0.70-1.36) for use in patients not suspected to have tonsillar pathology.

Prognostic significance of novel ¹8F-FDG PET/CT defined tumour variables in patients with oesophageal cancer.

PURPOSE: (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) combined with computed tomography (PET/CT) is now established as a routine staging investigation of oesophageal cancer (OC). The aim of the study was to determine the prognostic significance of PET/CT defined tumour variables including maximum standardised uptake value (SUVmax), tumour length (TL), metastatic length of disease (MLoD), metabolic tumour volume (MTV), total lesion glycolysis (TLG) and total local nodal metastasis count (PET/CT LNMC). MATERIALS AND METHODS: 103 pre-treatment OC patients (76 adenocarcinoma, 25 squamous cell carcinoma, 1 poorly differentiated and 1 neuroendocrine tumour) were staged using PET/CT. The prognostic value of the measured tumour variables were tested using log-rank analysis of the Kaplan-Meier method and Cox's proportional hazards method. Primary outcome measure was survival from diagnosis. RESULTS: Univariate analysis showed all variables to have strong statistical significance in relation to survival. Multivariate analysis demonstrated three variables that were significantly and independently associated with survival; MLoD (HR 1.035, 95% CI 1.008-1.064, p=0.011), TLG (HR 1.002, 95% CI 1.000-1.003, p=0.018) and PET/CT LNMC (HR 0.048-0.633, 95% CI 0.005-2.725, p=0.015). CONCLUSION: MLoD, TLG, and PET/CT LNMC are important prognostic indicators in OC. This is the first study to demonstrate an independent statistical association between TLG, MLoD and survival by multivariable analysis, and highlights the value of staging OC patients with PET/CT using functional tumour variables.