Improving Generalizability of PET DL Algorithms: List-Mode Reconstructions Improve DOTATATE PET Hepatic Lesion Detection Performance.

Deep learning (DL) algorithms used for DOTATATE PET lesion detection typically require large, well-annotated training datasets. These are difficult to obtain due to low incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and the high cost of manual annotation. Furthermore, networks trained and tested with data acquired from site specific PET/CT instrumentation, acquisition and processing protocols have reduced performance when tested with offsite data. This lack of generalizability requires even larger, more diverse training datasets. The objective of this study is to investigate the feasibility of improving DL algorithm performance by better matching the background noise in training datasets to higher noise, out-of-domain testing datasets. 68Ga-DOTATATE PET/CT datasets were obtained from two scanners: Scanner1, a state-of-the-art digital PET/CT (GE DMI PET/CT; n = 83 subjects), and Scanner2, an older-generation analog PET/CT (GE STE; n = 123 subjects). Set1, the data set from Scanner1, was reconstructed with standard clinical parameters (5 min; Q.Clear) and list-mode reconstructions (VPFXS 2, 3, 4, and 5-min). Set2, data from Scanner2 representing out-of-domain clinical scans, used standard iterative reconstruction (5 min; OSEM). A deep neural network was trained with each dataset: Network1 for Scanner1 and Network2 for Scanner2. DL performance (Network1) was tested with out-of-domain test data (Set2). To evaluate the effect of training sample size, we tested DL model performance using a fraction (25%, 50% and 75%) of Set1 for training. Scanner1, list-mode 2-min reconstructed data demonstrated the most similar noise level compared that of Set2, resulting in the best performance (F1 = 0.713). This was not significantly different compared to the highest performance, upper-bound limit using in-domain training for Network2 (F1 = 0.755; p-value = 0.103). Regarding sample size, the F1 score significantly increased from 25% training data (F1 = 0.478) to 100% training data (F1 = 0.713; p < 0.001). List-mode data from modern PET scanners can be reconstructed to better match the noise properties of older scanners. Using existing data and their associated annotations dramatically reduces the cost and effort in generating these datasets and significantly improves the performance of existing DL algorithms. List-mode reconstructions can provide an efficient, low-cost method to improve DL algorithm generalizability.

PET/CT Biomarkers Enable Risk Stratification of Patients with Relapsed/Refractory Diffuse Large B-cell Lymphoma Enrolled in the LOTIS-2 Clinical Trial.

PURPOSE: Significant progress has occurred in developing quantitative PET/CT biomarkers in diffuse large B-cell lymphoma (DLBCL). Total metabolic tumor volume (MTV) is the most extensively studied, enabling assessment of FDG-avid tumor burden associated with outcomes. However, prior studies evaluated the outcome of cytotoxic chemotherapy or chimeric antigen receptor T-cell therapy without data on recently approved FDA agents. Therefore, we aimed to assess the prognosis of PET/CT biomarkers in patients treated with loncastuximab tesirine. EXPERIMENTAL DESIGN: We centrally reviewed screening PET/CT scans of patients with relapsed/refractory DLBCL enrolled in the LOTIS-2 (NCT03589469) study. MTV was obtained by computing individual volumes using the SUV ≥4.0 threshold. Other PET/CT metrics, clinical factors, and the International Metabolic Prognostic Index (IMPI) were evaluated. Logistic regression was used to assess the association between biomarkers and treatment response. Cox regression was used to determine the effect of biomarkers on time-to-event outcomes. We estimated biomarker prediction as continuous and binary variables defined by cutoff points. RESULTS: Across 138 patients included in this study, MTV with a cutoff point of 96 mL was the biomarker associated with the highest predictive performance in univariable and multivariable models to predict failure to achieve complete metabolic response (OR, 5.42; P = 0.002), progression-free survival (HR, 2.68; P = 0.002), and overall survival (HR, 3.09; P < 0.0001). IMPI demonstrated an appropriate performance, however, not better than MTV alone. CONCLUSIONS: Pretreatment MTV demonstrated robust risk stratification, with those patients demonstrating high MTV achieving lower responses and survival to loncastuximab tesirine in relapsed/refractory DLBCL.

Clinical relevance of reporting fatty liver on ultrasound in asymptomatic patients during routine health checkups.

OBJECTIVE: Ultrasonography is an efficient technique for detecting fatty liver. Its sensitivity and specificity in detecting moderate to severe fatty liver are comparable to those of histology. Fatty liver is associated with abnormal lipid and lipoprotein metabolism and insulin resistance, metabolic syndrome, cardiovascular/renal disease, type 2 diabetes, and other conditions. This study was performed to compare the serum lipid profiles and serum glutamic pyruvic transaminase (GPT), glutamic oxaloacetic transaminase (GOT), and glycosylated hemoglobin (HbA1c) levels in patients diagnosed with fatty liver on ultrasonography versus controls without fatty liver and evaluate the clinical relevance of an ultrasound diagnosis of fatty liver in routine health checkups. METHODS: This hospital-based cross-sectional study included 390 patients who underwent health checkups; 226 were diagnosed with fatty liver (cases) and 164 were not (controls). The lipid profile, serum GOT and GPT levels, and HbA1c level were compared between the cases and controls. RESULTS: The cases had considerably higher levels of lipids, liver enzymes (serum GOT and GPT), and HbA1c than controls. CONCLUSION: Ultrasonography is a noninvasive simple tool for early detection of fatty liver in asymptomatic patients and can help clinicians achieve early detection of metabolic syndrome.