A Diagnostic Dilemma: Metastatic Neuroendocrine Tumor Mimicking Hepatocellular Carcinoma.

Carcinoid syndrome arises from neuroendocrine tumors, characterized by the presence of neurosecretory granules. The diagnosis of carcinoid syndrome involves biochemical testing and various imaging techniques. We report the case of a 62-year-old man with Parkinson's Disease who was found to have new-onset cirrhosis and multiple hepatic lesions with necrosis on CT imaging. These findings were concerning for metastatic malignancy of unknown primary origin. Subsequent MRI characterization of the liver lesions indicated hepatocellular carcinoma as the most likely diagnosis. However, a transthoracic echocardiogram, performed for anasarca and dyspnea on exertion, revealed a thickened tricuspid leaflet, highly suspicious for carcinoid valvulitis. A biopsy of one of the hepatic lesions was consistent with neuroendocrine tumor, confirming the diagnosis of carcinoid syndrome. This case highlights the limitations of diagnostic imaging approaches in distinguishing hepatocellular carcinoma from neuroendocrine tumors.

Prospective Margin Estimates Predict Local Tumor Progression Following Microwave Ablation of Small Renal Masses.

PURPOSE: To evaluate the relationship between prospectively generated ablative margin estimates and local tumor progression (LTP) among patients undergoing microwave ablation (MWA) of small renal masses (SRMs). MATERIALS AND METHODS: Between 2017 and 2020, patients who underwent MWA for SRM were retrospectively identified. During each procedure, segmented kidney and tumor shapes were coregistered with intraprocedural helical CT images obtained after microwave antenna placement. Predicted ablation zone shape and size were then overlaid onto the resultant model, and a model-to-model distance algorithm was employed to calculate multiple ablative margin estimates. LTP was modeled as a function of each margin estimate by hazard regression. Models were evaluated using hazard ratios and Akaike information criterion. Receiver operating characteristic curve area under the curve was also estimated using Harrell's and Uno's C indices (HI and UI, respectively). RESULTS: One hundred and twenty-eight patients were evaluated (median age 72.1 years). Mean tumor diameter was 2.4 ± 0.9 cm. LTP was observed in nine (7%) patients. Analysis showed that decreased estimated margin size as measured by first quartile (Q1; 25th percentile), maximum, and average ablative margin metrics was significantly associated with risk of LTP. For every one millimeter increase in Q1, maximum, and mean ablative margin, the hazard of LTP increased 67% (HR: 1.67; 95% CI = 1.25-2.20, UI = 0.93, HI = 0.77), 32% (HR: 1.32; 95% CI 1.09-1.60; UI = 0.93; HI = 0.76), and 48% (HR: 1.48; 95% CI 1.18-1.85; UI = 0.83; HI = 0.75), respectively. CONCLUSION: Prospectively generated ablative margin estimates can be used to predict the risk of local tumor progression following microwave ablation of small renal masses. LEVEL OF EVIDENCE 3: Retrospective cohort study.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.