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AIMS Lymphoepithelioma-like carcinomas (LELCs) are uncommon epithelial cancers characteristically showing two distinct components consisting of malignant epithelial cells and prominent dense lymphoid infiltrate. Hepatic LELCs consist of two types, the lymphoepithelioma-like hepatocellular carcinoma and lymphoepithelioma-like cholangiocarcinoma (LEL-CCA), with the latter being strongly associated with Epstein-Barr virus (EBV). METHODS AND RESULTS We present a series of three cases of intrahepatic biliary EBV-associated LEL tumours in which the biliary epithelial component showed a distinctly benign appearance, instead of the usual malignant epithelial features of a typical CCA or EBV-associated LEL-CCA. In the lesions, the biliary epithelium showed interconnecting glands or cords of cells. All had a very low proliferation (Ki-67) index. Immunohistochemistry for IDH1 and TP53 performed on two cases was negative and molecular tests for EGFR and KRAS gene mutations performed on one were negative. Prognosis was very good in all three cases, with patients alive with no evidence of disease 24-62 months after surgery. Intriguingly, all three cases had co-infection of HBV and EBV. These cases are also discussed in the context of the 63 cases of LEL-CCA available in the literature, with a focus on epidemiology, clinicopathological features and potential research interests. CONCLUSIONS Based on the distinct clinicopathological features and unique survival benefits, we believe these tumours represent the benign end of the spectrum of EBV-associated lymphoepithelial biliary carcinomas. Whether these tumours require a revision of the current nomenclature to 'lymphoepithelioma-like neoplasm of the biliary tract with probable low malignant potential' will require more detailed analysis with larger case-series.

Aims Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods and results We create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50–0.58, specificity 0.00–0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66–0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77–0.84) subgroups. Conclusion Synergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE.

Background: Clinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to CT pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection. Methods: We use clinical variables, annotated CTPAs, and ECG waveform and morphological parameter data from five hospitals to conduct a retrospective cohort study and develop three models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating tabular clinical data and an embedded representation of the ECG waveform. We benchmark the best model against four clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score. Finally, we investigate model robustness through feature sensitivity analyses and assess for demographic subgroup performance parity. Findings: We create a dataset linking 23,793 CTPAs (10·0% PE-positive) and 320,746 ECGs from 21,183 patients for model development and testing. We find that a Fusion model (area under receiver-operating characteristic [AUROC] 0·81 ± 0·01) outperforms both the ECG model (AUROC 0·59 ± 0·01) and EHR model (AUROC 0·65 ± 0·01). On a sample of 100 patients from the test set, the Fusion model has greater specificity (0·18) and performance (AUROC 0·84 ± 0.01) than the all four clinical criteria (AUROC 0·50-0·58, specificity 0·00-0·05). The model also retains superiority over clinical scores in feature sensitivity analyses (AUROC 0·66 to 0·84) and achieves comparable performance across different sex (AUROC 0·81) and racial (AUROC 0·77 to 0·84) subgroups. Interpretation: Integration of electrocardiogram waveforms with traditional clinical variables synergistically increases prediction performance and specificity for PE detection in those who are at least at moderate suspicion for PE. Funding: National Center for Advancing Translational Sciences, National Institutes of Health. Declaration of Interest: All authors declare no competing interests with this study. Ethical Approval: The study was approved by the Mount Sinai Institutional Review Board.

As modern abdominal imaging equipment advances, pancreatic lesion detection improves. Most of these lesions are incidental, and present a conundrum to the clinician and create great anxiety to the patient until a final diagnosis is made. For the practicing physician, the plethora of diagnostic options is overwhelming. The relevant question is, what is the most efficient algorithm to follow and to arrive at a timely and accurate diagnosis. This article presents a logical approach to the initial evaluation of a pancreatic lesion to get the most information possible with the least amount of testing, and to avoid duplicative measures.

SUMMARY The current algorithm for the imaging diagnosis of hepatocellular carcinoma accurately detects large, progressed tumors displaying the classical imaging features of arterial hyperenhancement with ‘washout’ and/or ‘capsule’ appearance. Liver MRI with the relatively newer hepatobiliary agent, gadoxetate disodium, provides information on hepatocellular function in addition to vascularity, facilitates detection of small progressed tumors, as well as early/vaguely nodular tumors, and shows promise for characterizing hepatocellular carcinoma biology. Prediction of tumor grade, presence of biliary and stem cell markers, microvascular invasion, future hypervascularization and post-treatment recurrence have all been studied with gadoxetate disodium-enhanced MRI with encouraging results. Incorporation of gadoxetate disodium-enhanced MRI into standard diagnostic and management algorithms will likely unfold in the future.

Purpose To prospectively evaluate comfort and image quality of prostate MRI using two different endorectal (ER) coils. Materials and Methods Thirty consecutive patients were prospectively randomized to receive prostate MRI using either a prostate endocoil (PEC) or colorectal endocoil (CEC). Patients and operators were surveyed with regard to endocoil placement. Four Body MRI trained radiologists rated image quality for each examination and additional selected blinded coronal T2 weighted images. Results Average patient discomfort (on a 0–10 pain scale) was greater with the PEC (5.0 for PEC and 2.7 for CEC) with a statistically significant difference (P

Introduction Aggressive angiomyxoma (AAM) is a rare, benign mass with propensity for local invasion and recurrence after resection. Infrequently, this tumor can be found arising from the scrotum or cord structures in males. Aim/Methods A case report is presented followed by a review of relevant literature addressing the diagnosis, imaging, management and follow-up for aggressive angiomyxoma of the scrotum. Results Imaging can assist in further characterization of masses noted on physical exam. Scrotal sonography is typically the primary imaging modality utilized and magnetic resonance imaging is able to provide further anatomic detail. Treatment mainstay is surgical resection with necessary long term surveillance.