Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen’s Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80–100% and 87–100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71–94% and positive predictive values in the range of 88–100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.

To develop and validate a predictive model for postembolization syndrome (PES) following transarterial hepatic chemoembolization (TACE) for hepatocellular carcinoma. Materials and Methods: In this single-center, retrospective study, 370 patients underwent 513 TACE procedures between October 2014 and September 2016. Seventy percent of the patients were randomly assigned to a training data set and the remaining 30% were assigned to a testing data set. Variables included demographic, laboratory, clinical, and procedural details. PES was defined as pain and/or nausea beyond 6 hours after TACE that required intravenous medication for symptom control. The predictive model was developed by using conditional inference trees and Lasso regression. Results: Demographics, laboratory data, performance, tumor characteristics, and procedural details were statistically similar for the training and testing data sets. Overall, 83 of 370 patients (22.4%) after 107 of 513 TACE procedures (20.8%) met the predefined criteria. Factors identified at univariable analysis included large tumor burden (P = .004), drug-eluting embolic TACE (P = .03), doxorubicin dose (P = .003), history of PES (P , .001) and chronic pain (P , .001), of which history of PES, tumor burden, and drug-eluting embolic TACE were identified as the strongest predictors by the multivariable analysis and were used to develop the predictive model. When applied to the testing data set, the model demonstrated an area under the curve of 0.62, sensitivity of 79% (22 of 28), specificity of 44.2% (53 of 120), and a negative predictive value of 90% (53 of 59). Conclusion: The model identified history of postembolization syndrome, tumor burden, and drug-eluting embolic chemoembolization as predictors of protracted recovery because of postembolization syndrome.

Two novel NPM1 gene mutations were detected among our study population of AML patients identified as: the insertion CACG associated with point mutation, deletion of one base, or associated with point mutation. NPM1 gene mutations may become a new tool for monitoring minimal residual disease in AML with normal karyotype. Whether these previously unreported NPM-1 mutations will confer the same better outcome as previously reported mutations is currently unknown and warrants a larger study.

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