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OBJECTIVE: Most of the functional neuroimaging studies of depression have focused primarily on the resting state or responses to negatively valenced stimuli. However, depression consists not only of an accentuation of negative affective processing but of an inability to experience pleasure or positive motivation. The authors tested the hypothesis that depressed subjects would show less activation than healthy comparison subjects, in response to positive stimuli, in ventral striatal regions associated with processing of reward and positive stimuli. METHOD: Positive, negative, and neutral words were presented to 10 unmedicated depressed patients and 12 healthy comparison subjects in the context of a 3T functional magnetic resonance imaging (MRI) paradigm. Image processing and analysis were performed using statistical parametric mapping with a mixed-effects model. Significant differences in neural responses were assessed, examining group, condition, and interaction effects of interest within the context of a general linear model. RESULTS: Relative to comparison subjects, depressed patients demonstrated significantly less bilateral ventral striatal activation to positive stimuli, correlating with decreased interest/pleasure in and performance of activities. They also displayed decreased activation to positive stimuli in a dorsomedial frontal region associated with processing of self-related stimuli. Responses of depressed subjects to negative stimuli were consistent with the growing literature on frontolimbic dysfunction in depression. CONCLUSIONS: This finding 1) supports a pathophysiological model of depression that includes reward/motivational pathway dysfunction, 2) suggests a contributing neural substrate of the inability to experience pleasure or engage in rewarding activities, 3) provides greater specification of abnormalities of basal ganglia function in depression, and 4) may help guide treatment approaches. [ABSTRACT FROM AUTHOR]

Background: Few eligible patients in the United States participate in lung cancer screening (LCS) with low-dose computed tomography (LDCT)., Objective: What is the efficacy of interventions to increase LCS participation?, Design: We performed a systematic review following a prespecified protocol registered in PROSPERO (CRD42021283984). In June/July of 2021, we searched Ovid MEDLINE, Embase, Cochrane, CENTRAL, ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, and Epistemonikos from 1946 to October 2021 to identify studies evaluating interventions to increase LCS participation., Participants: Thirteen of 2761 studies met inclusion criteria for data extraction. Of these, six had results available (five RCTs and one prospective observational study). The studies had predominantly White and non-Hispanic participants., Main Measures: An intention-to-treat analysis was used to calculate each study's relative risk (RR) to increase LCS. Effect sizes were pooled using a random-effects model with a subgroup analysis for multi- versus single-step interventions. Risk of bias was evaluated with the revised Cochrane risk-of-bias tool (RoB 2) and risk of bias in non-randomized studies of interventions (ROBINS-I)., Key Results: Overall, the proportion of screening LDCTs performed did not improve in the intervention group relative to the comparator group (RR [95% CI] of 1.30 [0.74, 2.29]), and meta-analysis indicated high heterogeneity of studies (I 2  = 91%). Subgroup analysis suggests that interventions targeting multiple barriers may increase LCS participation (RR [95% CI] for multistep vs single-step; 2.68 [1.77, 4.05] vs 0.99 [0.89, 1.10], P < 0.01). Quality assessment revealed that three of five RCTs showed some concerns or high risk of bias., Conclusion: Evidence on efficacy of interventions to increase LCS participation is limited due to a small number of prospective studies performed in non-diverse populations with critical risk of bias. Further, overall, studied interventions did not improve lung cancer screening participation, though interventions targeting multiple barriers may have some benefit., Competing Interests: Declarations: Conflict of Interest: The authors declare that they do not have a conflict of interest., (© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Introduction: Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. LungCT ScreeningReporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement. Unfortunately, due to variability in radiology reports, extraction of Lung-RADS scores is non-trivial, and best practices do not exist. The purpose of this project is to compare mechanisms to extract Lung-RADS scores from free-text radiology reports., Methods: We retrospectively analyzed reports of LCS low-dose computed tomography (LDCT) examinations performed at a multihospital integrated healthcare network in New York State between January 2016 and July 2023. We compared three methods of Lung-RADS score extraction: manual physician entry at time of report creation, manual LCS specialist entry after report creation, and an internally developed, rule-based natural language processing (NLP) algorithm. Accuracy, recall, precision, and completeness (i.e., the proportion of LCS exams to which a Lung-RADS score has been assigned) were compared between the three methods., Results: The dataset includes 24,060 LCS examinations on 14,243 unique patients. The mean patient age was 65 years, and most patients were male (54 %) and white (75 %). Completeness rate was 65 %, 68 %, and 99 % for radiologists' manual entry, LCS specialists' entry, and NLP algorithm, respectively. Accuracy, recall, and precision were high across all extraction methods (>94 %), though the NLP-based approach was consistently higher than both manual entries in all metrics., Discussion: An NLP-based method of LCS score determination is an efficient and more accurate means of extracting Lung-RADS scores than manual review and data entry. NLP-based methods should be considered best practice for extracting structured Lung-RADS scores from free-text radiology reports., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors have no conflicts of interest related to the submission of this manuscript. Dr. Bade is a site PI on industry-sponsored trials for Nucleix, Delfi Diagnostics, and Biodesix., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Novel strategies are needed to improve low rates of lung cancer screening (LCS) in the US. Seeking to determine hospitalists' perspectives on leveraging hospitalizations to identify patients eligible for LCS, we performed qualitative interviews with eight hospitalists from two hospitals within a large integrated healthcare system. The interviews used semi-structured questions to assess (1) knowledge and practice of general screening and LCS guidelines from the United States Preventive Services Task Force (USPSTF), (2) identification of smoking history, and (3) hospitalists' views on how data obtained during hospitalization may be utilized to improve general screening and LCS post hospitalization. We ultimately reached the conclusion that hospitalists would support a dedicated program to identify hospitalized patients eligible for LCS and facilitate testing after discharge. Efforts to identify patients and arrange subsequent screening should be performed by team members outside the inpatient team., Competing Interests: Declaration of interests The authors have nothing to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Topic Importance: Thoracic imaging with CT scan has become an essential component in the evaluation of respiratory and thoracic diseases. Providers have historically used conventional single-energy CT; however, prevalence of dual-energy CT (DECT) is increasing, and as such, it is important for thoracic physicians to recognize the utility and limitations of this technology., Review Findings: The technical aspects of DECT are presented, and practical approaches to using DECT are provided. Imaging at multiple energy spectra allows for postprocessing of the data and the possibility of creating multiple distinct image reconstructions based on the clinical question being asked. The data regarding utility of DECT in pulmonary vascular disorders, ventilatory defects, and thoracic oncology are presented. A pictorial essay is provided to give examples of the strengths associated with DECT., Summary: DECT has been most heavily studied in chronic thromboembolic pulmonary hypertension; however, it is increasingly being used across a wide spectrum of thoracic diseases. DECT combines morphologic and functional assessments in a single imaging acquisition, providing clinicians with a powerful diagnostic tool. Its role in the evaluation and treatment of thoracic diseases will likely continue to expand in the coming years as clinicians become more experienced with the technology., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: J. R. is a consultant for Siemens Healthineers. M. R.-J. receives clinical research support from Siemens Healthineers. T. F. is an employee for Siemens employee (Siemens Healthineers, Firchheim, Germany). None declared (J. M., E. A., S. R., J. C.)., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: Lung cancer screening (LCS) decreases lung cancer mortality. However, its benefit may be limited by nonadherence to screening. Although factors associated with LCS nonadherence have been identified, to the best of our knowledge, no predictive models have been developed to predict LCS nonadherence. The purpose of this study was to develop a predictive model leveraging a machine learning model to predict LCS nonadherence risk., Methods: A retrospective cohort of patients who enrolled in our LCS program between 2015 and 2018 was used to develop a model to predict the risk of nonadherence to annual LCS after the baseline examination. Clinical and demographic data were used to fit logistic regression, random forest, and gradient-boosting models that were internally validated on the basis of accuracy and area under the receiver operating curve., Results: A total of 1875 individuals with baseline LCS were included in the analysis, with 1264 (67.4%) as nonadherent. Nonadherence was defined on the basis of baseline chest computed tomography (CT) findings. Clinical and demographic predictors were used on the basis of availability and statistical significance. The gradient-boosting model had the highest area under the receiver operating curve (0.89, 95% confidence interval = 0.87 to 0.90), with a mean accuracy of 0.82. Referral specialty, insurance type, and baseline Lung CT Screening Reporting & Data System (LungRADS) score were the best predictors of nonadherence to LCS., Conclusions: We developed a machine learning model using readily available clinical and demographic data to predict LCS nonadherence with high accuracy and discrimination. After further prospective validation, this model can be used to identify patients for interventions to improve LCS adherence and decrease lung cancer burden., (© The Author(s) 2023. Published by Oxford University Press.)

Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy (LVH) in the absence of another causal disease. Several morphologic and histologic changes have been described. Given the morbidity and mortality associated with HCM, understanding these anatomic variations is key to interpreting imaging. This is especially important since many patients exhibit these associated findings in the absence of LVH and prompt early detection of these variations may lead to early diagnosis and treatment. This article describes the appearance of morphologic variations seen in HCM beyond myocardial thickening including: papillary muscle and mitral valve variants, myocardial crypts, left ventricular myocardial bands, and dystrophic calcification related to increased wall tension., (Copyright © 2023. Published by Elsevier Inc.)