Citations

Atelectasis, the partial or complete collapse of the lung, requires early diagnosis for effective treatment. This study investigates the effects of Region of Interest (ROI) cropping and transfer learning on the automatic classification of atelectasis from chest X-ray images. Four SqueezeNet-based models were developed using two image types (raw and cropped lung regions) and two training strategies (from-scratch and transfer learning). Models I and II were trained from scratch on raw and cropped images, respectively, while Models III and IV employed transfer learning on the same image sets. Performance was evaluated using standard classification metrics. Model IV, combining transfer learning and ROI-cropped images, achieved the best performance, with 98.43% accuracy, 0.984 F1-score, and 0.999 AUC. It also reached 0.9861 precision, 0.9828 sensitivity, and 0.9859 specificity. ROI-based cropping consistently improved classification performance. Combining transfer learning with ROI-focused preprocessing significantly enhances atelectasis detection and shows promise as a reliable clinical decision-support approach.

e16486 Background: Allele-specific KRAS G12C inhibitors have revolutionized lung and colorectal cancer treatment, yet the biology of G12C in other gastrointestinal (GI) malignancies remains undefined. We aimed to elucidate the clinicopathologic and genomic landscape of KRAS G12C-mutated tumors compared to other G12 variants in a large non-colorectal GI cohort. Methods: We queried cBioPortal for pancreatic, esophagogastric, hepatobiliary, ampullary, and appendiceal adenocarcinomas. Following data quality control, the final cohort (n = 6,352) was stratified into KRAS wild-type (n = 3,293), G12C (n = 63), and other G12 variants (n = 2,996). Clinicopathologic features, tumor mutational burden (TMB), microsatellite instability status, and co-alteration in 96 key genes across 11 signaling pathways were compared. Statistical significance was assessed using Fisher’s exact or Chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables. The Benjamini-Hochberg method was applied to correct for multiple hypothesis testing. Results: KRAS G12C prevalence was 1.0% overall, with significant heterogeneity across cancer subtypes (p < 0.001). Appendiceal adenocarcinoma demonstrated the highest G12C prevalence at 5.71% (10/175), followed by ampullary (1.44%, 2/139), pancreatic (1.17%, 34/2906), hepatobiliary (0.88%, 12/1360), and esophagogastric cancers (0.28%, 5/1772). G12C patients were significantly older than wild-type patients (64.8±11.2 vs 62.5±12.9 years, mean±SD, p < 0.001) but younger than other G12 variant carriers (65.8±10.4 years). G12C tumors demonstrated intermediate genomic instability, with a TMB of 4.82±6.17 compared to 4.98±10.55 in wild-type tumors and 3.67±10.12 in other G12 variants (p < 0.001). Notably, G12C tumors were universally microsatellite stable (0% MSI-H vs 4.0% wild-type vs 0.9% other G12, p < 0.001). Genomic analysis revealed that AKT1 mutations were significantly enriched in the G12C cohort (6.3% vs 0.4%; q = 0.025) as compared to other G12 variants. A trend of increased mutation frequency in HRAS was observed specifically in pancreatic cancers in G12C tumors as compared to other variants (8.8% vs 0.7%; q = 0.083). In pancreatic adenocarcinoma, G12C tumors were enriched for chromatin modifiers, notably ARID1A (17.6% vs 7.7%, p = 0.045) and SETD2 / SETD26 (8.8% vs 1.1%, p = 0.006), though these did not reach FDR significance. Notably, the immunotherapy-resistance drivers STK11 and KEAP1 were absent (0%) in the whole G12C cohort. Conclusions: KRAS G12C represents a biologically distinct subgroup of exon 2 KRAS mutations compared to other KRAS G12 pathogenic variants. The absence of resistance markers and enrichment of actionable AKT1 and chromatin-modifier mutations suggest distinct therapeutic vulnerabilities in non-colorectal KRAS G12C-mutated GI cancers.

ABSTRACT In 2018, the International Niemann‐Pick Disease Alliance (INPDA) and the International Niemann‐Pick Disease Registry (INPDR) developed and published comprehensive clinical management guidelines to support inclusive and standardized care pathways in Niemann‐Pick disease type C (NPC)—an ultra‐rare, autosomal recessive, neurovisceral lysosomal disorder. Since then, advances in diagnostics, care, and the approval of two novel disease‐modifying agents have underscored the need to revise these guidelines to ensure safe, consistent, and high‐quality care for those affected by NPC. In response, the INPDA and INPDR convened a multidisciplinary Guidelines Development Group (GDG) comprising individuals with NPC expertise from 14 countries across five continents, representing a broad range of specialties, as well as patients and families involved in NPC care. Informed by a comprehensive literature review and two meetings, the GDG systematically reviewed, revised, and updated the 2018 guideline statements, re‐evaluating the level of evidence, strength of recommendations, and expert agreement for each. The resulting 2025 consensus clinical management guidelines constitute a timely, up‐to‐date, and internationally applicable resource for the diagnosis, treatment, and holistic management of individuals with NPC. These guidelines serve as a critical resource for specialist centers, hospital‐based medical teams, staff involved in NPC patient care, family physicians and other primary caregivers, and, importantly, patients and their families.

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes.

Abstract Background Implicit cognitive markers may assist with the prediction of suicidality beyond clinical risk factors. The aim of this study was to investigate neural correlates associated with the Death/Suicide Implicit Association Test (DS-IAT) via event-related potentials (ERP) in suicidal adolescents. Methods Thirty inpatient adolescents with suicidal ideations and behaviors (SIBS) and 30 healthy controls from the community were recruited. All participants underwent 64-channel electroencephalography, DS-IAT, and clinical assessments. Hierarchical generalized linear models with spatiotemporal clustering were used to identify significant ERPs associated with the behavioral outcome of DS-IAT (D scores) and group differences. Results Behavioral results (D scores) showed that the adolescents with SIBS had stronger implicit associations between “death” and “self” than the healthy group (P = .02). Within adolescents with SIBS, participants with stronger implicit associations between “death” and “self” reported more difficulty in controllability of suicidal ideation in the past 2 weeks based on the Columbia-Suicide Severity Rating Scale (P = .03). For the ERP data, the D scores and N100 component over the left parieto-occipital cortex had significant correlations. Significant group differences without behavioral correlation were observed for a second N100 cluster (P = .01), P200 (P = .02), and late positive potential (5 clusters, all P ≤ .02). Exploratory predictive models combining both neurophysiological and clinical measures distinguished adolescents with SIBS from healthy adolescents. Conclusions Our results suggest that N100 may be a marker of attentional resources involved in the distinction of stimuli that are congruent or incongruent to associations between death and self. Combined clinical and ERP measures may have utility in future refinements of assessment and treatment approaches for adolescents with suicidality.