Oncology/Hematology

Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.

Breast cancer remains the most prevalent malignancy among women worldwide. The timely detection of this cancer type is critical for improving survival outcomes. Despite advancements, mammogram classification using deep learning strategies still faces challenges. These include inter-view feature inconsistency, loss of diagnostic details, and limited interpretability. In order to address these issue...

Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy often diagnosed late due to the limited sensitivity and specificity of current modalities. To explore alternative strategies, we developed a high-dimensional immune profiling framework using 40-marker spectral flow cytometry to characterize peripheral blood from healthy individuals and treatment-naive PDAC patients across diseas...

The emerging role of machine learning-based methods in cancer classification using microRNA.

Early detection and accurate classification of cancer are crucial to improving patient outcomes. Diagnosis and classification of tumors using conventi...

Bi-directional YOLOv10 with average convolution for brain tumor detection in MRI.

Medical professionals rely on MRI scans to evaluate brain tumors, a task prone to fatigue and subjective interpretation. This study enhances the YOLOv...

Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

PURPOSE: Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detect...

Deep learning for histopathological diagnosis of esophageal squamous cell carcinoma in biopsies: A multicenter analysis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer mortality in Asia, where histopathological diagnosis of endoscopic bi...

Technological synergy and convergent innovation: evolutionary trajectories of AI-integrated multi-omics in cancer research and clinical translation (2015-2024).

OBJECTIVE: This bibliometric analysis systematically examined cancer research literature (2015-2024), focusing on advancements driven by machine learn...

AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.

Artificial intelligence (AI) is being used in oncological drug development to address the high costs, low success rates, and long timelines that chara...

Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) remains a major global health challenge due to its molecular heterogeneity, late diagnosis, and limited therapeutic opt...

Association of cardiovascular-kidney-metabolic syndrome stages with MASLD prevalence and liver fibrosis severity: evidence from traditional and machine learning approaches.

INTRODUCTION: Cardiovascular-Kidney-Metabolic (CKM) syndrome reflects the convergence of cardiovascular, renal, and metabolic disorders. Metabolic dys...

CT-based multiple instance and ensemble learning for lymph node metastasis prediction in esophageal squamous cell carcinoma: a multicentre, retrospective study.

Enhancing breast cancer diagnostics: Shape-aware angular feature learning for precision in breast cancer classification.

Breast cancer is a life-threatening disease that is very common among women in the world. The early and correct diagnosis is necessary to enhance the ...

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Computational modeling and optimization of scFv-based receptors to support CAR-T design targeting CD19 for enhanced binding robustness and reduced off-target propensity.

Chimeric Antigen Receptor T-cell (CAR-T) therapy has revolutionized the treatment of B-cell malignancies, with CD19 being a primary target due to its ...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...

Late distant recurrence prediction model in premenopausal women with ER-positive/HER 2-negative breast cancer: A multicenter retrospective study.

BACKGROUND: Late distant recurrence (DR) remains a significant challenge in estrogen receptor (ER)-positive/Human Epidermal Growth Factor Receptor 2 (...

Development of a Predictive Calculator for the Need for Abdominoperineal Resection after Chemoradiation Therapy in Anal Squamous Cell Carcinoma.

BACKGROUND: Chemoradiation therapy (CRT) is standard treatment for anal squamous cell carcinoma (ASCC). Abdominoperineal resection (APR) is usually re...

Electrochemical biosensors for Calreticulin in lung Cancer.

Electrochemical biosensors represent an advanced and novel technology for the sensitive and specific detection of clinically relevant biomarkers. Of t...

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling.

Recent advancements in nondestructive 3D pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact cl...

Minor salivary gland biopsy in Sjogren's disease: a diagnostic and prognostic tool toward precision medicine.

Minor salivary gland biopsy occupies a distinctive position in the evaluation of Sjögren disease (SjD), offering diagnostic and prognostic insights th...