Oncology/Hematology

FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.

BACKGROUND AND OBJECTIVE: Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per patient. This diagnosis procedure is tedious, time-consuming, and prone to misdiagnosis due to human fatigue. AI-aided diagnosis systems can help alleviate this burden. However, vast amounts of data from multiple hospitals, diverse patient demographi...

Wavelet Spectrum in a Multi-Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors with BI-RADS Category 4a or Higher.

OBJECTIVES: BI-RADS 4a+ breast tumors have a high rate of unnecessary biopsies due to ambiguous diagnostic features. This study aims to develop and evaluate a computer-aided diagnosis method integrating wavelet spectrum analysis with deep learning for benign-malignant classification of these tumors, so as to reduce unnecessary invasive biopsies. METHODS: A retrospective study was conducted on 390 ...

A Single-Cell Guided Machine Learning Model Predicts Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Resistance to immune checkpoint inhibitors is a major clinical obstacle in the treatment of gastric cancer. Identifying drug-resistant cell population...

Functional Imaging in Hereditary Endocrine Neoplasms: Evolving Modalities and Clinical Implications.

Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...

Alzheimer's disease-cancer research (inception to 2025): trends, themes, translational pathways, and insights from highly cited studies.

Alzheimer's disease-cancer research (ADCR) has gained increasing attention due to paradoxical epidemiological associations and shared yet oppositely r...

Deep Learning Based Framework for Detection and Classification of Leukemia Using Microscopic Images.

Excessive proliferation of white blood cells (WBCs) in the bone marrow leads to a type of blood cancer known as leukemia. This blood cancer impairs th...

Multiome-based identification of molecular markers for prospective identification of platelet-biased HSCs.

Platelet-biased hematopoietic stem cells (PLT-HSCs) play key roles in normal physiology, aging, and blood cancer. However, currently, no markers allow...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personali...

Machine Learning-Driven Prognostic Modeling in Head and Neck Adenoid Cystic Carcinoma: A Retrospective Cohort Study.

INTRODUCTION: Adenoid cystic carcinoma (ACC) of the head and neck is a rare malignancy characterized by perineural invasion, local recurrence, and dis...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

Multimodal assessment of minimally conscious state and cognitive motor dissociation in neurocritical care: A critical review.

Minimally conscious state (MCS) is characterized by inconsistent but clearly discernible clinical and behavioral evidence of consciousness. Cognitive ...

Label-free classification of breast cancer subtypes in ex vivo human tissues using Raman spectroscopy and machine learning.

Breast conserving surgery (BCS) aims to excise breast tumors whilst preserving breast-related quality of life, but is complicated by the challenge of ...

Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.

Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accu...

A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.

Accurate International Society of Urological Pathology (ISUP)-grade classification of renal cell carcinoma (RCC) is challenging due to subtle histopat...

Deep neural architecture empowered by explainable artificial intelligence for accurate and early diagnosis of gynaecological cancer using medical images.

Early analysis is a necessity for the more effective treatment of cancers. In gynaecological cancers, like endometrial, ovarian, and cervical cancers,...

Integrative single-cell and spatial transcriptomics with machine learning identify a Luminal-inflam malignant program and reveal an RPN1-PERK UPR vulnerability in triple-negative breast cancer.

Triple-negative breast cancer is marked by extensive cellular heterogeneity and limited availability of actionable targeted treatments, which contribu...

PathTIGR: A pathway topology-informed graph representation learning framework for immunotherapy response prediction.

Immunotherapy has revolutionized cancer treatment, yet substantial inter-patient response heterogeneity limits therapeutic benefit to specific patient...

Swin UNETR-based prediction of [68Ga]Ga-FAPI-46 PET/CT dose rate maps in cancer patients: quantitative comparison with [18F]FDG PET/CT.

BACKGROUND: Fibroblast activation protein (FAP) is a promising theranostic target due to its high stroma expression in numerous malignancies. This stu...

Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.

Molecular subtypes of B-cell acute lymphoblastic leukemia (B-ALL) are essential in modern clinical treatment. However, the fast emerging subtypes and ...

Caregiver Preferences for AI-Supported Telemedicine in Pediatric Palliative Care: A Discrete Choice Experiment.

BackgroundAsynchronous telemedicine may support home-based pediatric palliative care (PPC) by improving access to professional guidance and reducing c...