Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Showing 13001-13020 of 19,032 articles

Precision Imaging to Evaluate Kaposi Sarcoma (PRIME-KS): protocol for a multicountry novel artificial intelligence-based imaging device

Abstract Background: Kaposi sarcoma (KS) is the most common cancer among men in several Eastern African countries, yet treatment monitoring relies on imprecise, time-consuming ruler-based measurements defined by the AIDS Clinical Trial Group (ACTG). This method suffers from inter-observer variability, fails to capture lesion height or true geometric area, and performs poorly on dark skin. SkinScan...

Understanding Human AI Discrepancy in Breast Cancer TIL Assessment: A Multi-Rater and Perceptual Bias Study

Objective: Tumor-infiltrating lymphocytes (TILs) in breast cancer are one of the most important indicators of the immune response within the tumor microenvironment. They play a particularly significant prognostic and predictive role in triple-negative and HER2-positive subtypes. However, substantial inter-observer variability has been reported in TIL scoring among pathologists, which limits its re...

To RAG, or Not to RAG? A Comparative Evaluation of Retrieval-Augmented Generation for ICD Coding of German Tumor Diagnoses

Introduction Coding tumor diagnoses from free-text clinical documentation currently requires substantial manual effort. Promising approaches for autom...

Prognostic performance of an AI-based recurrence risk model in clinically low-risk HR+/HER2- early breast cancer

Objective Accurate prognostication of recurrence risk in HR+/HER2- early breast cancer is central for therapeutic decision-making, including identifyi...

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expressio...

Knowledge-Driven Neuro-Symbolic Reasoning for Personalized Oncology Treatment Recommendation Based on Multi-Modal Medical Knowledge Graph

Personalized oncology treatment recommendation is a critical clinical task that requires in-tegrating complex, multi-modal patient data with establish...

A Pan-Cancer Multi-Omic SuperLearner for Regulated Cell Death Survival Topologies

Introduction: Regulated cell death (RCD) pathways profoundly influence tumor progression and immune modulation. In prior work, we constructed a compre...

Equitable Health Intelligence: An Open Benchmark of Multi-Population Machine Learning for Omics-Based Cancer Prognosis

Purpose: Machine learning (ML) models for omics-based cancer prognosis are often trained on data from predominantly European-ancestry populations, pro...

Real-time artificial intelligence prediction of peptide characteristics and MSFragger search improves multiplexed quantification of non-canonical HLA presented peptides in clear cell renal cell carcinoma.

Non-canonical HLA-presented peptides are promising therapeutic targets, but their low abundance makes them difficult to reproducibly identify and quan...

SNV and indel error modeling of deep targeted cell-free DNA sequencing data for sensitive detection of circulating tumor DNA in colorectal cancer

Circulating tumor DNA (ctDNA) is a promising biomarker for cancer detection, but low tumor burden makes it difficult to distinguish true signal from b...

Sensitive Glioma Detection and Recurrence Monitoring Using a Machine Learning Model Based on Circulating Monocytes

Background: Non-invasive diagnosis, reliable recurrence surveillance remain critical unmet needs in gliomas. Glioma induces profound systemic immune a...

UMITIC: An unsupervised framework for the joint characterization of cellular phenotypes and spatial neighborhoods in multiplex and hyperplex immunofluorescence imaging data

Multiplexed imaging technologies enable the simultaneous measurement of dozens of protein markers while preserving context, providing a high-resolutio...

A Foundation Model for the Cancer Genome

Cancer is a disease of the genome, in which somatic mutations and copy-number alterations determine tumour identity, clinical behaviour, and response ...

Pansoma, a machine learning tool for identifying somatic variants using pangenome graphs

Somatic variant calling, the identification of mutations in non-germline cells acquired over an individual's lifetime, is critical for studying diseas...

Development and Validation of a Machine Learning Model to Predict Prognosis in Patients with Advanced Head and Neck Cancer

Importance Prognostic tools beyond staging are needed to guide treatment and counseling in head and neck squamous cell carcinoma (HNSCC). Objective To...

Deep Learning Spatial Profiling of CD103+CD8+ T Cells and Survival in Rectal Cancer After Neoadjuvant Chemoradiotherapy

Background: CD8+ tumor-infiltrating lymphocytes (TILs) are established prognostic markers in colorectal cancer, yet the clinical significance of CD103...

A New Hybrid Method for Brain Tumor Detection Based on Deep Learning

Brain tumor detection using Magnetic Resonance Imaging (MRI) remains a challenging task due to tumor hetero-geneity and imaging variability. This pape...

Interconnecting ADC Structure with Tumor Cell Biology with Multimodal Learning

Antibody-drug conjugates (ADCs) represent a significant advancement in cancer therapy, yet their development remains constrained by high attrition rat...

Interpretable morphology mapping of peripheral blood leukocytes using annotation-efficient artificial intelligence

Background Peripheral blood smears (PBS) review is labor-intensive, subjective, and challenging for rare or morphologically heterogeneous cell types i...

dbGIST: An LLM-Assisted Multi-Omics Resource for Target Exploration and Cross-Dataset Validation in Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract, yet GIST-specific omics evidence rema...

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