Oncology/Hematology

Latent twins: a framework for scene recognition and fast radiative transfer inversion in FORUM all-sky observations.

The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for t...

Based on artificial intelligence-assisted generation and in-depth in-silico evaluation of potential inhibitors targeting Stearoyl-CoA desaturase 1 (SCD-1).

Stearoyl-CoA desaturase 1 (SCD-1), a key rate-limiting enzyme in lipid metabolism, catalyzes the con...

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep lea...

Identifying predictive hematological biomarkers for radiation exposure by machine learning in mouse models.

BACKGROUND: Population-scale radiation exposure assessment during radiological emergencies is hinder...

Exploring the immune landscape and immunotherapy relevance of ER stress-related lncRNAs in melanoma through multi-omics and explainable machine learning.

BACKGROUND: Melanoma is a highly aggressive malignancy with poor clinical outcomes, and endoplasmic ...

Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.

BACKGROUND: Immunotherapy has shown limited efficacy in a substantial subset of CRC patients, yet th...

Body composition radiomics integrated with machine learning to predict prognosis in advanced NSCLC treated with first-line immunochemotherapy and concurrent radiotherapy.

OBJECTIVE: Adipose tissue is a highly heterogeneous and plastic endocrine and immune organ whose cha...

Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.

BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis,...

Machine learning-driven QSAR modeling combined with single cell transcriptomics identifies novel drug targets for lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality, large...

Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.

Neoadjuvant dual HER2 blockade with trastuzumab and pertuzumab plus chemotherapy represents the curr...

A three-gene signature (ABCE1, ATAD5, FUT4) for colorectal cancer prognosis: integrative analysis of transcriptomic, single-cell, and machine learning data.

Colorectal cancer (CRC) remains a leading cause of cancer-related death, highlighting an unmet need ...

AI-driven cellular immunotherapy: transforming CAR-T engineering and translation.

Chimeric antigen receptor T (CAR-T) cell therapy has demonstrated remarkable success in hematologic ...

Validation of a QuPath-Based Artificial Intelligence Workflow for Plasma Cell Quantification on CD138 Immunohistochemistry in an Indian Hematopathology Cohort.

INTRODUCTION: Plasma cell percentage on CD138 immunohistochemistry of bone marrow trephine biopsies ...

Development and validation of an interpretable model for pre-chemotherapy triage of fatigue-pain-sleep disturbance burden in lung cancer.

PURPOSE: The aim of the study is to develop an interpretable machine learning model that can effecti...

Data-driven system identification in cancer systems biology: A multiscale modeling approach to melanoma.

BACKGROUND AND OBJECTIVE: Developing systems biology models of cancer is critical for improving unde...

Differentiating sub-centimeter lung metastases in colorectal cancer by deep learning: a multicenter retrospective study.

BACKGROUND: Early diagnosis of sub-centimeter lung metastases is critical for timely decision-making...

Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and validation of a machine learning model.

BACKGROUNDS: Reoperation is a key therapeutic strategy for recurrent or persistent papillary thyroid...

Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ...

Development and Validation of a Machine Learning Model to Prognosticate Hepatocellular Carcinoma.

BACKGROUND: Prognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed...

Perspectives on artificial intelligence-generated chronic disease risk predictions in early breast cancer: an international survey among radiation oncologists.

Frontiers in Raman nano-diagnostics: SERS and TERS of extracellular vesicles for cancer detection.

Efforts to create rapid, non-invasive, and reliable cancer diagnostics have increasingly focused on ...