Oncology/Hematology

A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).

BACKGROUND: T1 colorectal cancer (T1 CRC) is increasingly treated with curative-intent endoscopic resection, but tumor recurrence remains a critical factor influencing patient prognosis. However there is no validated biomarker exists to reliably predict post-resection recurrence, limiting risk-adapted follow-up and adjuvant therapy decisions. MATERIALS AND METHODS: In this multicenter retrospectiv...

Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.

BACKGROUND: Cancer remains one of the foremost global causes of mortality, with nearly 10 million deaths recorded by 2020. As incidence rates rise, there is a growing interest in leveraging machine learning (ML) to enhance prediction, diagnosis, and treatment strategies. Despite these advancements, insufficient attention has been directed toward the integration of sociodemographic variables, which...

An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation.

Gadolinium-based contrast agents (GBCAs) are commonly employed with T1-weighted (T1w) MRI to enhance lesion visualization but are restricted in patien...

Artificial Intelligence Driven Virtual Screening and Molecular Docking Approaches Identified LIFR, BTG2, EPHX2, and PAK3 as Targets and BI-2536, AP-24534, and AZ-628 as Repurposed Drugs for PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal tumors worldwide, with limited effective treatments. Globally, the in...

Multimodal Large Language Models for Cystoscopic Image Interpretation and Bladder Lesion Classification: Comparative Study.

BACKGROUND: Cystoscopy remains the gold standard for diagnosing bladder lesions; however, its diagnostic accuracy is operator dependent and prone to m...

Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.

Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Its tendency for postoperative distant metastasis significa...

A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.

OBJECTIVES: To develop and validate a multi-task deep learning (MTDL) model using multiphase contrast-enhanced CT (CECT) for simultaneously assessing ...

Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validation.

OBJECTIVE: To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer detection on MRI compared to fully...

Radiomics++: Review of Habitat Imaging Analysis for Decoding Tumor Heterogeneity.

Tumors display genomic and phenotypic heterogeneity, which holds prognostic significance and may influence therapy response. Radiographic imaging moda...

Large-scale DNA organization analysis of primary breast cancers for prediction of axillary lymph node metastases.

Breast cancer is the leading cause of cancer-related deaths among women worldwide. It is standard practice for patients to undergo a sentinel lymph no...

Predicting and interpreting protein and phosphoprotein abundance from pan-cancer and single-cell transcriptomes.

Proteins that impact phenotype and disease are often approximated by RNA expression, which poorly infers protein abundance. We developed DeepGxP, a de...

Mushroom-derived Immunomodulators: Mechanistic insights, omics integration, AI innovation, and clinical applications in precision immunotherapy.

Mushrooms are rich in structurally diverse bioactive compounds that exert potent immunomodulatory effects, making them promising candidates for next-g...

Implementation of electronic patient-reported outcomes in supportive care for oncology patients.

PURPOSE OF REVIEW: Patient-reported outcomes (PROs) have become increasingly important in oncology, capturing the patient perspective on symptoms, tre...

A Facile Method Based on Faster R-CNN for Cell Detection in Microfluidic Devices.

Cell detection is ubiquitous in the analysis of microfluidic cell assays. In cell biology, immunology, oncology, and toxicology research, studying cel...

Allosteric Insights into TCR-pMHC Dynamics: Understanding the Effects of Melanoma-Associated Epitopes.

Allostery, a crucial phenomenon for comprehending protein function, interactions, and regulation, involves the transmission of perturbations induced b...

Cervical cancer cell prediction and detection with high test accuracy based on a reliable automated system.

Cervical cancer ranks as the fourth most prevalent cancer among women worldwide. The increasing incidence and mortality rates are largely attributed t...

His-MMDM: Multi-Domain and Multi-Omics Translation of Histopathological Images with Diffusion Models.

Generative AI (GenAI) has advanced computational pathology through various image translation models. These models synthesize histopathological images ...

Integrating artificial intelligence (AI) into colorectal cancer reporting.

Artificial intelligence (AI) and deep learning (DL) are transforming cancer research and clinical care, with histopathology playing a central role in ...

Hierarchical image pyramid transformer framework for automated breast cancer molecular subtyping using tissue microarrays.

The heterogeneity of breast cancer at molecular and histological levels poses significant challenges for precise diagnosis and treatment. Current mole...

Spectral Fingerprinting of Engineered Nanomaterials for Precision Biosensing.

Biological systems comprise a complex milieu of macromolecules, small molecules, and ions comprising tens of thousands of distinct species. Various cl...