Oncology/Hematology

Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer.

BACKGROUND: The most frequent malignant tumor in women is breast cancer (BRCA). It has been discovered that T-cell exhaustion and macrophages play significant roles in BRCA. It was necessary to explore prognostic genes associated with T-cell exhaustion and macrophage polarization in BRCA.

Screening and identification of novel protein markers of early-stage lung cancer and construction and application of screening models.

OBJECTIVE: Molecular biomarkers have the potential to improve the current state of early screening of lung cancer. This investigation aimed to identify novel protein markers for early-stage lung cancer and combine them with traditional tumor markers to develop machine learning models for lung cancer screening.

Subregion-based radiomics analysis for predicting the histological grade of clear cell renal cell carcinoma.

PURPOSE: We explored the feasibility of constructing machine learning (ML) models based on subregion radiomics features (RFs) to predict the histologi...

Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision medicine.

BACKGROUND: Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Natural killer (NK) cells play a crucial rol...

Multilevel Inter-modal and Intra-modal Transformer network with domain adversarial learning for multimodal sleep staging.

Sleep staging identification is a fundamental task for the diagnosis of sleep disorders. With the development of biosensing technology and deep learni...

Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial in...

Machine learning-driven prognostic model based on sphingolipid-related gene signature in pancreatic cancer: development and validation.

BACKGROUND: Pancreatic cancer, a highly malignant tumor with poor prognosis, lacks effective early diagnosis and treatment strategies. Sphingolipids h...

Multimodal integration of longitudinal noninvasive diagnostics for survival prediction in immunotherapy using deep learning.

OBJECTIVES: Immunotherapies have revolutionized the landscape of cancer treatments. However, our understanding of response patterns in advanced cancer...

Multiomics in Renal Cell Carcinoma: Current Landscape and Future Directions for Precision Medicine.

PURPOSE OF REVIEW: Renal cell carcinoma (RCC) is a prevalent and increasingly diagnosed malignancy associated with high mortality and recurrence rates...

Applications of artificial intelligence in abdominal imaging.

The rapid advancements in artificial intelligence (AI) carry the promise to reshape abdominal imaging by offering transformative solutions to challeng...

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage...

A novel fractional computational neural framework for analyzing cancer model under chemotherapy drug.

In this study, a novel Caputo fractional-order model is proposed to represent the complex interactions among stem cells, effector cells, and tumor cel...

From development to implementation: a systematic review on the current maturity status of artificial intelligence models for patients with colorectal cancer liver metastases.

Introduction Artificial intelligence (AI) is increasingly being researched and developed in the medical field and holds potential to transform healthc...

Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma.

INTRODUCTION: To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node meta...

Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study.

The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical pr...

Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery.

The composition and function of glycans are very complex thus manual data interpretation of their structural elucidation is difficult. Capillary elect...

Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability.

: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, and its prognosis is closely linked to the International Society...

Validity and Reliability of an Artificial Intelligence-Based Posture Estimation Software for Measuring Cervical and Lower-Limb Alignment Versus Radiographic Imaging.

: Accurate postural assessment is essential for managing musculoskeletal disorders; however, routine screening is often limited by radiation exposure,...

A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.

: Magnetic resonance imaging (MRI) is crucial in detecting suspicious lesions and diagnosing clinically significant prostate cancer (csPCa). However, ...

High-fidelity in silico generation and augmentation of TCR repertoire data using generative adversarial networks.

Engineered T-cell receptor (eTCR) systems rely on accurately generated T-cell receptor (TCR) sequences to enhance immunotherapy predictability and eff...