Oncology/Hematology

Evaluating generative AI models for explainable pathological feature extraction in lung adenocarcinoma grading assessment and prognostic model construction.

BACKGROUND: Given the increasing prevalence of generative AI (GenAI) models, a systematically evaluation of their performance in lung adenocarcinoma histopathological assessment is crucial. This study aimed to evaluate and compare three visual-capable GenAI models (GPT-4o, Claude-3.5-Sonnet, and Gemini-1.5-Pro) for lung adenocarcinoma histological pattern recognition and grading, as well as to exp...

Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants.

BACKGROUND: Genetic variants play a pivotal role in the initiation and progression of many diseases, including cancer. Detecting these variants is the first step in understanding their contribution to disease mechanisms. RNA sequencing (RNA-Seq) has become a crucial assay in cancer research, offering insights beyond those provided by DNA sequencing. This study introduces VarRNA, a novel method tha...

Machine learning algorithms with body fluid parameters: an interpretable framework for malignant cell screening in cerebrospinal fluid.

OBJECTIVES: This study aimed to develop and validate a machine learning (ML) model utilizing cerebrospinal fluid (CSF) body fluid parameters from hema...

Gastrointestinal microbiota and inflammasomes interplay in health and disease: a gut feeling.

The intricate interplay between the gut microbiota and the GI tract has garnered significant attention, as growing evidence has identified the inflamm...

Creatine kinase in prostate cancer: A biosensor-driven diagnostic paradigm.

BACKGROUND: Prostate cancer (PC) remains a leading cause of cancer-related morbidity in men worldwide. Emerging evidence suggests that the brain-type ...

Cancer in a drop: Advances in liquid biopsy in 2024.

Over the past decade, liquid biopsy (LB) has emerged as a key tool in oncology. Its utility in non-invasive sampling and real-time monitoring has made...

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.

Accurate predictions of T cell receptor (TCR) specificity remain an important open problem in immunology, with broad implications for vaccine design, ...

The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer.

Differentiation of adrenal incidentalomas (AIs) remains a challenge in the oncological setting. The aim of the study was to explore the diagnostic ef...

Computed Tomography-Based Radiomics Diagnostic Model for Fat-Poor Small Renal Tumor Subtypes.

Differentiating histologic subtypes of fat-poor small renal masses using conventional imaging remains difficult due to their overlapping radiologic c...

Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction.

PURPOSE: Recent advances in machine learning have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemi...

The Role of PANoptosis-Related Genes in Predicting Breast Cancer Survival and Immune Prospect.

The function of PANoptosis in breast cancer (BC) remains indistinct. We constructed a nomogram model to predict the prognosis of BC to identify high-...

Corrigendum: Identification of hub genes and immune-related pathways in acute myeloid leukemia: insights from bioinformatics and experimental validation.

[This corrects the article DOI: 10.3389/fimmu.2024.1511824.].

Autophagy crosstalk with the immune microenvironment in chronic myeloid leukemia and serves as a biomarker for diagnosis and progression.

BACKGROUND: Previous studies have shown that autophagy is closely related to the occurrence, development, and treatment resistance of chronic myeloid ...

Predicting Response to Transarterial Chemoembolization in Hepatocellular Carcinoma Using Machine Learning Models.

BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with transarterial chemoembolization (TACE) being a key treatm...

The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper.

BACKGROUND: Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovati...

Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis.

OBJECTIVE: Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvement...

Identification of neuronal synapse-related signatures and potential therapeutic drugs in colorectal cancer based on machine learning algorithms and molecular docking.

BACKGROUND: Nervous system-cancer interactions can regulate tumorigenesis, invasion, and metastasis. However, specific biomarkers for targeting neuron...

Identification and validation of hypoxia-associated genes positively correlated with in pulmonary adenocarcinoma.

BACKGROUND: Hypoxia contributes to the proliferation, migration, and chemotherapy resistance of lung adenocarcinoma (LUAD). This study aimed to identi...

A Bayesian deep learning model with consolidation-to-tumor ratio (CTR) prior revolutionizes the prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma: a large-scale diagnostic study.

BACKGROUND: The preoperative prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma (LUAD) is crucial for sel...

Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.

Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples ...