Oncology/Hematology

Insights into AI advances in immunohistochemistry for effective breast cancer treatment: a literature review of ER, PR, and HER2 scoring.

Breast cancer is a significant health challenge, with accurate and timely diagnosis being critical to effective treatment. Immunohistochemistry (IHC) staining is a widely used technique for the evaluation of breast cancer markers, but manual scoring is time-consuming and can be subject to variability. With the rise of Artificial Intelligence (AI), there is an increasing interest in using machine l...

BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches.

BACKGROUND: Cancer rates are rising rapidly, causing global mortality. According to the World Health Organization (WHO), 9.9 million people died from cancer in 2020. Machine learning (ML) helps identify cancer early, reducing deaths. An ML-based cancer diagnostic model can use the patient's genetic information, such as microarray data. Microarray data are high dimensional, which can degrade the pe...

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, inclu...

Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer.

BACKGROUND: Programmed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was t...

Interaction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study.

INTRODUCTION: The current understanding of colorectal carcinogenesis is based on the adenoma-carcinoma sequence, where genetics, intestinal microbiota...

Integrating bioinformatics and machine learning to identify AhR-related gene signatures for prognosis and tumor microenvironment modulation in melanoma.

BACKGROUND: The Aryl Hydrocarbon Receptor (AhR) pathway significantly influences immune cell regulation, impacting the effectiveness of immunotherapy ...

Utilizing machine-learning techniques on MRI radiomics to identify primary tumors in brain metastases.

OBJECTIVE: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametr...

Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.

OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inh...

A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx.

"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset,...

Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat o...

Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aim...

Dual-path neural network extracts tumor microenvironment information from whole slide images to predict molecular typing and prognosis of Glioma.

BACKGROUND AND OBJECTIVE: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and progno...

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network.

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (...

mCNN-glucose: Identifying families of glucose transporters using a deep convolutional neural network based on multiple-scanning windows.

Glucose transporters are essential carrier proteins that function on the phospholipid bilayer to facilitate glucose diffusion across cell membranes. T...

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and mana...

Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states.

Prostate cancer is a disease which poses an interesting clinical question: Should it be treated? Only a small subset of prostate cancers are aggressiv...

Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.

Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive el...

Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer.

BACKGROUND: Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and...

Automated CAD system for early detection and classification of pancreatic cancer using deep learning model.

Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally...

Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.

PURPOSE: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [...