Oncology/Hematology

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective management and prognosis. Recent studies emphasise the critical role of the tumour microenvironment, particularly immune cells, in influencing tumour beh...

Advanced CNN Architecture for Brain Tumor Segmentation and Classification using BraTS-GOAT 2024 Dataset.

BACKGROUND: The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tumor characteristics and imaging modalities across clinical settings presents a significant challenge.

SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.

BACKGROUND AND OBJECTIVE: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection method...

Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.

AIM: Patient-reported outcome measures (PROMs) are increasingly used in palliative care to evaluate patients' symptoms and conditions. Healthcare prov...

Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the...

Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.

PURPOSE: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-the...

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and c...

Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.

Cancer is a complex disease driven by mutations in the genes that play critical roles in cellular processes. The identification of cancer driver genes...

Deep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.

Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been...

Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.

BACKGROUND: Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mit...

Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.

Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However, identifying hub genes regulating mitophagy and de...

Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and ...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as...

Deep learning based semantic segmentation of leukemia effected white blood cell.

Medical image segmentation has numerous applications in diagnosing different diseases. Various types of diseases are found in white blood and Red bloo...

Ensemble learning guided survival prediction and chemotherapy benefit analysis in high-grade chondrosarcoma: A study based on the surveillance, epidemiology, and end results (SEER) database.

The chemotherapy benefit for high-grade chondrosarcoma remains controversial. Ensemble learning has better overall performance than single computatio...

Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers.

RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aimi...

Brain tumor classification using MRI images and deep learning techniques.

Brain tumors pose a significant medical challenge, necessitating early detection and precise classification for effective treatment. This study aims t...

A Hybrid Deep Learning and Model-Checking Framework for Accurate Brain Tumor Detection and Validation

Model checking, a formal verification technique, ensures systems meet predefined requirements, playing a crucial role in minimizing errors and enhan...

Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning

Lung cancer is an extremely lethal disease primarily due to its late-stage diagnosis and significant mortality rate, making it the major cause of ca...

Diagnostic Performance of Deep Learning Applications in Hepatocellular Carcinoma Detection Using Computed Tomography Imaging.

Hepatocellular carcinoma (HCC) is a prevalent cancer that significantly contributes to mortality globally, primarily due to its late diagnosis. Early ...