Lung Cancer

Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

BACKGROUND: The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk.

Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning

Lung cancer remains a leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) being the most common subtype. This study aimed to identify key biomarkers associated with stage III NSCLC in non-smoking females using gene expression profiling from the GDS3837 dataset. Utilizing XGBoost, a machine learning algorithm, the analysis achieved a strong predictive performa...

Temporal Characterization and Visualization of Revolving Therapy-Events in Lung Cancer Patients.

This paper presents a comprehensive workflow for integrating revolving events into the transitive sequential pattern mining (tSPM+) algorithm and Mach...

Personalized graph feature-based multi-omics data integration for cancer subtype identification

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment ch...

Hybrid Artificial Intelligence Solution Combining Convolutional Neural Network and Analytical Approach Showed Higher Accuracy in A-lines Detection on Lung Ultrasound in Thoracic Surgery Patients Compared with Radiology Resident.

OBJECTIVES: Lung ultrasound reduces the number of chest X-rays after thoracic surgery and thus the radiation. COVID-19 pandemic has accelerated resear...

Identification of effective diagnostic genes and immune cell infiltration characteristics in small cell lung cancer by integrating bioinformatics analysis and machine learning algorithms.

OBJECTIVES: To identify potential diagnostic markers for small cell lung cancer (SCLC) and investigate the correlation with immune cell infiltration.

A preliminary study on the diagnostic performance of the uPath PD-L1 (SP263) artificial intelligence (AI) algorithm in patients with NSCLC treated with PD-1/PD-L1 checkpoint blockade.

OBJECTIVE: The uPath PD-L1 (SP263) is an AI-based platform designed to aid pathologists in identifying and quantifying PD-L1 positive tumor cells in n...

Distilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism

Multiple Instance Learning (MIL) has garnered widespread attention in the field of Whole Slide Image (WSI) classification as it replaces pixel-level...

Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning

Due to the lack of fine-grained annotation guidance, current Multiple Instance Learning (MIL) struggles to establish a robust causal relationship be...

Deep Bayesian segmentation for colon polyps: Well-calibrated predictions in medical imaging

Colorectal polyps are generally benign alterations that, if not identified promptly and managed successfully, can progress to cancer and cause affec...

Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.

Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, e...

A 3D Pancreatic Cancer Model with Integrated Optical Sensors for Noninvasive Metabolism Monitoring and Drug Screening

A distinct feature of pancreatic ductal adenocarcinoma (PDAC) is a prominent tumor microenvironment (TME) with remarkable cellular and spatial heter...

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic option...

Predictive value of circulating lymphocyte subsets and inflammatory indexes for neoadjuvant chemoradiotherapy response in rectal mucinous adenocarcinoma patients: A machine learning approach.

INTRODUCTION: In this study, we aimed to evaluate the predictive value of circulating lymphocyte subsets and inflammatory indexes in response to neoad...

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approach...

Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a n...

Contrastive Pre-Training and Multiple Instance Learning for Predicting Tumor Microsatellite Instability.

Accurate classification between tumor MicroSatellite Stability (MSS) and Instability (MSI) is crucial in gastrointestinal (GI) cancer prognosis and tr...

Hard example mining in Multi-Instance Learning for Whole-Slide Image Classification.

Multiple instance learning(MIL) has shown superior performance in the classification of whole-slide images(WSIs). The implementation of multiple insta...

Knowledge Models for Cancer Clinical Practice Guidelines: Construction, Management and Usage in Question Answering.

An automated knowledge modeling algorithm for Cancer Clinical Practice Guidelines (CPGs) extracts the knowledge contained in the CPG documents and tra...

EDRAM-Net: Encoder-Decoder with Residual Attention Module Network for Low-dose Computed Tomography Reconstruction.

The medical application of Computed Tomography (CT) is to provide detailed anatomical structures of patients without the need for invasive procedures ...