Lung Cancer

Artificial Intelligence for radiation dose reduction in computed tomography: a narrative synthesis of clinical evidence from 2020 to 2025.

Computed tomography (CT) is essential to modern clinical practice but contributes substantially to population radiation exposure, particularly in oncology, paediatric and screening pathways. In line with the ALARP principle and IR(ME)R requirements, there is growing interest in the use of artificial intelligence (AI) to reduce dose without compromising diagnostic performance. This work synthesised...

Clinical-grade autonomous cytopathology through whole-slide edge tomography.

Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing to its speed, simplicity and minimally invasive nature1-9. However, its effectiveness is limited by variability in diagnostic accuracy stemming from subjective visual interpretation10-21. Although many artificial intelligence (AI)-powered systems have...

The Overlooked Autoantibody Repertoire: Exploring the Biomarker Potential of Downregulated Autoantibodies in NSCLC.

Autoantibodies (AAbs) represent promising biomarkers in cancer. While most AAbs are elevated in cancer, a substantial subset is downregulated, and the...

Radiomics for early detection of pancreatic cancer: a systematic review and meta-analysis.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, in which accurate and faster detection is required in high-risk...

Comparable survival after lobectomy and total thyroidectomy for T3b differentiated thyroid carcinoma: A population-based study.

BACKGROUND: The 2025 American Thyroid Association guidelines recommend total thyroidectomy for all T3b differentiated thyroid carcinoma (DTC). However...

Exploratory identification of COL1A1 as a potential gene linking endocrine-disrupting chemicals and lung adenocarcinoma: a bioinformatics and machine learning analysis.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are common environmental pollutants that affect human health. These chemicals have been linked to th...

CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.

UNLABELLED: Single-cell RNA sequencing facilitates the discovery of gene expression signatures that define cell states across patients, which could be...

Clinical validation of perfusion imaging with pulmonary function test data using Voronoi-based discretization.

Accurate lung function assessment is essential for diagnosing and managing diseases like COPD, pulmonary emboli, and lung cancer. Single-photon emissi...

Emerging Roles of Multiparametric Biodosimetry and Artificial Intelligence in Advancing Radiation Dose Assessment for Triage.

Biodosimetry plays a crucial role in radiation emergency preparedness and response by enabling efficient allocation of medical resources through prior...

A Flexible Hybrid Quantum-classical Training Framework of Organ-at-Risk and Tumor Segmentation Models for Radiation Therapy Planning.

Deep learning-based Organ-at-Risk (OAR) and tumor segmentation is vital for radiation therapy planning but often suffers from over-parameterization, r...

A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs.

PROteolysis TArgeting Chimeras (PROTACs) are bifunctional molecules that offer a novel approach to targeted protein degradation, showing particular pr...

Utilisation of intramuscular and intermuscular fat to develop a new skeletal muscle grading score which can predict treatment outcomes for locally advanced rectal cancer.

BACKGROUND: Sarcopenia has been widely studied in rectal cancer with increasing evidence to suggest that other body composition parameters, in particu...

Toward Timely Diagnosis of Pancreatic Cancer: Revolutionizing Early Detection Through Genomics, Artificial Intelligence, and Noninvasive Biomarkers.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive cancers, typically diagnosed at an advanced stage due to its su...

Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy often diagnosed late due to the limited sensitivity and specificity of current m...

Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

PURPOSE: Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detect...

Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.

OBJECTIVES: To evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction alg...

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.

Aflatoxin B1 (AFB1), a known mycotoxin and environmental hazard, has been linked to breast cancer, yet the exact biological pathways remain poorly cha...

Pathomic model to predict the expression of UQCRH and overall survival of lung adenocarcinoma patients.

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the ...