Lung Cancer

Differentiating adenocarcinoma and squamous cell carcinoma in lung cancer using semi automated segmentation and radiomics.

INTRODUCTION: Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are frequently observed forms of non-small cell lung cancer (NSCLC), playing a significant role in global cancer mortality. This research categorizes NSCLC subtypes by analyzing image details using computer-assisted semi-automatic segmentation and radiomic features in model development.

Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT Radiomic subtypes: a multi-cohort study.

BACKGROUND: Radiomics analyzes quantitative features from medical images to reveal tumor heterogeneity, offering new insights for diagnosis, prognosis, and treatment prediction. This study explored radiomics based biomarkers to predict immunotherapy response and its association with the tumor microenvironment in non-small cell lung cancer (NSCLC) using unsupervised machine learning models derived ...

Axial Skeletal Assessment in Osteoporosis Using Radiofrequency Echographic Multi-spectrometry: Diagnostic Performance, Clinical Utility, and Future Directions.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture preve...

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms.

AIMS: To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lun...

Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related de...

Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.

With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction for non-small cell lung cancer (NSCLC) patients ha...

HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet the response rate remains limited, with only about 30% of solid tumor pa...

Machine learning-based forest fire susceptibility mapping of Southern Mizoram, a part of Indo-Burma Biodiversity Hotspot.

Forest fires are a significant global environmental hazard, causing widespread economic losses and ecological damage to natural habitats. Biodiversity...

Innovative technologies and their clinical prospects for early lung cancer screening.

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lacking effective early-stage screening approaches. Im...

A multi-gene predictive model for the radiation sensitivity of nasopharyngeal carcinoma based on machine learning.

Radiotherapy resistance in nasopharyngeal carcinoma (NPC) is a major cause of recurrence and metastasis. Identifying radiotherapy-related biomarkers i...

Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of...

Quasi-supervised MR-CT image conversion based on unpaired data.

. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and tre...

Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.

Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progr...

Computational theranostics strategy for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in modern medicine, characterized by its insidious progression, early systemic metas...

Advances in molecular pathology and therapy of non-small cell lung cancer.

Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substant...

Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.

OBJECTIVE: Accurate segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding anatomical structures is critical for diagnosis, treatment...

Comparative analysis of pre-transcatheter aortic valve implantation CTA protocols: Optimizing radiation dose and contrast volume.

BACKGROUND: To establish the most effective and safe pre-transcatheter aortic valve implantation (TAVI) CT angiography (CTA) protocol by comparing two...

'Bill': An artificial intelligence (AI) clinical scenario coach for medical radiation science education.

INTRODUCTION: The integration of artificial intelligence (AI) into medical radiation science (MRS) education offers significant potential to enhance s...

Stereotactic Body Radiation Therapy for Primary Renal Cancer and Genetic Markers of Response: A Phase 2 Trial.

Controlled outcome assessment of radiotherapy for primary renal cell carcinoma (RCC) remains limited, particularly regarding its impact on ipsilateral...

Integrated analysis of oral rinse-derived and plasma circulating tumour DNA for mutation profiling and outcome prediction with oral squamous cell carcinoma.

This study evaluates the potential of oral rinse-derived and plasma circulating tumour DNA (ctDNA) in HPV-negative oral squamous cell carcinoma (OSCC)...