Lung Cancer

Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.

PURPOSE: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed tomography (CECT) in detecting pancreatic ductal adenocarcinoma (PDAC).

Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.

Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. The present study identified 59 exosome-associated differentially expressed genes (EA-DEGs) from the Gene Expression Omnibus (GEO) and GeneCards databases. Functional analysis indicated the involvement of the EA-DEGs in NSCLC-related pathways, includi...

Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.

Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated wi...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Prognostic model of lung adenocarcinoma from the perspective of cancer-associated fibroblasts using single-cell and bulk RNA-sequencing.

Cancer-associated fibroblasts (CAFs) play important roles in the progression of lung adenocarcinoma (LUAD). We examined CAF subgroups via gene ontolog...

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies amo...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow...

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with ...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...

Activated cancer-associated fibroblasts correlate with poor survival and decreased lymphocyte infiltration in infiltrative type distal cholangiocarcinoma.

Cancer-associated fibroblasts promote tumor progression through growth facilitation, invasion, and immune evasion. This study investigated the impact ...

Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.

PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastri...

2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing ...

Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery.

BACKGROUND: Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lea...

Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end results (SEER) database.

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal adenocarcinoma (PDAC), significantly contributing to...

Radiation Dose Reduction and Image Quality Improvement of UHR CT of the Neck by Novel Deep-learning Image Reconstruction.

PURPOSE: We evaluated a dedicated dose-reduced UHR-CT for head and neck imaging, combined with a novel deep learning reconstruction algorithm to asses...

Advances in Breast Cancer Detection: Biomarkers, Mechanisms, and Biosensor Technologies.

Breast cancer (BC), the most common cancer in women, remains a global health concern. According to the World Health Organization, in the year 2022, BC...

Artificial Intelligence-Powered Spatial Analysis of Immune Phenotypes in Resected Pancreatic Cancer.

IMPORTANCE: Although tumor-infiltrating lymphocytes (TILs) have been implicated as prognostic biomarkers across various malignancies, the clinical app...

A hybrid multi-instance learning-based identification of gastric adenocarcinoma differentiation on whole-slide images.

OBJECTIVE: To investigate the potential of a hybrid multi-instance learning model (TGMIL) combining Transformer and graph attention networks for class...