Lung Cancer

Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.

Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, no...

Impact of Normal Lung Volume Choices on Radiation Pneumonitis Risk Prediction in Locally Non-small Cell Lung Cancer Radiation Therapy.

PURPOSE: This study aims to evaluate the impact of varying definitions of normal lung volume on the ...

Exploring the impact of neutrophils on lung adenocarcinoma using Mendelian randomization and transcriptomic study.

Tumor immune microenvironment plays a crucial role in determining the prognosis of lung adenocarcino...

Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy.

In nanostructure extraction, advanced techniques like synchrotron radiation and electron microscopy ...

Multichannel deep learning prediction of major pathological response after neoadjuvant immunochemotherapy in lung cancer: a multicenter diagnostic study.

OBJECTIVES: This study aimed to develop a pretreatment CT-based multichannel predictor integrating d...

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 intell...

Intelligent assistant in radiation protection based on large language model with knowledge base.

Radiation protection is a critical pillar supporting the use of nuclear energy and nuclear technolog...

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 a...

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-associate...

Benford's Law in histology.

Digital pathology is an emerging field that is gaining popularity due to its numerous advantages ove...

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 ad...

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.

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 arteriov...

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 undete...

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 medi...

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 i...

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,...

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...

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 ...

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 don...

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 ...