Lung Cancer

A Novel Theranostic Strategy for Malignant Pulmonary Nodules by Targeted CECAM6 with Zr/I-Labeled Tinurilimab.

Lung adenocarcinoma (LUAD) constitutes a major cause of cancer-related fatalities worldwide. Early identification of malignant pulmonary nodules constitutes the most effective approach to reducing the mortality of LUAD. Despite the wide application of low-dose computed tomography (LDCT) in the early screening of LUAD, the identification of malignant pulmonary nodules by it remains a challenge. In ...

POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps ( -map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose an innovative Population-prior-aided Ove...

Development of Multiparametric Prognostic Models for Stereotactic Magnetic Resonance Guided Radiation Therapy of Pancreatic Cancers.

PURPOSE: Stereotactic magnetic resonance guided adaptive radiation therapy (SMART) is a new option for local treatment of unresectable pancreatic duct...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1...

Development and validation of machine learning models for early diagnosis and prognosis of lung adenocarcinoma using miRNA expression profiles.

ObjectiveStudy aims to develop diagnostic and prognostic models for lung adenocarcinoma (LUAD) using Machine learning（ML）algorithms, aiming to enhance...

Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.

BACKGROUND: Unlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effec...

Predictive models of epidermal growth factor receptor mutation in lung adenocarcinoma using PET/CT-based radiomics features.

BACKGROUND: Lung adenocarcinoma (LAC) comprises a substantial subset of non-small cell lung cancer (NSCLC) diagnoses, where epidermal growth factor re...

A CNN-transformer fusion network for predicting high-grade patterns in stage IA invasive lung adenocarcinoma.

BACKGROUND: Invasive lung adenocarcinoma (LUAD) with the high-grade patterns (HGPs) has the potential for rapid metastasis and frequent recurrence. Th...

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging.

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical prop...

Deep Learning-driven Microfluidic-SERS to Characterize the Heterogeneity in Exosomes for Classifying Non-Small Cell Lung Cancer Subtypes.

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to ...

Feasibility and Impact of a Radiation Therapy Technologist-Driven Workflow for Cone Beam Computed Tomography Guided Partial Breast Adaptive Radiation Therapy.

PURPOSE: Cone beam computed tomography-based online adaptive radiation therapy (ART) allows significantly smaller planning target volume margins for p...

Taking the 3Rs to a higher level: replacement and reduction of animal testing in life sciences in space research.

Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in the next few decades. Human presence in space, espec...

Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the V...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer ...

Internal Target Volume Estimation for Liver Cancer Radiation Therapy Using an Ultra Quality 4-Dimensional Magnetic Resonance Imaging.

PURPOSE: Accurate internal target volume (ITV) estimation is essential for effective and safe radiation therapy in liver cancer. This study evaluates ...

Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis.

BACKGROUND: Radiation-induced esophagitis remains a significant challenge in thoracic and neck cancer treatment, impacting patient quality of life and...

Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy.

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A de...

Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma.

PURPOSE: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locall...

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limit...

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread p...