Lung Cancer

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 is crucial for improving patient survival outcomes. This study developed the nasopharyngeal carcinoma radiotherapy sensitivity score (NPC-RSS) to predict radiotherapy response. By evaluating 113 machine learning algorithm combinations, the glmBoost+Na...

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 NPC patients after intensity-modulated radiation therapy (IMRT) has not been fully studied, and there remains a high risk of recurrence. This study aims to construct a reliable model for predicting post-treatment recurrence by integrating high-accur...

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)...

Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.

PURPOSE: Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for im...

CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.

OBJECTIVES: To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent su...

Artificial intelligence for radiotherapy dose prediction: A comprehensive review.

Patient outcomes are significantly impacted by the effectiveness and quality of radiation treatment planning. Deep learning, a branch of artificial in...

Multiscale-fusion models with genomic, topological and pathomic features to predict response to radiation therapy for non-small cell lung cancer patients.

Artificial intelligence models with biomarkers to predict treatment responses to radiation would be necessary to maximise the treatment outcomes of in...

Monitoring of Early Skin Reactions by Optical Coherence Tomography and Dermatoscopy in Patients Receiving Radiation Therapy for Head and Neck Cancer.

PURPOSE: Patients with head and neck cancer undergoing radiation therapy (RT) may experience pronounced acute skin reactions. We tested whether optica...

Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study.

Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer ...

Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.

The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possib...

Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.

The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML)...

Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.

Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learni...