Lung Cancer

Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.

Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). This study aims to compare and identify abnormal gene expression patterns in LUAD and LUSC sam...

Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy.

BACKGROUND AND PURPOSE: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' workload, challenges persist due to the microscopic spread of tumor cells undetectable in CT imaging, low-intensity contrast between organs, and inter...

Descriptive overview of AI applications in x-ray imaging and radiotherapy.

Artificial intelligence (AI) is transforming medical radiation applications by handling complex data, learning patterns, and making accurate predictio...

Accuracy of machine learning models for pre-diagnosis and diagnosis of pancreatic ductal adenocarcinoma in contrast-CT images: a systematic review and meta-analysis.

PURPOSE: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast C...

A unified deep-learning framework for enhanced patient-specific quality assurance of intensity-modulated radiation therapy plans.

BACKGROUND: Modern radiation therapy techniques, such as intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT), use...

Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated...

Automated Measurement of Effective Radiation Dose by F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography.

BACKGROUND/OBJECTIVES: Calculating the radiation dose from CT in F-PET/CT examinations poses a significant challenge. The objective of this study is t...

Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current application...

Improving prediction of solar radiation using Cheetah Optimizer and Random Forest.

In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensa...

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need fo...

Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.

PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasophary...

Can knowledge-based planning models validated on ethnically diverse patients lead to global standardisation of external beam radiation therapy for locally advanced cervix cancer?

BACKGROUND AND PURPOSE: Knowledge-based planning (KBP) can consistently and efficiently create high-quality Volumetric Arc Therapy (VMAT) plans for ce...

Exploring tumor microenvironment interactions and apoptosis pathways in NSCLC through spatial transcriptomics and machine learning.

BACKGROUND: The most common type of lung cancer is non-small cell lung cancer (NSCLC), accounting for 85% of all cases. Programmed cell death (PCD), a...

High density of TCF1+ stem-like tumor-infiltrating lymphocytes is associated with favorable disease-specific survival in NSCLC.

INTRODUCTION: Tumor-infiltrating lymphocytes are both prognostic and predictive biomarkers for immunotherapy response. However, less is known about th...

Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.

BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular a...

The establishment of PD-1 inhibitor treatment prognosis model based on dynamic changes of peripheral blood indexes in patients with advanced lung squamous cell carcinoma.

BACKGROUND: Unlike patients with lung adenocarcinoma, patients with lung squamous cell carcinoma (LUSC) do not derive significant benefits from target...

Impact of race-based calculations of eGFR on the management of muscle invasive bladder cancer.

PURPOSE: The estimated glomerular filtration rate (eGFR) has historically been calculated with a race-coefficient multiplier (RCM); however, the RCM h...

Real-World and Clinical Trial Validation of a Deep Learning Radiomic Biomarker for PD-(L)1 Immune Checkpoint Inhibitor Response in Advanced Non-Small Cell Lung Cancer.

PURPOSE: This study developed and validated a novel deep learning radiomic biomarker to estimate response to immune checkpoint inhibitor (ICI) therapy...

Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis.

OBJECTIVES: Endoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This stu...

Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.

The presence, location, and extent of prostate cancer is assessed by pathologists using H&E-stained tissue slides. Machine learning approaches can acc...