Lung Cancer

Mechanistic study of Lonicerae Japonicae Flos (Caprifoliaceae) in non-small cell lung cancer prevention and treatment through integrative pharmacology, multi-machine learning, artificial intelligence, and in vitro experiments.

ETHNOPHARMACOLOGICAL RELEVANCE: Lonicerae Japonicae Flos (Caprifoliaceae) (LJF), an herb with the homology of medicine and food, is traditionally utilized for its heat-clearing, detoxifying, and anticancer properties. Yet, the mechanism by which LJF may assist in the treatment of non-small cell lung cancer (NSCLC) remains unclear.

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high radiation dose generated by the two-scan protocol. We aimed to solve the overexposure problem by training a U-Net-based CA segmentation model using a low-dose co...

Conversion of Mixed-Language Free-Text CT Reports of Pancreatic Cancer to National Comprehensive Cancer Network Structured Reporting Templates by Using GPT-4.

OBJECTIVE: To evaluate the feasibility of generative pre-trained transformer-4 (GPT-4) in generating structured reports (SRs) from mixed-language (Eng...

The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel ins...

Enhancing security in electromagnetic radiation therapy using fuzzy graph theory.

This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems....

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains lim...

The role of patient outcomes in shaping moral responsibility in AI-supported decision making.

INTRODUCTION: Integrating decision support mechanisms utilising artificial intelligence (AI) into medical radiation practice introduces unique challen...

Explainable AI for lung cancer detection via a custom CNN on CT images.

Lung cancer, which claims 1.8 million lives annually, is still one of the leading causes of cancer-related deaths globally. Patients with lung cancer ...

Tumor-educated platelets in lung cancer.

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-r...

DNA Molecular Computing with Weighted Signal Amplification for Cancer miRNA Biomarker Diagnostics.

The expression levels of microRNAs (miRNAs) are strongly linked to cancer progression, making them promising biomarkers for cancer detection. Enzyme-f...

Novel CT radiomics models for the postoperative prediction of early recurrence of resectable pancreatic adenocarcinoma: A single-center retrospective study in China.

PURPOSE: To assess the predictive capability of CT radiomics features for early recurrence (ER) of pancreatic ductal adenocarcinoma (PDAC).

Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.

Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical image...

MLG2Net: Molecular Global Graph Network for Drug Response Prediction in Lung Cancer Cell Lines.

Drug response prediction (DRP) is a central task in the era of precision medicine. Over the past decade, the emergence of deep learning (DL) has great...

Exploring 4 generation EGFR inhibitors: A review of clinical outcomes and structural binding insights.

Epidermal growth factor receptor (EGFR) is a potential target for anticancer therapies and plays a crucial role in cell growth, survival, and metastas...

Preoperative assessment in lymph node metastasis of pancreatic ductal adenocarcinoma: a transformer model based on dual-energy CT.

BACKGROUND: Deep learning(DL) models can improve significantly discrimination of lymph node metastasis(LNM) of pancreatic ductal adenocarcinoma(PDAC),...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (...

A comparison of neural networks and regression-based approaches for estimating kidney function in pediatric chronic kidney disease: Practical predictive epidemiology for clinical management of a progressive disease.

PURPOSE: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determin...

A neural network approach to glomerular filtration rate estimation: a single-centre retrospective audit.

OBJECTIVES: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race correction factor is frequently used for an est...

Gap-App: A sex-distinct AI-based predictor for pancreatic ductal adenocarcinoma survival as a web application open to patients and physicians.

In this study, using RNA-Seq gene expression data and advanced machine learning techniques, we identified distinct gene expression profiles between ma...

5-5-5 ABRT (Dose of 5 Gy per Fraction for up to 5 Fractions Over 5 Weeks Adaptive Bridging Radiation Therapy)-Artificial Intelligence Enters the CAR (-T) (Chimeric Antigen Receptor-T) in Relapsed/Refractory Large B Cell Lymphoma.

PURPOSE: Bridging radiation therapy (BRT) is effective for local control in patients with relapsed or refractory large B cell lymphoma who are undergo...