Lung Cancer

CT image denoising methods for image quality improvement and radiation dose reduction.

With the ever-increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low-dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)-based appro...

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques...

Hyperpolarized Gas Imaging in Lung Diseases: Functional and Artificial Intelligence Perspective.

Pathophysiologic changes in lung diseases are often accompanied by changes in ventilation and gas exchange. Comprehensive evaluation of lung function ...

Exposure-response analysis using time-to-event data for bevacizumab biosimilar SB8 and the reference bevacizumab.

This analysis aimed to characterize the exposure-response relationship of bevacizumab in non-small-cell lung cancer (NSCLC) and evaluate the efficacy...

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

BACKGROUND: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small ce...

Predicting occult lymph node metastasis in solid-predominantly invasive lung adenocarcinoma across multiple centers using radiomics-deep learning fusion model.

BACKGROUND: In solid-predominantly invasive lung adenocarcinoma (SPILAC), occult lymph node metastasis (OLNM) is pivotal for determining treatment str...

Artificial intelligence in biology and medicine, and radioprotection research: perspectives from Jerusalem.

While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Particip...

Pharmacokinetics of recombinant human annexin A5 (SY-005) in patients with severe COVID-19.

Annexin A5 is a phosphatidylserine binding protein with anti-inflammatory, anticoagulant and anti-apoptotic properties. Preclinical studies have show...

Using Vision Transformer for high robustness and generalization in predicting EGFR mutation status in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma is a common cause of cancer-related deaths worldwide, and accurate EGFR genotyping is crucial for optimal treatment ou...

Deep learning-radiomics integrated noninvasive detection of epidermal growth factor receptor mutations in non-small cell lung cancer patients.

This study focused on a novel strategy that combines deep learning and radiomics to predict epidermal growth factor receptor (EGFR) mutations in patie...

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

BACKGROUND: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into trea...

Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer.

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection a...

Management and outcomes of brain metastases from pancreatic adenocarcinoma: a pooled analysis and literature review.

BACKGROUND: Brain metastases (BM) are rare in pancreatic ductal adenocarcinoma (PDAC) and little data exists concerning these patients and their outco...

The role of artificial intelligence in informed patient consent for radiotherapy treatments-a case report.

Recent advancements in large language models (LMM; e.g., ChatGPT (OpenAI, San Francisco, California, USA)) have seen widespread use in various fields,...

CT-Based Super-Resolution Deep Learning Models with Attention Mechanisms for Predicting Spread Through Air Spaces of Solid or Part-Solid Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma (LUAD), and preoperative knowledge of STA...

A personalized prediction model for urinary tract infections in type 2 diabetes mellitus using machine learning.

Patients with type 2 diabetes mellitus (T2DM) are at higher risk for urinary tract infections (UTIs), which greatly impacts their quality of life. Dev...

T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study.

Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR ...

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework.

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using th...

Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.

INTRODUCTION: Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted softwar...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within ...