Lung Cancer

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Breast cancer continues to be a leading cause of death among women in the world. The prediction of survival outcomes based on treatment modalities, i.e., chemotherapy, hormone therapy, surgery, and radiation therapy is an essential step towards personalization in treatment planning. However, Machine Learning (ML) models may improve these predictions by investigating intricate relationships between...

Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.

OBJECTIVES: To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is tra...

Radiomics in Early Detection of Pancreatic Ductal Adenocarcinoma: A Close Look at Its Current Status and Challenges to Clinical Implementation.

Radiomics is a mathematical approach to medical images to extract quantitative features generating a "radiomics signature." The radiomics workflow inv...

Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma.

Bone metastasis (BM) is common in high-grade lung neuroendocrine tumors (NETs). This study aimed to use multiple machine learning algorithms to explor...

Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection.

Artificial intelligence models using digital histopathology slides stained with hematoxylin and eosin offer promising, tissue-preserving diagnostic to...

Deep Learning-Based Classification of NSCLC-Derived Extracellular Vesicles Using AFM Nanomechanical Signatures.

Nonsmall cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality, with liquid biopsy emerging as a promising tool for noninvasive...

Metabolic alterations driven by LDHA in CD8 + T cells promote immune evasion and therapy resistance in NSCLC.

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths worldwide. Despite advancements in treatment, prognosis for patients wi...

Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination.

BACKGROUND: Lung cancer is a highly aggressive and lethal cancer requiring prognostic and predictive biomarkers for improving patient outcomes. Here, ...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within ...

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interventions, medical images are acquired to provide gui...

pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

pyDOSEIA is a Python package designed for meteorological data processing and radiological impact assessment in diverse scenarios, including nuclear an...

AQP9 and IFITM1 as drivers of immune infiltration and tumor progression in IBD-associated colorectal cancer: from computational insights to experimental validation.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract associated with an increased risk of colorectal canc...

Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors.

Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and a persistent challenge in o...

Robot-Assisted Retrograde Intrarenal Surgery: A Mini Review of the Literature on Robotic Surgery Platforms for Endoscopic Stone Surgery.

Retrograde intrarenal surgery (RIRS) has become a cornerstone in renal stone management, with robotic platforms recently entering clinical practice. T...

Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.

Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heur...

Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy.

In nanostructure extraction, advanced techniques like synchrotron radiation and electron microscopy are often hindered by radiation damage and chargin...

Exploring the impact of neutrophils on lung adenocarcinoma using Mendelian randomization and transcriptomic study.

Tumor immune microenvironment plays a crucial role in determining the prognosis of lung adenocarcinoma (LUAD), with the interaction of immune cells wi...

Multichannel deep learning prediction of major pathological response after neoadjuvant immunochemotherapy in lung cancer: a multicenter diagnostic study.

OBJECTIVES: This study aimed to develop a pretreatment CT-based multichannel predictor integrating deep learning features encoded by Transformer model...

Intelligent assistant in radiation protection based on large language model with knowledge base.

Radiation protection is a critical pillar supporting the use of nuclear energy and nuclear technologies. The radiation protection system has been esta...