AI Medical Compendium Journal:
Cancer medicine

Showing 51 to 60 of 86 articles

Leveraging Artificial Intelligence and Radiomics for Improved Nasopharyngeal Carcinoma Prognostication.

Cancer medicine
INTRODUCTION: Nasopharyngeal carcinoma (NPC) typically presents as advanced disease due to the lack of significant symptoms in the early stages. Accurate prognostication is therefore challenging as current methods based on anatomical staging often la...

Exploring Mechanisms and Biomarkers of Breast Cancer Invasion and Migration: An Explainable Gene-Pathway-Compounds Neural Network.

Cancer medicine
BACKGROUNDS: Exploring the molecular features that drive breast cancer invasion and migration remains an important biological and clinical challenge. In recent years, the use of interpretable machine learning models has enhanced our understanding of ...

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.

Cancer medicine
INTRODUCTION: Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetua...

Predicting Neoplastic Polyp in Patients With Gallbladder Polyps Using Interpretable Machine Learning Models: Retrospective Cohort Study.

Cancer medicine
OBJECTIVE: Gallbladder polyps (GBPs) are increasingly prevalent, with the majority being benign; however, neoplastic polyps carry a risk of malignant transformation, highlighting the importance of accurate differentiation. This study aimed to develop...

Multidisciplinary clinician perceptions on utility of a machine learning tool (ALERT) to predict 6-month mortality and improve end-of-life outcomes for advanced cancer patients.

Cancer medicine
BACKGROUND: There are significant disparities in outcomes at the end-of-life (EOL) for minoritized patients with advanced cancer, with most dying without a documented serious illness conversation (SIC). This study aims to assess clinician perceptions...

Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.

Cancer medicine
BACKGROUND: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for ad...

Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

Cancer medicine
BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics ...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

Cancer medicine
BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture informatio...