AIMC Topic: Neoplasm Staging
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Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence.
JAMA otolaryngology-- head & neck surgery
Dec 1, 2024
IMPORTANCE: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intell...
Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.
Briefings in bioinformatics
Nov 22, 2024
Breast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an ef...
Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer.
Cancer medicine
Nov 1, 2024
INTRODUCTION: The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone.
Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning.
The journal of pathology. Clinical research
Nov 1, 2024
Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle co...
Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.
Medicine
Sep 27, 2024
Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predict...
Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.
Cancer medicine
Sep 1, 2024
OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve t...
Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.
The journal of gene medicine
Sep 1, 2024
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...
Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.
The journal of pathology. Clinical research
Sep 1, 2024
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. W...
Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations.
Cancer medicine
Aug 1, 2024
BACKGROUND: Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic featur...
Evolutionary learning-derived lncRNA signature with biomarker discovery for predicting stage of colon adenocarcinoma.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as valuable biomarkers for cancer diagnosis and prognosis prediction. This work proposes an evolutiona...
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