Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Showing 1282-1302 of 15,250 articles
Evaluation of Artificial Intelligence Models for Nutritional Symptom Management in Breast Cancer Patients Undergoing Chemotherapy.

The purpose of the study is to determine if artificial intelligence (AI) models could provide dietar...

Artificial Intelligence Measured Tumor Burden and Pre-Treatment Circulating Tumor DNA in Human Papilloma Virus-Associated Oropharynx Cancer.

BACKGROUND: Artificial intelligence (AI)-based imaging analysis and circulating tumor-associated DNA...

Training, Validating, and Testing Machine Learning Prediction Models for Endometrial Cancer Recurrence.

PURPOSE: Endometrial cancer (EC) is the most common gynecologic cancer in the United States with ris...

Enhancing lung cancer detection through integrated deep learning and transformer models.

Lung cancer has been stated as one of the prevalent killers of cancer up to this present time and th...

PSMA PET/CT for prostate cancer diagnosis: current applications and future directions.

Prostate cancer (PCa) requires improved diagnostic strategies beyond conventional imaging. This revi...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery a...

Surgical and radiological outcomes of giant cell tumor of the bone: prognostic value of Campanacci grading and selective use of denosumab.

BACKGROUND: Advancements in diagnostic and therapeutic modalities for giant cell tumors of bone (GCT...

Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.

Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to ...

Development of an activity-based ratiometric electrochemical substrate for measuring circulating dipeptidyl peptidase-IV/CD26 in whole blood samples.

Dipeptidyl peptidase-IV (DPP-IV) is a circulating blood biomarker that diagnose pancreatic and thyro...

PFHxA and PFHxS promote breast cancer progression in 3D culture: MEX3C-associated immune infiltration revealed by bioinformatics and machine learning.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with widespread...

Application of multivalent aptamers in tumor diagnosis, analysis and therapy (Review).

Cancer remains one of the leading causes of mortality worldwide, making early diagnosis and precise ...

Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic algorithm.

BACKGROUND AND OBJECTIVES: Prostate cancer is the most common form of cancer in the male population....

Detecting the left atrial appendage in CT localizers using deep learning.

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example,...

Metabolomics as a tool for understanding and treating triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous variant of breast cancer dis...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI respon...

Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.

BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tom...

Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data.

Building deep learning models that can rapidly segment whole slide images (WSIs) using only a handfu...

Diagnostic accuracy of ChatGPT-4 in orthopedic oncology: a comparative study with residents.

BACKGROUND: Artificial intelligence (AI) is increasingly being explored for its potential role in me...

Reirradiation for recurrent glioblastoma: the significance of the residual tumor volume.

PURPOSE: Recurrent glioblastoma has a poor prognosis, and its optimal management remains unclear. Re...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients o...

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