Breast cancer remains a global health burden, with an increase in deaths related to this particular cancer. Accurately predicting and diagnosing breast cancer is important for treatment development ...
Breast cancer ranks among the most prevalent cancers in women globally, with its treatment efficacy heavily reliant on the early identification and diagnosis of the disease. The importance of early ...
Breast cancers can be classified into subgroups that hint at the aggressiveness of the cancer and the likelihood that the patient will experience a recurrence years after their initial diagnosis. Now, ...
A machine learning (ML) model incorporating both clinical and genomic factors outperformed models based solely on either clinical or genomic data in predicting which patients with hormone receptor (HR ...
n this study, 773 untreated breast cancer patients from all over China were collected and followed up for at least 5 years. We obtained clinical data from 773 cases, RNA sequencing data from 752 cases ...
Breast cancer is a biologically heterogeneous disease in which genomic complexity, dynamic tumor evolution, and variable therapeutic response continue to ...
A machine learning (ML) model incorporating both clinical and genomic factors outperformed models based solely on either clinical or genomic data in predicting which patients with hormone receptor (HR ...
Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care This study aims to investigate the impact of ...
Ensuring Reliability of Curated Electronic Health Record–Derived Data: The Validation of Accuracy for Large Language Model–/Machine Learning–Extracted Information and Data (VALID) Framework AJCC ...
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