Article

Machine Learning for Predicting and Maximizing the Response of Breast Cancer Patients to Neoadjuvant Therapy

2025-10-14

Abstract excerpt

<h4>Purpose</h4> Neoadjuvant therapy (NAT) is an established treatment for certain high-risk, locally advanced, or unresectable breast cancers, often facilitating breast-conserving surgery. Recent studies show that achieving pathologic complete response (pCR) after NAT correlates with higher event-free survival rates. Thus, accurate prediction of pCR is essential for personalizing breast cancer (BC) treatment to...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ff62066d-9690-5963-8926-397128c071fa
DOI: 10.1101/2025.10.11.25337587

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine Learning for Predicting and Maximizing the Response of Breast Cancer Patients to Neoadjuvant TherapyDOI 10.1101/2025.10.11.25337587Read

Genomic Machine Learning Model Predicts Radiation Therapy Benefit in Early-Stage Breast Cancer Patients with High AccuracySemantic proximity Read

Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patientsSemantic proximity Read

Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning modelSemantic proximity Read

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response MechanismSemantic proximity Read

18F-FDG PET/CT-Based Deep Radiomic Models for Enhancing Chemotherapy Response Prediction in Breast CancerSemantic proximity Read

Transcriptomics-Driven Machine Learning Models Accurately Predict Chemotherapy Response in Muscle-invasive Bladder CancerSemantic proximity Read

Integrated Genomic Profiling Identifies Predictive Biomarkers for Neoadjuvant Therapy Response in Chinese Breast CancersSemantic proximity Read

Immune Effective Score As A Predictor Of Response To Neoadjuvant Trastuzumab Therapy And A Prognostic Indicator For HER2-positive Breast CancerSemantic proximity Read

a pathomic model to predict ifn-γ expression and patients’ survival in breast cancer: a machine learning-based studySemantic proximity Read

Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision medicineSemantic proximity Read

Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning modelSemantic proximity Read

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteinsSemantic proximity Read