Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.
Timothy B FisherGeetanjali SainiT S RekhaJayashree KrishnamurthyShristi BhattaraiGrace CallagyMark WebberEmiel A M JanssenJun KongRitu AnejaPublished in: Breast cancer research : BCR (2024)
Our machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.
Keyphrases
- machine learning
- neoadjuvant chemotherapy
- transcription factor
- end stage renal disease
- ejection fraction
- locally advanced
- newly diagnosed
- artificial intelligence
- lymph node
- sentinel lymph node
- chronic kidney disease
- big data
- peritoneal dialysis
- prognostic factors
- squamous cell carcinoma
- deep learning
- patient reported outcomes
- radiation therapy
- patient reported
- replacement therapy