Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment.
Josip VrdoljakZvonimir BobanDomjan BarićDarko ŠegvićMarko KumrićManuela AvirovićMelita Perić BaljaMarija Milković PerišaČedna TomasovićSnježana TomićEduard VrdoljakJosko BozicPublished in: Cancers (2023)
Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.
Keyphrases
- lymph node
- end stage renal disease
- lymph node metastasis
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- squamous cell carcinoma
- peritoneal dialysis
- neoadjuvant chemotherapy
- radiation therapy
- young adults
- rectal cancer
- locally advanced
- mass spectrometry
- combination therapy
- breast cancer risk