Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer

PURPOSE: To predict pathological complete response (pCR) after neoadjuvant chemotherapy using extreme gradient boosting (XGBoost) with MRI and non-imaging data at multiple treatment timepoints. MATERIAL AND METHODS: This retrospective study included breast cancer patients (n = 117) who underwent neo...

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Detalles Bibliográficos
Autores principales: Syed, Aaquib, Adam, Richard, Ren, Thomas, Lu, Jinyu, Maldjian, Takouhie, Duong, Tim Q.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844845/
https://www.ncbi.nlm.nih.gov/pubmed/36649274
http://dx.doi.org/10.1371/journal.pone.0280320

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9844845/
https://www.ncbi.nlm.nih.gov/pubmed/36649274
http://dx.doi.org/10.1371/journal.pone.0280320

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