Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

Abstract Purpose This study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy. Materials and me...

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Bibliographic Details
Main Authors: Lal Hussain, Pauline Huang, Tony Nguyen, Kashif J. Lone, Amjad Ali, Muhammad Salman Khan, Haifang Li, Doug Young Suh, Tim Q. Duong
Format: Article
Language:English
Published: BMC 2021-06-01
Series:BioMedical Engineering OnLine
Subjects:
Molecular subtypes
Neoadjuvant chemotherapy
Magnetic resonance imaging
Artificial intelligence
Texture features
Radiomics
Online Access:https://doi.org/10.1186/s12938-021-00899-z

Internet

https://doi.org/10.1186/s12938-021-00899-z

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