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

HIGHLIGHTS

• who: Aaquib Syed and colleagues from the Editor: Marco Giannelli, Pisa University Hospital have published the research work: Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer, in the Journal: PLOS ONE of April/12,/2022
• what: The aim of this study was to apply the extreme gradient boosting (XGBoost) algorithm to predict pCR using multiparametric MRI data along with non-imaging data at multiple treatment timepoints as inputs. This approach has the potential to non-invasively identify patients who are . . .
  

   

  

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