Machine learning with textural analysis of longitudinal multiparametric MRI and molecular subtypes accurately predicts pathologic complete response in patients with invasive breast cancer.
Aaquib SyedRichard AdamThomas RenJinyu LuTakouhie MaldjianTimothy Q DuongPublished in: PloS one (2023)
Using XGBoost on extracted GLCM features and non-imaging data accurately predicts pCR. This early prediction of response can minimize exposure to toxic chemotherapy, allowing regimen modification mid-treatment and ultimately achieving better outcomes.
Keyphrases
- machine learning
- locally advanced
- big data
- high resolution
- magnetic resonance imaging
- neoadjuvant chemotherapy
- electronic health record
- contrast enhanced
- cross sectional
- type diabetes
- adipose tissue
- deep learning
- young adults
- magnetic resonance
- insulin resistance
- skeletal muscle
- replacement therapy
- data analysis
- fluorescence imaging