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. 2019 Nov 23;49:115–122. doi: 10.1016/j.breast.2019.11.009

Table 1.

List of clinical and imaging variables used. Reprinted from: Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. J Am Med Inform Assoc. 2013; 20(4):688-95.

Clinical Variable Description Imaging Variable Key Term Description
Age Age at the time of diagnosis Delta ADC Delta t1, t2 difference
ER+ Estrogen receptor Delta Ktrans FXL Ktrans Pharmocokinetic transfer constant
PR+ Progesterone receptor Delta Ktrans FXLvp FXL Fast exchange limit
HER2+ Human epidermal growth factor receptor Delta Ktrans FXR FXR Fast exchange regime
Clinical Grade Pretreatment clinical grade Delta ve FXL vp Blood plasma volume fraction
Proliferative rate Delta ve FXvp ve Extravascular extracellular volume fraction
Pre-treatment nodal status Pathologically confirmed by fine needle aspiration or sentinel node evaluation Delta ve FXR ti Intra cellular water lifetime of wated molecule
Clinical-T Pretreatment clinical size based on clinical findings judged most accurate for that case (physical exam, ultrasound, mammogram, conventional MRI) Delta vp FXL
Clinical-N Pretreatment nodal stage based on pathologically confirmed by fine needle aspiration of node or sentinel evaluation Delta ti FXR
Pre-treatment clinical stage Staging of the breast cancer prior to initiation of systemic chemotherapy Ktrans, t1 FXL
Pre-treatment physical exam Longest diameter by physical exam (CM) Ktrans, t1 FXLvp
Pre-treatment longest diameter (ultra sound) Longest dimension (CM) Clinical judgment is used to determine the modality most accurate for that case (physical exam, ultrasound, mammogram, conventional MRI) Ktrans, t1 FXR
Delta tumor volume