Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning

Abstract

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Figure 1.

Temporal relationship of clinical and imaging parameters to treatment and assessment of its response.

Figure 2:

A General Schema for Predictive Model Building and Evaluation. pCR: Pathological Complete Response.

Figure 3.

A representative Cart tree for predicting neoadjuvant treatment response.