Quantitative predictions of neoadjuvant chemotherapy effects in breast cancer by individual patient data assimililation

TL;DR

This paper introduces a personalized data assimilation method to quantitatively predict tumor shrinkage in breast cancer patients undergoing neoadjuvant chemotherapy, aiding treatment planning.

Contribution

It presents a novel, patient-specific algorithm for predicting tumor response to chemotherapy using individual data, validated on a sample of 37 patients.

Findings

Tumor size at diagnosis and after first dose predicts subsequent shrinkage.

Predictions are within 10-20% accuracy for about 90% of patients.

Method can inform optimal timing for surgery.

Abstract

Neoadjuvant chemotherapy has been used for breast cancer aiming at downgrading before surgery. In this article we propose a new quantitative analysis of the effects of the neoadjuvant therapy to obtain numerical, personalized, predictions on the shrinkage of the tumor size after the drug doses, by data assimilation of the individual patient. The algorithm has been validated by a sample of 37 patients with histological diagnosis of locally advanced primary breast carcinoma. The biopsy specimen, the initial tumor size and its reduction after each treatment were known for all patients. We find that: a) the measure of tumor size at the diagnosis and after the first dose permits to predict the size reduction for the follow up; b) the results are in agreement with our data sample, within 10-20 %, for about 90% of the patients. The quantitative indications suggest the best time for surgery. The analysis is patient oriented, weakly model dependent and can be applied to other cancer phenotypes.