Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy

Abstract

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient’s tumor on multiparametric MRI is insufficient to predict that patient’s response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation.

The Diagnostic Performance of Minimally Invasive Biopsy in Predicting Breast Pathological Complete Response After Neoadjuvant Systemic Therapy in Breast Cancer: A Meta-Analysis

“…With more and more research data accumulated, it is possible to incorporate multiple potentially relevant factors into a predictive model, which would dramatically improve the accuracy of confirming residual tumors after NST. In the construction of a predictive model with multiple parameters, artificial intelligence can play an important role ( 42 – 44 ). Only when a reliable non-surgical tool to rule out residual disease has been developed can omission of breast surgery be clinically feasible and safe.…”

Heath-PRIOR: An Intelligent Ensemble Architecture to Identify Risk Cases in Healthcare

“…Lo Gullo et al 2020 [1] research focuses on patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC). Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery.…”

“…Smart city environments, when applied to healthcare, improve the quality of people's lives, enabling, for instance, disease prediction and treatment monitoring. In medical settings, case prioritization is of great importance, with beneficial outcomes both in terms of patient health and physicians' daily work [1].…”

“…The data produced by the Internet of Things (IoT) devices can be integrated [2] with other systems, and Recommender Systems (RS) are an alternative to automatically integrate data with predictive models. Several studies have adopted predictive approaches to diagnosis and ways of treatment [3], risk and detection of chronic diseases [1], [4]- [6], detection of the possibility of heart disease [7]- [9], and the use of medical notes to make predictions in health centers [10] and to estimate the likelihood of adverse events in postoperative cases [11], [12].…”

“…Boosting is an iterative approach combining various weak learners, which results in low training error [33], [35]. It 1 www.ufjf.br/pgcc/dissertacoes/ is performed by sequentially updating selected instances to the ensemble subspace by giving more weight to difficult examples, i.e., the most informative instances, which are not correctly classified in the previous steps Tuysuzoglu and Birant 2020 [34]. Weighted majority voting is applied as the combination rule for the ensemble outputs.…”