Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning.

ABSTRACT: Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These

SUBMITTER: Moghadas-Dastjerdi H 

PROVIDER: S-EPMC8319580 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature