Online ISSN: 2515-8260

Keywords : residual cancer burden


A REVIEW OF MACHINE LEARNING FRAMEWORKS FOR EARLY AND ACCURATE PREDICTION OF NEOADJUVANT CHEMOTHERAPY RESPONSES

Uddaraju Susmitha; Narasingarao, M. R

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1040-1050

The ability to predict the reaction of breast tumors to neoadjuvant chemotherapy from the get-go over the span of treatment can delineate patient’s dependent on the reaction for explicit tolerant treatment procedures. From now on, reaction to neoadjuvant chemotherapy is measured as being based on physical examination or breast imaging (mammogram, mri, or normal MRI). There is a powerless connection with these projections and with the actual tumor size as measured by the pathologist through authoritative procedure. Given the numerous options open to Neoadjuvant chemotherapy (NAC), it is important to develop a plan to predict response over the care period. Sadly, as long as certain people are not seen as responding, their condition can never again be specifically resectable, so this situation should be preserved at a strategic remove from progressing response appraisal protocols throughout the care regimen. This paper provides a review of all the existing frameworks of machine learning involved to perform accurately neoadjuvant chemotherapy responses