To verify its effectiveness after anticancer treatment, criteria for assessing tumor reactions to treatments were presented, and some of standards are being used with constant development and improvement. However, while evaluation criteria have been revised steadily with an empirical and statistical approach, methods of measuring the condition of a tumor is relatively classic and usually depends on people or tools.
The most widely used criteria as of 2020 is RECIST1.1. After ‘WHO Criteria’ was first published in 1981, followed by Response Evaluation Criteria in Solid Tumors (RECIST) by research groups from Europe and North America in 2000. Finally, after some revised, RECIST1.1 was announced in 2009. It is mainly used for the purpose of assessing the effectiveness of surgery, chemotherapy, and radiation therapy.
The purpose of this study is to evaluate the treatment response of solid cancers more objectively, accurately and quickly than conventional methods by measuring and classifying the tumor’s condition using deep learning based on RECIST1.1. Furthermore, the ultimate goal is to improve existing assessment criteria and present new standards to provide optimal treatment for each patients.