<p>Our MICCAI 2020 tutorial is motivated by the need for developing radiomic and image analytics tools for post-treatment response assessment in oncology. While significant strides have recently been made in the development of radiomics tools through multiple open-source efforts (pyRadiomics, CapTk, CERR), these have been primarily seen application in improved disease characterization on diagnostic imaging. However, nearly 80-90% of over 1.6 million patients diagnosed with cancer annually in the U.S have to be re-evaluated following neoadjuvant or adjuvant chemo-, radiation, or combination therapies, to identify those with residual or progressive disease (i.e. non-responders) compared to those with stable or regressing disease (i.e. responders). Unfortunately, benign “tumor-mimicking” treatment changes (i.e. pseudo-progression, fibrosis, radiation necrosis) confound the appearance of residual disease on routine imaging. There is hence an increasing awareness of the need for specialized quantitative tools to reliably assess post-treatment changes, preferably using routine imaging to distinguish non-responders from responders.
0 commit comments