Excerpt from Cancer Network:
“A deep learning approach to assessing cancer outcomes appears feasible, according to results of a study in patients with lung cancer. Machine curation yielded similarly accurate assessments of progression and times to improvement and response compared with human counterparts. This development could speed up oncology care processes.
” ‘Important clinical end points, such as response to therapy and disease progression, are often recorded in the EHR [electronic health record] only as unstructured text,’ wrote authors led by Kenneth L. Kehl, MD, MPH, of the Dana-Farber Cancer Institute in Boston. Standards such as RECIST are not routinely applied outside of clinical trials.”
Go to full article published by Cancer Network.
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