Scientists have been using two main forms of clinical data to predict cancer outcomes: images (either photographs, as in the case of skin cancer, or pathology slides) and -omes of various sorts. Applying ever-more sophisticated machine learning approaches to these datasets can yield accurate diagnoses and prognoses, and even infer how tumors evolve (yellow arrows). Now, scientists are finding that images can predict -omics (blue arrows). Combining the two data sources gives researchers even better predictions of how long a cancer patient will live (thick purple arrows). The ultimate goal of these algorithms, currently under development in basic biology labs, is to help doctors select treatments and forecast survival.
Read the full story.