TECH OUTLOOKTECH OUTLOOKAUGUST - 20219When thinking of the future, one can find even more data with a higher likelihood of finding more predictive variables of drug response by integrating digital pathology images with orthogonally related radiology imagescould provide an additional dimension of encoded data for interpretation. Obtaining these 3 orthogonally related types of data from tumors is relatively easy to obtain with current technologies. However, current practice in drug development is limited by analyzing these data sets independently, and thus we end up sub-sampling the tumor data and are limited by the type and amount of knowledge we can obtain.What needs to be developed are multi-modal AI/ML/DL tools that can analyze these orthogonally related data sets simultaneously. I refer to this burgeoning field as pathology data science. While pathologist already integrate molecular data into their pathological visual interpretation of the tumor; pathologists of the future-- pathology data scientists, will begin to leverage AI/ML/DL algorithms to synthesize and interpret the pixel level data in a pathology image with the pixel level data of a radiology image with -Omics data as one potential diagnostic source as a CDx, rather than having separate types of analyses. In addition, this new approach will place demands on pathology labs for slide staining quality that is fitted for digital and computational analysis rather than visual assessment by pathologists.In summary, I believe the future role of imaging and the pathology data scientist in drug discovery will be in the ability to analyze and synthesize the pixels of tumor images at all length scales (pathologically, radiologically) and its synthesis with high throughput molecular data derived from the same tumor, quantify perturbations in the TME and potentially predict various types of clinical variables and drug response as a CDx that is currently not possible. Pathology data scientists will play a critical role in developing and applying this technology as they sit at the intersection of these data sets and are the only discipline (not radiology or molecular alone) to diagnose cancer alone.
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