As many radiologists will tell you, comparing their imaging interpretations with corresponding tissue obtained at biopsy or surgery is a critical part of their job, according to presenter Dr. Geoffrey McWilliams from the University of California, Davis School of Medicine.
"If a radiologist suggests the presence of cancer, it's extremely helpful to know if cancer was actually present or not," he told AuntMinnie.com.
Correlation of radiologic findings with pathologic specimens helps radiologists and institutions calibrate their interpretations, improve their accuracy, and track their progress. Unfortunately, there currently isn't an automated radiologic-pathologic correlation system, McWilliams noted.
"Manually following pathology results, comparing to imaging interpretations, and processing the data is time-consuming and requires constant input and oversight that is often infeasible for the average practice," he said.
As a result, McWilliams and his mentor, Dr. Thomas Loehfelm, PhD, sought to create a natural language processing tool to perform automated radiologic-pathologic correlation. Due to the relative simplicity of processing data from structured reports, the researchers elected to focus on reports produced using structured reporting. Their application was initially deployed for lesions using the prostate and thyroid imaging reporting and data systems (PI-RADS and TI-RADS) and is currently in use at their institution.
"The implications of this project are already being felt, with plans to build automated radiologic-pathological correlation tools across many different sections of diagnostic and interventional radiology," McWilliams said. "We expect that similar tools will be deployed across multiple sections within radiology over the coming year. This project also demonstrates a reproducible method for building a radiologic-pathologic correlation tool, and we would welcome any inquiries from institutions wishing to implement such a system."
