As the second-leading cause of death in the United States, cancer is a public health crisis that afflicts nearly one in two people during their lifetime. Cancer is also an oppressively complex disease. Hundreds of cancer types affecting more than 70 organs have been recorded in the nation's cancer registries - databases of information about individual cancer cases that provide vital statistics to doctors, researchers, and policymakers.
"Population-level cancer surveillance is critical for monitoring the effectiveness of public health initiatives aimed at preventing, detecting, and treating cancer," said Gina Tourassi, director of the Health Data Sciences Institute and the National Center for Computational Sciences at the Department of Energy's Oak Ridge National Laboratory.
The image visualizes how the team's multitask convolutional neural network classifies primary cancer sites.
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"Collaborating with the National Cancer Institute, my team is developing advanced artificial intelligence solutions to modernize the national cancer surveillance program by automating the time-consuming data capture effort and providing near real-time cancer reporting."
Through digital cancer registries, scientists can identify trends in cancer diagnoses and treatment responses, which in turn can help guide research dollars and public resources. However, like the disease they track, cancer pathology reports are complex. Variations in notation and language must be interpreted by human cancer registrars trained to analyze the reports.
To better leverage cancer data for research, scientists at ORNL are developing an artificial intelligence-based natural language processing tool to improve information extraction from textual pathology reports. The project is part of a DOE-National Cancer Institute collaboration known as the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) that is accelerating research by merging cancer data with advanced data analysis and high-performance computing.
To train and test the multitask CNNs with real health data, the team used ORNL's secure data environment and over 95,000 pathology reports from the Louisiana Tumor Registry. They compared their CNNs to three other established AI models, including a single-task CNN.
"In addition to offering HPC and scientific computing resources, ORNL has a place to train and store secure data - all of these together are very important," Alawad said.
During testing they found that the hard parameter sharing multitask model outperformed the four other models (including the cross-stitch multitask model) and increased efficiency by reducing computing time and energy consumption. Compared with the single-task CNN and conventional AI models, the hard sharing parameter multitask CNN completed the challenge in a fraction of the time and most accurately classified each of the five cancer characteristics.
"The next step is to launch a large-scale user study where the technology will be deployed across cancer registries to identify the most effective ways of integration in the registries' workflows. The goal is not to replace the human but rather augment the human," Tourassi said.
MEDICA-tradefair.com; Source: Oak Ridge National Laboratory