AI IDs cancer cells in pathology images, could improve patient treatment plans

Researchers have developed an AI algorithm that can identify cancer cells in digital pathology images, sharing their findings in EBioMedicine.

“As there are usually millions of cells in a tissue sample, a pathologist can only analyze so many slides in a day,” Guanghua Xiao, PhD, a professor at UT Southwestern Medical Center in Dallas, said in a prepared statement. “To make a diagnosis, pathologists usually only examine several ‘representative’ regions in detail, rather than the whole slide. However, some important details could be missed by this approach.”

Xiao et al. hope their algorithm, called ConvPath, can help bring about significant changes in that paradigm. Using tumor images from four different datasets, the “cell type classification pipeline” was trained to automatically perform several segmentation and classification tasks on digital pathology images and turn each image into a “spatial map” for the pathologist.

Overall, the team achieved a classification accuracy of more than 90% with both training and testing datasets, noting that their algorithm could “quickly pinpoint the tumor cells” for pathologists.

“It is time-consuming and difficult for pathologists to locate very small tumor regions in tissue images, so this could greatly reduce the time that pathologists need to spend on each image,” Xiao said in the same statement.

ConvPath could also help pathologists and clinicians “predict the patient prognosis” and “tailor the treatment plan of individual patients,” the authors wrote. And its detailed analysis “could potentially provide information for patient response to immunotherapy.”

To assist other researchers looking to explore ConvPath, the authors shared all source scrips for the software here.