According to the American Cancer Society, prostate cancer is the second most common cancer in American men, averaging around 175,000 new cases every year. During the diagnosis process, more than one million men in the U.S. alone undergo a prostate biopsy, a procedure that results in 10-12 needle cores for patients, and more than 10 million tissue samples that need to be examined by pathologists.
To help alleviate the strain on uropathologists, reduce workloads, and harmonize grading, a team of researchers from 26 worldwide organizations developed a deep learning-based solution to detect and grade cancer in prostate needle biopsy samples.
“AI has potential to reduce high intra-observer variability and to provide diagnostic expertise in regions where this is currently unavailable,” the researchers stated in their paper. “It is recognized that there is a shortage of pathologists internationally. In China, there is only one pathologist per 130,000 population, while in many African countries the ratio is of the order of one per million.”
Using two NVIDIA GPU clusters, comprised of NVIDIA Tesla P100 GPUs, distributed among 27 nodes at the Tampere Center for Scientific Computing in Finland, running CUDA, cuDNN, MATLAB, Keras, TensorFlow and XGBoost, the team trained two deep neural networks for classification of image patches and multiple convolutional neural networks on a selection of 6,682 biopsies from 976 men.
The cases selected for this study were chosen to represent the full range of diagnoses, the researchers said.
In addition to the training set, the team also used 1,630 biopsies from 245 men to evaluate the performance of the deep learning system.
The algorithm achieved accuracy in the range of 0.997 and 0.999, on par with a human pathologist who achieved an accuracy level of 0.96.
“Here, we have for the first time demonstrated AI-based grading of prostate biopsies on the level of leading urological pathologists,” the researchers said. “We believe that the use of this system can increase sensitivity and promote patient safety by providing decision-support and by focusing the attention of the pathologist on regions of interest.”