News: AI turned to be better than Pathologists in Predicting Survival in Brain Cancer
According to a recent study by the scientists from Emory and Northwestern Universities, published in Proceedings of the National Academy of Sciences of the United States of America, convolutional networks turned to be better than pathologists (who undergo years of highly-specialized training) in predicting survival in brain cancer.
A brain tumor occurs when abnormal cells form within the brain. There are two main types of tumors: malignant or cancerous tumors and benign tumors. Cancerous tumors can be divided into primary tumors that start within the brain, and secondary tumors that have spread from somewhere else, known as brain metastasis tumors. All types of brain tumors may produce symptoms that vary depending on the part of the brain involved. These symptoms may include headaches, seizures, problem with vision, vomiting, and mental changes. The headache is classically worse in the morning and goes away with vomiting. More specific problems may include difficulty in walking, speaking, and with sensation. As the disease progresses unconsciousness may occur. Source: Wikipedia.org
According to the development team, predicting the expected outcome of patients diagnosed with cancer is a critical step in treatment.
Today advances in genomic and imaging technologies provide physicians with vast amounts of data, yet prognostication remains largely subjective, leading to suboptimal clinical management. A computational approach based on deep learning was developed by Pooya Mobadersany, Safoora Yousefi, Mohamed Amgad, David A. Gutman, Jill S. Barnholtz-Sloan, José E. Velázquez Vega, Daniel J. Brat, and Lee A. D. Cooper to predict the overall survival of patients diagnosed with brain tumors from microscopic images of tissue biopsies and genomic biomarkers.
This method uses adaptive feedback to simultaneously learn the visual patterns and molecular biomarkers associated with patient outcomes. This approach surpasses the prognostic accuracy of human experts using the current clinical standard for classifying brain tumors and presents an innovative approach for objective, accurate, and integrated prediction of patient outcomes.
The breakthrough resulted from combining the SCNN technology with more conventional methods that statisticians use to analyze patient outcomes.
When the software was trained using both images and genomic data, its predictions of how long patients survive beyond diagnosis were more accurate than those of human pathologists. The study used public data produced by the National Cancer Institute’s Cancer Genome Atlas project to develop and evaluate the algorithm.
Deep learning networks are often criticized for being black box approaches that do not reveal insights into their prediction mechanisms.
To investigate the visual patterns that SCNN models associate with poor outcomes, the scientists used heat map visualizations to display the risks predicted by their network in different regions of whole-slide images. Transparent heat map overlays are frequently used for visualization in digital pathology, and in this study, these overlays enable pathologists to correlate the predictions of highly accurate survival models with the underlying histology over the expanse of a whole-slide image. Heat maps were generated using a trained SCNN model to predict the risk for each nonoverlapping HPF in a whole-slide image. The predicted risks were used to generate a color-coded transparent overlay, where red and blue indicate higher and lower SCNN risk, respectively.
“The eventual goal is to use this software to provide doctors with more accurate and consistent information. We want to identify patients where treatment can extend life,” says Lee A.D. Cooper, PhD, the study’s lead author, a professor of biomedical informatics at Emory University School of Medicine and member of the Winship Cancer Institute.
What the pathologists do with a microscope is amazing. That an algorithm can learn a complex skill like this was an unexpected result. This is more evidence that AI will have a profound impact in medicine, and we may experience this sooner than expected.
The scientists are looking forward to future studies to evaluate whether the software can be used to improve outcomes for newly diagnosed patients.
Find the original paper here