Machine Learning Tool Accurately Diagnoses Esophageal Cancer
November 08, 2019 – Machine learning methods could accurately identify cancerous esophagus tissue on microscopy images without the time-consuming manual data input that is required for current methods, according to a study published in JAMA Network Open.
Researchers at Dartmouth and Dartmouth-Hitchcock Norris Cotton Cancer Center have developed an innovative machine learning approach that automatically learns clinically important regions on whole-slide images to classify them.
Histopathology image analysis requires a manual annotation process that outlines the regions of interest on a high-resolution whole slide image to train the computer model. Although the method is advanced, the process is still tedious.
“Data annotation is the most time-consuming and laborious bottleneck in developing modern deep learning methods,” said Saeed Hassanpour, PhD, lead author of the study.
“Our study shows that deep learning models for histopathology slides analysis can be trained with labels only at the tissue level, thus removing the need for high-cost data annotation and creating new opportunities for expanding the application of deep learning in digital pathology.”
The team tested their method for identifying cancerous and precancerous esophagus tissue on high-resolution microscopy images without training on region-of-interest annotations. Researchers then applied the network to Barrett esophagus and esophageal adenocarcinoma detection and found that their method achieved better results than the traditional method.
“Our new approach outperformed the current state-of-the-art approach that requires these detailed annotations for its training,” said Hassanpour.
“The result is significant because our method is based solely on tissue-level annotations, unlike existing methods that are based on manually annotated regions.”
Machine learning technology has consistently demonstrated its potential to improve diagnostics and care management. Recently, a team of researchers used machine learning tools to accurately predict patients with cancer who were at high risk of six-month mortality, which could help clinicians engage in timely conversations with their patients.
“Our findings demonstrated that machine learning algorithms can predict a patient’s risk of short-term mortality with good discrimination and PPV. Such a tool could be very useful in aiding clinicians’ risk assessments for patients with cancer as well as serving as a point-of-care prompt to consider discussions about goals and end-of-life preferences,” the researchers stated.
“Machine learning algorithms can be relatively easily retrained to account for emerging cancer survival patterns. As computational capacity and the availability of structured genetic and molecular information increase, we expect that predictive performance will increase and there may be a further impetus to implement similar tools in practice.”
The research team on the esophageal study believes that this new machine learning approach could improve cancer diagnosis and care.
“Our method would facilitate a more extensive range of research on analyzing histopathology images that were previously not possible due to the lack of detailed annotations,” Hassanpour concluded.
“Clinical deployment of such systems could assist pathologists in reading histopathology slides more accurately and efficiently, which is a critical task for the cancer diagnosis, predicting prognosis, and treatment of cancer patients.”
In future work, the team plans to further validate the model by testing it on data from other institutions and running prospective clinical trials. Additionally, the group will apply the method to histological images of other types of tumors and lesions that have limited training data.