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New machine learning model classifies lung cancer slides as accurately as doctors

Credit: Hassanpour Lab, Dartmouth’s Norris Cotton Cancer Center

Researchers from Dartmouth’s Norris Cotton Cancer Center have utilised machine learning to visually grade tumour patterns and subtypes of lung adenocarcinoma.

This classification plays an important role in prognosis and determination of treatment for lung cancer, but is a subjective and challenging task for scientists, given the unpredictability of cancerous cells.

The team led by Saeed Hassanpour, PhD, developed a deep neural network to classify different types of lung adenocarcinoma on histopathology slides and found that the model performed as well as three practising pathologists.

“Our study demonstrates that machine learning can achieve high performance on a challenging image classification task and has the potential to be an asset to lung cancer management,” Hassanpour said.

“Clinical implementation of our system would be able to assist pathologists for accurate classification of lung cancer subtypes, which is critical for prognosis and treatment.”

But the research results could also apply to other histopathology image analysis tasks, according to Hassanpour. The team plans to use the method to other challenging histopathology image analysis tasks in breast, oesophagal, and colorectal cancer.

“If validated through clinical trials, our neural network model can potentially be implemented in clinical practice to assist pathologists,” says Hassanpour. “Our machine learning method is also fast and can process a slide in less than one minute, so it could help triage patients before examination by physicians and potentially greatly assist pathologists in the visual examination of slides.”

Because of the many potential applications of the research, Hassanpour’s team also made their code publicly available to promote new research and collaborations in this domain.

The results of the study have been shown in a paper called “Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks”, which was published in Nature’s Scientific Reports site.


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