A New Machine Learning Model can Classify Lung Cancer Slides at the Pathologist Level

Machine learning has improved dramatically in recent years and shown great promise in the field of medical image analysis. A team of research specialists at Dartmouth's Norris Cotton Cancer Center have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma, the most common form of the leading cause of cancer-related deaths worldwide.

Currently, lung adenocarcinoma, requires pathologist's visual examination of lobectomy slides to determine the tumor patterns and subtypes. This classification has an important role in prognosis and determination of treatment for lung cancer, however is a difficult and subjective task. Using recent advances in machine learning, 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 on par with three practicing 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," says Hassanpour. "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."

The team's conclusions, "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks" are newly published in Scientific Reports. Recognizing that the approach is potentially applicable to other histopathology image analysis tasks, Hassanpour's team made their code publicly available to promote new research and collaborations in this domain.

In addition to testing the deep learning model in a clinical setting to validate its ability to improve lung cancer classification, the team plans to apply the method to other challenging histopathology image analysis tasks in breast, esophageal, 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."

Jason W Wei, Laura J Tafe, Yevgeniy A Linnik, Louis J Vaickus, Naofumi Tomita, Saeed Hassanpour.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.
Scientific Reportsvolume 9, Article number: 3358 (2019). doi: 10.1038/s41598-019-40041-7.

Most Popular Now

AI can Help Improve Emergency Room Admis…

Generative artificial intelligence (AI), such as GPT-4, can help predict whether an emergency room patient needs to be admitted to the hospital even with only minimal training on a limited...

Philips ePatch and AI Analytics Platform…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, announced the successful nationwide rollout of its ambulatory cardiac monitoring service in Spain using its unique wearable ePatch...

Is 'Smart Health Tech' Solving…

Opinion Article by Dr. Paul Deffley, Chief Medical Officer, Alcidion. Where would you position the NHS in relation to other countries, when it comes to the adoption of innovative technologies to...

ChatGPT Extracts Data for Ischaemic Stro…

In an ischaemic stroke, an artery in the brain is blocked by blood clots and the brain cells can no longer be supplied with blood as a result. Doctors must...

Comprehensive Bibliographic Dataset Adva…

A groundbreaking study published in Health Data Science, a Science Partner Journal, introduces a curated bibliographic dataset that aims to revolutionize the landscape of Health Artificial Intelligence (AI) research. Led...

New AI Algorithm may Improve Autoimmune …

A new advanced artificial intelligence (AI) algorithm may lead to better - and earlier - predictions and novel therapies for autoimmune diseases, which involve the immune system mistakenly attacking their...

AI Health Coach Lowers Blood Pressure an…

A new study in JMIR Cardio, published by JMIR Publications, shows that a fully digital, artificial intelligence (AI)-driven lifestyle coaching program can effectively reduce blood pressure (BP) in adults with...

Will Generative AI Change the Way Univer…

Since the launch of ChatGPT 3 in November 2022, we've been abuzz with talk of artificial intelligence: is it an unprecedented opportunity, or will it rob everyone of jobs and...

New Deep Learning Model is 'Game Ch…

Research led by the University of Plymouth has shown that a new deep learning AI model can identify what happens and when during embryonic development, from video. Published in the Journal...

Huge NHS Cloud Deals Mean Tough Question…

Opinion Article by Chris Scarisbrick, Deputy Managing Director, Sectra. The largest public cloud projects to ever take place within the NHS are beginning. Regional procurements for public cloud hosted diagnostic imaging...

A Three-Point Plan for Digital Delivery

Sam Shah has seen health tech policy up-close and worries that little progress has been made over the past five-years. However, he has a plan for any health and social...

AI Tech should Augment Physician Decisio…

The use of artificial intelligence (AI) in clinical health care has the potential to transform health care delivery but it should not replace physician decision-making, says the American College of...