Neural network can determine lung cancer severity

Computed tomography (CT) is a major diagnostic tool for assessment of lung cancer in patients. Now NIBIB-funded researchers at Stanford University have created an artificial neural network that analyzes lung CT scans to provide information about lung cancer severity that can guide treatment options.

Photo
Visualization of LungNet predictions in two dimensional space showed that patients in the high-risk group are clustered away from those in the low-risk group. Lesions of low-risk patients were characterized by regular and uniform shaped nodules. Nodules of patients in the high-risk group had sharp and irregular margins.
Source: Mukherjee, et al., Nature Machine Intelligence, May-2020

CT imaging is an important diagnostic tool to measure the location, extent, size, and shape of lung lesions used to guide therapeutic decisions for patients with lung cancer—the most common fatal malignancy in adults worldwide. However, analysis of CT images is limited to what is visible to the human eye, and reader variability results in differences in clinical care across different oncology centers.

A multidisciplinary group of experts in biomedical informatics, radiology, data science, electrical engineering, and radiation oncology have teamed up to create a machine learning neural network called LungNet designed to obtain consistent, fast, and accurate information from lung CT scans from patients. The group worked with scans from adults with non-small cell lung cancer (NSCLC), which accounts for 85% of lung cancer diagnoses.

"LungNet demonstrates the benefits of designing and training machine learning tools directly on medical images from patients," said Qi Duan, Ph.D., director of the NIBIB Program in Image Processing, Visual Perception and Display. "This is an outstanding example of how machine learning technology can be a cost-effective approach to advance disease detection, diagnosis, and treatment."

The research group was led by Olivier Gevaert, Assistant Professor of Medicine in Biomedical Informatics Research, at Stanford who specializes in the development of machine learning methods for biomedical decision support using multi-scale biomedical data. "Quantitative image analysis has demonstrated that radiological images, such as CT scans of patients with lung cancer, contain more minable information than what is observed by radiologists," explained Gevaert. "Using CT image datasets from several different oncology clinics, we set out to determine whether our neural network could be trained to accurately and reproducibly analyze the scans and deliver consistent, useful clinical information."

The neural network called LungNet was trained and evaluated on four independent cohorts of patients with NSCLC from four medical centers, with each center averaging several hundred patients. The LungNet analysis accurately predicted overall survival in all four patient groups. LungNet also accurately classified benign versus malignant nodules and was able to further stratify nodules regarding cancer progression.

The research team expects LungNet to be extremely valuable for not only classifying benign versus malignant tumors but also for stratifying patients into low, medium, and high risk groups. This allows for intensified treatment for patients in the high-risk group as well as reducing unnecessary treatments for patients in the low-risk group.

Subscribe to our newsletter

Related articles

AI used to identify different types of brain injuries

AI used to identify different types of brain injuries

Researchers have developed an AI algorithm that can detect and identify different types of brain injuries.

ConvPath software uses AI to identify cancer cells

ConvPath software uses AI to identify cancer cells

A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.

3D models used in assessing coronary artery disease

3D models used in assessing coronary artery disease

Researchers announce critical advances in the use of 3D-printed coronary phantoms with diagnostic software, further developing a non-invasive diagnostic method for Coronary Artery Disease risk assessment.

Deep learning system automatically detects diseases

Deep learning system automatically detects diseases

Patients could soon get faster and more accurate diagnoses with new software that can automatically detect signs of diabetes, heart disease and cancer from medical images.

AI improves stroke and dementia diagnosis in brain scans

AI improves stroke and dementia diagnosis in brain scans

Machine learning has detected one of the commonest causes of dementia and stroke, in CT brain scans, more accurately than current methods.

AI system to prevent imaging cyberthreats

AI system to prevent imaging cyberthreats

Researchers have developed an AI technique that will protect medical devices from malicious operating instructions in a cyberattack.

AI finds patterns of mutations in tumour images

AI finds patterns of mutations in tumour images

Researchers have developed an AI algorithm that uses computer vision to analyze tissue samples from cancer patients.

The next-gen 'smart pill' for cancer detection

The next-gen 'smart pill' for cancer detection

Researchers are developing a pill-sized capsule that uses AI-enabled sensing to diagnose gastrointestinal cancers and bleeding earlier.

Neural networks helps grow artificial organs

Neural networks helps grow artificial organs

Researchers have developed a neural network capable of recognizing retinal tissues during the process of their differentiation in a dish.

Popular articles