New Delhi: Scientists in India and Japan have developed a highly accurate machine tool to help clinicians choose the most effective treatment strategy for patients with glioma brain tumour.
A new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98 per cent accuracy, researchers reported in the journal IEEE Access.
“Our method outperformed other state-of-the-art approaches for predicting glioma grades from brain MRI scans. This is quite considerable,” said study researcher Balasubramanian Raman from the Indian Institute of Technology Roorkee (IIT-R).
According to the researchers, gliomas are a common type of brain tumour affecting glial cells, which provide support and insulation for neurons.
The treatment varies depending on the tumour”s aggressiveness, so it”s important to get the diagnosis right for each individual. Radiologists obtain a very large amount of data from MRI scans to reconstruct a 3D image of the scanned tissue.
Much of the data available in the MRI scans cannot be detected by the naked eye, such as details related to the tumour shape, texture, or the image”s intensity. Artificial intelligence (AI) algorithms help extract this data.
Medical oncologists have been using this approach, called radiomics, to improve patient diagnoses, but its accuracy still needs to be enhanced.
For the current study, Kyoto University”s Institute for Integrated Cell-Material Sciences (iCeMS, Japan) bioengineer Ganesh Pandian Namasivayam collaborated with Indian data scientist Raman to develop a machine learning approach that can classify gliomas into low or high grade with 97.54 per cent accuracy.
The choice of patient treatment largely depends on being able to determine glioma”s grading. The team, including Rahul Kumar, Ankur Gupta and Harkirat Singh Arora, used a dataset from MRI scans belonging to 210 people with high-grade glioma and another 75 with low-grade glioma.
They developed an approach called CGHF, which stands for: A computational decision support system for glioma classification using hybrid radiomics and stationary wavelet-based features.
They chose specific algorithms for extracting features from some of the MRI scans and then trained another predictive algorithm to process this data and classify the gliomas. They then tested their model on the rest of the MRI scans to assess its accuracy.
The method outperformed other approaches for predicting glioma grades from brain MRI scans.
“We hope AI helps develop a semi-automatic or automatic machine predictive software model that can help doctors, radiologists, and other medical practitioners tailor the best approaches for their individual patients,” Ganesh said.