The new machine learning approach classifies a common type of brain tumour into...
The new machine learning approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy.
Source: Mindy Takamiya/Kyoto University iCeMS, CC BY 4.0)
09.06.2020 •

Machine learning enhances brain tumour diagnosis

A new AI approach classifies a common type of brain tumour into low or high grades with almost 98% accuracy, researchers report. Scientists in Japan and India, including from Kyoto University’s Institute for Integrated Cell-Material Sciences (iCeMS), developed the method to help clinicians choose the most effective treatment strategy for individual patients.

Gliomas are a common type of brain tumour affecting glial cells, which provide support and insulation for neurons. Patient 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 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 accuracy still needs to be enhanced.

CeMS bioengineer Ganesh Pandian Namasivayam collaborated with Indian data scientist Balasubramanian Raman from Roorkee to develop a machine learning approach that can classify gliomas into low or high grade with 97.54% accuracy. Low grade gliomas include grade I pilocytic astrocytoma and grade II low-grade glioma. These are the less aggressive and less malignant of the glioma tumours. High grade gliomas include grade III malignant glioma and grade IV glioblastoma multiforme, which are much more aggressive and more malignant with a relatively short post-diagnosis survival time. The choice of patient treatment largely depends on being able to determine the 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 gliomas and another 75 with low grade gliomas. They developed an approach called CGHF, which stands for: 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. “Our method outperformed other state-of-the-art approaches for predicting glioma grades from brain MRI scans,” says Balasubramanian. “This is quite considerable.”

Namasivayam added: “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.”

Subscribe to our newsletter

Related articles

AI & MRI look into the genome of brain tumors

AI & MRI look into the genome of brain tumors

Researcher have developed a computer method that uses MRI and machine learning to rapidly forecast genetic mutations in glioma tumors,

Federated learning allows hospitals to share data privately

Federated learning allows hospitals to share data privately

Researchers have shown that federated learning is successful in the context of brain imaging, by being able to analyze MRI scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

AI and laser-based imaging system identify brain tumors

AI and laser-based imaging system identify brain tumors

A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.

AI predicts effectiveness of immunotherapy

AI predicts effectiveness of immunotherapy

Scientists can determine which lung-cancer patients will benefit from expensive immunotherapy.

AI for very young brains

AI for very young brains

Scientists have developed an innovative new technique that uses artificial intelligence to better define the different sections of the brain in newborns during a magnetic resonance imaging (MRI) exam.

Surgeons successfully treat brain aneurysms using a robot

Surgeons successfully treat brain aneurysms using a robot

Using a robot to treat brain aneurysms is feasible and could allow for improved precision when placing stents, coils and other devices.

Deep learning identifies molecular patterns of cancer

Deep learning identifies molecular patterns of cancer

An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.

Video game to treat children with ADHD

Video game to treat children with ADHD

Researchers have developed advanced brain-computer interface technology that harnesses machine learning to personalise brain-training for children with ADHD.

Machine learning improves diagnostics of head and neck cancers

Machine learning improves diagnostics of head and neck cancers

Researchers used artificial intelligence to develop a new classification method which identifies the primary origins of cancerous tissue based on chemical DNA changes.

Popular articles