New algorithm saves time for brain cancer patients
Our expert's opinion:
"When the subject is brain tumor treatment, time is of the essence. Researchers from Osaka University are using new technologies to discover, predict and classify genetic mutations in patients with brain tumors. Classic MRI differentiate the mutations by their size, shape and intensity but the new thing would be to gather the data, use algorithm and machine-learning on a lot of images to understand what kind of mutations the Glioma patients has. This is very promising as it could mean showing the patients what is the best treatments and all of this way faster than through a surgical sampling (which is needed for brain cancer and takes a lot of time). I think that this research is very promising and I’ll always be amazed when hearing what computers can help us achieve in the healthcare sector.
What is your take on this ? Should the Medical sector invest even more into new technologies ?"
- Antoine Desprez, Associate Consultant
New method uses MRI, machine learning to forecast genetic mutations in glioma tumors
Researchers at Osaka University have developed a computer method that uses magnetic resonance imaging (MRI) and machine learning to rapidly forecast genetic mutations in glioma tumors, which occur in the brain or spine. The work may help glioma patients to receive more suitable treatment faster, giving better outcomes. The research was recently published in Scientific Reports.
Cancer treatment has undergone a revolution in recent years. Spurred by recognition that each cancer case is unique, the specific genetic mutations tumor cells carry are now sequenced to discover which chemotherapy drugs will work best. However, certain types of cancer, especially brain tumors, are less accessible for genetic testing. The tumor's genotype can't be found until a sample is taken during surgery, and this can significantly delay treatment.
Glioma is a type of cancer that originates in the brain's supporting cells. Two types of mutations are especially important; these are changes in the gene for the enzyme isocitrate dehydrogenase (IDH) or the promoter region of telomerase (TERT). Identifying these mutations can help direct the proper course of treatment. The researchers produced a machine-learning algorithm that can predict which mutations are present using only the MR images of the tumors.
"Machine learning is increasingly used to diagnose medical images. But our work is one of the first to even attempt to classify something as hidden as the genotype based on image data alone."
Ryohei Fukuma, Researcher at Osaka university
The algorithm was found to be significantly better at predicting the mutations compared with conventionally used radiomic features of the MR images, such as size, shape, and intensity.
To construct the algorithm, the researchers used a convolutional neural network to extract features from the MR images. Then, using a machine-learning method called support vector machines, they classified the patients into groups based on the presence or absence of mutations.
"We hope to expand this approach to other types of cancer, so we can take advantage of the large cancer gene databases already collected"
Haruhiko Kishima, senior author research
The end result could remove the need for surgical tissue sampling. Even more, it could lead to better clinical outcomes for patients as the process of delivering personalized medicine becomes easier and faster.
Source: news medical
