What is it about?
In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas, a type of brain tumor, are classified into astrocytoma, oligodendroglioma, and glioblastoma. Astrocytoma and oligodendroglioma are also called low grade glioma (LGG), and glioblastoma is also called glioblastoma multiforme (GBM). LGG patients frequently have isocitrate dehydrogenase (IDH) mutations. Patients with IDH mutations have been reported to have a better prognosis than patients without IDH mutations. Therefore, IDH mutations are an essential indicator for the classification of glioma. In this paper, we aimed to classify the presence or absence of the IDH1 mutation using WSIs and clinical data of glioma patients. Ensemble learning between the WSIs model and the clinical data model is used to classify the presence or absence of IDH1 mutation.
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Why is it important?
Our experimental results indicate that the overall accuracy of the AI models can be improved by using both clinical data and images.
Perspectives
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Surya Prasath
Cincinnati Children's Hospital Medical Center
Read the Original
This page is a summary of: Deep learning-based IDH1 gene mutation prediction using histopathological imaging and clinical data, Computers in Biology and Medicine, September 2024, Elsevier,
DOI: 10.1016/j.compbiomed.2024.108902.
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