好色先生

好色先生

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Abstract Details

Deep learning for prediction of IDH mutation in gliomas- a meta-analysis of evaluation of diagnostic test performance with a Bayesian approach
Neuro-oncology
P1 - Poster Session 1 (9:00 AM-5:00 PM)
427

We aim to evaluate the diagnostic performance of deep learning (DL) for determination of IDH mutation status in gliomas.

Conventionally, identification of IDH mutation in gliomas is based on histopathological analysis of tissue specimen acquired via stereotactic biopsy or definitive resection. Magnetic resonance imaging (MRI) is done routinely in glioma workup. Accurate pre-treatment prediction of IDH mutation status using MRI can guide clinical decision making.

A systematic search of Cochrane Library, Web of Science, Medline, and Embase was conducted to identify relevant publications until 01/08/2021. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess studies' quality. Bayes theorem was utilized to calculate the posttest probability using likelihood ratios and predetermined pretest probabilities.

A total of four studies that included 1295 patients with glioma were included and analyzed in this study. The sample sizes for the training and validation sets were 1007 and 437, respectively. For training sets, using a known pretest probability of 80.2%, Bayes theorem yielded a posttest probability of 97.6% for a positive test and 27% for a negative test. On the other hand, a posttest probability of 96% for a positive test and 30.6% for a negative test were found for validation sets.

DL algorithms demonstrate an excellent diagnostic performance in predicting IDH mutation in gliomas. However, more studies are required to optimize and increase its reliability.

Authors/Disclosures
Mert Karabacak
PRESENTER
Mr. Karabacak has nothing to disclose.
No disclosure on file
Seren Mordag III Ms. Mordag has nothing to disclose.
No disclosure on file