Research Information System
Artificial Intelligence Theory and Application, vol.1, no.2, pp.191-201, 2021 (Peer-Reviewed Journal)
Introduction-Objectives: In recent years, it has been revealed that the
codeletion of the 1p/19q chromosomal arms in low-grade glioma (LGG)
patients is an important biomarker. Accordingly, codeletion is highly
correlated with a patient’s positive response to treatment (chemotherapy,
radiotherapy) and longer survival. Therefore, it is important to determine
1p/19q codeletion to make the correct treatment planning. For this purpose, a
low complexity, single-step deep learning-based method is proposed that
localizes the tumor region and predicts the codeletion simultaneously.
Materials-Methods: In the study, the lightweight EfficientNet-B0 model is
transformed into a multi-scale single-stage object detector model by inspiring
the YOLO model architecture. Then, the model is trained by using the transfer
learning strategy. Besides, pre-trained models with low complexity, i.e. Tiny
YOLO v2, Tiny YOLO v3, and Tiny YOLO v4, are fine-tuned. Results: All
the models are cross-validated on a public data set consisting of 159 patients
with LGG (codeletion: 102, no-deletion: 57). The data set includes biopsyproven samples. The model performance scores, namely the log-average miss
rate and mean average precision (mAP@[0.5:0.95]), are 0.14 and 40.21%,
respectively. The method predicts codeletion with 90.2% sensitivity in 67ms
inference time. Conclusions: In the study, promising results are obtained in
the prediction of 1p/19q codeletion status by a fully automatic method. The
performance obtained shows that the proposed model can be used as an
assistive diagnostic tool for experts. As the data sets are updated with more
samples, the robustness of the model can be increased by improving the
performance.