| Abstract |
Predicting 1p/19q co-deletion status in patients with lower-grade Gliomas using convolutional neural networks (CNNs) has attracted a lot of interest because of the potential influence it might have on personalized diagnosis, treatment planning, and prognosis evaluation. To reliably predict 1p/19q codeletion status, this research compares the performance of several CNN models and the combination of MRI sequences. Results show that many CNN models do well in training and testing, including ResNet50, VGG16, InceptionV3, DenseNet201, ResNet152, MobileNetV2, and EfficientNet-B0. Diagnostic accuracy is further enhanced by combining various MRI sequences, T1Gd and T2. The excellent levels of sensitivity, specificity, accuracy, and F1 score attained by CNN models are also highlighted in the paper, demonstrating their potential therapeutic relevance. However, there are cautions about the accessibility of datasets, the ease of interpretation, and ethical concerns. Clinical confirmation, incorporation into clinical practice, and further follow-up investigations are all suggested next steps. Multi-center research, AI with an explanation of its actions, the integration of new types of data, and decision support systems that operate in real time are all promising avenues for future study. CNNs show potential for enhancing patient treatment in the setting of lower-grade Gliomas by reliably predicting 1p/19q co-deletion status.
Keywords: CNNs, 1p/19q Co-Deletion Status, Lower-Grade Glioma, Medical Imaging Analysis, Personalized Diagnosis, Treatment Planning, Prognosis Assessment.
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