Abstract
AB093. Pixel-wise classification of glioma using deep learning for accurate tumour mapping on magnetic resonance imaging
Kiran Aftab1, Salma Asif2, Ansar Rahman2, Ummul Wara1, Faryal Raees1, Ahmad Raza Shahid2, Amna Farrukh1, Ceemal Fareed1, Manal Nasir1, Rabeet Tariq1, Muhammad Sameer1, Meher Angez1, Zeba Saleem2, Komal Naeem1, Muhammad Nouman Mughal1, Fatima Mubarak1, Syed Ather Enam1
1Center of Oncological Research in Surgery (COORS), Aga Khan University, Karachi, Pakistan;
2FAST National University of Computer and Emerging Sciences, Karachi, Pakistan
Correspondence to: Syed Ather Enam, MBBS, PhD. Center of Oncological Research in Surgery (COORS), Aga Khan University, National Stadium Road, Karachi, Sindh 74800, Pakistan. Email: ather.enam@aku.edu.
Background: Central nervous system (CNS) tumours, especially glioma, are a complex disease and many challenges are encountered in their treatment. Artificial intelligence (AI) has made a colossal impact in many walks of life at a low cost. However, this avenue still needs to be explored in healthcare settings, demanding investment of resources towards growth in this area. We aim to develop machine learning (ML) algorithms to facilitate the accurate diagnosis and precise mapping of the brain tumour.
Methods: We queried the data from 2019 to 2022 and brain magnetic resonance imaging (MRI) of glioma patients were extracted. Images that had both T1-contrast and T2-fluid-attenuated inversion recovery (T2-FLAIR) volume sequences available were included. MRI images were annotated by a team supervised by a neuroradiologist. The extracted MRIs thus obtained were then fed to the preprocessing pipeline to extract brains using SynthStrip. They were further fed to the deep learning-based semantic segmentation pipelines using UNet-based architecture with convolutional neural network (CNN) at its backbone. Subsequently, the algorithm was tested to assess the efficacy in the pixel-wise diagnosis of tumours.
Results: In total, 69 samples of low-grade glioma (LGG) were used out of which 62 were used for fine-tuning a pre-trained model trained on brain tumor segmentation (BraTS) 2020 and 7 were used for testing. For the evaluation of the model, the Dice coefficient was used as the metric. The average Dice coefficient on the 7 test samples was 0.94.
Conclusions: With the advent of technology, AI continues to modify our lifestyles. It is critical to adapt this technology in healthcare with the aim of improving the provision of patient care. We present our preliminary data for the use of ML algorithms in the diagnosis and segmentation of glioma. The promising result with comparable accuracy highlights the importance of early adaptation of this nascent technology.
Keywords: Glioma; brain tumor segmentation (BraTS); convolutional neural network (CNN); UNet; magnetic resonance imaging (MRI)
Acknowledgments
We would like to acknowledge the support of Aga Khan University Hospital, and FAST for providing us with facilities to conduct the experiments.
Funding: The study was supported by the HEC-GCF Grant for the Virtual Biopsy for Classification, Outcome Prediction and Treatment Planning of Brain Tumours (ViBCOT) Project.
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cco.amegroups.com/article/view/10.21037/cco-24-ab093/coif). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the Aga Khan University Review Board (2021-6138-19411). Because of the retrospective nature of the research, the requirement for informed consent was waived. But for, prospective cases written/verbal consent is taken.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the noncommercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
Cite this abstract as: Aftab K, Asif S, Rahman A, Wara U, Raees F, Shahid AR, Farrukh A, Fareed C, Nasir M, Tariq R, Sameer M, Angez M, Saleem Z, Naeem K, Mughal MN, Mubarak F, Enam SA. AB093. Pixel-wise classification of glioma using deep learning for accurate tumour mapping on magnetic resonance imaging. Chin Clin Oncol 2024;13(Suppl 1):AB093. doi: 10.21037/cco-24-ab093
