Revolutionizing Brain Tumor Diagnosis: An insight into Convolutional Neural Network Model's Efficacy in Central Nervous System Tumor Analysis with Squash Smear Cytology

Abstract

Background

Intraoperative squash smear cytology is a valuable diagnostic technique for brain tumors. However, its accuracy and availability are often limited by the need for expert neuropathologists. Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), has the potential to revolutionize this diagnostic process by providing rapid and reliable classifications.

Objective

This study aims to develop and evaluate a CNN-based AI model for classifying intraoperative squash smear images of gliomas into high-grade and low-grade tumors.

Methods

A dataset of 10,000 digitally scanned squash smear images (6,000 high-grade and 4,000 low-grade gliomas) was preprocessed and used to train, validate, and test a CNN model. The model's performance was assessed using accuracy, positive predictive value, and F1-score.

Results

The CNN model achieved training and validation accuracies of 96.2 and 96.39%, respectively. On the test dataset, it obtained 91% accuracy for high-grade gliomas and 77% for low-grade gliomas. The model also demonstrated a positive predictive value of 86.6% and an F1-score of 0.887. Additionally, feature visualization techniques provided insights into the model's decision-making process.

Conclusion

The results indicate that deep learning can serve as a reliable intraoperative diagnostic tool for gliomas, offering neurosurgeons rapid decision support. This approach could significantly improve diagnostic accessibility in centers lacking neuropathological expertise, potentially reducing intraoperative delays and enhancing patient care.

Keywords

AI - CNN - Squash - glioma - CNS tumor - Artificial Intelligence - Convolutional Neural Network - neurosurgery - neuro-oncology - neuropathology

^* These authors contributed equally to this work.

Publikationsverlauf

Eingereicht: 06. Februar 2025

Angenommen: 01. März 2025

Accepted Manuscript online:
10. März 2025

Artikel online veröffentlicht:
15. April 2025

• References

• 1 Nair M, Varghese C, Swaminathan R. Cancer: current scenario, intervention strategies and projections for 2015. Burden Disease India 2015
• 2 Yeole BB. Trends in the brain cancer incidence in India. Asian Pac J Cancer Prev 2008; 9 (02) 267-270
• 3 Brain and Other Nervous System Cancer - Cancer Stat Facts. . Accessed January 10, 2018 at: https://seer.cancer.gov/statfacts/html/brain.html
• 4 Emblem KE, Nedregaard B, Hald JK, Nome T, Due-Tonnessen P, Bjornerud A. Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering. J Magn Reson Imaging 2009; 30 (01) 1-10
• 5 Vankdothu R, Hameed MA, Fatima H. A brain tumor identification and classification using deep learning based on CNN-LSTM method. Comput Electr Eng 2022; 101: 107960
• 6 Akter A, Nosheen N, Ahmed S. et al. Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Syst Appl 2024; 238: 122347
• 7 Jindal A, Diwan H, Kaur K, Sinha VD. Intraoperative Squash smear in central nervous system tumors and its correlation with histopathology: 1 year study at a tertiary care centre. J Neurosci Rural Pract 2017; 8 (02) 221-224
• 8 Chilamkurthy S, Ghosh R, Tanamala S. et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 2018; 392 (10162) 2388-2396
• 9 Xu Y, Jia Z, Ai Y, Zhang F, Lai M, Chang EIC. Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2015: 947-951
• 10 Araújo T, Aresta G, Castro E. et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS One 2017; 12 (06) e0177544
• 11 Bora K, Chowdhury M, Mahanta LB, Kundu MK, Das AK. Pap smear image classification using convolutional neural network. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing - ICVGIP '16. ACM Press; 2016: 1-8
• 12 Mitra S, Kumar M, Sharma V, Mukhopadhyay D. Squash preparation: a reliable diagnostic tool in the intraoperative diagnosis of central nervous system tumors. J Cytol 2010; 27 (03) 81-85
• 13 Sanyal P, Mukherjee T, Barui S, Das A, Gangopadhyay P. Artificial intelligence in cytopathology: a neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears. J Pathol Inform 2018; 9: 43
• 14 Carneiro G, Mateus D, Peter L. et al. Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings.
• 15 Rawat W, Wang Z. Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 2017; 29 (09) 2352-2449
• 16 Cappabianca P, Spaziante R, Caputi F. et al. Accuracy of the analysis of multiple small fragments of glial tumors obtained by stereotactic biopsy. Acta Cytol 1991; 35 (05) 505-511
• 17 Roessler K, Dietrich W, Kitz K. High diagnostic accuracy of cytologic smears of central nervous system tumors. A 15-year experience based on 4,172 patients. Acta Cytol 2002; 46 (04) 667-674