Ozer Erdener, Bilecen Ali Enver, Ozer Nur Basak, Yanikoglu Berrin
Department of Pathology, Dokuz Eylul University School of Medicine, Izmir, Turkey.
Division of Anatomical Pathology, Sidra Medicine and Research Center, Doha, Qatar.
Cytopathology. 2023 Mar;34(2):113-119. doi: 10.1111/cyt.13192. Epub 2022 Dec 25.
Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncology. Frozen section slides and cytological preparations provide architectural and cellular information that is analysed by pathologists to reach an intraoperative diagnosis. Progress in the fields of artificial intelligence and machine learning means that AI systems have significant potential for the provision of highly accurate real-time diagnosis in cytopathology.
To investigate the efficiency of machine-learning models in the intraoperative cytological diagnosis of CNS tumours.
We trained a deep neural network to classify biopsy material for intraoperative tissue diagnosis of four major brain lesions. Overall, 205 medical images were obtained from squash smear slides of histologically correlated cases, with 18 high-grade and 11 low-grade gliomas, 17 metastatic carcinomas, and 9 non-neoplastic pathological brain tissue samples. The neural network model was trained and evaluated using 5-fold cross-validation.
The model achieved 95% and 97% diagnostic accuracy in the patch-level classification and patient-level classification tasks, respectively.
We conclude that deep learning-based classification of cytological preparations may be a promising complementary method for the rapid and accurate intraoperative diagnosis of CNS tumours.
中枢神经系统（CNS）肿瘤的术中病理诊断对于神经肿瘤学患者管理方案的制定至关重要。冰冻切片和细胞学标本可提供结构和细胞信息，病理学家通过分析这些信息来做出术中诊断。人工智能和机器学习领域的进展意味着人工智能系统在细胞病理学中提供高度准确的实时诊断方面具有巨大潜力。
研究机器学习模型在中枢神经系统肿瘤术中细胞学诊断中的效率。
我们训练了一个深度神经网络，用于对四种主要脑病变的活检材料进行分类，以进行术中组织诊断。总共从组织学相关病例的压片涂片上获取了205张医学图像，其中包括18例高级别和11例低级别胶质瘤、17例转移性癌以及9例非肿瘤性病理脑组织样本。使用五折交叉验证对神经网络模型进行训练和评估。
该模型在斑块级分类和患者级分类任务中的诊断准确率分别达到了95%和97%。
我们得出结论，基于深度学习的细胞学标本分类可能是一种有前景的辅助方法，用于中枢神经系统肿瘤的快速准确术中诊断。
