Department of Pathology, IMS & SUM Hospital, Bhubaneswar, India.
Adobe Systems, Noida, India.
Diagn Cytopathol. 2024 Nov;52(11):679-686. doi: 10.1002/dc.25382. Epub 2024 Jul 15.
Cytological analysis of effusion specimens provides critical information regarding the diagnosis and staging of malignancies, thus guiding their treatment and subsequent monitoring. Keeping in view the challenges encountered in the morphological interpretation, we explored convolutional neural networks (CNNs) as an important tool for the cytological diagnosis of malignant effusions.
A retrospective review of patients at our institute, over 3.5 years yielded a dataset of 342 effusion samples and 518 images with known diagnoses. Cytological examination and cell block preparation were performed to establish correlation with the gold standard, histopathology. We developed a deep learning model using PyTorch, fine-tuned it on a labelled dataset, and evaluated its diagnostic performance using test samples.
The model exhibited encouraging results in the distinction of benign and malignant effusions with area under curve (AUC) of 0.8674, F-measure or F1 score which denotes the harmonic mean of precision and recall, to be 0.8678 thus, demonstrating optimal accuracy of our CNN model.
The study highlights the promising potential of transfer learning in enhancing the clinical pathology laboratory efficiency when dealing with malignant effusions.
细胞学分析积液标本可为恶性肿瘤的诊断和分期提供关键信息，从而指导其治疗和后续监测。鉴于在形态学解释方面遇到的挑战，我们探讨了卷积神经网络（CNN）作为恶性积液细胞学诊断的重要工具。
对我院 3.5 年以上的患者进行回顾性研究，获得了 342 例积液样本和 518 张已知诊断的图像数据集。进行细胞学检查和细胞块制备，与金标准（组织病理学）建立相关性。我们使用 PyTorch 开发了一个深度学习模型，在标记数据集上进行微调，并使用测试样本评估其诊断性能。
该模型在区分良性和恶性积液方面表现出令人鼓舞的结果，曲线下面积（AUC）为 0.8674，F 度量或 F1 分数表示精度和召回率的调和平均值，为 0.8678，因此，展示了我们的 CNN 模型的最佳准确性。
该研究强调了迁移学习在处理恶性积液时提高临床病理实验室效率的有前途的潜力。
