Diagnostic utility of transfer learning by using convolutional neural network for cytological diagnosis of malignant effusions.

Introduction: 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.

Materials and methods: 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.

Results: 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.

Conclusion: The study highlights the promising potential of transfer learning in enhancing the clinical pathology laboratory efficiency when dealing with malignant effusions.