A novel validated real-world dataset for the diagnosis of multi-class serous effusion cytology according to TIS and ground-truth validation data.

Researchers

Journal

Modalities

Models

Abstract

The application of AI algorithms in serous fluid cytology is lacking due to the deficiency in standardized publicly available datasets. Here, we develop a novel public serous effusion cytology dataset. Furthermore, we apply AI algorithms on it to test its diagnostic utility and safety in clinical practice.The work is divided into three phases. Phase 1 entails building the dataset based on the multi-tiered evidence-based classification system proposed by the international system (TIS) of serous fluid cytology along with ground truth tissue diagnosis for malignancy. To ensure reliable results of future AI research on this dataset, we carefully consider all the steps of the preparation and staining from a real-world cytopathology perspective. In Phase 2, we pay special consideration to the image acquisition pipeline to ensure image integrity. Then we utilize the power of transfer learning using the convolutional layers of the VGG16 deep learning model for feature extraction Finally, in Phase 3, we apply the random forest classifier on the constructed dataset.The dataset comprises 3731 images distributed among the four TIS diagnostic categories. The model achieves 74 % accuracy in this multiclass classification problem. Using a one versus all classifier, the fall-out rate for images that are misclassified as negative for malignancy despite being a higher risk diagnosis is 0.13. Most of these misclassified images (77%) belong to the atypia of undetermined significance category in concordance with real-life statistics.This is the first and largest publicly available serous fluid cytology dataset based on a standardized diagnostic system. It is also the first dataset to include various types of effusions and is the first dataset to include pericardial fluid specimens. In addition, it is the first dataset to include the diagnostically challenging atypical categories. AI algorithms applied on this novel dataset show reliable results that can incorporated in actual clinical practice with minimal risk of missing a diagnosis of malignancy. This work provides a foundation for researchers to develop and test further AI algorithms for the diagnosis of serous effusions.S. Karger AG, Basel.