A machine learning model for screening of body fluid cytology smears

Abstract

Introduction: Body fluid cytology is one of the commonest investigations performed in indoor patients, both for diagnosis of suspected carcinoma as well as staging of known carcinoma. Carcinoma is diagnosed in body fluids by the pathologist through microscopic examination and searching for malignant epithelial cell clusters. The process of screening body fluid smears is a time consuming and error prone process. Aim: We have attempted to construct a machine learning model which can screen body fluid cytology smears for malignant cells. Materials and methods: MGG stained Ascitic / pleural fluid cytology smears were included from 21 cases (14 malignant, 07 benign) in this study. A total of 693 microphotographs were taken at 40x magnification at the same illumination and after correction of white balance. A Magnus Microphotography system was used for photography. The images were split into the training set (195 images), test set (120 images) and validation set (378 images). A machine learning model, a convolutional neural network, was developed in the Python programming language using the Keras deep learning library. The model was trained with the images of the training set. After completion of training, the model was evaluated on the test set of images. Results: Evaluation of the model on the test set produced a sensitivity of 97.87%, specificity 85.26%, PPV 95.18%, NPV 93.10% In 06 images, the model has failed to detect singly scattered malignant cells/ clusters. 14 (3.7%) false positives was reported by the model. The machine learning model shows potential utility as a screening tool. However, it needs improvement in detecting singly scattered malignant cells and filtering inflammatory infiltrate.

A Novel Validated Real-World Dataset for the Diagnosis of Multiclass Serous Effusion Cytology according to the International System and Ground-Truth Validation Data

“…Studies in the current literature approach dataset preparation in two ways. Most of the studies excluded these categories altogether and included only the benign and MAL cells [9, 10, 12, 14‒16, 18‒22, 24, 25]. In the few studies that included atypical cells, the ultimate goal was to place these cells into benign or MAL classes [11, 13, 17].…”

“…The literature dealing with effusion cytology is limited and cannot be generalized in the routine clinical setting. One of the most notable shortcomings in the literature addressing serous fluid cytology automation is that the datasets are locally prepared and are not publicly available [9][10][11][12][13][14][15][16][17][18][19][20][21]. In addition, these datasets do not follow a clinically reproducible diagnostic system and are not accompanied by ground-truth validation data.…”

“…In addition, these datasets do not follow a clinically reproducible diagnostic system and are not accompanied by ground-truth validation data. Besides, in many instances, these datasets are designed to include only specific regions of interest on the slides [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”