Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins

Abstract

Purpose

The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images.

Methods

Tissue blocks with residual MALT lymphoma and data from histological and flow cytometric studies and molecular genetic analyses such as gene rearrangement were procured for 129 patients treated between April 2008 and April 2020. We collected pathological hematoxylin and eosin-stained (HE) images of lymphoma from these patients and cropped 10 different image patches at a resolution of 2048 × 2048 from pathological images from each patient. A total of 990 images from 99 patients were used to create and evaluate machine-learning models. Each image patch of three different magnification rates at ×4, ×20, and ×40 underwent texture analysis to extract features, and then seven different machine-learning algorithms were applied to the results to create models. Cross-validation on a patient-by-patient basis was used to create and evaluate models, and then 300 images from the remaining 30 cases were used to evaluate the average accuracy rate.

Results

Ten-fold cross-validation using the support vector machine with linear kernel algorithm was identified as the best algorithm for discriminating between conjunctival mucosa-associated lymphoid tissue and orbital MALT lymphomas, with an average accuracy rate under cross-validation of 85%. There were ×20 magnification HE images that were more accurate in distinguishing orbital and conjunctival MALT lymphomas among ×4, ×20, and ×40.

Conclusion

Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.

Keywords:

• Conjunctival MALT lymphoma
• orbital MALT lymphoma
• artificial intelligence
• machine learning
• computer-assisted diagnosis

Acknowledgements

We gratefully acknowledge the technical assistance of the Clinical Laboratory Department at Kobe Kaisei Hospital.

Author contributions

MT and MN, as equal first authors, wrote the main text of the manuscript and prepared the figures. Datasets were prepared by AK, NM, AS, and YH. AA and SH reviewed the manuscript and checked the statistical analyses. All authors read and approved the manuscript in its final form.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [M.T.], upon reasonable request.

Additional information

Funding

This work was supported by JSPS KAKENHI Grant Number 23K09013, the Charitable Trust Fund for Ophthalmic Research in Commemoration of Santen Pharmaceutical’s Founder 2022, and 2023 Osaka Community Foundation (M.T.).