Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms

Mina Umemoto
ORCID logo ,
Tasuku Mariya
* ,
Yuta Nambu
,
Mai Nagata
,
Toshihiro Horimai
,
Shintaro Sugita
,
Takayuki Kanaseki
,
Yuka Takenaka
,
Shota Shinkai
,
Motoki Matsuura
,
Masahiro Iwasaki
,
Yoshihiko Hirohashi
ORCID logo ,
Tadashi Hasegawa
,
Toshihiko Torigoe
ORCID logo ,
Yuichi Fujino
,
Tsuyoshi Saito
Version 1 : Received: 7 March 2024 / Approved: 7 March 2024 / Online: 8 March 2024 (09:24:54 CET)

How to cite: Umemoto, M.; Mariya, T.; Nambu, Y.; Nagata, M.; Horimai, T.; Sugita, S.; Kanaseki, T.; Takenaka, Y.; Shinkai, S.; Matsuura, M.; Iwasaki, M.; Hirohashi, Y.; Hasegawa, T.; Torigoe, T.; Fujino, Y.; Saito, T. Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms. Preprints 2024, 2024030474. https://doi.org/10.20944/preprints202403.0474.v1 Umemoto, M.; Mariya, T.; Nambu, Y.; Nagata, M.; Horimai, T.; Sugita, S.; Kanaseki, T.; Takenaka, Y.; Shinkai, S.; Matsuura, M.; Iwasaki, M.; Hirohashi, Y.; Hasegawa, T.; Torigoe, T.; Fujino, Y.; Saito, T. Prediction of Mismatch Repair Status in Endometrial Cancer from Histological Slide Images Using Various Deep Learning-Based Algorithms. Preprints 2024, 2024030474. https://doi.org/10.20944/preprints202403.0474.v1

Abstract

The application of deep learning algorithms to predict the molecular profiles of various cancers from digital images of hematoxylin and eosin (H&E)-stained slides has been reported in recent years, mainly in gastric and colon cancers. In this study, we investigated the potential use of H&E-stained endometrial cancer slide images to predict the associated mismatch repair (MMR) status. H&E-stained slide images were collected from 127 cases of the primary lesion of endometrial cancer. After digitization using a Nanozoomer virtual slide scanner (Hamamatsu Photonics), we segmented the scanned images into 5,397 tiles of 512 × 512 pixels. The MMR proteins (PMS2, MSH6) were immunohistochemically stained, classified into MMR proficient/deficient, and annotated for each case and tile. We trained several neural networks, including convolutional and attention-based networks, using tiles annotated with the MMR status. Among the tested networks, ResNet50 exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.91 for predicting the MMR status. The constructed prediction algorithm may be applicable to other molecular profiles and useful for pre-screening before implementing other, more costly genetic profiling tests.

Keywords

endometrial cancer; deep learning; artificial intelligence; biomarker; mismatch repair; molecular classification; whole slide imaging; digital pathology

Subject

Medicine and Pharmacology, Obstetrics and Gynaecology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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