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

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 for gastric and colon cancers. In this study, we investigated the potential use of H&E-...

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Vydané v:Cancers Ročník 16; číslo 10; s. 1810
Hlavní autori: Umemoto, Mina, Mariya, Tasuku, Nambu, Yuta, Nagata, Mai, Horimai, Toshihiro, Sugita, Shintaro, Kanaseki, Takayuki, Takenaka, Yuka, Shinkai, Shota, Matsuura, Motoki, Iwasaki, Masahiro, Hirohashi, Yoshihiko, Hasegawa, Tadashi, Torigoe, Toshihiko, Fujino, Yuichi, Saito, Tsuyoshi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.05.2024
Predmet:
Algorithms
Artificial intelligence
Cancer
Classification
Cloning
Colon cancer
Data mining
Deep learning
Diagnosis
Digitization
Endometrial cancer
Endometrium
Genomes
Ligands
Machine learning
Medical imaging equipment
Methods
Mismatch repair
Monoclonal antibodies
MSH6 protein
Neural networks
Oncology, Experimental
Prognosis
Proteins
Tumors
Uterine cancer
Yeast
Japan
deep learning
digital pathology
endometrial cancer
whole-slide imaging
mismatch repair
biomarker
artificial intelligence
molecular classification
ISSN:2072-6694, 2072-6694
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Shrnutí: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 for 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 5397 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.
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ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16101810