Deep learning for the standardized classification of Ki-67 in vulva carcinoma: A feasibility study.

ABSTRACT:

Background

The aim of this study is to demonstrate the feasibility of automatic classification of Ki-67 histological immunostainings in patients with squamous cell carcinoma of the vulva using a deep convolutional neural network (dCNN).

Material and methods

For evaluation of the dCNN, we used 55 well characterized squamous cell carcinomas of the vulva in a tissue microarray (TMA) format in this retrospective study. The tumor specimens were classified in 3 different categories C1 (0-2%), C2 (2-20%) and C3 (>20%), representing the relation of the number of KI-67 positive tumor cells to all cancer cells on the TMA spot. Representative areas of the spots were manually labeled by extracting images of 351 × 280 pixels. A dCNN with 13 convolutional layers was used for the evaluatio

SUBMITTER: Choschzick M 

PROVIDER: S-EPMC8346648 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature