Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

ABSTRACT:

Background

Histopathological growth patterns are one of the strongest prognostic factors in patients with resected colorectal liver metastases. Development of an efficient, objective and ideally automated histopathological growth pattern scoring method can substantially help the implementation of histopathological growth pattern assessment in daily practice and research. This study aimed to develop and validate a deep-learning algorithm, namely neural image compression, to distinguish desmoplastic from non-desmoplastic histopathological growth patterns of colorectal liver metastases based on digital haematoxylin and eosin-stained slides.

Methods

The algorithm was developed using digitalized whole-slide images obtained in a single-centre (Erasmus MC Cancer Institute, the Neth

SUBMITTER: Hoppener DJ 

PROVIDER: S-EPMC11523050 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature