Unraveling a Histopathological Needle-in-Haystack Problem: Exploring the Challenges of Detecting Tumor Budding in Colorectal Carcinoma Histology

Background: In this study focusing on colorectal carcinoma (CRC), we address the imperative task of predicting post-surgery treatment needs by identifying crucial tumor features within whole slide images of solid tumors, analogous to locating a needle in a histological haystack. We evaluate two appr...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (2), p.949
Main Authors: Rusche, Daniel, Englert, Nils, Runz, Marlen, Hetjens, Svetlana, Langner, Cord, Gaiser, Timo, Weis, Cleo-Aron
Format: Article
Language:English
Subjects:
Biomarkers
budding
Cancer
Carcinoma
Classification
Colorectal cancer
CRC
Datasets
Decision making
Ethics
Histology
histopathology
Hypotheses
Inflammatory bowel disease
Lymphatic system
Morphology
supervised segmentation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background: In this study focusing on colorectal carcinoma (CRC), we address the imperative task of predicting post-surgery treatment needs by identifying crucial tumor features within whole slide images of solid tumors, analogous to locating a needle in a histological haystack. We evaluate two approaches to address this challenge using a small CRC dataset. Methods: First, we explore a conventional tile-level training approach, testing various data augmentation methods to mitigate the memorization effect in a noisy label setting. Second, we examine a multi-instance learning (MIL) approach at the case level, adapting data augmentation techniques to prevent over-fitting in the limited data set context. Results: The tile-level approach proves ineffective due to the limited number of informative image tiles per case. Conversely, the MIL approach demonstrates success for the small dataset when coupled with post-feature vector creation data augmentation techniques. In this setting, the MIL model accurately predicts nodal status corresponding to expert-based budding scores for these cases. Conclusions: This study incorporates data augmentation techniques into a MIL approach, highlighting the effectiveness of the MIL method in detecting predictive factors such as tumor budding, despite the constraints of a limited dataset size.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020949