Deep learning can predict lymph node status directly from histology in colorectal cancer - Kiehl, Lennard; Kuntz, Sara; Höhn, Julia; Jutzi, Tanja; Krieghoff-Henning, Eva; Kather, Jakob Nikolas; Holland-Letz, Tim; Kopp-Schneider, Annette; Chang-Claude, Jenny; Brobeil, Alexander; Kalle, Christof von; Fröhling, Stefan; et al. (European journal of cancer, 2021)
Background - Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). - Objectives - The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). - Methods - Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. - Results - On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. - Conclusion - Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.
Gesehen am 22.01.2022