Computational & Structural Biotechnology: Cell type prediction with neighborhood-enhanced cellular embedding using deep learning on hematoxylin and eosin-stained images (Chen Lab)

This study aimed to predict the cell types that infiltrate the tumor microenvironment using hematoxylin and eosin-stained images from colon cancer and breast cancer samples.

Two datasets, one focused on colon cancer and the other on breast cancer, were used to develop deep learning models. Cell segmentation was performed using Stardist, followed by the K-Nearest Neighbor method to construct a neighborhood-enhanced cellular extraction matrix for model training. Transductive semi-supervised learning was applied to the breast cancer dataset, where the Base-4 model was trained on S1 and S2 samples and subsequently used to generate assigned labels for the S3, S4, and S5 sets, on which the Base-4+ model was trained.

The Base-7 model trained on colon cancer cell images achieved an accuracy of 0.85 on the hold-out test set and 0.74 on the independent test set, with six neighboring cells identified as the optimal condition for prediction. In addition, the Base-4 model achieved a prediction accuracy of 0.69 with four neighboring cells as the optimal condition in the breast cancer dataset. In contrast, the Base-4+ model reached an accuracy of up to 0.93 on the validation set. The model also captured invasive and ductal carcinoma cells with overall agreement relative to spot-based cell types (0.63).

Deep learning models accurately predicted cell types in breast and colon cancer datasets using only cell morphology and neighborhood embedding.