HookNet-TLS

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About

Summary

HookNet-TLS is a powerful deep-learning algorithm designed for the automated detection of tertiary lymphoid structures (TLS) and germinal centers (GC). The model has been trained on a large set of histology slides from various solid tumors, including clear cell renal cell carcinoma, muscle-invasive bladder cancer, and lung squamous cell carcinoma.

Mechanism

We developed HookNet-TLS, a multi-resolution deep learning model. This model automates the process of TLS quantification and identification of germinal centers in hematoxylin and eosin (H&E) stained digital pathology slides. HookNet-TLS integrates information from multiple image resolutions, mimicking the process a pathologist would use to identify these structures under a microscope. It was developed using 1019 manually annotated slides from The Cancer Genome Atlas (TCGA), encompassing various cancer types. The model's multi-resolution approach allows for a comprehensive analysis of the tissue slides, capturing both the macroscopic and microscopic details necessary for accurate TLS identification.

To use this algorithm, you will need to provide a multiresolution TIFF file that contains 0.5um and 2.0um spacings. If your image is in a different format (e.g. MRXS, SVS, NDPI, etc..), you can use the wholeslidedata package and pyvips to convert it.

Furthermore, as a second input, a tissue background segmentation is required. These can be made with the Tissue-Background Segmentation algorithm

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Interfaces

This algorithm implements all of the following input-output combinations:

Validation and Performance

Please refer to our paper: Multi-resolution deep learning characterizes tertiary lymphoid structures in solid tumors for validation and performance analysis

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

No warnings are currently applicable.

Common Error Messages

No common error messages are currently known.