A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images

Nikita Shvetsov; Morten Grønnesby; Edvard Pedersen; Kajsa Møllersen; Lill-Tove Rasmussen Busund; Ruth Schwienbacher; Lars Ailo Bongo; Thomas Karsten Kilvaer

doi:10.3390/cancers14122974

A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images

Cancers (Basel). 2022 Jun 16;14(12):2974. doi: 10.3390/cancers14122974.

Authors

Nikita Shvetsov¹, Morten Grønnesby², Edvard Pedersen¹, Kajsa Møllersen³, Lill-Tove Rasmussen Busund^{2

4}, Ruth Schwienbacher^{2

4}, Lars Ailo Bongo¹, Thomas Karsten Kilvaer^{5

6}

Affiliations

¹ Department of Computer Science, UiT The Arctic University of Norway, N-9038 Tromsø, Norway.
² Department of Medical Biology, UiT The Arctic University of Norway, N-9038 Tromsø, Norway.
³ Department of Community Medicine, UiT The Arctic University of Norway, N-9038 Tromsø, Norway.
⁴ Department of Clinical Pathology, University Hospital of North Norway, N-9038 Tromsø, Norway.
⁵ Department of Oncology, University Hospital of North Norway, N-9038 Tromsø, Norway.
⁶ Department of Clinical Medicine, UiT The Arctic University of Norway, N-9038 Tromsø, Norway.

Abstract

Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17-0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15-0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14-0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.

Keywords: NSCLC; deep learning; digital pathology; tumor-infiltrating lymphocytes.

Grants and funding

This work was funded by the Research Council of Norway, grant no. 309439, SFI Visual Intelligence, and the North Norwegian Health Authority, grant no. HNF1521-20.