Persistent Identifier
|
doi:10.18710/4YN9SZ |
Publication Date
|
2021-04-23 |
Title
| UiT_TILs - Replication Data for "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images" |
Author
| Kilvaer, Thomas K (UiT The Arctic University of Norway) - ORCID: 0000-0003-1669-0117 |
Point of Contact
|
Use email button above to contact.
Kilvaer, Thomas K (UiT The Arctic University of Norway) |
Description
| This dataset can be used to replicate the findings in "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images". The motivation for this paper is that increased levels of tumor infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. Our aim is to leverage computational pathology to automatically quantify TILs in standard diagnostic whole-tissue hematoxylin and eosin stained section slides (H&E slides). Our approach is to transfer an open source machine learning method for segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of our data. Our results show that improved data augmentation improves immune cell detection in H&E WSIs. Moreover, the resulting TIL quantification correlates to patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small 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 Model HR 0.30 95% CI 0.15-0.60). Moreover, we implemented a cloud based system to train, deploy, and visually inspect machine learning based annotation for H&E slides. Our pragmatic approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, validation in prospective studies is needed to assert that the method works in a clinical setting. The dataset is comprised of three parts: 1) Twenty image patches with and without overlays used by pathologists to manually evaluate the output of the deep learning models, 2) The models trained and subsequently used for inference in the paper, 3) the patient dataset with corresponding image patches used to clinically validate the output of the deep learning models. The tissue samples were collected from patients diagnosed between 1993 and 2003. Supplementing information was collected retrospectively in the time period 2006-2017. The images were produced in 2017. (2021-01-19) |
Subject
| Medicine, Health and Life Sciences |
Keyword
| machine learning
ML
deep learning
DL
non-small cell lung cancer
Carcinoma, Non-Small-Cell Lung (MeSH) https://www.ncbi.nlm.nih.gov/mesh/D002289
immune cell
tissue infiltrating lymphycytes
TIL (MeSH) |
Related Publication
| submitted for review |
Language
| English |
Producer
| UiT The Arctic University of Norway (UiT) https://en.uit.no/ |
Production Date
| 2020-01-01 |
Production Location
| Tromso |
Funding Information
| North Norwegian Health Authority: HNF1521-20 |
Distributor
| UiT The Arctic University of Norway (UiT The Arctic University of Norway) https://dataverse.no/dataverse/uit |
Depositor
| Kilvaer, Thomas K |
Deposit Date
| 2021-01-19 |
Time Period
| Start Date: 1993-01-01 ; End Date: 2003-01-01 |
Date of Collection
| Start Date: 2006-01-01 ; End Date: 2017-01-01 |
Data Type
| clinical data; observation data/ratings |