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Part 1: Document Description
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Citation |
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Title: |
UiT_TILs - Replication Data for "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images" |
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Identification Number: |
doi:10.18710/4YN9SZ |
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Distributor: |
DataverseNO |
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Date of Distribution: |
2021-04-23 |
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Version: |
2 |
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Bibliographic Citation: |
Kilvaer, Thomas K, 2021, "UiT_TILs - Replication Data for "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images"", https://doi.org/10.18710/4YN9SZ, DataverseNO, V2 |
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Citation |
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Title: |
UiT_TILs - Replication Data for "A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images" |
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Identification Number: |
doi:10.18710/4YN9SZ |
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Authoring Entity: |
Kilvaer, Thomas K (UiT The Arctic University of Norway) |
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Producer: |
UiT The Arctic University of Norway |
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Date of Production: |
2020-01-01 |
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Grant Number: |
HNF1521-20 |
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Distributor: |
DataverseNO |
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Distributor: |
UiT The Arctic University of Norway |
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Access Authority: |
Kilvaer, Thomas K |
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Depositor: |
Kilvaer, Thomas K |
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Date of Deposit: |
2021-01-19 |
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Holdings Information: |
https://doi.org/10.18710/4YN9SZ |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences, machine learning, ML, deep learning, DL, non-small cell lung cancer, Carcinoma, Non-Small-Cell Lung, immune cell, tissue infiltrating lymphycytes, TIL |
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Abstract: |
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. |
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Time Period: |
1993-01-01-2003-01-01 |
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Date of Collection: |
2006-01-01-2017-01-01 |
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Kind of Data: |
clinical data |
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Kind of Data: |
observation data/ratings |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
submitted for review |
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Bibliographic Citation: |
submitted for review |
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0_README.txt |
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text/plain |
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manual_validation.tar.0000 |
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manual_validation.tar.0001 |
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