Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911292876390400 |
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| author | Shvetsov, Nikita Sildnes, Anders Tafavvoghi, Masoud Busund, Lill-Tove Rasmussen Dalen, Stig Møllersen, Kajsa Bongo, Lars Ailo Kilvaer, Thomas K. |
| author_facet | Shvetsov, Nikita Sildnes, Anders Tafavvoghi, Masoud Busund, Lill-Tove Rasmussen Dalen, Stig Møllersen, Kajsa Bongo, Lars Ailo Kilvaer, Thomas K. |
| contents | Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide images (WSIs) is laborious and subject to variability, potentially undermining patient outcomes. Our study introduces an automated pipeline that utilizes semi-stochastic patch sampling, patch classification to retain prognostically relevant patches, and cell quantification using the HoVer-Net model to streamline the TIL evaluation process. This pipeline efficiently excludes approximately 70% of areas not relevant for prognosis and requires only 5% of the remaining patches to maintain prognostic accuracy (c-index = 0.65). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score's strong association with patient survival, which outperforms traditional CD8 IHC scoring methods. While the pipeline demonstrates potential for enhancing NSCLC prognostication and personalization of treatment, comprehensive clinical validation is still required. Future research should focus on verifying its broader clinical utility and investigating additional biomarkers to improve NSCLC prognosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer Shvetsov, Nikita Sildnes, Anders Tafavvoghi, Masoud Busund, Lill-Tove Rasmussen Dalen, Stig Møllersen, Kajsa Bongo, Lars Ailo Kilvaer, Thomas K. Computer Vision and Pattern Recognition 68T07 I.4.6; I.4.9; J.3 Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide images (WSIs) is laborious and subject to variability, potentially undermining patient outcomes. Our study introduces an automated pipeline that utilizes semi-stochastic patch sampling, patch classification to retain prognostically relevant patches, and cell quantification using the HoVer-Net model to streamline the TIL evaluation process. This pipeline efficiently excludes approximately 70% of areas not relevant for prognosis and requires only 5% of the remaining patches to maintain prognostic accuracy (c-index = 0.65). The computational efficiency achieved does not sacrifice prognostic accuracy, as demonstrated by the TILs score's strong association with patient survival, which outperforms traditional CD8 IHC scoring methods. While the pipeline demonstrates potential for enhancing NSCLC prognostication and personalization of treatment, comprehensive clinical validation is still required. Future research should focus on verifying its broader clinical utility and investigating additional biomarkers to improve NSCLC prognosis. |
| title | Fast TILs -- A Pipeline for Efficient TILs Estimation in Non-Small Cell Lung Cancer |
| topic | Computer Vision and Pattern Recognition 68T07 I.4.6; I.4.9; J.3 |
| url | https://arxiv.org/abs/2405.02913 |