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L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
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Minhas, Fayyaz, Toss, Michael S., Wahab, Noor ul, Rakha, Emad and Rajpoot, Nasir M. (2022) L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Online, 13-17 Sep 2021. Published in: Proceedings of the ECML PKDD 2021: Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 1525 pp. 390-400. ISBN 9783030937324. doi:10.1007/978-3-030-93733-1_27 ISSN 1865-0929.
An open access version can be found in:
Official URL: http://dx.doi.org/10.1007/978-3-030-93733-1_27
Abstract
“Can we predict if an early stage cancer patient is at high risk of developing distant metastasis and what clinicopathological factors are associated with such a risk?” In this paper, we propose a ranking based censoring-aware machine learning model for answering such questions. The proposed model is able to generate an interpretable formula for risk stratification using a minimal number of clinicopathological covariates through L1-regulrization. Using this approach, we analyze the association of time to distant metastasis (TTDM) with various clinical parameters for early stage, luminal (ER + /HER2-) breast cancer patients who received endocrine therapy but no chemotherapy (n = 728). The TTDM risk stratification formula obtained using the proposed approach is primarily based on mitotic score, histological tumor type and lymphovascular invasion. These findings corroborate with the known role of these covariates in increased risk for distant metastasis. Our analysis shows that the proposed risk stratification formula can discriminate between cases with high and low risk of distant metastasis (p-value < 0.005) and can also rank cases based on their time to distant metastasis with a concordance-index of 0.73.
| Item Type: | Conference Item (Paper) | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
| Series Name: | Communications in Computer and Information Science | ||||
| Journal or Publication Title: | Proceedings of the ECML PKDD 2021: Machine Learning and Principles and Practice of Knowledge Discovery in Databases | ||||
| Publisher: | Springer | ||||
| ISBN: | 9783030937324 | ||||
| ISSN: | 1865-0929 | ||||
| Book Title: | Machine Learning and Principles and Practice of Knowledge Discovery in Databases | ||||
| Official Date: | 1 January 2022 | ||||
| Dates: |
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| Volume: | 1525 | ||||
| Page Range: | pp. 390-400 | ||||
| DOI: | 10.1007/978-3-030-93733-1_27 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Reuse Statement (publisher, data, author rights): | The final authenticated version is available online at https://doi.org/10.1007/978-3-030-93733-1_27. | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Copyright Holders: | Springer Nature Switzerland AG | ||||
| Conference Paper Type: | Paper | ||||
| Title of Event: | Joint European Conference on Machine Learning and Knowledge Discovery in Databases | ||||
| Type of Event: | Conference | ||||
| Location of Event: | Online | ||||
| Date(s) of Event: | 13-17 Sep 2021 | ||||
| Open Access Version: |
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