Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers
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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_ | 1866915091637600256 |
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| author | Singh, Vivek Chaganti, Shikha Siebert, Matthias Rajesh, Sowmya Puiu, Andrei Gopalan, Raj Gramz, Jamie Comaniciu, Dorin Kamen, Ali |
| author_facet | Singh, Vivek Chaganti, Shikha Siebert, Matthias Rajesh, Sowmya Puiu, Andrei Gopalan, Raj Gramz, Jamie Comaniciu, Dorin Kamen, Ali |
| contents | Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19646 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers Singh, Vivek Chaganti, Shikha Siebert, Matthias Rajesh, Sowmya Puiu, Andrei Gopalan, Raj Gramz, Jamie Comaniciu, Dorin Kamen, Ali Machine Learning Artificial Intelligence Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the screening method and frequency, primarily to optimize resource allocation by targeting screening towards individuals who draw most benefit. For most screening programs, age and clinical risk factors such as family history are part of the initial risk stratification algorithm. In this paper, we focus on developing a blood marker-based risk stratification approach, which could be used to identify patients with elevated cancer risk to be encouraged for taking a diagnostic test or participate in a screening program. We demonstrate that the combination of simple, widely available blood tests, such as complete blood count and complete metabolic panel, could potentially be used to identify patients at risk for colorectal, liver, and lung cancers with areas under the ROC curve of 0.76, 0.85, 0.78, respectively. Furthermore, we hypothesize that such an approach could not only be used as pre-screening risk assessment for individuals but also as population health management tool, for example to better interrogate the cancer risk in certain sub-populations. |
| title | Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.19646 |