Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence-Reference-Cited by-同舟云学术

Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence

Published:2023-09 Issue:7 Volume: Page:
ISSN:2473-4276
Container-title:JCO Clinical Cancer Informatics
language:en
Short-container-title:JCO Clin Cancer Inform

Author:

Rodriguez Patricia J.¹^ORCID,Heagerty Patrick J.²^ORCID,Clark Samantha¹^ORCID,Khor Sara¹^ORCID,Chen Yilin¹^ORCID,Haupt Eric³^ORCID,Hahn Erin E.³⁴^ORCID,Shankaran Veena⁵^ORCID,Bansal Aasthaa¹⁵^ORCID

Affiliation:

1. The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA

2. Department of Biostatistics, University of Washington, Seattle, WA

3. Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA

4. Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA

5. Fred Hutchinson Cancer Center, Seattle, WA

Abstract

Using machine learning to leverage CEA biomarker change and predict colorectal cancer recurrence.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

Link

https://ascopubs.org/doi/pdfdirect/10.1200/CCI.23.00066

Reference33 articles.

1. Predicting Risk of Recurrence After Colorectal Cancer Surgery in the United States: An Analysis of a Special Commission on Cancer National Study

2. Association Between Intensity of Posttreatment Surveillance Testing and Detection of Recurrence in Patients With Colorectal Cancer

3. Breast and colorectal cancer recurrence and progression captured by five U.S. population-based registries: Findings from National Program of Cancer Registries patient-centered outcome research

4. Predictors of recurrence free survival for patients with stage II and III colon cancer

5. Individualized Prediction of Colon Cancer Recurrence Using a Nomogram

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