Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study Article Swipe
Chengkun Sun
,
Erin M. Mobley
,
Michael B. Quillen
,
Max Parker
,
Meghan Daly
,
Rui Wang
,
Isabela Visintin
,
Ziad T. Awad
,
Jennifer N. Fishe
,
Alexander S. Parker
,
Thomas J. George
,
Jiang Bian
,
Jie Xu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/64506
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2196/64506
Background Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effective prevention and treatment, particularly for patients below the recommended screening age. Objective Our study aims to predict EOCRC using machine learning (ML) and structured electronic health record data for individuals under the screening age of 45 years, with the aim of exploring potential risk and protective factors that could support early diagnosis. Methods We identified a cohort of patients under the age of 45 years from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (ie, 0, 1, 3, and 5 y) and ensured robustness through propensity score matching to account for confounding variables including sex, race, ethnicity, and birth year. We conducted a comprehensive performance evaluation using metrics including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and F 1 -score. Both linear (ie, logistic regression, support vector machine) and nonlinear (ie, Extreme Gradient Boosting and random forest) models were assessed to enable rigorous comparison across different classification strategies. In addition, we used the Shapley Additive Explanations to interpret the models and identify key risk and protective factors associated with EOCRC. Results The final cohort included 1358 CC cases with 6790 matched controls, and 560 RC cases with 2800 matched controls. The RC group had a more balanced sex distribution (2:3 male-to-female) compared to the CC group (2:5 male-to-female), and both groups showed diverse racial and ethnic representation. Our predictive models demonstrated reasonable results, with AUC scores for CC prediction of 0.811, 0.748, 0.689, and 0.686 at 0, 1, 3, and 5 years before diagnosis, respectively. For RC prediction, AUC scores were 0.829, 0.771, 0.727, and 0.721 across the same time windows. Key predictive features across both cancer types included immune and digestive system disorders, secondary malignancies, and underweight status. In addition, blood diseases emerged as prominent indicators specifically for CC. Conclusions Our findings demonstrate the potential of ML models leveraging electronic health record data to facilitate the early prediction of EOCRC in individuals under 45 years. By uncovering important risk factors and achieving promising predictive performance, this study provides preliminary insights that could inform future efforts toward earlier detection and prevention in younger populations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2196/64506
- OA Status
- gold
- Cited By
- 1
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410946881
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410946881Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2196/64506Digital Object Identifier
- Title
-
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-02Full publication date if available
- Authors
-
Chengkun Sun, Erin M. Mobley, Michael B. Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad T. Awad, Jennifer N. Fishe, Alexander S. Parker, Thomas J. George, Jiang Bian, Jie XuList of authors in order
- Landing page
-
https://doi.org/10.2196/64506Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.2196/64506Direct OA link when available
- Concepts
-
Logistic regression, Medicine, Random forest, Cohort, Machine learning, Confounding, Colorectal cancer, Artificial intelligence, Propensity score matching, Nomogram, Cancer, Oncology, Internal medicine, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | JMIR Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.is_published | True |
| primary_location.raw_source_name | JMIR Cancer |
| primary_location.landing_page_url | https://doi.org/10.2196/64506 |
| publication_date | 2025-06-02 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4322719806, https://openalex.org/W3007297990, https://openalex.org/W4231576061, https://openalex.org/W3216550296, https://openalex.org/W3014031346, https://openalex.org/W2997822868, https://openalex.org/W2987654150, https://openalex.org/W4385420940, https://openalex.org/W3122334537, https://openalex.org/W4223430324, https://openalex.org/W2971130507, https://openalex.org/W4321595817, https://openalex.org/W2998807005, https://openalex.org/W3013783227, https://openalex.org/W3112048136, https://openalex.org/W2979089242, https://openalex.org/W4221116282, https://openalex.org/W2889522041, https://openalex.org/W2765619636, https://openalex.org/W2556625312, https://openalex.org/W4366085127, https://openalex.org/W2988699204, https://openalex.org/W2238461883, https://openalex.org/W3186606013, https://openalex.org/W1998883620, https://openalex.org/W2969967802, https://openalex.org/W2040228556, https://openalex.org/W2923287877, https://openalex.org/W3144015606, https://openalex.org/W3000376411, https://openalex.org/W3032885674, https://openalex.org/W3095990913, https://openalex.org/W2400773242, https://openalex.org/W2127129197, https://openalex.org/W4200212107, https://openalex.org/W4311862982, https://openalex.org/W4206629261, https://openalex.org/W3120118518, https://openalex.org/W2433575101 |
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