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Cited by 89Head and Neck Cancer StudiesEsophageal Cancer Research and TreatmentBreast Cancer Treatment Studies

Development of a machine learning model for survival risk stratification of patients with advanced oral cancer

Open access
Tseng, Yi-Ju, Wang, Hsin-Yao, Lin, Ting-Wei, Lu, Jang-Jih, Hsieh, Chia-Hsun, Liao, Chun-Ta·2020·JAMA Network Open
DOI 10.1001/jamanetworkopen.2020.11768PMC PMC7442932
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Abstract

Importance: A tool for precisely stratifying postoperative patients with advanced oral cancer is crucial for the treatment plan, such as intensifying or deintensifying the regimen to improve their quality of life and prognosis. Objective: To develop and validate a machine learning-based algorithm that can provide survival risk stratification for patients with advanced oral cancer who have comprehensive clinicopathologic and genetic data. Design, Setting, and Participants: In this prognostic cohort study, the elastic net penalized Cox proportional hazards regression-based risk stratification model was developed and validated using single-center data collected between January 1, 1996, and December 31, 2011. In total, comprehensive clinicopathologic and genetic data (including clinical, pathologic, and 44 cancer-related gene variant profiles) of 334 patients with stage III or IV oral squamous cell carcinoma were used to develop and validate the algorithm in this 15-year cohort study. Data analysis was conducted between February 1, 2018, and May 6, 2020. Main Outcomes and Measures: The main outcomes were cancer-specific survival, distant metastasis-free survival, and locoregional recurrence-free survival. Model performance was compared in terms of the Akaike information criterion and the Harrell concordance index (C index). Results: Complete data were available for 334 patients (315 men; median age at onset, 48 years [interquartile range, 42-56 years]). The predictive models using comprehensive clinicopathologic and genetic data outperformed those using clinicopathologic data alone. In the groups of postoperative patients receiving adjuvant concurrent chemoradiotherapy, the models demonstrated higher classification performance than those using clinicopathologic data alone in cancer-specific survival (mean [SD] C index, 0.689 [0.050] vs 0.673 [0.051]; P = .02) and locoregional recurrence-free survival (mean [SD] C index, 0.693 [0.039] vs 0.678 [0.035]; P = .004). The classification performance in distant metastasis-free survival was not different (mean [SD] C index, 0.702 [0.056] vs 0.688 [0.048]; P = .09). Conclusions and Relevance: A risk stratification model using comprehensive clinicopathologic and genetic data accurately differentiated the high-risk group from the low-risk group in cancer-specific survival and locoregional recurrence-free survival for postoperative patients with advanced oral cancer. This algorithm could be used through an online calculator to provide additional personalized information for postoperative management of patients with advanced oral squamous cell carcinoma.

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  • Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated.

    “Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16, 20–25).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [20]

  • ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.

    “These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [20]

  • Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.

    “Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [20]

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Authors
Tseng, Y.-J., Wang, H.-Y., Lin, T.-W., Lu, J.-J., Hsieh, C.-H., & Liao, C.-T.
Affiliations
  • Chang Gung University (TW)
  • Chang Gung Memorial Hospital (TW)
Corresponding author
Hsieh, Chia-Hsun
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2020-08-21
Volume
3
Issue
8
Pages
e2011768
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2020.11768
PMC
PMC7442932
OpenAlex
W3081120827
Cited by
89
References
37
Citations by year
2026: 5 · 2025: 19 · 2024: 23 · 2023: 18 · 2022: 12 · 2021: 11 · 2020: 1
FWCI
8.45
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JAMA Network Open (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Otorhinolaryngology › Head and Neck Cancer Studies
  • Health Sciences › Medicine › Surgery › Esophageal Cancer Research and Treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
oral squamous cell carcinoma, advanced oral cancer, survival risk stratification, prognostic model, adjuvant chemoradiotherapy, cancer-specific survival, locoregional recurrence, distant metastasis, postoperative patients, precision oncology, model validation
MeSH terms
Machine Learning, Adult, Female, Humans, Male, Middle Aged, Mouth Neoplasms, Prognosis, Retrospective Studies, Risk Assessment, Disease-Free Survival, Kaplan-Meier Estimate, Chemoradiotherapy, Adjuvant

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