Dataset Information

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer.

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.

SUBMITTER: Tseng YJ

PROVIDER: S-EPMC7442932 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer.

Tseng Yi-Ju YJ Wang Hsin-Yao HY Lin Ting-Wei TW Lu Jang-Jih JJ Hsieh Chia-Hsun CH Liao Chun-Ta CT

JAMA network open 20200803 8

<h4>Importance</h4>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.<h4>Objective</h4>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.<h4>Design, setting, and participants</h4>In t ...[more]

PMID: 32821921

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Dataset Information

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer.

Importance

Objective

Design, setting, and participants

Main outcomes and measures

Results

Conclusions and relevance

Publications

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer.

Similar Datasets

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