Open AccessA Diagnostic Analysis Workflow to Optimal Multiple Tumor Markers to Predict the Nonmetastatic Breast Cancer from Breast Lumps
Author(s) -
Nan Jiang,
Tian Tian,
Xianyang Chen,
Zhang Guo-fen,
Longfa Pan,
Chengping Yan,
Guangdie Yang,
Lili Wang,
Xuchen Cao,
Xin Wang
Publication year - 2021
Publication title -
journal of oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.228
H-Index - 54
eISSN - 1687-8469
pISSN - 1687-8450
DOI - 10.1155/2021/5579373
Subject(s) - medicine , breast cancer , random forest , ca 15 3 , logistic regression , oncology , cancer , decision tree , artificial intelligence , ca15 3 , computer science
Objective To assess the diagnostic performance of clinically common single markers and combinations to distinguish nonmetastatic breast cancer and benign breast tumor. A predictive model with a better diagnostic ability for nonmetastatic breast cancer was established by using the diagnostic process.Methods A total of 222 patients with nonmetastatic breast cancer and 265 patients with benign breast disease were enrolled in this study. CEA, Ca 15-3, Ca 125, Ca 72-4, CYFRA 21-1, FERR, AFP, and NSE were measured by an electrochemiluminescent immunoenzymometric assay on the Elecsys system. There are four key steps for our diagnostic workflow, that is, feature selection, algorithm selection, parameter optimization, and outer test data was used to validate the optimal algorithm and markers.Results CEA, Ca 15-3, CYFRA 21-1, AFP, and FERR were selected using the t -test in our inner development set. The optimal algorithm among logical regression, decision tree, support vector machine, random forest, and gradient boost machine was selected by 10-fold cross-validation, and we found that random forest and logistic regression are the better classification. The outer test data was used to validate the best markers and classification. The random forest with CEA, Ca 15-3, CYFRA 21-1, AFP, and FERR showed the optimal combination for distinguishing breast cancer and benign breast disease. The AUC value was 0.888, the cut-off point was 0.484, and sensitivity and specificity were 78.9% and 90.1%.Conclusions No single marker of these eight markers was good at identifying nonmetastatic breast cancer from benign tumors. But a diagnostic analysis workflow was established to develop a predictive model with better diagnostic capability for nonmetastatic breast cancer. This workflow is also applicable to the optimization of other disease markers and diagnostic models. The predictive model showed good diagnostic performance, and it could be gradually incorporated as a support method for the diagnosis of nonmetastatic breast cancer.