title: A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.
abstract: Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence.
authors.affiliation: Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Hezar Jerib St., 81746-73441, Isfahan, Iran.
authors.forename: Mohammad R
authors.initials: MR
authors.lastname: Mohebian
authors.affiliation: Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Hezar Jerib St., 81746-73441, Isfahan, Iran; Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), C. Pau Gargallo, 5, 08028 Barcelona, Spain.
authors.forename: Hamid R
authors.initials: HR
authors.lastname: Marateb
authors.affiliation: Department of Biostatistics and Epidemiology, School of Public Health, Isfahan University of Medical Sciences, Hezar Jerib St., 81745 Isfahan, Iran.
authors.forename: Marjan
authors.initials: M
authors.lastname: Mansourian
authors.affiliation: Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), C. Pau Gargallo, 5, 08028 Barcelona, Spain.
authors.forename: Miguel Angel
authors.initials: MA
authors.lastname: Mañanas
authors.affiliation: Cancer Prevention Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
authors.forename: Fariborz
authors.initials: F
authors.lastname: Mokarian
keywords: Breast cancer; CAD, computer-aided diagnosis; Cancer recurrence; Computer-assisted diagnosis; DT, decision tree; FH, family history of cancer; HPBCR, the proposed hybrid predictor of breast cancer recurrence; HRT, hormone therapy; I. Node, number of involved axillary lymph nodes; Machine learning; NR, lymph node involvement ratio; Prognosis; T. Node, number of dissected axillary lymph nodes; TS, tumor size; XRT, radiotherapy