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Original Research

Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes

ORCID Icon, , , , &
Pages 909-923 | Published online: 01 Mar 2022

Figures & data

Figure 1 Flowchart of the study design and patient selection.

Figure 1 Flowchart of the study design and patient selection.

Table 1 Basic Information of the Training and Validation Sets

Figure 2 Evaluating the number of variables contained in the optimal set using the root mean square error.

Figure 2 Evaluating the number of variables contained in the optimal set using the root mean square error.

Figure 3 Variable importance values derived from the random forest-recursive feature elimination analysis.

Figure 3 Variable importance values derived from the random forest-recursive feature elimination analysis.

Figure 4 Change in the prediction error rate of the recurrence risk model of breast cancer patients with tree number.

Figure 4 Change in the prediction error rate of the recurrence risk model of breast cancer patients with tree number.

Figure 5 Receiver operating characteristic curve of the developed random survival forest model.

Figure 5 Receiver operating characteristic curve of the developed random survival forest model.

Figure 6 Kaplan-Meier survival curves of recurrence-free survival for the training set.

Figure 6 Kaplan-Meier survival curves of recurrence-free survival for the training set.

Figure 7 Receiver operating characteristic curve of the developed random survival forest model assessment by the validation set.

Figure 7 Receiver operating characteristic curve of the developed random survival forest model assessment by the validation set.

Figure 8 Kaplan-Meier survival curves of recurrence-free survival for the validation set.

Figure 8 Kaplan-Meier survival curves of recurrence-free survival for the validation set.