BMC Med Inform Decis Mak - Prediction of axillary lymph node metastasis in primary breast cancer patients using a decision tree-based model.

Tópicos

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Resumo

CKGROUND: The aim of this study was to develop a new data-mining model to predict axillary lymph node (AxLN) metastasis in primary breast cancer. To achieve this, we used a decision tree-based prediction method-the alternating decision tree (ADTree).METHODS: Clinical datasets for primary breast cancer patients who underwent sentinel lymph node biopsy or AxLN dissection without prior treatment were collected from three institutes (institute A, n = 148; institute B, n = 143; institute C, n = 174) and were used for variable selection, model training and external validation, respectively. The models were evaluated using area under the receiver operating characteristics (ROC) curve analysis to discriminate node-positive patients from node-negative patients.RESULTS: The ADTree model selected 15 of 24 clinicopathological variables in the variable selection dataset. The resulting area under the ROC curve values were 0.770 [95% confidence interval (CI), 0.689-0.850] for the model training dataset and 0.772 (95% CI: 0.689-0.856) for the validation dataset, demonstrating high accuracy and generalization ability of the model. The bootstrap value of the validation dataset was 0.768 (95% CI: 0.763-0.774).CONCLUSIONS: Our prediction model showed high accuracy for predicting nodal metastasis in patients with breast cancer using commonly recorded clinical variables. Therefore, our model might help oncologists in the decision-making process for primary breast cancer patients before starting treatment.

Resumo Limpo

ckground aim studi develop new datamin model predict axillari lymph node axln metastasi primari breast cancer achiev use decis treebas predict methodth altern decis tree adtreemethod clinic dataset primari breast cancer patient underw sentinel lymph node biopsi axln dissect without prior treatment collect three institut institut n institut b n institut c n use variabl select model train extern valid respect model evalu use area receiv oper characterist roc curv analysi discrimin nodeposit patient nodeneg patientsresult adtre model select clinicopatholog variabl variabl select dataset result area roc curv valu confid interv ci model train dataset ci valid dataset demonstr high accuraci general abil model bootstrap valu valid dataset ci conclus predict model show high accuraci predict nodal metastasi patient breast cancer use common record clinic variabl therefor model might help oncologist decisionmak process primari breast cancer patient start treatment

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