TY - JOUR
T1 - Prediction of axillary lymph node metastasis in primary breast cancer patients using a decision tree-based model
AU - Takada, Masahiro
AU - Sugimoto, Masahiro
AU - Naito, Yasuhiro
AU - Moon, Hyeong Gon
AU - Han, Wonshik
AU - Noh, Dong Young
AU - Kondo, Masahide
AU - Kuroi, Katsumasa
AU - Sasano, Hironobu
AU - Inamoto, Takashi
AU - Tomita, Masaru
AU - Toi, Masakazu
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - Background: 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.
AB - Background: 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.
KW - Alternating decision tree
KW - Breast cancer
KW - Data mining
KW - Lymph node metastasis
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U2 - 10.1186/1472-6947-12-54
DO - 10.1186/1472-6947-12-54
M3 - Article
C2 - 22695278
AN - SCOPUS:84862207098
VL - 12
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
SN - 1472-6947
IS - 1
M1 - 54
ER -