Validation and update of a lymph node metastasis prediction model for breast cancer

Si Qi Qiu, Merel Aarnink, Marissa C. van Maaren, Monique D. Dorrius, Arkajyoti Bhattacharya, Jeroen Veltman, Caroline A.H. Klazen, Jan H. Korte, Susanne H. Estourgie, Pieter Ott, Wendy Kelder, Huan Cheng Zeng, Hendrik Koffijberg, Guo Jun Zhang, Gooitzen M. Van Dam, Sabine Siesling (Corresponding Author)

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Abstract

Purpose: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making.

Methods: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability.

Results: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%.

Conclusions: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.

Original languageEnglish
Pages (from-to)700-707
Number of pages8
JournalEuropean journal of surgical oncology
Volume44
Issue number5
DOIs
Publication statusPublished - May 2018

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Lymph Nodes
Breast Neoplasms
Neoplasm Metastasis
Area Under Curve
Calibration
Linear Models
Estrogen Receptors
Ultrasonography
Neoplasms
Population

Keywords

  • Axillary lymph node metastasis
  • Axillary surgery omission
  • Breast cancer
  • Model
  • Prediction model

Cite this

Qiu, Si Qi ; Aarnink, Merel ; van Maaren, Marissa C. ; Dorrius, Monique D. ; Bhattacharya, Arkajyoti ; Veltman, Jeroen ; Klazen, Caroline A.H. ; Korte, Jan H. ; Estourgie, Susanne H. ; Ott, Pieter ; Kelder, Wendy ; Zeng, Huan Cheng ; Koffijberg, Hendrik ; Zhang, Guo Jun ; Van Dam, Gooitzen M. ; Siesling, Sabine. / Validation and update of a lymph node metastasis prediction model for breast cancer. In: European journal of surgical oncology. 2018 ; Vol. 44, No. 5. pp. 700-707.
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title = "Validation and update of a lymph node metastasis prediction model for breast cancer",
abstract = "Purpose: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making.Methods: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability.Results: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10{\%} at all cut-off points when the predictive probability was less than 12.0{\%}.Conclusions: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.",
keywords = "Axillary lymph node metastasis, Axillary surgery omission, Breast cancer, Model, Prediction model",
author = "Qiu, {Si Qi} and Merel Aarnink and {van Maaren}, {Marissa C.} and Dorrius, {Monique D.} and Arkajyoti Bhattacharya and Jeroen Veltman and Klazen, {Caroline A.H.} and Korte, {Jan H.} and Estourgie, {Susanne H.} and Pieter Ott and Wendy Kelder and Zeng, {Huan Cheng} and Hendrik Koffijberg and Zhang, {Guo Jun} and {Van Dam}, {Gooitzen M.} and Sabine Siesling",
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language = "English",
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journal = "European journal of surgical oncology",
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Qiu, SQ, Aarnink, M, van Maaren, MC, Dorrius, MD, Bhattacharya, A, Veltman, J, Klazen, CAH, Korte, JH, Estourgie, SH, Ott, P, Kelder, W, Zeng, HC, Koffijberg, H, Zhang, GJ, Van Dam, GM & Siesling, S 2018, 'Validation and update of a lymph node metastasis prediction model for breast cancer' European journal of surgical oncology, vol. 44, no. 5, pp. 700-707. https://doi.org/10.1016/j.ejso.2017.12.008

Validation and update of a lymph node metastasis prediction model for breast cancer. / Qiu, Si Qi; Aarnink, Merel; van Maaren, Marissa C.; Dorrius, Monique D.; Bhattacharya, Arkajyoti; Veltman, Jeroen; Klazen, Caroline A.H.; Korte, Jan H.; Estourgie, Susanne H.; Ott, Pieter; Kelder, Wendy; Zeng, Huan Cheng; Koffijberg, Hendrik; Zhang, Guo Jun; Van Dam, Gooitzen M.; Siesling, Sabine (Corresponding Author).

In: European journal of surgical oncology, Vol. 44, No. 5, 05.2018, p. 700-707.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Validation and update of a lymph node metastasis prediction model for breast cancer

AU - Qiu, Si Qi

AU - Aarnink, Merel

AU - van Maaren, Marissa C.

AU - Dorrius, Monique D.

AU - Bhattacharya, Arkajyoti

AU - Veltman, Jeroen

AU - Klazen, Caroline A.H.

AU - Korte, Jan H.

AU - Estourgie, Susanne H.

AU - Ott, Pieter

AU - Kelder, Wendy

AU - Zeng, Huan Cheng

AU - Koffijberg, Hendrik

AU - Zhang, Guo Jun

AU - Van Dam, Gooitzen M.

AU - Siesling, Sabine

PY - 2018/5

Y1 - 2018/5

N2 - Purpose: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making.Methods: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability.Results: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%.Conclusions: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.

AB - Purpose: This study aimed to validate and update a model for predicting the risk of axillary lymph node (ALN) metastasis for assisting clinical decision-making.Methods: We included breast cancer patients diagnosed at six Dutch hospitals between 2011 and 2015 to validate the original model which includes six variables: clinical tumor size, tumor grade, estrogen receptor status, lymph node longest axis, cortical thickness and hilum status as detected by ultrasonography. Subsequently, we updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. The area under the receiver operator characteristic curve (AUC) and calibration curve were used to assess the original and updated models. Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability.Results: Data from 1416 patients were analyzed. The AUC for the original model was 0.774. Patients were classified into four risk groups by GLM analysis, for which four updated models were created. The AUC for the updated models was 0.812. The calibration curves showed that the updated model predictions were better in agreement with actual observations than the original model predictions. FNRs of the updated models were lower than the preset 10% at all cut-off points when the predictive probability was less than 12.0%.Conclusions: The original model showed good performance in the Dutch validation population. The updated models resulted in more accurate ALN metastasis prediction and could be useful preoperative tools in selecting low-risk patients for omission of axillary surgery.

KW - Axillary lymph node metastasis

KW - Axillary surgery omission

KW - Breast cancer

KW - Model

KW - Prediction model

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JF - European journal of surgical oncology

SN - 0748-7983

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