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

TLDR: 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.

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.

Figures

Table 2. FNRs of the updated models at different model-predicted probability cut-off points

Table 1. Comparison between the Dutch and Chinese patients by clinicopathological characteristics

Full text

University of Groningen
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
Published in:
EJSO
DOI:
10.1016/j.ejso.2017.12.008
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from
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2018
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Citation for published version (APA):
Qiu, S-Q., Aarnink, M., van Maaren, M. C., Dorrius, M. D., Bhattacharya, A., Veltman, J., Klazen, C. A. H.,
Korte, J. H., Estourgie, S. H., Ott, P., Kelder, W., Zeng, H-C., Koffijberg, H., Zhang, G-J., van Dam, G. M.,
& Siesling, S. (2018). Validation and update of a lymph node metastasis prediction model for breast cancer.
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44
(5), 700-707. https://doi.org/10.1016/j.ejso.2017.12.008
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Accepted Manuscript
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
PII: S0748-7983(18)30037-4
DOI: 10.1016/j.ejso.2017.12.008
Reference: YEJSO 4822
To appear in:
European Journal of Surgical Oncology
Received Date: 3 November 2017
Revised Date: 30 November 2017
Accepted Date: 21 December 2017
Please cite this article as: Qiu S-Q, Aarnink M, van Maaren MC, Dorrius MD, Bhattacharya A, Veltman
J, Klazen CAH, Korte JH, Estourgie SH, Ott P, Kelder W, Zeng H-C, Koffijberg H, Zhang G-J, van Dam
GM, Siesling S, Validation and update of a lymph node metastasis prediction model for breast cancer,
European Journal of Surgical Oncology (2018), doi: 10.1016/j.ejso.2017.12.008.
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MANUS CRIP T
ACCEP TED
ACCEPTED MANUSCRIPT
1
Validation and update of a lymph node metastasis prediction model for breast cancer
Si-Qi Qiu
a,e
*, Merel Aarnink
f
*, Marissa C. van Maaren
g
, Monique D. Dorrius
c
, Arkajyoti Bhattacharya
d
,
Jeroen Veltman
h
, Caroline A.H. Klazen
i
, Jan H. Korte
j
, Susanne H. Estourgie
k
, Pieter Ott
l
, Wendy
Kelder
m
, Huan-Cheng Zeng
e
, Hendrik Koffijberg
f
, Guo-Jun Zhang
n
, Gooitzen M. van Dam
a,b
, Sabine
Siesling
f,g
,
a
Department of Surgery,
b
Department of Nuclear Medicine and Molecular Imaging & Intensive Care,
c
Department of Radiology,
d
Department of Medical Oncology, University Medical Center Groningen,
University of Groningen, Groningen, The Netherlands
e
The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
f
Department of Health Technology and Services Research, MIRA Institute for Biomedical Technology
and Technical Medicine, University of Twente, Enschede, The Netherlands
g
Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, the Netherlands
h
Department of Radiology, ZiekenhuisgroepTwente, Almelo, The Netherlands
i
Department of Radiology, Medisch Spectrum Twente, Enschede, The Netherlands
j
Department of Radiology, Isala, Zwolle, The Netherlands
k
Department of Surgery, Medisch Centrum Leeuwarden, Friesland, The Netherlands
l
Department of Radiology,
m
Department of Surgery, Martini Hospital, Groningen, The Netherlands
n
Changjiang Scholar’s Laboratory of Shantou University Medical College, Guangdong, China
* Both authors contributed equally to this work.
Corresponding author: Sabine Siesling, PhD, Department of Health Technology and Services
Research, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente,
Enschede, the Netherlands; Department of Research, Netherlands Comprehensive Cancer
Organisation, 3501 DB, Utrecht, the Netherlands (Tel: +31-6-13193806; e-mail address:
s.siesling@iknl.nl
).

MANUS CRIP T
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ACCEPTED MANUSCRIPT
2
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 1,416 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: breast cancer; axillary lymph node metastasis; model; prediction model; axillary surgery
omission

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Introduction
Axillary lymph node (ALN) status is an important prognostic factor and a major determinant for
postoperative treatment decision-making for breast cancer patients
1,2
.
ALN staging evolved together
with the shift in surgical treatment from the largest tolerable surgery to less invasive surgery. During
this process, sentinel lymph node biopsy (SLNB) replaced ALN dissection and has become the
standard of care for ALN staging in breast cancer patients with clinically negative ALN for over 10
years. SLNB has significantly reduced the incidence of surgical complications such as upper limb
lymphedema and impaired shoulder function, and has improved patients’ quality of life without
compromising their survival
3–7
. However, the surgical complications from SLNB cannot be ignored.
Lymphedema occurs in approximately 5-8% of patients receiving a SLNB and paresthesia in 10-15%
6–
12
. In addition, 28-49% of the patients experience shoulder-arm function impairment
11–13
. Notably, 60-
70% of the patients receiving a SLNB are shown to have negative SLNs after histopathological
analysis and thus do not benefit from the procedure
4,14,15
.
Due to early detection through the national screening program, more patients are being diagnosed
with early breast cancer and are more often free from ALN metastasis
16
.
If patients with a
pathologically negative ALN can be preoperatively predicted, omission of axillary surgery could avoid
the above-mentioned surgical complications and improve their quality of life, without affecting the
postoperative treatment decision-making. Consequently, accurate assessment of the preoperative
patients’ risk of ALN metastasis is required. However, all currently used imaging modalities have low
sensitivity in predicting ALN metastasis, resulting in a false-negative prediction of around 40-70%
17–20
.
Therefore, new tools for prediction of preoperative ALN metastasis are urgently needed.
We previously developed a predictive model for ALN metastasis in a Chinese breast cancer population
based on clinicopathological features from the primary tumor and axillary ultrasound
21
.
The model was
based on six independent predictors for ALN metastasis: clinical tumor size, histological tumor grade,
estrogen receptor (ER) status, longest axis, cortical thickness and hilum status of the ALN as detected
by ultrasonography. The model was validated on an additional set of 234 Chinese patients, generating
an area under the receiver operating characteristic curve (AUC) of 0.864
21
, indicating a good
performance in ALN metastasis prediction.
In this study we validated the performance of the Chinese model for predicting ALN metastasis in a
large Dutch breast cancer population. The model was updated using the Dutch and Chinese patient

Frequently asked questions

Q1. What are the contributions in "Validation and update of a lymph node metastasis prediction model for breast cancer" ?

In this paper, a lymph node metastasis prediction model based on axillary lymph node ( ALN ) status is proposed. 

Q2. What tests were used to compare the different groups?

Differences of categorical and continuous variables between groups were analyzed using the Chisquare and Mann-Whitney U test, respectively. 

Q3. What software was used to perform the statistical analyses?

Statistical analyses were performed using the statistical software SPSS, version 19 and R, version 3.3.2.M ANUS CRIP TAC CEPT ED6 

Q4. What is the role of a SLNB in breast cancer?

During this process, sentinel lymph node biopsy (SLNB) replaced ALN dissection and has become the standard of care for ALN staging in breast cancer patients with clinically negative ALN for over 10 years. 

Q5. Why are more patients being diagnosed with early breast cancer?

Due to early detection through the national screening program, more patients are being diagnosed with early breast cancer and are more often free from ALN metastasis16. 

Q6. What imaging modalities have been reported to predict the risk of ALN metastasis?

Other imaging modalities e.g. magnetic resonance imaging (MRI) or positron emission tomography-computed tomography (PET-CT) have also been reported to predict the risk of ALN metastasis with a high FNR (MRI 18% vs PET-CT 36%)29. 

Q7. How many Chinese patients were used in the present study?

In addition to the Dutch patients, the Chinese patients (n=322) diagnosed at Cancer Hospital of Shantou University Medical College between 2009 and 2014 for developing the model were also used in the present study for updating the original model21. 

Q8. How many patients have a negative SLN after histopathological analysis?

60- 70% of the patients receiving a SLNB are shown to have negative SLNs after histopathological analysis and thus do not benefit from the procedure4,14,15. 

Q9. What is the role of axillary surgery in breast cancer?

Keywords: breast cancer; axillary lymph node metastasis; model; prediction model; axillary surgery omissionM ANUS CRIP TAC CEPT ED3Axillary lymph node (ALN) status is an important prognostic factor and a major determinant for postoperative treatment decision-making for breast cancer patients1,2. 

Q10. What is the impact of residual metastatic disease on survival of patients?

Given the improvement of systemic treatments for breast cancer, the impact of residual metastatic disease in ALNs on survival of patients has become less important. 

Q11. What is the effect of the updated models on the patient’s survival?

The updated models resulted in more accurate ALN metastasis predictions and could therefore be useful preoperative tools in selecting low-risk patients for axillary surgery omission. 

Q12. How did the authors update the original model?

the authors updated the original model using generalized linear model (GLM) tree analysis and by adjusting its intercept and slope. 

Q13. How was the clinical usefulness of the model evaluated?

Clinical usefulness of the model was evaluated by false-negative rates (FNRs) at different cut-off points for the predictive probability. 

Q14. How can the authors obtain the data for all of the variables incorporated in their models?

the necessary data for all of the six variables incorporated in their models can be obtained preoperatively, for example, by a core needle biopsy of the primary tumor and axillary ultrasound examination. 

Q15. How many patients could have been selected for axillary surgery?

Using the updated models, 415 patients (29.3% of entire study population) could have been selected for axillary surgery omission.