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10.1245/ASO.2005.07.022
Annals of Surgical Oncology 12:1066-1072 (2005)
© 2005 Society of Surgical Oncology
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Original Article

Can the Memorial Sloan-Kettering Cancer Center Nomogram Predict the Likelihood of Nonsentinel Lymph Node Metastases in Breast Cancer Patients in The Netherlands?

Marjolein L. Smidt, MD1, Deborah M. Kuster, MD1, Gert Jan van der Wilt, PhD2, Frederik B. Thunnissen, MD, PhD3, Kimberley J. Van Zee, MD4 and Luc J. A. Strobbe, MD, PhD1

1 Department of Surgical Oncology, C22, Canisius Wilhelmina Hospital, P.O. Box 9015, 6500 GS, Nijmegen, The Netherlands
2 Department of Medical Technology Assessment, Radboud University Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
3 Department of Pathology, Canisius Wilhelmina Hospital, P.O. Box 9015, 6500 GS, Nijmegen, The Netherlands
4 Department of Surgery, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, New York 10021

Correspondence: Address correspondence and reprint requests to: Marjolein L. Smidt, MD; E-mail: marjoleinsmidt{at}yahoo.com.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: According to Dutch guidelines, an axillary lymph node dissection (ALND) is recommended whenever a sentinel lymph node (SLN) contains metastatic disease. However, only in approximately 50% of patients with metastatic disease in the SLN are additional nodal metastases detected in the completion ALND. To identify the individual patient’s risk for non-SLN metastases, a nomogram containing eight predictors was developed by the Breast Service of Memorial Sloan-Kettering Cancer Center (New York, NY). The aim of this study was to test the accuracy of the nomogram on a population of Dutch breast cancer patients.

Methods: Patient, tumor, and SLN metastasis characteristics were collected for 222 consecutive patients who underwent a completion ALND. The data of the index and test populations were compared. A receiver operating characteristic curve was drawn, and the area under the curve was calculated to assess the discriminative power of the nomogram.

Results: Even though our patient population differed in many respects from the source population, the area under the receiver operating characteristic curve amounted to .77, a value very much comparable to the one found in the source population.

Conclusions: The nomogram provides a fairly accurate predicted probability for the likelihood of non-SLN metastases in a general population of breast cancer patients at a regional teaching hospital in The Netherlands. This suggests that the nomogram’s originally calculated predictive accuracy may be valid for patient populations that differ considerably from the population in which it was developed.

Key Words: Breast neoplasms • Sentinel lymph node biopsy • Nomogram • Axillary metastases • Prediction


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The histological status of the axillary lymph nodes is the most important prognostic indicator in patients with breast cancer.1 The sentinel lymph node (SLN) procedure is a reliable technique for assessing axillary lymph node involvement in clinical T1/2N0 breast cancer.2–7 The morbidity of an axillary lymph node dissection (ALND) can be avoided in case of a histopathologically negative SLN.8 In case of an SLN containing metastatic disease, however minimal, prevailing Dutch guidelines recommend an ALND.

A completion ALND is performed for staging, achieving regional control, and improving survival.9–13 Accurate staging requires information on the total number of nodes involved.14 Opponents of a routine ALND after tumor-positive SLN biopsy (SLNB) argue that the added therapeutic benefit is low.15 In earlier articles, only in approximately 50% of patients with metastatic disease in the SLN were additional nodal metastases detected in the completion ALND.16 A study on the follow-up of 31 SLN-positive patients who declined a completion ALND showed no axillary recurrences after a mean follow-up of 30 months. All patients had received adjuvant chemotherapy and breast and chest wall radiation.17 This is in accordance with the present guidelines, which recommend that virtually all SLN-positive patients should receive adjuvant chemotherapy. This, in combination with radiotherapy, might eradicate residual metastatic disease.18

After the introduction of the SLNB, several studies addressed the predictors of non-SLN metastases after a positive SLNB. To identify the individual patient’s risk for non-SLN metastases, a nomogram (Fig. 1Go) was developed by the Breast Service of Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY). A nomogram is a graphical tool to depict a complicated calculation.19 The association of prognostic features with the likelihood of non-SLN metastases was assessed by multivariate logistic regression analysis of a retrospective group of 702 patients. The nomogram was created by using pathologic size, tumor type and nuclear grade, lymphovascular invasion, multifocality, estrogen receptor status, method of detection of the SLN metastases, number of positive SLNs, and number of negative SLNs. The outcome of the nomogram is the predicted probability of non-SLN metastases. This predicted probability can be determined graphically or, to facilitate use in daily practice and greater accuracy, can be calculated by the Web site http://www.mskcc.org/nomograms or through a personal digital assistant–compatible application. The nomogram was validated in a prospective set of patients from MSKCC: it reasonably accurately predicted the probability of non-SLN metastases for an individual breast cancer patient with a positive SLN at MSKCC. The aim of this study was to assess the nomogram’s generalizability by testing its predictive accuracy in a population of breast cancer patients from a regional teaching hospital in The Netherlands.


Figure 1
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  		FIG. 1. The nomogram to predict the likelihood of nonsentinel lymph node (non-SLN) metastases after a positive SLN biopsy, as developed by Memorial Sloan-Kettering Cancer Center. NUCGRADE, tumor type and nuclear grade (ductal, nuclear grade I; ductal, nuclear grade II; ductal, nuclear grade III; lobular); LVI, lymphovascular invasion; MULTIFOCAL, multifocality of primary tumor; ER, estrogen receptor status; NUMNEGSLN, number of negative SLNs; NUMSLNPOS, number of positive SLNs; PATHSIZE, pathologic size in centimeters; METHDETECT2, method of detection of SLN metastases (routine hematoxylin and eosin, serial hematoxylin and eosin, and immunohistochemistry). The first row (Points) is the point assignment for each variable. Rows 2 through 9 represent the variables included in the model. For an individual patient, each variable is assigned a point value (uppermost scale, Points) according to the histopathologic characteristics. A vertical line is made between the appropriate variable value and the Points line. The assigned points for all 8 variables are summed, and the total is found in row 10 (Total Points). Once the total is located, a vertical line is made between Total Points and the final row (row 11). Row 11 presents the predicted probability for non-SLN metastases after a positive SLN biopsy.

 

   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Between June 1997 and December 2003, 696 consecutive patients underwent an SLNB for a clinical T1/2N0 breast cancer in a teaching hospital. Data concerning these patients were prospectively collected in a breast cancer database. The SLNB revealed metastatic disease in 229 patients (33%). Almost all (n = 222) of these patients underwent a completion ALND. To be included in the study population, patients had to meet the following selection criteria: (1) the patient had to undergo operation for a primary invasive breast cancer, without neoadjuvant therapy; (2) the SLNB had to be successful, and the SLN had to contain any amount of metastatic disease; and (3) the total lymph node count of SLNB and ALND had to be at least 10. From 47 (24%) of the 222 patients, fewer than 10 lymph nodes were retrieved.

SLN Identification
The SLN was identified by using 60 MBq of 99mTc nanocolloid as a radioactive tracer before surgery and 2 mL of blue dye (Bleu Patente V; Guerbet, Aulnaysous-Bois, France) for lymphatic mapping. During surgery, the SLN was harvested; this procedure was guided by the triple technique consisting of preoperative lymphoscintigraphy, blue lymphatic vessels, and detection of radioactivity by gamma probe (Neoprobe; Johnson & Johnson Medical, Hamburg, Germany).

SLN Histopathologic Examination
The SLN was bisected, after which both halves were embedded in paraffin. Each part was step-sectioned at 500-µm intervals at three levels and stained with hematoxylin and eosin (H&E) and immunohistochemical (IHC) staining. Cam5.2 is a mono-clonal antibody directed against cytokeratin 8, which is localized in the cytoplasm of the tumor cells (Becton Dickinson, San Jose, Calif). The nomogram differentiates three or four methods of detection of metastatic disease in the SLN: IHC only, serial, routine H&E, and possible frozen section. Because SLNs in this study were retrieved with use of local anesthesia, the nomogram without the frozen-section method of detection was used. No distinction was made in the database between serial and routine H&E, and all slides were reviewed by an experienced pathologist. If the first slide was positive, it was judged as routine H&E positive. Metastases found on further slides were judged as serial H&E or IHC positive, depending on the method of detection.

Data Analysis
Patient and tumor characteristics were collected from the prospective database for each variable of the MSKCC nomogram, including tumor type (ductal vs. lobular carcinoma), pathologic size (centimeters), and nuclear grade; presence of lymphovascular invasion; multifocality; estrogen receptor status; method of detection of the SLN metastasis (routine histopathology, serial H&E, or IHC); and number of positive and negative SLNs (Fig. 1Go). These characteristics were compared with the prospective MSKCC data and tested for significance (Table 1Go).


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  		TABLE 1. Comparison of descriptive characteristics of both prospective patient populations of CWH and MSKCC
 
By using the MSKCC Web site, the predicted probability was calculated for each patient. The 222 patients were grouped in deciles (groups of 22 patients) according to their predicted probabilities. For each group, the actual probability was calculated by assessing the incidence of additional non-SLN metastases. A calibration plot was drawn showing for each decile the actual versus predicted probability. To measure the discrimination of the nomogram, a receiver operating characteristic (ROC) curve was construed, and the area under the curve was calculated. Calculations were performed for the entire cohort (n = 222) and for the cohort excluding patients for whom fewer than 10 lymph nodes could be retrieved (n = 167). All analyses were conducted with SPSS 11.0 (SPSS Inc., Chicago, IL).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Patient and tumor characteristics were collected and compared with the MSKCC data. All variables differed significantly except age and the number of positive SLNs (Table 1Go). Among the 47 patients from whom fewer than 10 lymph nodes were retrieved, the median number of lymph nodes was 8.

To assess the accuracy of the nomogram, actual probabilities were plotted against the calculated predicted probability for each decile of patients. The trend line shows almost complete concordance with the ideal line, with a slope of 1 (Fig. 2Go). An ROC curve was drawn to assess the discrimination of the nomogram (Fig. 3Go). The area under the ROC curve was .76 (95% confidence interval, .69–.83). Both graphics and statistical analyses were repeated after exclusion of the patients in whom fewer than 10 lymph nodes were recovered. As a result, the calibration plot shifted to the left, and the area under the ROC curve amounted to .77 (95% confidence interval, .70–.84; Fig. 4Go).


Figure 2
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  		FIG. 2. Calibration plot for the nomogram. The entire cohort of 222 patients was classified in deciles according to their predicted probabilities. For each group, the actual probability (incidence of additional nonsentinel lymph node metastases) was calculated. A calibration plot was drawn showing for each decile the actual versus predicted probability.

 

Figure 3
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  		FIG. 3. A receiver operating characteristic (ROC) curve assessing the discrimination of the nomogram. The area under the ROC curve is .76.

 

Figure 4
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  		FIG. 4. Calibration plot for the nomogram. The cohort of 167 patients in whom 10 or more lymph nodes could be retrieved were classified in deciles according to their predicted probabilities. For each group, the actual probability (incidence of additional nonsentinel lymph node metastases) was calculated. A calibration plot was drawn showing for each decile the actual versus predicted probability.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Soon after the validation phase of the SLN concept, several articles were published concerning prognostic factors for non-SLN metastatic disease after a positive SLNB. Various predictors were identified, such as a palpable breast mass, tumor size, histological grade, lymphovascular invasion, SLN micrometastasis or size of the nodal metastasis, extranodal extension, and more than one positive SLN.16,20–31 The MSKCC nomogram combines these prognostic factors revealed by multivariate analysis. The nomogram is a tool that provides a risk estimate for the likelihood of non-SLN metastases and can be used in current practice.

Because the nomogram was developed in MSKCC, which is a tertiary referral center, the question arose whether it would be applicable to the population of a Dutch regional teaching hospital. The difference of the studied populations was reflected in significantly deviating patient and tumor characteristics. The area under the curve, however, amounted to .76 in the Dutch population for the entire group and to .77 for the group comparable to the source group. For the New York group, the area under the curve was .78. The scale of the area under the curve ranges from .5 for a test as good as the toss of a coin to 1.0, which makes a perfect test. An area under the curve of .77 is therefore a reasonably accurate predictive test.

According to the MSKCC inclusion criteria, at least 10 lymph nodes need to be retrieved. This criterion was not met in 24% of the Dutch patients. The likelihood for detecting a non-SLN containing metastatic disease increases with the number of examined nodes. A higher yield of nodes per patient would therefore result in a shift of the calibration plot to the left. Our data suggest, however, that the influence of the number of retrieved lymph nodes is minimal and that this should not preclude use of the nomogram.

Recently a study was presented that compared surgeons’ predictions of the probability of non-SLN metastatic disease with the performance of the MSKCC nomogram. The surgeons’ predictive accuracy corresponded with an area under the curve of only .54. Clearly, the MSKCC nomogram outperformed expert judgment, thus emphasizing its clinical utility.32

The observed differences in patient populations can be explained by the function of the hospitals: MSKCC is an oncological referral center, and the Canisius Wilhelmina Hospital is a regional teaching hospital for the general population. The higher incidence of smaller tumors at MSKCC could be the result of annual screening for breast cancer in all patients, compared with the Dutch screening scheme, which consists of 2-yearly screening of women aged 50 to 75 years. Approximately 30% of the study patients had a screening-detected tumor. Except for size, all tumor characteristics had a better profile in the Dutch population, which corresponds to a more general patient population mix.

A subset of 40% of the 50% of breast cancer patients with an SLN containing metastatic disease will have non-SLN metastases. Consequently, a cutoff value of 10% predicted probability would concern 2.0% of all patients. According to the predicted probability of 10%, only .2% would be at risk for residual metastatic disease in non-SLNs. Axillary recurrence rates after negative SLNB are so far lower than expected. This may be explained by the increasing number of patients in this group receiving adjuvant therapy.18,33 Patients with an SLN containing metastatic disease will always be considered for adjuvant therapy. As a consequence, axillary recurrence rates will even be lower than expected, and a cutoff point of 10% seems therefore reasonable and defensible. In current practice, the nomogram was used to avoid completion ALND in several patients with a low likelihood (<10%) of non-SLN metastases. The Dutch guidelines, however, recommend a completion ALND for all patients in the presence of only one prognostic factor: a positive SLN. This nomogram enables the formation of an individualized risk estimate for the probability of non-SLN metastases based on eight predictors. The nomogram does not make a statement on treatment recommendations. Its results can support the multi-specialty oncological team as well as the doctor or patient regarding the choice of whether an ALND is desirable, taking into account, for instance, patients’ preferences and risk aversiveness. Further research on estimates of non-SLN metastases by oncologists versus the MSKCC nomogram is warranted.

In conclusion, the nomogram provides a fairly accurate prediction of the probability of non-SLN metastases in a general population of breast cancer patients differing from the population in which the nomogram was originally developed. The resulting risk estimate can help to individualize a patient’s treatment.


   ACKNOWLEDGMENTS
 
The authors thank J. M. M. Groenewoud of the Department of Medical Technology Assessment, Radboud University Medical Centre, for statistical support.

Received for publication July 21, 2004. Accepted for publication July 29, 2005.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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