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10.1245/s10434-006-9150-5
Annals of Surgical Oncology 14:615-620 (2007)
© 2007 Society of Surgical Oncology
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Original Article

Can Surgical Oncologists Reliably Predict the Likelihood for Non-SLN Metastases in Breast Cancer Patients?

Marjolein L. Smidt1, Luc J. A. Strobbe1, Hans M. M. Groenewoud2, Gert Jan der Wilt2, Kimberley J. Van Zee4 and Theo Wobbes3

1 Department of Surgical Oncology, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
2 Departments of Medical Technology Assessment, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
3 Departments of Surgery, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
4 Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, USA

Correspondence: Address correspondence and reprint requests to: Marjolein L Smidt; Department of Surgical Oncology, Canisius Wilhelmina Hospital, PO Box 9015 6500 GS, Nijmegen, The Netherlands; E-mail: marjoleinsmidt{at}yahoo.com


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: In approximately 40% of the breast cancer patients with sentinel lymph node (SLN) metastases, additional nodal metastases are detected in the completion axillary lymph node dissection (cALND). The MSKCC nomogram can help to quantify a patient’s individual risk for non-SLN metastases with fairly accurate predicted probability. The aim of this study was to compare the predictions of surgical oncologists for non-SLN metastases with nomogram results and to clarify the impact of nomogram results on clinical decision-making.

Methods: Questionnaires, containing patient scenarios, were sent to surgical oncologists involved in breast cancer care. The surgeon was asked to predict the probability for non-SLN metastases for the first five scenarios. For the remaining scenarios, the patient’s actuarial likelihood, calculated by the nomogram, was supplied. The surgeon was asked whether or not (s)he would perform a cALND. The type of hospital and the surgeon’s experience were registered.

Results: The concordance-index amounted to 0.78, indicating moderate concurrence between the surgical predictions and nomogram results. The intersurgeon variation was important. About 25% of the surgeons was influenced by nomogram information and decided in one or more patients to abandon the cALND. Neither the type of hospital nor experience influenced predicting abilities or the clinical decision-making process.

Conclusion: Individual predictions of surgical oncologists for non-SLN metastases do not correlate well with the MSKCC nomogram. The distribution between intersurgeon predictions for one scenario is important. Therefore, the nomogram is superior to clinical estimations for predicting the likelihood for non-SLN metastases.

Key Words: Breast neoplasms • Sentinel Lymph node biopsy • Lymphatic metastasis and risk assessment


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The sentinel lymph node biopsy (SLNB) is now widely accepted as an accurate staging procedure in breast cancer patients.1–4 If any amount of metastatic disease is found in the sentinel lymph node (SLN), a completion axillary lymph node dissection (cALND) is recommended according to the Dutch guidelines to optimize ultimate staging, regional control and survival enhancement.5–7 When the SLN is tumor-free, patients can be spared a cALND and its morbidity in terms of lymphedema, seroma formation, reduced shoulder motility and chronic pain.8,9

In approximately 40% of the breast cancer patients with SLNs containing metastatic disease, additional nodal metastases are detected in the cALND.10–23 The SLN procedure already caused a decrease of the number of ALND in case of a negative SLN. A further reduction could be achieved through selection of a subset of patients with low suspicion for non-SLN metastases after a positive SLNB. Many authors determined factors that attempt to predict the presence of non-SLNs containing metastatic disease. The most frequently identified predictors are the size of the primary tumor (size in general12,16,22–24 and size larger than 2 cm14,17,19,20) and SLN metastasis (size in general10,12,16,17,23 and macrometastasis13,14,18–20), extranodal growth10,13,15,24 and lymphovascular invasion.10,14,20–22,24 Some authors identified subsets with such a small risk for non-SLN metastases that an ALND could safely be omitted, but all concerned small studies and subset groups of patients.24 Several groups emphasize the importance of the recently closed trial of the American College of Surgeons (Z0011), in which patients with a positive SLN were randomized to ALND or no further axillary treatment. The aim of this trial was to reveal a subset of patients in which an ALND can be omitted. The trial was closed to poor patient accrual.

The MSKCC breast cancer group used the relevant predictors to develop a nomogram to help quantify a patient’s individual risk for non-SLN metastases (Fig. 1Go). It provides a reasonably accurate predicted probability and was validated for a general population of Dutch breast cancer patients.11,22 For daily practice, however, it is essential to know how clinical predictions compare with the nomogram results and how these nomogram results influence the decisions that clinicians take. Comparisons of clinical versus computer-aided decision-making are rare in medical literature. One author examined prostate nomogram results against urologists’ predictions. He concluded that nomogram results could be of significant benefit in certain settings of clinical decision-making.25 Almost no literature could be found about the influence (nomogram) results have on clinical decision-making.26 To this purpose a systematic search in Pubmed under the MeSH-terms "forecasting, outcome assessment and breast neoplasms" and the heading "clinical decision-making" was performed.


Figure 1
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  		FIG. 1. The nomogram to predict the likelihood of non-sentinel lymph node (SLN) metastases after a positive SLN biopsy (SLNB), as developed by MSKCC. 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, pathological size in cm; METHDETECT, method of detection of SLN metastases (routine H&E, serial H&E and IHC). The first row (POINTS) is the point assignment for each variable. Rows 2–9 represent the variables included in the model. For an individual patient, each variable is assigned a point value (uppermost scale, POINTS) based on the histopathological characteristics. A vertical line is made between the appropriate variable value and the POINTS line. The assigned points for all eight 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 11. Row 11 presents the predicted probability for non-SLN metastases after a positive SLNB.

 
The aim of the first part of this study was to establish the surgical oncologists’ estimate or "guesstimate" for axillary lymph node involvement after a positive SLNB in breast cancer patients. These findings were compared with the MSKCC nomogram results. The second part of this study should clarify to what extent the additional information provided by the nomogram, influences clinical decision-making.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
To examine clinical predictive ability, a questionnaire was sent to all surgical oncologists involved in breast cancer care in the Netherlands. It contained ten scenarios presenting female breast cancer patients with a positive SLN and added relevant prognostic features. An example of a scenario is presented here.

"What is the likelihood for residual disease in the axilla in a 50-year-old female with a 3 cm centrally located multifocal tumor. Tumor characteristics are invasive ductal cancer, nuclear grade II, estrogen-and progesteron-receptor-negative and no sign of lymphovascular invasion. One out of two SLNs is positive by immunohistochemical staining." All other scenarios are listed after the discussion.

In addition to the data required to use the nomogram, age, location of the tumor and progesteron-receptor status were supplied. The surgeon was asked to estimate the probability for non-SLN metastases for each of the first five scenarios. The accuracy of the surgical oncologists’ prediction was established by comparing the results with the nomogram findings.

For the second five similar scenarios, the patient’s individualized predicted probability for metastatic disease in non-SLNs, calculated by the nomogram, was supplied. To clarify the impact of the nomogram results on clinical decision-making, the surgeon was asked whether or not (s)he would perform a cALND, not taking into account the Dutch breast cancer guidelines.5,27

Clinicians completing the questionnaire provided the type of hospital they worked at: academic or cancer centre, regional teaching or local hospital. Surgical experience—expressed in years after graduation from medical school—of each surgeon was registered.28 To examine the influence of experience on predicting abilities, years were transposed to a number of decades.

An experienced statistician performed all statistical analyses with help of the statistical package SAS for Windows release 8.02.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
One hundred and seven questionnaires were returned, coming from 8 out of 10 academic or cancer centers, 29 out of 41 regional teaching hospitals and 19 out of 61 local hospitals.

The probability predicted by the nomogram for the first five consecutive scenarios was 4, 10, 10, 32 and 43%. The median clinical guesstimate was 10, 10, 15, 30 and 30% (Fig. 2Go). A concordance index was calculated to demonstrate concordance or discorcordance between the sequence of nomogram results and clinical estimates. The c-index scale varies from 0.5, which represents any toss of a coin, to 1.0, which represents perfection. The c-index in this study amounts to 0.78 indicating moderate concurrence. The variation between the predictions of the individual surgeons for each scenario was important, minimum and maximum values varied from 2.5–90 to 0–100% (Fig. 3Go). There is no significant difference between the predictions of surgical oncologists of the various types of hospitals. Experience did not make a difference in clinical predicting abilities either. Several models were used to test both relationships but no correlation could be found (P > 0.20, always).


Figure 2
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  		FIG. 2. Likelihood for non-sentinel lymph node (SLN) metastases for the various scenarios: the bar heights represent the nomogram results versus the median clinical estimate for the various scenarios.

 

Figure 3
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  		FIG. 3. Distribution of the clinical estimates for non-sentinel lymph node (SLN) metastases for the various scenarios drawn as a jitplot. One dot represents two surgical oncologists. The actuarial predictions amounted to 4, 10, 10, 32, 43 for the consecutive scenarios.

 
In the second part of the five scenarios, the surgical oncologist determined on the basis of the clinical data and the supplied nomogram results, if (s)he would perform a cALND. The likelihood for non-SLN metastases was 7, 8, 14, 21 and 52%. Surgical oncologists considered the need for a cALND 90, 78, 88, 95 and 99% (Fig. 4Go). About 25% of the surgeons was influenced by the information supplied by the nomogram and decided in one or more patients to abandon the cALND despite the positive SLN. Neither the type of hospital, a surgeon works at, nor the experience, did seem to influence the clinical decision-making process (P > 0.20, always).


Figure 4
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  		FIG. 4. Surgical preference for completion axillary lymph node dissection (cALND) versus actuarial likelihood for non-sentinel lymph node (SLN) metastases for the various scenarios.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Clinical and actuarial predictions concerning non-SLN metastasis after a positive SLNB in breast cancer patients do not correlate well. The distribution of clinical predictions of the individual surgeon per scenario is important as shown in Fig. 3Go. The similarity of the bar heights in Fig. 2Go is striking in scenarios B, C and D. The "median" surgical oncologist can discriminate the likelihood for non-SLN metastases for the various scenarios. When compared with Fig. 3Go, however, the interobserver variability is striking. The individual surgical oncologist, regardless of his/her experience, is not able to make a solid prediction of the need for a cALND. Neither the type of hospital nor the clinical experience influenced predicting abilities.

In the second part of the questionnaire, the influence of the actuarial probability on clinical decision-making was determined. Of all surgeons, about 25% appears to be influenced by the nomogram results. This could not be attributed to the type of the hospital or the clinical experience.

The bar heights in Fig. 4Go, representing the actuarial likelihood for non-SLN metastases and the percentage of surgical oncologists favoring the cALND, have a similar upward course. For the first scenario, however, the predicted clinical considered need for a completion ALND and actuarial likelihood for non-SLN metastases deviate widely. This is probably caused by the young age of this patient, 28 years, compared with the mean of 51 years of the other patients in this part of the questionnaire.

One author described several studies comparing actuarial and clinical predictions.26 Actuarial predictions always exceeded the mean accuracy of the clinicians and even the single best clinical prediction. If clinicians were provided with the calculated likelihood, improvement of predicting abilities occurred, but never matched the calculated prediction. Though none of the studied articles judged age as a predictive variable for non-SLN metastasis, the young age of the patient in scenario 1 may have been regarded by various surgical oncologists as exceptional.10,12–16,18–23 This resulted in a deviating median considered need to perform a cALND. Human judgment is colored by many factors. Fatigue, recent experience, changes in order of information, over-confidence in one’s clinical judgement, inability to distinguish between valid and invalid variables and the weight of various variables influence clinical prediction. Further, a tendency exists to overrate information consistent with one’s hypothesis and ignore contradictory information.26,29

Clinical experience influences diagnostic and therapeutic performance. A study on diagnostic skills of general practitioners in the first moment of consultation demonstrated a strong correlation between experience and diagnostic performance.30 Another study proved an inverse relationship between experience expressed as the number of years since graduation of medical school and performance of internists.31 A third author determined that time in practice as well as the type of the hospital had an influence on physician performance. A physician would perform optimally between 6 and 15 years after graduation in a large, multispecialty group.32 A recently published systemic review reported a decrease in performance with increasing experience in more than half of the examined studies.28 A possible drawback in this study could be that experience in treating a specific condition may not be necessarily correlated to the number of years after graduation. The present study, however, could not detect any relation between predicting abilities and clinical experience, nor the type of hospital, where the interviewee worked.

In conclusion, our study demonstrates that the individual predictions of surgical oncologists for non-SLN metastases do not correlate well with the MSKCC nomogram. Furthermore, the wide range of abilities of individual surgeons to predict non-SLN metastases is important and thus, the nomogram outperforms expert judgment.

The Scenarios
What is the risk for residual disease in the axilla? (1) In a 55-year-old woman with a 1.3 cm LIQ tumor: nuclear grade II, ER–/PR+, not multifocal, no LVI and one of two SLNs positive by immunohistochemistry (IHC). (2) In a 34-year-old woman with a 0.6 cm UOQ tumor: nuclear grade III, ER+/PR–, not multifocal, no LVI and one of one SLNs positive by IHC. (3) In a 50-year-old woman with a 3 cm central tumor: nuclear grade II, ER–/PR–, multifo-cal, no LVI and one of two SLNs positive by IHC. (4) In a 67-year-old woman with a 2.2 cm UOQ tumor: nuclear grade II, ER+/PR+, not multifocal, LVI and one of four SLNs positive by routine histology. (5) In a 61-year-old woman with a 0.9 cm UOQ tumor: nuclear grade II, ER+/PR+, multifocal, LVI and one of two SLNs positive by routine histology. Would you dissect the axilla?

(1) In a 28 year old woman with a 1.2 cm UIQ tumor: nuclear grade II, ER–/PR–, not multifocal, LVI and one of three SLNs positive by serial HE. The risk for residual disease calculated by the nomogram is 7%. (2) In a 62-year old woman with a 2.5 cm UIQ tumor: nuclear grade II, ER+/PR+, not multifocal, no LVI and one of three SLNs positive by serial HE. The risk for residual disease calculated by the nomogram is 8%. (3) In a 38-year-old woman with a 3.5 cm LOQ tumor: nuclear grade III, ER+/PR+, not multifocal, no LVI and one of three SLNs positive by serial HE. The risk for residual disease calculated by the nomogram is 14%. (4) In a 53-year-old woman with a 0.5 cm UOQ tumor: nuclear grade II, ER+/PR–, not multifocal, no LVI and two of five SLNs positive by routine histology. The risk for residual disease calculated by the nomogram is 21%. (5) In a 50-year-old woman with a 1.5 cm UOQ tumor: lobular cancer, ER+/PR+, multifocal, no LVI and one of one SLNs positive by routine histology. The risk for residual disease calculated by the nomogram is 52%. These scenarios were presented at the Breast conference on 16 December 2004 at Memorial Sloan-Kettering Cancer Center, New York, USA, by Michelle Specht MD. The "no frozen section" nomogram was used. (LIQ—lower inner quadrant, LOQ—lower outer quadrant, UO-Q—upper outer quadrant, UIQ—upper inner quadrant, ER—estrogen receptor, PR—progesterone, LVI—lymphovascular invasion)


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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