Multicentre validation of different predictive tools of non-sentinel lymph node involvement in breast cancer

Surgical Oncology 21 (2012) 59e65

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Review

Multicentre validation of different predictive tools of non-sentinel lymph node involvement in breast cancer G. Cserni a, b, *, G. Boross c, R. Maráz c, M.H.K. Leidenius d, T.J. Meretoja d, P.S. Heikkila e, P. Regitnig f, G. Luschin-Ebengreuth g, J. Zgajnar h, A. Perhavec h, B. Gazic i, G. Lázár j, T. Takács j, A. Vörös a, R.A. Audisio k a

Department of Pathology, University of Szeged, Állomás u. 2., H-6720 Szeged, Hungary Bács-Kiskun County Teaching Hospital, Department of Pathology, Nyiri ut 38, H-6000 Kecskemét, Hungary Bács-Kiskun County Teaching Hospital, Department of Surgery, Nyiri ut 38, H-6000 Kecskemét, Hungary d Breast Surgery Unit, Helsinki University Central Hospital, P.O. Box 140, Helsinki, FI-00029 HUS, Finland e Department of Pathology, Helsinki University Central Hospital, P.O. Box 400, Helsinki, FI-00029 HUS, Finland f Department of Pathology, Medical University of Graz, Auenbruggerplatz 25, 8036 Graz, Austria g Department of Obstetrics and Gynecology, Medical University of Graz, Auenbruggerplatz 14, 8036 Graz, Austria h Department of Surgical Oncology, Institute of Oncology, Zaloska c. 2, 1105 Ljubljana, Slovenia i Department of Pathology, Institute of Oncology, Zaloska c. 2, 1105 Ljubljana, Slovenia j Department of Surgery, University of Szeged, Pecsi u 6, H-6720 Szeged, Hungary k Department of Surgery, St Helens Teaching Hospital, Marshalls Cross Road, WA93DA, St Helens, UK b c

a r t i c l e i n f o

a b s t r a c t

Article history: Accepted 2 December 2011

Sentinel lymph node (SN) biopsy offers the possibility of selective axillary treatment for breast cancer patients, but there are only limited means for the selective treatment of SN-positive patients. Eight predictive models assessing the risk of non-SN involvement in patients with SN metastasis were tested in a multi-institutional setting. Data of 200 consecutive patients with metastatic SNs and axillary lymph node dissection from each of the 5 participating centres were entered into the selected non-SN metastasis predictive tools. There were significant differences between centres in the distribution of most parameters used in the predictive models, including tumour size, type, grade, oestrogen receptor positivity, rate of lymphovascular invasion, proportion of micrometastatic cases and the presence of extracapsular extension of SN metastasis. There were also significant differences in the proportion of cases classified as having low risk of non-SN metastasis. Despite these differences, there were practically no such differences in the sensitivities, specificities and false reassurance rates of the predictive tools. Each predictive tool used in clinical practice for patient and physician decision on further axillary treatment of SN-positive patients may require individual institutional validation; such validation may reveal different predictive tools to be the best in different institutions. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Axillary lymph node dissection Breast cancer Non-sentinel lymph nodes Predictive model Sentinel lymph node Validation

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Authorship statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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* Corresponding author. Department of Pathology, Bács-Kiskun County Teaching Hospital, Nyiri ut 38., H-6000 Kecskemét, Hungary. Tel.: þ36 76 516 768; fax: þ36 76 481 219. E-mail address: [email protected] (G. Cserni). 0960-7404/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.suronc.2011.12.001

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G. Cserni et al. / Surgical Oncology 21 (2012) 59e65

Introduction With the widespread use of sentinel lymph node (SN) biopsy (SNB), axillary lymph node dissection (ALND) has been largely abandoned as an initial staging procedure for clinically nodenegative early breast cancer. As node-negative breast carcinomas are nowadays more common than those with nodal metastasis, this policy has saved a lot of SN-negative breast cancer patients from the potential morbidity of ALND without influencing their chances for cure [1,2]. However, patients with a metastatic axillary SN (considered metastatic in a first echelon lymph node) generally undergo completion ALND, and again, the majority reveal no further metastases in the second or further echelon lymph nodes. This has stimulated considerable research to find out which factors influence further nodal involvement, i.e. non-SN (NSN) metastasis. Several models have been developed, and different predictive tools (including nomograms, scores, predictive rules) have been constructed to assist clinicians and patients to predict the risk of NSN metastasis after the finding of a positive SN. The early results of the American College of Surgeons Oncology Group Z-11 trial suggest that in the study population, survival is not affected by not removing potentially involved NSNs by ALND, provided that the patients receive whole breast irradiation with opposing tangential fields which generally includes nearly the whole level I and II of the axilla and almost all patients receive systemic adjuvant treatment [3]. The role of axillary radiotherapy instead of completion ALND is also explored in the EORTC 10981-22023 (AMAROS) trial [4]. Although it seems very likely that further surgery in the form of ALND has an alternative in the form of radiotherapy, the role of predictive tools for NSN involvement allowing a selective approach to any form of regional treatment is important. The majority of such predictive tools have been developed from single institutional databases using part of the data to validate the predictive model on a relatively independent set of patients. A few models were derived from multi-institutional collaborations. Many of these tools were independently validated by other centres, with different rates of success. For example, the first nomogram produced by the Memorial Sloan-Kettering Cancer Centre (MSKCC) [5] is certainly the most studied one, with many supportive validation series [6], although it has been found to perform poorly in a subset of patients with micrometastatic breast cancer [7]. These predictive tools vary largely in the magnitude of their source data, the data found statistically important in predicting NSN metastases, the weight these data receive in the model, etc (Table 1). On the other hand, what these models share in general, is that they are not perfect in predicting the actual NSN status, although they perform better than tossing a coin and are superior to simple clinical judgement [8]: the area under the receiver operating characteristic (ROC) curve is generally between 0.7 and 0.85. It is well documented that both the procedure of SNB (in terms of timing, dosage, volume, tracers, injection sites, etc.) [9] and the pathological assessment of the SLNs [10,11] (as well as the tumours themselves) vary to a great extent. We therefore hypothesized that such variations may be a source of divergent models, but also of divergent usability of a predictive tool in a given institution. To prove or refute this hypothesis, retrospective cases were collected from different institutions and were tested in different predictive tools.

Materials and methods Data Each participating institution provided data on 200 consecutive female patients with invasive breast carcinoma with one or more

positive SNs and completion ALND collected retrospectively since December 2010. Any metastatic SN involvement (including isolated tumour cells, ITCs) was considered positive in this study. All patients underwent primary surgery, while patients receiving neoadjuvant systemic treatment were excluded. No case with reSNB (i.e. SNB after previous removal of SNs) was included. The data collected included pathological size of the primary tumour, grade (both histological and nuclear), histological type (ductal, lobular, mixed ductal and lobular versus other), oestrogen receptor, progesterone receptor and HER2 status, the presence of lymphovascular invasion, the number of positive and negative SNs, category of the SN involvement (isolated tumour cells e up to 0.2 mm, micrometastasis e greater than 0.2 and up to 2 mm; and macrometastasis e greater than 2 mm), the size of the SN metastasis (if available), the presence of extracapsular extension (ECE) of SN metastasis, the number of negative and positive NSNs. Data were entered into the following previously published predictive tools: - MSKCC nomogram available at http://www.mskcc.org/mskcc/ html/15938.cfm [5]. The methods of detection had to be adopted at some institutions: e.g. imprint cytology replaced frozen sections [12e14] or intraoperative immunohistochemistry (IHC) substituted permanent section IHC [15,16] or serial sections were omitted from the analysis (Table 2). - MD Anderson score [17], - Tenon score [18], - Mayo nomogram [19], - Louisville clinical prediction rule [20], - Stanford nomogram available at: https://www3-hrpdcc. stanford.edu/nsln-calculator [21], - French micrometastasis nomogram [22], and - Masaryk nomogram [23]. All variables required for the above mentioned models are summarized in Table 1. Although the aim was to have complete datasets for all the 200 cases per each institution, the size of the SN metastasis were not readily available for several centres, therefore these variables were originally considered as optional. The number and proportion of patients predicted to have a low rate of NSN involvement (10% or having a given score as defined by the original description of the model e i.e. 0 for the MDA score, 3.5 for the Tenon score and 60 points for the Mayo nomogram and score 1 for the Louisville prediction rule) were calculated for each centre to reflect the usefulness of the given model. This was followed by the determination of the eventual rate of NSN involvement in this subgroup of patients. Statistics The distribution of different variables and results were compared with a two-tailed Students’ t-test or the chi-square test using the Vassar Stat statistical software (VassarStats, Vassar College, Poughkeepsie, NY USA). For the comparison of false reassurance rates, where the case numbers were generally too low, the Fisher exact test (two-tailed) was used instead of the chisquare test. A p value < 0.05 was taken as significance level. The model values for low risk of NSN metastasis were taken into consideration as test values for predicting negative NSNs, and the sensitivity, specificity and negative predictive value of the tests were calculated on this basis. Of these, sensitivity and negative predictive value were given more consideration, due to the clinical question of reliably identifying patients at low risk of NSN involvement.

G. Cserni et al. / Surgical Oncology 21 (2012) 59e65

61

Table 1 Variables included in the different predictive models tested. Variables

MSKCC nomogram

MD Anderson score

Tenon score

Mayo nomogram

Louisville clinical prediction rule

Stanford nomogram

French micrometastasis nomogram

Masaryk nomogram

Age Tumour size Categorical Continuous Tumour type Nuclear grade LVI ER status Triple negativity Multifocality of the primary tumour Tumour location Number of positive SNs Number of negative SNs Number of SNs Proportion of positive SNs Detection method of SN metastasis Size of SN metastasis Categorical Continuous Extracapsular spread

 þ  þ þ þ þ þ  þ  þ þ   þ    

 þ þ    þ     

 þ þ  e e e e e    e  þ  þ þ  

þ þ e þ        þ þ    þ e þ þ

 þ þ         þ   þ     

 þ e þ   þ          þ þ  

 þ þ  þ  þ         þ    

 þ þ  þ  þ   þ  þ þ    þ þ  þ

þ   þ þ  

Ethics This retrospective work with no influence on patients therapy or outcome was submitted to institutional review boards for permission where necessary. No ethical objection was raised at either site. Data safety monitoring for anonymity of the patients concerned was also obtained where necessary.

Results The 5 centres contributing with data to the present study provided parameters on 1000 patients and related breast tumours. The relevant characteristics of each institutional subset is presented in Table 3, showing how most variables presented significant differences across the five participating institutions. The proportion of patients classified to have a low risk of NSN involvement and the actual rate of NSN involvement in this group (false reassurance rate) is shown in Table 4. The same table also

shows sensitivity, specificity and negative predictive value for each of the predictive tools. The proportion of patients identified as having low risk of NSN involvement varied significantly from predictive model to predictive model and to a lesser degree from institution to institution. The Mayo nomogram was not able to identify a subset of patients with low risk of NSN involvement in any of the five centres. Fluctuations were also seen in the false reassurance rates, but when considering predictive tools by institutions, there was only one significant difference (p ¼ 0.004) concerning the Tenon score in centres B and E (Table 4) whereas there were no significant differences when institutions were looked at by predictive tool. Overall false reassurance rates of the predictive tools were also compared, and there was a significant difference between the French micrometastasis nomogram (with the lowest rate) and the MSKCC nomogram or the Louisville clinical prediction rule and also between the Louisville clinical prediction rule and the Stanford nomogram.

Table 2 Major details of the pathological assessment of SNs by centres. Centre

Preoperative nodal assessment

Intraoperative assessment

Permanent section assessment

A

Palpation, axillary ultrasound (US) and US-guided fine needle aspiration cytology of suspicious nodes

Slicing or halving SNs >5 mm and subjecting the cut surfaces to imprint cytology stained with HE; no intraoperative assessment for smaller SNs

B

Palpation, axillary ultrasound (US) and US-guided fine needle aspiration cytology of suspicious nodes

C

Palpation of the axilla

Slicing SNs at 1e1.5 mm thick slices and subjecting the cut surfaces to imprint cytology stained with toluidine blue, followed by frozen sections from 3 levels with steps of 0.3 mm (levels 1 and 3 stained with toluidine blue, level 2 with ultrarapid cytokeratin) Slicing or halving SNs >5 mm and using one part for 2 frozen sections stained with HE or methylen-blue; SNs up to 5 mm are frozen in toto

Step sectioning of each slice with steps of 0.25 mm till the extinction of the block (HE); cytokeratin AE1/AE3 immunostain of every 3rd level if negative by HE 2 HE sections per slice (approximately 0.2 mm apart), cytokeratin 8/18 (CAM5.2) or AE1/AE3 if negative by HE or if ITC was identified on HE

D

Palpation, axillary ultrasound (US) and US-guided fine needle aspiration cytology of suspicious nodes

E

Palpation, axillary ultrasound (US) and US-guided fine needle aspiration cytology of suspicious nodes

Halving SNs, then slicing them at 2 mm intervals and subjecting the cut surfaces to imprint cytology stained with Hemacolor fast staining kit (Merck KGaA, Darmstadt, Germany) Slicing or halving SNs >5 mm and subjecting the cut surfaces to imprint cytology stained with HE

SN: sentinel node; HE: haematoxylin and eosin; IHC: immunohistochemistry.

Step sectioning of each slice with steps of 0.125 mm till the extinction of the block (HE); cytokeratin AE1/AE3 immunostain of every 3rd level if negative by HE Step sectioning of each slice with steps of 0.25 mm for both HE and cytokeratin IHC if initial HE stained level is negative Step sectioning of each slice with steps of 0.25 mm till the extinction of the block (HE); cytokeratin IHC only in doubtful cases

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G. Cserni et al. / Surgical Oncology 21 (2012) 59e65

Table 3 Variables included in the models by institutions.

Age: mean  SD (median) Tumour size in mm: mean  SD (median) Tumour type IDC ILC IDC þ ILC Other LVI present ER positive Histological grade 1/2/3 Nuclear grade 1/2/3 SN number: mean  SD (median) NSN number: mean  SD (median) NSN positive cases (%) SN metastasis size (mm): mean  SD (median) SN metastasis MIC or ITC/MAC (%MIC-ITC) ECE present

Centre A

Centre B

Centre C

Centre D

Centre E

58  12 (59)a 20  14 (17)a

59  11 (60)a 21  18 (18)a

57  13 (57) 18  9 (16)a

58  10 (58)a 22  11 (21)a

56  11 (57)a 22  11 (19)a

143 (72%) 21 (11%) 10 (5%) 26 (13%) 76 (38%)a 178 (89%)a 48a/85/67 15a/79a/106a 1.9  1 (2)a 12  5 (12)a 79 (40%)a 5.7  4.9 (4.8)a 57/143 (29%)a 90 (45%)a

149 (75%) 31 (16%) 4 (2%) 16 (8%) 52 (26%)a 189 (95%)a 50a/89/61 24a/101a/75a 2.4  1.4 (2)a 19  6 (18)a 55 (28%)a NA 85/115 (43%)a 54 (27%)a

160 (80%) 15 (8%) 21 (11%) 4 (2%) 51 (26%)a 161 (81%)a 20a/91/89 45a/91a/64a 1.8  1.2 (1)a 15  6 (14)a 70 (35%) 6.4  7.1 (3)a 74/126 (37%)a 43 (22%)a

168 (84%) 21 (11%) 9 (4.5%) 2 (1%) 80 (40%)a 181 (91%)a 27a/104/69 4a/124a/72a 1.8  1.0 (2)a 17  6 (16)a 53 (27%)a 4.6  4.1 (3.5)a 63/137 (32%)a 53 (27%)a

169 (85%) 13 (7%) 4 (2%) 14 (7%) 57 (29%)a 152 (76%)a 21a/100/79 10a/76a/114a 1.9  1 (2)a 10  5 (9)a 70 (35%) NA 34/166 (17%)a 36 (18%)a

a

a

a

a

a

IDC: invasive ductal carcinoma, no special type/not otherwise specified; ILC: invasive lobular carcinoma; ITC: isolated tumour cells/clusters; ECE: extracapsular extension; ER: oestrogen receptor; LVI: lymphovascular invasion; MAC: macrometastasis, i.e. >2 mm; MIC: micrometastasis; NA: not applicable; NSN: non-sentinel (lymph) node; SD: standard deviation; SN: sentinel (lymph) node. a Significantly different from at least one other set.

Variations in the negative predictive values, sensitivities and specificities of the models are also shown in Table 4. Discussion SN biopsy allows a selective approach to the regional surgical treatment of breast cancer patients. Patients with negative SNs can be safely spared an axillary lymph node dissection and its potential morbidity, they can reliably be considered as regional lymph nodenegative [1,2]. Patients with positive SNs present a challenge in further management plans, as about two thirds of them have no further nodal metastasis. Likewise, less than one third (327) of the 1000 patients undergoing ALND at the five different centres participating in this study had NSN metastases. The dilemma of how to offer best treatment to SN-positive patients is also reflected by the fact that about one fifth of the nearly hundred thousand such patients in the National Cancer Data Base had no completion ALND [24]. If the aim of surgery is the most adequate removal of locoregional disease with the safest way of avoiding overtreatment in patients who have no identifiable disease in the axilla, the identification of SN-positive patients at low risk of NSN metastasis is more than understandable. Several models have been built, in order to predict the risk of NSN metastasis [5,17e23,25e29]. Of these the MSKCC nomogram is the first one to be developed as well as the most studied up to date [5]. It has been validated in several institutions. The area under receiver operating characteristic (ROC) curves has often been used to describe these models and has been found to fall between 0.7 and 0.86 [6], suggesting that the predictive models are not perfect (area under ROC curve: 1), but are much better than random selection by chance alone (area under ROC curve: 0.5). The models were generally derived from single institutional data [5,17,18,21], with a few considering data from two or more institutions [20,22]. As shown in Table 1, the models included in this study identify different parameters as significant predictors of NSN metastasis, but they considered different sets of parameters to derive the model from. Table 1 demonstrates that tumour size, lymphovascular invasion (LVI), SN metastasis size (sometimes indirectly reflected by the method of detection), the number and/or proportion of positive SNs have often been integrated in the models as parameters of importance. This is in keeping with the findings of

an earlier [30] as well as a more recent [31] meta-analysis of predictive models, both of which also identified extracapsular nodal extension of the SN metastasis as a relevant parameter. Most of these parameters involve data derived from pathology reports (Table 1). Several parameters may differ in SN biopsy, including the pathological work-up of the SNs. In a questionnaire based survey, the European Working Group for Breast Screening Pathology found 123 somewhat different histology protocols reported from 240 European pathology departments, with the most common one being adopted by only 8 departments [11]. This diversity was identified as one of the contributors to the wide range of upstaging (9e47%) reached by SN biopsy reported in the literature [10]. However, not only the pathology processing of the SNs might be different, but also that of the primary tumours. This may be hypothesized by comparing larger series of breast cancers for pathology derived prognostic factors. Likewise, the distribution of several parameters showed significant differences among some of the institutions participating in this multicentre study. (The variability in the distribution of these parameters does not necessarily reflect divergent pathology in the background, although this might be an important cause; it can partly be due to differences in the populations as well as tumours). With this diversification in the variables considered by the predictive models we expected different predictive tools to perform differently in different institutions. Although the area under the ROC curve has frequently been used to characterise NSN metastasis predictive tools, this reflects the overall value of the tools, including their specificity and sensitivity for both low and high risk patients. This study concentrated on patients with low risk of NSN metastasis, therefore a different approach was adopted, by viewing the predictive tool as a test selecting between NSN positive and negative patients. Some of the tools do not seem very useful due to the very limited proportion of patients they highlight as belonging to the low risk group (e.g. the Louisville clinical prediction rule [20]). The Mayo nomogram failed to identify a subset with low risk, which contrasts with the original description where the authors reported a low (3%; 16/531) proportion of patients belonging to this category. It may be speculated, that the nomogram reported in the printed paper, which was also used in this study, could be incorrect [19].

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Table 4 The predicted and observed rate of NSN involvement in patients with calculated low risk. Predictive tool (low risk definition)

Proportion of cases with low predicted risk

MSKCC nomogram (10%) Centre A 23/200 (11.5%)a Centre B 12/200 (6%)a Centre C 38/200 (19%)a Centre D 38/200 (19%)a Centre E 44/200 (22%)a Overall/Mean 155/1000 (15.5%) S.D. MDA score (0) Centre A 46/200 (23%)a Centre B 85/200 (42.5%)a Centre C 60/200 (30%)a Centre D 46/200 (23%)a Centre E 28/200 (14%)a Overall/Mean 265/1000 (26.5%) S.D. Tenon score (3.5) Centre A 58/200 (29%)a Centre B 83/200 (41.5%)a Centre C 70/200 (35%)a Centre D 48/200 (24%)a Centre E 51/200 (25.5%)a Overall/Mean 310/1000 (31%) S.D. Mayo nomogram (60 points) Centre A 0/200 (0%) Centre B NA Centre C 0/200 (0%) Centre D 0/200 (0%) Centre E NA Overall/Mean 0/600 (0%) Louisville prediction rule (score 1) Centre A 7/200 (3.5%) Centre B 3/200 (1.5%) Centre C 6/200 (3%) Centre D 0/200 (0%) Centre E 0/200 (0%) Overall/Mean 16/1000 (1.6%) S.D. Stanford nomogram (10%) Centre A 19/200 (9.5%)a Centre B 49/200 (24.5%)a Centre C 41/200 (20.5%)a Centre D 13/200 (6.5%)a Centre E 11/200 (5.5%)a Overall/Mean 143/1000 (14.3%) S.D. French micrometastasis nomogram (10%) Centre A 24/57 (42.1%; 12% of all) Centre B 25/85 (29.4%; 12.5% of all) Centre C 23/74 (31%; 11.5% of all) Centre D 25/63 (39.7%; 12.5% of all) Centre E 9/34 (26.5%; 4.5% of all) Overall/Mean 106/313 (33.9%) S.D. Masaryk nomogram (10%) Centre A 19/200 (9.5%)a Centre B NA Centre C 30/200 (15%)a Centre D 5/200 (2.5%)a Centre E NA Overall/Mean 54/600 (9%) S.D. a

Actual rate of NSN metastasis in the low risk patients (False reassurance rate) (%95%CI) 6/23 1/12 7/38 5/38 11/44 30/155

(26%  18%) (8%  16%) (18%  12%) (13%  11%) (25%  13%) (19.4%  6.2%)

10/46 12/85 6/60 7/46 5/28 40/265

(22% (14% (10% (15% (18% (15%

8/58 7/83 10/70 6/48 14/51 45/310

(14%  9%) (8%  6%)a (14%  8%) (13%  9%) (28%  12%)a (15%  4%)

     

12%) 7%) 8%) 10%) 14%) 4%)

Sensitivity

2/7 (29%  33%) 0/3 (0%) 3/6 (50%  40%) NA NA 5/16 (31.3%  22.7%)

Negative predictive value

92% 98% 90% 91% 84% 91% 5%

14% 8% 24% 22% 25% 19% 8%

74% 92% 82% 87% 75% 82% 8%

87% 78% 91% 87% 93% 87% 6%

30% 50% 42% 27% 18% 33% 13%

78% 86% 90% 84% 82% 84% 4%

90% 87% 86% 89% 80% 86% 4%

41% 52% 46% 29% 29% 39% 11%

86% 92% 86% 86% 73% 85% 7%

e e e e e e

NA NA NA NA NA NA

Specificity

e e e e e e

98% 100% 96% e e 98% 3%

e e e e e e 4% 2% 2%

3% 1%

71% 100% 50% e e 74% 25%

e e

2/19 3/49 7/41 1/13 1/11 13/143

(11%  14%) (6%  7%) (17%  12%) (8  15%) (9%  17%) (9%  5%)

98% 95% 90% 98% 99% 96% 4%

14% 32% 26% 8% 8% 18% 11%

90% 94% 83% 92% 91% 90% 4%

2/24 1/25 3/23 2/25 0/9 8/106

(8%  11%) (4%  8%) (13%  14%) (8%  11%) (0%) (8%  5%)

75% 83% 63% 71% 100% 78% 14%

45% 30% 30% 41% 31% 36% 7%

92% 96% 87% 92% 100% 93% 5%

2/19 NA 6/30 1/5 NA 9/54

(11%  14%)

98% e

(20%  14%) (20%  35%)

91% 98% e

(17%  10%)

14% e

96% 4%

90% e

19% 3% e 12% 8%

80% 80% e 83% 6%

Proportion that is significantly different from the findings of at least one other centre.

The present results suggest that some predictive tools select a meaningful proportion of SN-positive patients, and have a reasonable false-negative rate for acceptance and everyday use (Table 4). The French micrometastasis nomogram might be of special interest. Patients with SN micrometastasis have been

variably interpreted in term of risk of NSN involvement. Some studies suggest no considerable risk for further metastasis in NSNs whereas others argue in favour of a substantial risk mandating completion ALND [32,33]. This nomogram identified one third of patients having ITC or micrometastasis as having a low risk of NSN

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G. Cserni et al. / Surgical Oncology 21 (2012) 59e65

metastasis: this was the model that performed the best in this study. Although the last StGallen consensus meeting ended with the majority of the expert voting against ALND for a micrometastatic SN, there might be a subset of patients with a considerable risk of NSN involvement even in this group [34] and patients’ wish must be also considered [35]. However, the aim of the present study was not to evaluate different predictive tools, but to prove that these may perform differently in different institutions. This difference is shown by the variability in the proportions of patients highlighted as having low risk of NSN involvement in different institutions. On the other hand, the variations in the false reassurance rates observed with the predictive tools by institution were not significant with the exception of the Tenon score in Centres B and E. A possible limitation of the present study could be due to the fact that we only collected 200 patients per institution, and this may not be enough to reliably highlight the differences we monitored. It must be remembered that these 200 cases per institution reflect a minority of patients selected for SN biopsy, as SN metastatic cases reflect 25 to 40 percent of all cases undergoing sentinel lymphadenectomy. This rate is lower in patients pre-screened by axillary ultrasound, where 13% of patients with a negative lymph node status based on palpation only proved to be positive after ultrasound-guided fine needle aspiration of suspicious nodes and therefore underwent ALND instead of SNB [36]. Indeed several published validation series presented smaller series than the individual institutional contributions analysed in this series [6]: it is also believed that changes that are not highlighted by this study might not be of the scale of influencing current practices. Conclusions Although we were not able to demonstrate significant performance differences of a given predictive tool by institution, the variables used in the models often showed significant differences from site to site, and likewise, the proportion of low risk patients selected was also rather variable. Therefore it can be suggested that any predictive tool planned to be used somewhere in decision making and patient advising might be better validated at the institution where it was planned to be used. This approach is more likely to select the tool that might best perform for the given setting; this is also expected to be better than selecting a predictive tool from the literature and apply it to the data that are likely to be divergent at least in some respects. Conflict of interest statement The authors have declared no conflict of interest. Authorship statement Guarantor of the integrity of the study: G. Cserni. Study concepts: G. Cserni, M. H. K. Leidenius, P. Regitnig, J. Zgajnar, G. Lázár, R. A. Audisio. Study design: G. Cserni, R. A. Audisio. Definition of intellectual content: G. Cserni, R. A. Audisio, M. H. K. Leidenius, P. Regitnig, J. Zgajnar, G. Lázár. Literature research: G. Cserni, M. K. H. Leidenius, J. Zgajnar, P. Regitnig. Clinical studies: None. Experimental studies: None. Data acquisition: G. Cserni, G. Boross, R. Maráz, M. H. K. Leidenius, T. J. Meretoja, P. S. Heikkila, P. Regitnig, G. Luschin-Ebengreuth, J. Zgajnar, A. Perhavec, B. Gazic, G. Lázár, T. Takács, A. Vörös. Data analysis: G. Cserni. Statistical analysis: G. Cserni.

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