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  • Research article
  • Open Access
  • Open Peer Review

Lymph node positivity in different early breast carcinoma phenotypes: a predictive model

  • 1, 14Email author,
  • 1, 14,
  • 2,
  • 3,
  • 4,
  • 1,
  • 5,
  • 6,
  • 7,
  • 8,
  • 9,
  • 10,
  • 11,
  • 12,
  • 13,
  • 1,
  • 9,
  • 2,
  • 4 and
  • 1
BMC Cancer201919:45

https://doi.org/10.1186/s12885-018-5227-3

  • Received: 24 January 2018
  • Accepted: 16 December 2018
  • Published:
Open Peer Review reports

Abstract

Background

A strong correlation between breast cancer (BC) molecular subtypes and axillary status has been shown. It would be useful to predict the probability of lymph node (LN) positivity. Objective: To develop the performance of multivariable models to predict LN metastases, including nomograms derived from logistic regression with clinical, pathologic variables provided by tumor surgical results or only by biopsy.

Methods

A retrospective cohort was randomly divided into two separate patient sets: a training set and a validation set. In the training set, we used multivariable logistic regression techniques to build different predictive nomograms for the risk of developing LN metastases. The discrimination ability and calibration accuracy of the resulting nomograms were evaluated on the training and validation set.

Results

Consecutive sample of 12,572 early BC patients with sentinel node biopsies and no neoadjuvant therapy. In our predictive macro metastases LN model, the areas under curve (AUC) values were 0.780 and 0.717 respectively for pathologic and pre-operative model, with a good calibration, and results with validation data set were similar: AUC respectively of 0.796 and 0.725.

Among the list of candidate’s regression variables, on the training set we identified age, tumor size, LVI, and molecular subtype as statistically significant factors for predicting the risk of LN metastases.

Conclusions

Several nomograms were reported to predict risk of SLN involvement and NSN involvement. We propose a new calculation model to assess this risk of positive LN with similar performance which could be useful to choose management strategies, to avoid axillary LN staging or to propose ALND for patients with high level probability of major axillary LN involvement but also to propose immediate breast reconstruction when post mastectomy radiotherapy is not required for patients without LN macro metastasis.

Keywords

  • Breast cancer
  • Sentinel node
  • Risk prediction
  • Nomogram
  • Molecular subtype

Synopsis

A retrospective cohort of 12.572 early BC patients with SN biopsies was randomly divided into two separate patient sets to develop, validate and compare different predictive nomograms for the risk of developing LN metastases from clinical and pathologic variables provided by tumor surgical results or by biopsy.

Background

In breast cancer (BC), nodal status is a major prognostic factor that determines therapeutic decisions to a large extent. Sentinel lymph node biopsy (SLNB) provides a reliable assessment of the axilla status in early clinically node-negative BC [1]. Since it also causes less morbidity than axillary lymph node dissection (ALND), it is now considered as a standard of care procedure. The omission of completion ALND in patients with negative sentinel lymph nodes (SLN) has been recognized as a reasonable attitude since the publication of the NSABP B-32 results [2]. Moreover, it is likely that it can be safely expanded to patients with minimal SLN involvement (isolated tumor cells and micro metastases), with regard to survival outcomes [3, 4]. Indeed, 40 to 70% of these patients do not have metastatic non-sentinel lymph nodes (NSLN) [5]. Main predictors of LN metastases are tumor size, grade, lymphovascular invasion (LVI), age at diagnosis, extracapsular extension of the positive SLN, and hormonal and HER2 receptor status [610]. In addition, a strong correlation between BC molecular subtypes and /or tumor phenotypes on the one hand (determined by hormonal receptor and HER2 status) and axillary status on the other hand has been shown in numerous studies [1116].

The determination of the risk of positive axillary LN can significantly contribute to therapeutic decisions. However, this risk cannot be immediately induced from the results of multivariate analyses that provide broad statistical information. Only an appropriate prediction tool, using a nomogram, can indicate the individual risk of a given patient. These nomograms can also be used to compare populations from different studies. A large cohort is necessary to reliably determine the probability of positive SN, particularly for less frequent tumor phenotypes. Reyal et al. published such a nomogram predictive of the risk of developing SN metastases in 2011 [11], built on a training set made of 1543 early-stage BC patients, and validated on two cohorts of 615 and 496 patients respectively. This model was further validated in a cohort of 755 consecutive patients treated at Institut Curie in 2009 [17].

The aim of our study was to develop and compare the performance of multivariable models to predict LN metastases, including nomograms derived from logistic regression with clinical, pathologic variables provided by tumor surgical results or only provided by biopsy as explanatory variables.

Methods

Patients

Our cohort consisted of 12,572 consecutive patients with small (≤ 30 mm based on clinical and radiologic findings), clinically node-negative invasive BC, who did not receive neoadjuvant therapy, and underwent SLNB between 1999 and 2012 at 13 French centers. HER2 status was determined for all patients. During the first years of the study, ALND was systematically performed in some sites; thereafter, ALND was performed only in case of SN involvement, this attitude being homogeneous within all the participating sites.

Evaluation

The following data were retrieved: characteristics of patients (age at the time of SLNB), and tumors [size, clinical stage, histological type, estrogen (ER), progesterone (PR) and HER2 status, LVI, Scarff-Bloom-Richardson (SBR) grade], description of ALND (number of LN sampled and involved), and results of the pathological examination of surgical resection specimens. Tumor size was determined on the results of pathological examination but could be evaluated pre operatively by mammography, sonography and in selected cases by MRI (clinical T stage). LVI was detected on surgical specimen.

Tumor phenotype was defined by the combination of ER, PR and HER2 status, evaluated by immuno-histochemistry (IHC) and confirmed by FISH in case of IHC-HER2 2+. Positivity for ER and PR was determined according to French guidelines (≥ 10% of cancer cells expressing ER/PR). Five molecular subtypes were defined according to clinico-pathological criteria [18]. Because information on Ki-67 was not available, we used grade to capture cell proliferation, as described by von Minckwitz et al [19] The following definitions were used: triple-negative (basal-like, HER2-/HR-), HER2 positive (non-luminal, HER2+/HR-), and luminal (HR+), divided into luminal A (HR+/HER2−/grade1 or 2), luminal B-HER2-negative like (HR+/HER2−/grade 3), and luminal B-HER2-positive like (HR+/HER2+ all grades).

Although the methods used for histological examination were not standardized in the protocol, all sites proceeded similarly: serial sections were performed every 200 μm and stained with standard hematoxylin and eosin. The number of sections was six to ten, or pursued until node exhaustion in case of large SN. Additional IHC analysis was done in case of negative results at standard examination. For additional nodes identified by completion ALND, routine HE analysis was performed.

Five categories of LN status were defined: negative LN (pN0i-), isolated tumor cells (pN0(i+): < 0.2 mm), detected either by hematoxylin and eosin (HE) staining or by cytokeratin IHC, micro metastases (pN1mi: > 0.2 mm and < 2 mm), and macro metastases (> 2 mm), divided into single and multiple macro metastases [20].

Statistical methods

Our main objective was to create prediction models for the risk of LN positivity and the risk of LN macroscopic metastases from clinical and pathologic variables provided by tumor surgical results or by biopsy, and evaluate their performance with respect to three main features: discrimination (i.e. whether the relative ranking of individual predictions is in the correct order), calibration (i.e. agreement between observed outcomes and predictions) and clinical utility defined as proportions of patients classified into risk categories using predefined cutoff values (< 10%, between 10 and 20%, between 20 and 30%, between 30 and 40%, and > = 40%). Our main evaluation criteria were based on the final status of LN metastases (pN0(i+), pN1mi or pN1ma) as the result of SLNB alone or the final result of both SLNB and ALND. LN positivity was defined as the presence of isolated tumor cells, micro or macro LN metastases. We used logistic regression models [21] including age (<=40, 41–75,> 75), tumor size (<=20, 20–30, > = 30 mm) or clinical T stage (T0-T1, T2, T3-T4), tumor grade, histology type, LVI, and molecular subtypes as predictor factors to predict each individual risks. The list of predictor factors was set beforehand, based on the investigator’s experience and some reference papers [611, 1315, 17]. No additional procedure was used in regression analysis to reduce the list of only 5 or 6 predictor factors identified beforehand. Prior to analysis, we randomly divided our initial cohort (N = 12,572) in two separate sub-cohorts: a large training cohort (N = 8381) to create prediction models and a confirmatory cohort (N = 4191) to evaluate their individual’s prediction performance. A split-sample approach was adopted in order to estimate unbiasedly the model performance, as these estimates are known to be biased upwards when regression parameters are estimated on the same dataset [22]. First we performed a descriptive analysis using the following criteria: patient’s age at SN biopsy, clinical and pathological tumor size, tumor grade and histology type, lymphovascular invasion or not (LVI), presence of estrogen (ER), progesterone (PR) and hormonal receptors (RH), Her2 positivity, tumor subtype, number of SN removed and final LN status. The evaluation of each model was assessed in the training sample and the confirmatory sample. Differences in patient’s and tumor’s characteristics were compared using Chi Square or exact Fisher test, Student or Wilcoxon rank sum tests as appropriate. The discrimination ability was evaluated by the area under the ROC (Receiver Operating Characteristic) curve (AUC). We used the functions roc and pROC implemented in R to estimate AUC with 95%CI and test for difference in AUCs along the Delong’s method in the confirmatory sample [23]. Empirical distributions of AUC observed after re-fitting a model on bootstrap replicates (B = 2000) were used to estimate AUC and difference in AUCs with 95% Ci in the training sample. Model calibration was evaluated using Hosmer goodness-of-fit test [24]. All statistical analyses were conducted in the R Language and Environment for Statistical Computing version 3.2.5 (The R Foundation, Vienna, Austria).

Results

Patients’ characteristics

Patients’ main characteristics are summarized in Table 1. SBR grade was 1, 2 and 3 in 34, 46 and 20% of cases respectively. Hormone receptor-positive tumors (ER+ and/or PR+) accounted for 88% of cases (11,013 patients). Final LN status, taking into account ALND results when performed, was: pN0(i-) in 8253 patients (66%), pN0(i+) in 355 (3%), pN1mi in 970 (8%) and macro metastasis in 2994 (24%). The comparison between patients with positive and negative final LN status, and between patients with LN macro metastases versus pN0 or pNo(i+) or pN1mi showed statistically significant differences with regard to age, pathologic tumor size, SBR grade, LVI, histological type and distribution of molecular subtypes (Tables 2 and 3).
Table 1

Population: all patients and patients according to initial data set or validation set

 

All patients

Initial set

Validation set

 

Nb

%

Nb

%

Nb

%

Nb patients

12,572

 

8381

 

4191

 

Age median (range)

58 (18–101)

58 (18–101)

58 (18–100)

<  60

7231

58

4857

58

2374

57

 61–65

1810

14

1166

14

644

15

 >  65

3525

28

2355

28

1170

28

Median tumor size

14

 

14

 

14

 

<  10

4701

38

3136

38

1565

38

 11 to 20

5053

41

3368

41

1685

41

 >  20

2679

22

1784

22

895

22

No SN removed

 1

3268

31

2203

31

1065

31

 2

3374

32

2231

31

1143

33

 3

2034

19

1382

20

652

19

  > 4

1886

18

1271

18

615

18

Tumor type

 Ductal

9793

78

6522

78

3271

78

 Lobular

1645

13

1110

13

535

13

 Mixt

226

2

144

2

82

2

 Others

899

7

599

7

300

7

Grade

 1

4246

34

2891

35

1355

33

 2

5756

46

3800

46

1956

47

 3

2448

20

1611

19

837

20

LVI

 Negative

8430

78

5661

78

2769

77

 Positive

2400

22

1595

22

805

23

Estrogen receptors

 negative

1730

14

1146

14

584

14

 positive

10,828

86

7227

86

3601

86

Progesterone receptors

 negative

3464

29

2281

29

1183

30

 positive

8522

71

5701

71

2821

70

Hormonal receptors

 negative

1541

12

1020

12

521

12

 positive

11,013

88

7349

88

3664

88

Her2 status

 negative

11,350

90

7570

90

3780

90

 positive

1222

10

811

10

411

10

Tumor sub types

 Luminal A

8998

72

6026

72

2972

71

 Luminal B Her2-

1178

9

756

9

422

10

 HR+ Her2+

766

6

521

6

245

6

 HR- Her 2+

450

4

288

3

162

4

 Triple Negative

1091

9

732

9

359

9

pN final

 pN0(i-)

8253

66

5507

66

2746

66

 pN0(i+)

355

3

233

3

122

3

 pN1mi

970

8

660

8

310

7

 Macro

2994

24

1981

24

1013

24

Clinical size

 T0

2764

23

1831

23

933

23

 T1

6784

57

4544

57

2246

56

 T2

2115

18

1383

17

732

18

  > T3

283

2

187

2

96

2

Table 2

Initial data set and validation set results according to axillary nodal involvement

 

Initial set

Validation set

pN0

i+/mi/macro

pN0

i+/mi/macro

Nb

%

Nb

%

p

Nb

%

Nb

%

p

Nb patients

5507

 

2874

  

2746

 

1445

  

Age median (range)

59 (18–101)

55 (20–98)

 

59 (18–100)

56 (22–90)

 

<  60

3001

55

1856

65

< 0.0001

1464

53

910

63

< 0.0001

 61–65

818

15

348

12

 

443

16

201

14

 

 >  65

1686

31

669

23

 

836

30

334

23

 

Tumor size (median)

12

 

19

  

12

 

20

  

<  10

2642

49

494

17

< 0.0001

1340

49

225

16

< 0.0001

 11 to 20

2185

40

1183

41

 

1081

40

604

42

 

 >  20

608

11

1176

41

 

294

11

601

42

 

Tumor type

 Ductal

4244

77

2278

79

< 0.0001

2133

78

1138

79

< 0.0001

 Lobular

711

13

399

14

 

332

12

203

14

 

 Mixt

73

1

71

2

 

38

1

44

3

 

 Others

473

9

126

4

 

241

9

59

4

 

Grade

 1

2142

39

749

26

< 0.0001

1007

37

348

24

< 0.0001

 2

2410

44

1390

49

 

1245

46

711

49

 

 3

887

16

724

25

 

459

17

378

26

 

LVI

 Negative

4077

89

1584

59

< 0.0001

2004

89

765

58

< 0.0001

 Positive

510

11

1085

41

 

256

11

549

42

 

Estrogen receptors

 negative

747

14

399

14

0.6964

371

14

213

15

0.296

 positive

4757

86

2470

86

 

2371

86

1230

85

 

Progesterone receptors

 negative

1568

30

713

26

0.0004

782

30

401

29

0.6346

 positive

3678

70

2023

74

 

1841

70

980

71

 

Hormonal receptors

 negative

660

12

360

13

0.4835

337

12

184

13

0.6948

 positive

4841

88

2508

87

 

2406

88

1258

87

 

Her2 status

 negative

5837

91

1733

87

< 0.0001

2909

92

871

86

< 0.0001

 positive

563

9

248

13

 

269

8

142

14

 

Tumor sub types

 Luminal A

4100

75

1926

67

< 0.0001

2029

75

943

66

< 0.0001

 Luminal B Her2-

364

7

392

14

 

217

8

205

14

 

 HR+ Her2+

336

6

185

6

 

140

5

105

7

 

 HR- Her 2+

151

3

137

5

 

93

3

69

5

 

 Triple Negative

509

9

223

8

 

244

9

115

8

 

Clinical size

 T0

1458

28

373

14

< 0.0001

756

29

177

13

< 0.0001

 T1

3166

61

1378

50

 

1565

60

675

48

 

 T2

546

11

837

30

 

272

10

460

33

 

  > T3

18

0

169

6

 

16

1

80

5

 
Table 3

Initial data set and validation set results according to axillary nodal macro metastasis involvement

 

Initial set

Validation set

pN0 pN1mi

macro

 

pN0pN1mi

macro

 

Nb

%

Nb

%

p

Nb

%

Nb

%

p

Nb patients

6400

 

1981

  

3178

 

1013

  

Age median (range)

58.4 (18–101)

55 (20–98)

 

59 (18–100)

56 (25–90)

 

<  60

3573

56

128/4

65

< 0.0001

1743

55

631

62

0.0002

 61–65

934

15

232

12

 

510

16

134

13

 

 >  65

1891

30

464

23

 

922

29

248

24

 

Tumor size

  < 10

2872

45

264

13

< 0.0001

1453

46

112

11

< 0.0001

 11 to 20

2665

42

703

36

 

1310

42

375

37

 

  > 20

786

12

998

51

 

380

12

515

51

 

Tumor type

 Ductal

4981

78

1541

78

< 0.0001

2494

79

777

77

< 0.0001

 Lobular

800

13

310

16

 

382

12

153

15

 

 Mixt

82

1

62

3

 

43

1

39

4

 

 Others

531

8

68

3

 

257

8

43

4

 

Grade

 1

2442

39

449

23

< 0.0001

1147

37

208

21

< 0.0001

 2

2841

45

959

49

 

1453

46

503

50

 

 3

1048

17

563

29

 

540

17

297

29

 

LVI

 Negative

4655

86

1006

55

< 0.0001

2287

86

482

53

< 0.0001

 Positive

780

14

815

45

 

373

14

432

47

 

Estrogen receptors

 negative

827

13

319

16

0.0003

414

13

170

17

0.0031

 positive

5570

87

1657

84

 

2760

87

841

83

 

Progesterone receptors

 negative

1751

29

530

28

0.4585

885

29

298

30

0.6346

 positive

4330

71

1371

72

 

2140

71

681

70

 

Hormonal receptors

 negative

732

11

288

15

0.0002

375

12

146

14

0.0306

 positive

5662

89

1687

85

 

2800

88

864

86

 

Tumor sub types

 Luminal A

4780

75

1246

63

< 0.0001

2345

74

627

62

< 0.0001

 Luminal B Her2-

458

7

298

15

 

272

9

150

15

 

 HR+ Her2+

383

6

138

7

 

161

5

84

8

 

 HR- Her 2+

178

3

110

6

 

106

3

56

6

 

 Triple Negative

554

9

178

9

 

269

9

90

9

 

Clinical size

 T0

1601

26

230

12

< 0.0001

828

27

105

11

< 0.0001

 T1

3731

61

813

43

 

1835

61

405

42

 

 T2

710

12

673

36

 

348

11

384

40

 

  > T3

30

0

157

8

 

20

1

76

8

 
We first predicted the individual probabilities of final LN positivity and of detecting LN macro metastases from selected clinico-pathologic predictor factors provided by tumor surgical results. The model AUCs with 95% CIs for confirmatory and training samples were respectively 0.767 [0.750–0.783] and 0.755 [0.744–0.767]. Calibration plot and Hosmer-Lemeshow test revealed that the calibration is adequate (p = 0.332 in confirmatory sample, p = 0.158 in training sample). With respect to clinical utility in confirmatory and training samples, the probability of positive LN were respectively below 10% for 7 patients (< 1%) and 19 patients (< 1%), between 10 and 20% for 1096 (31%) and 2255 (32%), and ≥ 20% for 2409 (68.6%) and 4859 (68.1%) patients (Table 4) (Fig. 1A, Additional file 1: Figure S1A and Additional file 2: Figure S2A). The second pathological model estimated the probability of detecting LN macro metastases only. The AUC values for confirmatory and training samples were respectively 0.798 (0.780–0.815) and 0.780 [0.767–0.790]. Clinical utility measures, estimated the probability of LN macro metastases respectively in confirmatory and training samples below 10% for 1004 patients (29%) and 2029 patients (28%), between 10 and 20% for 1075 patients (31%) and 2289 patients (32%), and >  20% for 1433 patients (41%) and 2815 patients (39.4%). The Hosmer-Lemeshow test revealed a poor calibration of the model (p = 0.024 in confirmatory sample, p = 0.427 in training sample) (Table 4 and Additional file 1: Table S1) (Fig. 1B, Additional file 2: Figure S1B, Additional file 3: Figure S2B).
Table 4

Discrimination, calibration and clinical utility measures of pathologic and pre-operative prediction models

 

Pathologic model

Pre-operative model

Probability of LN positivity

Criteria

Parametre

Initial set

Validation set

Initial set

Validation set

AUC

Est

0.754

0.767

0.681

0.687

 

95%CI

[0.742–0.765]

[0.75–0.783]

[0.668–0.693]

[0.669–0.705]

AUC (Bootstrap,

Est

0.755

0.766

0.682

0.686

B = 2000)

95%CI

[0.744–0.767]

[0.762–0.769]

[0.669–0.694]

[0.682–0.69]

Clinical utility

< 10%

19 (0%)

7 (0%)

1 (0%)

0 (0%)

10–20%

2255 (32%)

1096 (31%)

579 (7%)

279 (7%)

20–30%

1258 (18%)

586 (17%)

3137 (40%)

1487 (38%)

30–40%

1108 (16%)

559 (16%)

2478 (32%)

1315 (33%)

40–50

391 (5%)

188 (5%)

228 (3%)

125 (3%)

> = 50%

2102 (29%)

1076 (31%)

1429 (18%)

746 (19%)

Calibration

p-value

0.158

0.332

0.815

0.200

Probability of macrometastases

AUC (Delong)

Est

0.780

0.798

0.718

0.727

95%CI

[0.767–0.792]

[0.78–0.815]

[0.703–0.732]

[0.707–0.746]

AUC (Bootstrap,

Est

0.780

0.796

0.717

0.725

B = 2000)

95%CI

[0.767–0.793]

[0.793–0.799]

[0.703–0.732]

[0.721–0.728]

Clinical utility

< 10%

2029 (28%)

1004 (29%)

358 (5%)

184 (5%)

10–20%

2289 (32%)

1075 (31%)

5049 (64%)

2450 (62%)

20–30%

512 (7%)

262 (7%)

829 (11%)

465 (12%)

30–40%

726 (10%)

358 (10%)

307 (4%)

162 (4%)

40–50

644 (9%)

378 (11%)

573 (7%)

306 (8%)

> = 50%

933 (13%)

435 (12%)

736 (9%)

385 (10%)

Calibration

p-value

0.427

0.024

0.568

0.174

Fig. 1
Fig. 1

Nomograms. 1a: Nomogram predictive of LN Involvement– Pathologic model. 1b: Nomogram predictive of LN macro metastases – Pathologic model. 1c: Nomogram predictive of LN Involvement– Clinical model. 1d: Nomogram predictive of LN macro metastases – Clinical model.

We evaluated the loss in discrimination ability in pre-operative prediction models omitting the information about LVI and substituting pathological tumor size information by clinical T stage. For the overall probability of LN positivity, the AUC values for confirmatory and training samples were respectively 0.687 [0.669–0.705] and 0.682 (0.669–0.694). For the probability of detecting LN macro metastases, the observed AUC results for confirmatory and training samples were respectively 0.727 [0.707–0.746] and 0.717 (0.703–0.732). The calibration of both pre-operative models was found satisfactory. (Table 4) (Fig. 1 C-D, Additional file 2: Figure S1C-D, Additional file 3: Figure S2C-D).

The change in AUCs between pathological and per-operative model were found statistically significantly decreased (p < 0.001). We also evaluated in the confirmatory sample the discrimination ability of the prediction models obtained when treating the variable age and tumor size as continuous. The AUC values for predicting LN positivity and the presence of LN metastases were respectively 0.774 [0.758, 0.79] and 0.805 [0.789–0.823]. The observed increases were significantly (p = 0.041 and p = 0.026), but the results in terms of calibration were judged inadequate (Hosmer-Lemeshow p value < 0.001).

Discussion

The aim of this study was to better understand the relationships between tumor characteristics and the probability of axillary LN positivity. The large cohort used in our study is appropriate for less frequent tumor phenotypes (namely Her2+ and HR-Her2-). We distinguished between various histological tumor types, showing a lower LN positivity rate in tumors other than ductal, lobular or mixt, as previously reported for BC with favorable histology (tubular, mucinous, papillary, medullary, adenoid cystic and secretory) that are associated with a very low LN positivity rate [25].

In our model, we used the same independent variables as Reyal et al. [11], namely age, tumor size, molecular subtypes and LVI, and we added grade and histological type. However, age intervals were different, as well as tumor phenotype definitions (ER only in the Reyal model) and tumor size description (continuous variable in the Reyal model). We obtained different odds ratios for the same variables and clinical utility results were different and higher for low probability of positive lymph node, particularly for macro metastases in our population for both models. Clinical utility results for low probability of positive lymph node could be contributive to avoid surgical axillary staging by sentinel lymph node biopsy or axillary lymph node dissection.

The models were less reliable when information about LVI was missing. LVI could be detected on pre-operative biopsies but the difference in accuracy is obviously large in comparison with surgical specimen analysis.

The HER2 status was unknown in old studies [8] and others studies were based on small number of patients. We found that HER2 negative tumors were associated with LN positivity less frequently than HER2 positive tumors (22.9% vs. 31.9%). Lu et al. published that the lowest probability of node metastasis was for ER- / HER2- tumors [12]. Similarly in our study, triple negative tumors had the lowest probability of node metastasis, while HR- / Her2+ tumors had the highest probability. Reyal et al. hypothesized that the axillary LN metastatic process is predominantly related to intrinsic biological properties in ER-negative and HER2-negative BC, while tumor size, proliferation rate and LVI are the main determinants in the ER positive or HER2 positive breast cancers. However, positive axillary lymph nodes in triple negative BC were pejorative prognostic factors for sentinel node macro-metastases but also for occult sentinel node involvement (pN0(i+) and pN1mi) [26].

A reliable predictive model of LN positivity, based on pathologic parameters, can be used to compare populations from different studies, particularly for trials with or without axillary surgical procedure. Above all, it might allow avoiding SN biopsy when the probability of positivity is very low (< 10%). Some authors already suggested that SN biopsy could be omitted in tumors with good-prognosis subtypes [25] or that axillary dissection is useless in older patients [27]. We believe that these criteria lack accuracy and we prefer a decision-making approach, based on molecular subtypes. However, we must be aware of the risk of insufficient treatment in small tumors with favorable prognostic factors, in which LN status is a major determinant of adjuvant chemotherapy and regional radiotherapy. Moreover, the model is less reliable when LVI is not documented, which is usually the case before surgery. Ultra-sonography of the axilla and percutaneous biopsy is a growing practice. These clinical predictive tools may be helpful relative to the use of axillary ultra-sonography with percutaneous LN biopsy for patients with high level risk of axillary LN involvement.

These models can also be contributive in order to determined indications of post mastectomy radiotherapy for patients with axillary lymph nodes macro-metastases [28], particularly when immediate breast reconstruction can be proposed.

Conclusions

We reported a reliable predictive model of LN positivity according to different early breast carcinoma phenotypes in a large cohort. The determination of the risk of positive axillary LN can significantly contribute to therapeutic decisions. These models, with or without LVI results, can also be used to determine the risk of positive axillary LN or the risk of LN macro-metastasis. Before surgery, clinical models can be used to propose SLNB or not according to LN involvement probability. After surgery, in case of SLNB omission, if LN involvement probability is high, with eventually modifications of adjuvant treatment indications according to LN status, a re-operation can be proposed (SLNB or cALND). Thus clinical and pathologic models should be helpful in surgical planning, in the setting of a clinical trial and in clinical practice to avoid SLNB for very low risk of LN involvement and to avoid re-operation in case of SLNB omission or to propose ALND for patients with high level probability of major axillary LN involvement but also to propose immediate breast reconstruction when PMRT is not required for.

Declarations

Acknowledgments

Not applicable.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and materials

The datasets generated and analyzed during the current study are available in the ClinicalTrials.gov (Identifier: NCT02869607) repository, https://clinicaltrials.gov/ct2/show/NCT02869607?cond=Breast+Cancer&cntry=FR&city=Marseille&rank=2

Authors’ contributions

All authors have participated to the data collection: acquisition, analysis and interpretation.

All authors been involved in drafting the manuscript.

GH, EL, JMB and MC have written this document.

All authors have read and approve the final version of paper for submission in BMC CANCER. They assure that the manuscript is not, and will not be, under simultaneous consideration by any other publication. The paper reports previously unpublished work. All authors given final approval of the version to be published.

Ethics approval and consent to participate

This work was approved by our institutional review board (IPC - Comité d’Orientation Stratégique). All procedures performed in this study involving human participants were done in accordance with the French ethical standards and with the 2008 Helsinki declaration. All included patients provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institut Paoli Calmettes et CRCM, 232 boulevard de Sainte Marguerite, 13009 Marseille, France
(2)
Institut René Gauducheau, Site Hospitalier Nord, St Herblain, France
(3)
Institut Gustave Roussy, 114 rue Edouard Vaillant, Villejuif, France
(4)
Centre Oscar Lambret, 3 rue Frédéric Combenal, Lille, France
(5)
Centre Léon Bérard, 28 rue Laennec, Lyon, France
(6)
Centre Claudius Regaud, 20-24 rue du Pont St Pierre, Toulouse, France
(7)
Centre René Huguenin, 35 rue Dailly, Saint Cloud, France
(8)
Hôpital Tenon, 4 rue de la Chine, Paris, France
(9)
Centre Georges François Leclerc, 1 rue du Professeur Marion, Dijon, France
(10)
Hôpital de Grasse, Chemin de Clavary, Grasse, France
(11)
Hôpital des Diaconnesses, 18 rue du Sergent Bauchat, Paris, France
(12)
Centre Jean Perrin, 58 rue Montalembert, Clermont Ferrand, France
(13)
Institut Bergonié, 229 Cours de l’Argonne, Bordeaux, France
(14)
Aix-Marseille University, Unité Mixte de Recherche S912, Institut de Recherche pour le Développement, 13385 Marseille, France

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Copyright

© The Author(s). 2019

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