Prediction of High Nodal Burden in Patients With Sentinel Node–Positive Luminal ERBB2-Negative Breast Cancer

Ida Skarping; Pär-Ola Bendahl; Robert Szulkin; Sara Alkner; Yvette Andersson; Leif Bergkvist; Peer Christiansen; Tove Filtenborg Tvedskov; Jan Frisell; Oreste D Gentilini; Michalis Kontos; Thorsten Kühn; Dan Lundstedt; Birgitte Vrou Offersen; Roger Olofsson Bagge; Toralf Reimer; Malin Sund; Lisa Rydén; Jana de Boniface

doi:10.1001/jamasurg.2024.3944

. 2024 Sep 25;159(12):1393–1403. doi: 10.1001/jamasurg.2024.3944

Prediction of High Nodal Burden in Patients With Sentinel Node–Positive Luminal ERBB2-Negative Breast Cancer

Ida Skarping ^1,^2,^✉, Pär-Ola Bendahl ¹, Robert Szulkin ^3,⁴, Sara Alkner ^1,⁵, Yvette Andersson ^6,⁷, Leif Bergkvist ⁸, Peer Christiansen ⁹, Tove Filtenborg Tvedskov ^10,¹¹, Jan Frisell ^12,¹³, Oreste D Gentilini ^14,¹⁵, Michalis Kontos ¹⁶, Thorsten Kühn ^17,¹⁸, Dan Lundstedt ^19,²⁰, Birgitte Vrou Offersen ^21,^22,²³, Roger Olofsson Bagge ^24,^25,²⁶, Toralf Reimer ²⁷, Malin Sund ^28,²⁹, Lisa Rydén ^1,³⁰, Jana de Boniface ^31,³²

¹Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden

²Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden

³Cytel Inc, Sweden

⁴Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden

⁵Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden

⁶Department of Surgery, Västmanland Hospital, Västerås, Sweden

⁷Centre for Clinical Research Uppsala University, Västmanland Hospital Västerås, Sweden

⁸Centre for Clinical Research Uppsala University, Västmanland Hospital Västerås, Sweden

⁹Department of Plastic and Breast Surgery, Aarhus University Hosoital, Denmark

¹⁰Department of Breast Surgery, Gentofte Hospital, Denmark

¹¹Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

¹²Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

¹³Breast Center Karolinska, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden

¹⁴Università Vita-Salute San Raffaele, Milano, Italy

¹⁵IRCCS Ospedale San Raffaele, Milano, Italy

¹⁶1st Department of Surgery, National and Kapodistrian University of Athens, Laiko Hospital, Athens, Greece

¹⁷Interdisciplinary Breast Center, University of Ulm, Ulm, Germany

¹⁸Breast Center Die Filderklinik, Filderstadt, Germany

¹⁹Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

²⁰Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden

²¹Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark

²²Department of Oncology, Aarhus University Hospital, Aarhus, Denmark

²³Aarhus University, Faculty of Health, Aarhus, Denmark

²⁴Sahlgrenska Center for Cancer Research, Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

²⁵Department of Surgery, Sahlgrenska University Hospital, Gothenburg, Sweden

²⁶Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

²⁷Department of Obstetrics and Gynecology, University of Rostock, Rostock, Germany

²⁸Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

²⁹Department of Diagnostics and Intervention/Surgery, Umeå University, Umeå, Sweden

³⁰Department of Surgery and Gastroenterology, Skane University Hospital, Lund, Sweden

³¹Department of Surgery, Capio St Göran’s Hospital, Stockholm, Sweden

³²Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden

Accepted for Publication: July 9, 2024.

Published Online: September 25, 2024. doi:10.1001/jamasurg.2024.3944

^✉

Corresponding Author: Ida Skarping, MD, PhD, Division of Oncology, Department of Clinical Sciences, Lund University, 223 81 Lund, Sweden (ida.skarping@med.lu.se).

Author Contributions: Drs Szulkin and de Boniface had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Rydén and Boniface are co–senior authors responsible for study oversight.

Concept and design: Skarping, Bendahl, Szulkin, Andersson, Filtenborg Tvedskov, Frisell, Lundstedt, Vrou Offersen, Olofsson Bagge, Sund, Rydén, de Boniface.

Acquisition, analysis, or interpretation of data: Skarping, Szulkin, Alkner, Andersson, Bergkvist, Christiansen, Frisell, Gentilini, Kontos, Kühn, Lundstedt, Vrou Offersen, Olofsson Bagge, Reimer, Sund, Rydén, de Boniface.

Drafting of the manuscript: Skarping, Bendahl, Szulkin, Frisell, Lundstedt, Rydén, de Boniface.

Critical review of the manuscript for important intellectual content: Skarping, Szulkin, Alkner, Andersson, Bergkvist, Christiansen, Filtenborg Tvedskov, Frisell, Gentilini, Kontos, Kühn, Lundstedt, Vrou Offersen, Olofsson Bagge, Reimer, Sund, Rydén, de Boniface.

Statistical analysis: Bendahl, Szulkin.

Obtained funding: Skarping, Rydén, de Boniface.

Administrative, technical, or material support: Skarping, Frisell, Lundstedt, Olofsson Bagge, Rydén, de Boniface.

Supervision: Skarping, Bergkvist, Frisell, Gentilini, Lundstedt, Vrou Offersen, Olofsson Bagge, Reimer, Rydén, de Boniface.

Conflict of Interest Disclosures: Dr Gentilini reported compensation for consultation/advisory board service for Merck Sharp & Dohme, AstraZeneca, Eli Lilly, BD Biosciences, and Bayer. Dr Kühn reported personal fees from Merck Sharp & Dohme, Eli Lilly, Endomag, Sirius Medical, Merit Medical, and AstraZeneca outside the submitted work. Dr Olofsson Bagge reported an institutional research grant from Endomagnetics, a speaker honorarium from Pierre-Fabre, and shareholdings with Sahlgrenska Translational Melanoma Group Ventures outside the submitted work. No other disclosures were reported.

Funding/Support: This trial is supported by grants from the Swedish Research Council, the Swedish Cancer Society, the Swedish Society of Medicine, the Swedish Breast Cancer Association, and the Nordic Cancer Union. In addition, we acknowledge funding from the Governmental Funding of Clinical Research in the National Health Services (Avtal om läkarutbildning och forskning young researcher Dr Skarping) and Erling Persson Foundation (Drs Bendahl and Rydén).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank all the participants in the trial.

^✉

Corresponding author.

PMCID: PMC11425194 PMID: 39320882

Key Points

Question

Can high nodal burden (≥4 axillary metastases/≥N2) be predicted without completion axillary lymph node dissection (CALND) in patients with luminal ERBB2-negative tumors and, separately, in those with invasive lobular carcinoma, and 1 or 2 sentinel lymph node (SLN) macrometastases?

Findings

In this diagnostic/prognostic study including the CALND arm (n = 1010) of the randomized SENOMAC trial, 2 prediction models for the identification of patients with ≥N2 were developed, including number of SLN macrometastases, additional SLN micrometastases, SLN ratio, extracapsular extension, and tumor size (only luminal model).

Meaning

The prediction models may be used to identify patients at risk of high nodal burden eligible for intensified systemic treatment strategies without performing CALND.

This diagnostic/prognostic study evaluates prediction models for high nodal burden in luminal ERBB2-negative breast cancer.

Abstract

Importance

In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment.

Objective

To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND.

Design, Setting, and Participants

The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024.

Exposure

Predictors of high nodal burden.

Main Outcomes and Measures

High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration.

Results

Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83).

Conclusions and Relevance

The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed.

Trial Registration

ClinicalTrials.gov Identifier: NCT02240472

Introduction

Sentinel lymph node (SLN) biopsy is the standard procedure for axillary staging in patients with clinically node-negative (cN0) breast cancer. While completion axillary lymph node dissection (CALND) was previously routine care in patients with positive SLNs, it is today safely omitted in patients with 1 or 2 SLN metastases.^1,2,3 However, some systemic treatment recommendations in estrogen receptor (ER)–positive Erb-B2 receptor tyrosine kinase 2 (ERBB2)–negative breast cancer rely on the identification of high nodal burden (ie, ≥4 metastatic axillary lymph nodes [ALNs]), which can only be determined by CALND.⁴ The recent Abemaciclib Plus Endocrine Therapy for Hormone Receptor–Positive, HER2-Negative, Node-Positive, High-Risk Early Breast Cancer (MONARCHE) trial^5,6 showed that patients with high-risk early ER-positive breast cancer, defined as 4 or more metastatic ALNs or 1 to 3 metastatic ALNs together with either tumor size of 5 cm or greater or histologic grade 3, had improved invasive disease-free survival after 2 years of adjuvant CDK 4/6 inhibitor treatment in addition to endocrine therapy compared to the control group. In the Microarray in Node-Negative and 1 to 3 Positive Lymph Node Disease May Avoid Chemotherapy (MINDACT) trial,⁷ axillary nodal burden was indicative of systemic treatment. In addition, screening for distant metastases and locoregional radiotherapy target extension are considered in breast cancer with high nodal burden.⁸ High nodal burden can thus alter adjuvant treatment decisions. Therefore, CALND omission may lead to undertreatment.

Negative outcomes of CALND, such as arm lymphedema (affecting up to 25% of patients), numbness, and restricted shoulder mobility, are well documented.³ Arm morbidity is considerably worse after CALND than after SLN biopsy only.^9,10 Therefore, alternative strategies to correctly identify high nodal burden without exposing patients to the risk of long-term arm morbidity are warranted.

Known indicators of high nodal burden are larger tumor size and a higher number of suspicious nodes at axillary imaging.¹¹ Risk factors for additional non-SLN metastases in patients with metastatic SLNs (ie, not necessarily a high nodal burden) also include a high ratio of metastatic to excised SLNs (SLN ratio), extracapsular extension, and lymphovascular invasion in the primary tumor.¹²

Invasive lobular carcinoma (ILC) is most often of luminal A subtype and accounts for 10% to 15% of all breast cancer cases.^13,14,15,16 In comparison to invasive breast cancer of no special type (NST), ILC is associated with a higher nodal burden and a higher rate of non-SLN metastases.^17,18,19 In ILC, axillary ultrasonography, fine-needle aspiration cytology, and core needle biopsy have a lower sensitivity for detection of nodal metastases than in NST.^20,21,22 In addition, SLN biopsy has a higher false negative rate in ILC than in NST.²³ Unfortunately, no published axillary de-escalation trials have presented subgroup analyses addressing ILC.^1,2,3 Additionally, ILC is associated with late recurrences, which may not be fully captured by follow-up times ranging from 5.0 to 9.3 years.^1,2,3

Prediction models aimed at detecting high nodal burden offer a tool to identify individuals who require intensified adjuvant treatments while avoiding CALND.^{24,25,26,27,28} However, existing models are based on retrospective data encompassing any histopathological and molecular subtypes. None of the models have focused on luminal breast cancer, where nodal burden influences adjuvant strategies and no distinction between NST and ILC has been made. The primary aim of the present study was to develop prediction models for high nodal burden in patients with 1 or 2 SLN macrometastases and luminal ERBB2-negative breast cancer and ILC, respectively, which could reduce the risk of undertreatment without exposing patients to CALND.

Methods

Patient Characteristics

The Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial is a randomized (1:1), prospective, international, noninferiority trial. The study protocol was approved by the ethics committee at Karolinska Institutet, Stockholm, Sweden, and the trial was conducted according to good clinical practice. Enrollment was from January 2015 to December 2021. Randomized assignment was to CALND or its omission. Adjuvant treatment was according to national guidelines. Informed written consent was obtained from all individual participants prior to study inclusion.

The SENOMAC trial design and end points have been previously described, and initial results have been presented.^29,30 Adult patients diagnosed with primary invasive cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases were eligible. Additional SLN micrometastases were allowed. Preoperative axillary ultrasonography was mandatory. While patients having an SLN biopsy prior to preoperative systemic therapy were eligible, 55 such patients were excluded from the present analysis since their CALND results, obtained after systemic therapy, may not reflect the initial nodal status. Overall, 1010 patients with luminal ERBB2-negative breast cancer randomized to CALND constituted the present study cohort, 212 of whom (21.0%) had ILC (eFigure in Supplement 1). Data analysis for this article took place from June 2023 to April 2024. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline was followed.

Important Definitions

The TNM classification system was used. Thus, pathological nodal status (pN) was pN1 in case of 1 to 3 nodal metastases, pN2 for 4 to 9, and pN3 for 10 or more metastases.³¹ High nodal burden was defined as pN2 or pN3. Histopathological tumor type was categorized into 3 groups: NST, ILC, or other/mixed.³²

Statistical Analysis

Sample size calculations were performed for the primary and secondary outcomes of the SENOMAC trial, but not for the present analysis.²⁹ Patient and tumor demographic characteristics were summarized using standard descriptive statistical measures. Differences between the subgroups were evaluated using χ² tests for categorical variables, linear trend tests for characteristics measured on an ordinal scale, and t tests for continuous variables.

Two prediction models for pN2-3 vs pN1 were developed: one for the entire luminal ERBB2-negative cohort and another for the ILC subgroup. For the development of the prediction model for the luminal ERBB2-negative cohort, patients were randomly split into a training set (80%) and a test set (20%) for validation. The split was stratified on outcome (≥pN2) to get the same prevalence of high nodal burden in both datasets. To minimize the risk of overfitting, the prediction model was developed in the training set and evaluated in the test set. The ILC subgroup was not split due to its smaller size.

Routinely accessible variables after SLN biopsy were selected based on published literature and considered as candidates for the prediction models (eAppendix 1 in Supplement 1). Univariable logistic regression analysis was used to estimate associations between candidate predictors and high nodal burden in the luminal ERBB2-negative cohort and the ILC subgroup, respectively.

The model development steps are described in detail in eAppendix 1 in Supplement 1. Briefly, backward logistic regression analysis was combined with bootstrap to select a subset of predictor variables for the final model. Since the fraction of missing data was low, models were developed using data from patients with complete data.

The diagnostic performance of the prediction model for the luminal ERBB2-negative cohort was evaluated in terms of discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (Hosmer-Lemeshow plots, calibration slope, and intercept). The model for the luminal ERBB2-negative cohort was validated in the test set. Since no test set was set aside for the ILC subgroup, this model was not internally validated and should be regarded as exploratory. The equations for the final prediction models are presented in eAppendix 2 in Supplement 1. Nomograms were created. Sensitivity, specificity, positive predictive value, negative predictive value, true negative, true positive, false negative, and false positive rates for the models were calculated for clinically relevant predetermined cutoffs.

The prognostic value of the model for the luminal ERBB2-negative cohort was evaluated with recurrence-free survival (RFS) as the end point, using Kaplan-Meier estimates together with 1-df log-rank test for trend over the risk groups, and Cox regression. RFS was defined according to the Standardized Definitions for Efficacy End Points (STEEP) criteria.³³ The proportional hazards assumption in the Cox models was tested using Schoenfeld residuals.

The evidence against each null hypothesis was quantified by a P value and the word significant used for 2-sided P < .05. The reported P values have not been adjusted for multiple comparisons. R version 4.2.2 (R Foundation) was used for all statistical analyses.

Results

Descriptive Results

High vs Low Nodal Burden

Of 1010 patients with luminal ERBB2-negative breast cancer assigned to CALND in the SENOMAC trial (luminal cohort; median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden (Table). In 846 patients with 1 SLN macrometastasis, 83 (9.8%) had ≥pN2, while in 164 with 2 SLN macrometastases, 55 had ≥pN2 (33.5%) (P < .001). The median (range) tumor size was significantly smaller in patients with a low nodal burden (19.0 mm [1.1-136.0] vs 25.0 mm [4.0-155.0]; P < .001). Additional SLN micrometastases were less common in the low nodal burden group (84 of 872 [9.6%] vs 22 of 138 [15.9%]; P = .07), and SLN extracapsular extension significantly less prevalent (296 of 868 [34.1%] vs 66 of 137 [48.2%]; P = .002). No difference was seen between low vs high nodal burden for lymphovascular invasion in the primary tumor (242 of 870 [27.8%] vs 38 of 137 [27.7%]; P > .99).

Table. Baseline Characteristics of Patients.

Characteristic	No. (%)			P value	Trend test
Characteristic	Low nodal burden, pN1 (n = 872)	High nodal burden, ≥pN2 (n = 138)	Overall (N = 1010)	P value	Trend test
Sex
Female	868 (99.5)	138 (100)	1006 (99.6)	.95	NA
Male	4 (0.5)	0	4 (0.4)	.95	NA
Age at randomization, y
<40	19 (2.2)	2 (1.4)	21 (2.1)	.92	>.99
40-49	137 (15.7)	24 (17.4)	161 (15.9)
50-64	346 (39.7)	55 (39.9)	401 (39.7)
65-74	262 (30.0)	38 (27.5)	300 (29.7)
≥75	108 (12.4)	19 (13.8)	127 (12.6)
Mean (SD)	61.19 (11.49)	60.91 (11.43)	61.15 (11.47)	.79	NA
Median (range)	61.00 (34.00-90.00)	60.00 (34.00-88.00)	61.00 (34.00-90.00)	NA	NA
T stage
pT1	501 (57.5)	56 (40.6)	557 (55.1)	<.001	<.001
pT2	331 (38.0)	61 (44.2)	392 (38.8)
pT3	40 (4.6)	21 (15.2)	61 (6.0)
Tumor size, mm
Mean (SD)	22.78 (15.61)	31.14 (21.67)	23.92 (16.81)	<.001	NA
Median (range)	19.00 (1.10-136.00)	25.00 (4.00-155.00)	19.00 (1.10-155.00)	NA	NA
Tumor type
NST	673 (77.2)	92 (66.7)	765 (75.7)	.01	NA
ILC	170 (19.5)	42 (30.4)	212 (21.0)
Other/mixed	29 (3.3)	4 (2.9)	33 (3.3)
Histological grade (NHG)
1	175 (20.2)	31 (22.5)	206 (20.5)	.78	.48
2	559 (64.5)	88 (63.8)	647 (64.4)
3	133 (15.3)	19 (13.8)	152 (15.1)
Missing	5 (0.6)	0	5 (0.5)
Lymphovascular invasion
No	628 (72.2)	99 (72.3)	727 (72.2)	>.99	NA
Yes	242 (27.8)	38 (27.7)	280 (27.8)
Missing	2 (0.2)	1 (0.7)	3 (0.3)
Breast surgery performed
Breast-conserving surgery	592 (67.9)	73 (52.9)	665 (65.8)	<.001	NA
Mastectomy	280 (32.1)	65 (47.1)	345 (34.2)	<.001	NA
Suspicious lymph nodes on ultrasonography
No	760 (87.2)	113 (81.9)	873 (86.4)	.12	NA
Yes	112 (12.8)	25 (18.1)	137 (13.6)	.12	NA
SLNs removed, No.
1-2	618 (70.9)	102 (73.9)	720 (71.3)	.57	.35
3-4	221 (25.3)	33 (23.9)	254 (25.1)
>4	33 (3.8)	3 (2.2)	36 (3.6)
Mean (SD)	2.10 (1.15)	2.03 (1.03)	2.09 (1.13)	.47	NA
Median (range)	2.00 (1.00-9.00)	2.00 (1.00-6.00)	2.00 (1.00-9.00)	NA	NA
SLN macrometastases, No.
1	763 (87.5)	83 (60.1)	846 (83.8)	<.001	NA
2	109 (12.5)	55 (39.9)	164 (16.2)	<.001	NA
SLN micrometastases, No.
0	788 (90.4)	116 (84.1)	904 (89.5)	.07	.02
1	79 (9.1)	20 (14.5)	99 (9.8)
2	5 (0.6)	2 (1.4)	7 (0.7)
Total SLN metastases, No.
1	685 (78.6)	67 (48.6)	752 (74.5)	<.001	<.001
2	176 (20.2)	63 (45.7)	239 (23.7)
3	11 (1.3)	8 (5.8)	19 (1.9)
Mean (SD)	1.23 (0.45)	1.57 (0.60)	1.27 (0.49)	<.001	NA
Median (range)	1.00 (1.00-3.00)	2.00 (1.00-3.00)	1.00 (1.00-3.00)	NA	NA
SLN ratio (SLN metastases/excised SLN)
Mean (SD)	0.70 (0.29)	0.85 (0.23)	0.72 (0.29)	<.001	NA
Median (range)	0.67 (0.11-1.00)	1.00 (0.25-1.00)	0.67 (0.11-1.00)	NA	NA
Extracapsular extension SLN
No	572 (65.9)	71 (51.8)	643 (64.0)	.002	NA
Yes	296 (34.1)	66 (48.2)	362 (36.0)
Missing	4 (0.5)	1 (0.7)	5 (0.5)
Total lymph nodes removed, No.
Mean (SD)	15.09 (6.86)	17.72 (8.00)	15.45 (7.08)	<.001	NA
Median (range)	14.00 (1.00-51.00)	17.00 (4.00-50.00)	14.00 (1.00-51.00)	NA	NA
Metastases, No.
Mean (SD)	1.52 (0.67)	7.54 (6.00)	2.34 (3.09)	<.001	NA
Median (range)	1.00 (1.00-3.00)	5.00 (4.00-42.00)	1.00 (1.00-42.00)	NA	NA
Final nodal stage
pN1	872 (100)	0	872 (86.3)	<.001	<.001
pN2	0	108 (78.3)	108 (10.7)
pN3	0	30 (21.7)	30 (3.0)

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Abbreviations: ILC, invasive lobular carcinoma; NA, not applicable; NHG, Nottingham Histological Grade; NST, invasive breast cancer of no special type; SLN, sentinel lymph node.

Invasive Lobular Cancer

ILC constituted 21.0% of the entire luminal ERBB2-negative cohort. High nodal burden was more common in ILC than other histopathological tumor types (42 of 212 [19.8%] vs 96 of 798 [12.0%]; P = .005) (eTable in Supplement 1). The median (range) tumor size was significantly larger in patients with ILC (32.0 mm [6.0-155.0] vs 18.0 mm [1.1-97.0]; P < .001).

Prediction Models

Luminal ERBB2-Negative Cohort

Two vs 1 SLN macrometastases, higher SLN ratio, additional SLN micrometastases, SLN extracapsular extension, and larger tumor size were independently associated with high nodal burden and selected for the multivariable prediction model (eAppendix 2 in Supplement 1), which reached an AUC of 0.77 (95% CI, 0.72-0.82) in the training set and 0.74 (95% CI, 0.62-0.85) in the test set (Figure 1A and B). The calibration plot showed that the mean predicted probabilities of high nodal burden did not deviate systematically from observed fractions in deciles based on the predicted probabilities of high nodal burden in the test set (minor deviations from the dashed identity line, which indicates perfect calibration). This was also supported by the summary measures calibration intercept 0.05 and calibration slope 0.83, which are close to the optimal values 0 and 1.00, respectively, for a perfectly calibrated model (Figure 1C). At a sensitivity threshold of 80% or greater, 111 of the 201 patients in the test set (55.2%) were identified as high risk (Figure 1B). While this proportion seems high compared to the observed prevalence for ≥pN2 of 13.4%, the negative predictive value was 94.4% (85/90), correctly classifying all but 5 patients as low risk at this threshold. The model is visualized in a nomogram (Figure 2A).

Figure 1. — Threshold refers to the predicted probability of high nodal burden (≥pN2). In the calibration plot, the dashed oblique reference line indicates perfect calibration with an intercept of 0 and a slope of 1. The cluster of points in the lower left corner of the figure indicates that observed, as well as predicted, risk of high nodal burden is low for most patients in the cohort. In addition, the absence of a linear trend at this end of the risk spectrum suggests that the discrimination is poor for low-risk patients. However, the points in the upper right corner indicate that the model might be useful for identification of patients at high risk of high nodal burden, which was the aim of the present work. AUC indicates area under the ROC curve; FN, false negative; FP, false positive; N, nodal status; ILC, invasive lobular carcinoma; NPV, negative predicted value; PPV, positive predicted value; TN, true negative; TP, true positive; ROC, receiver operating characteristic curve.

Figure 2. — SLN ratio refers to (SLN micrometastases + SLN marcrometastases) / (all removed lymph nodes at SLN biopsy). SLN indicates sentinel lymph node.

Lobular Subgroup

In the ILC subgroup, 2 SLN macrometastases vs 1 had an odds ratio (OR) of 4.96 (95% CI, 2.17-11.47) in the final multivariable model for high vs low nodal burden (eAppendix 2 in Supplement 1). All independent factors identified in the luminal ERBB2-negative cohort, apart from tumor size, were associated with high nodal burden in ILC also and were included in the lobular prediction model with an AUC of 0.74 (95% CI, 0.66-0.83) in the development set (Figure 1D). The model is visualized in a nomogram (Figure 2B).

RFS

To investigate the prognostic potential of the prediction model, the total nomogram score was related to RFS in the luminal ERBB2-negative cohort. Cox regression analysis revealed that a 10-unit increase in the score corresponded to a 7% increase in the incidence of RFS events (hazard ratio [HR], 1.07; 95% CI, 1.02-1.13; P = .01) (Figure 3A). When categorizing the score into quartiles, the HR for the high-risk group (quartile 4) vs the low-risk group (quartile 1) was 2.11 (95% CI, 1.02-4.35; P = .04) and the HR for the medium-risk group (quartiles 2-3) vs quartile 1 was 1.59 (95% CI, 0.81-3.14; P = .18). The corresponding Kaplan-Meier estimates of cumulative incidence of RFS events are shown in Figure 3B.

Figure 3. — Patients were followed up from randomization to the date of first recurrence or death, defined according to the Standardized Definitions for Efficacy End Points (STEEP) criteria or, for event-free patients, to the date of last follow-up. Contralateral breast cancer was not considered a recurrence-free survival (RFS) event. Follow-up times for recurrence-free and alive participants were censored at the date of last visit. No follow-up times were censored for other reasons. The proportional hazards assumption was fulfilled for both Cox models (P = .48 in model with continuous score and P = .41 with score categories). Risk groups were created by dividing the total nomogram score into 3 groups: low (quartile 1), medium (quartiles 2-3), and high (quartile 4). The hazard ratio (HR) was calculated for a 10-unit nomogram score increase.

^aDifferent spline functions were assessed using Akaike Information Criterion (AIC), but a linear model for total points gave the best fit to the data after adjustment for model complexity.

^bInterpretation of this HR: for each 10-step increase of total nomogram points, the incidence of RFS events increases with 7%.

Discussion

In this diagnostic/prognostic study, the predictive models and corresponding nomograms for high nodal burden (≥pN2) in patients with 1 or 2 SLN macrometastases and luminal ERBB2-negative breast cancer and ILC, respectively, may help select patients for intensified adjuvant therapies while avoiding CALND. At a threshold corresponding to a sensitivity of at least 80%, all but 5 low-risk patients were correctly classified, corresponding to a negative predictive value of 94%. In the luminal ERBB2-negative cohort, the predicted probability of high nodal burden (≥pN2) was also prognostic of RFS, underscoring the clinical relevance of the prediction model as an alternative to CALND.

A minority of patients with 1 or 2 macrometastases on SLN biopsy have a high nodal burden: in the American College of Surgeons Oncology Group Z0011 (ACOSOG Z0011) trial,¹ 47 of 343 (13.7%) had 4 or more metastatic ALNs; in the European Organisation for Research and Treatment of Cancer 10981-22023 (AMAROS) trial,³ 52 of 672 (8%) had 4 or more positive additional nodes (besides SLN metastasis); and in the Optimal Treatment of the Axilla—Surgery or Radiotherapy After Positive Sentinel Lymph Node Biopsy in Early-Stage Breast Cancer (OTOASOR) trial,³⁴ 54 of 244 (22%) had ≥pN2a. Despite the presence of ≥pN2 disease in these trials, the omission of CALND did not increase long-term locoregional recurrence rates. However, there are currently no data from randomized clinical trials comparing CALND vs its omission in a population of patients with verified ≥pN2 disease. The proportion of ≥pN2 status ranges from 3.5% to 16% in cN0 breast cancer.^35,36 Thus, the therapeutic effect of CALND is debatable. Here we present a tool for procuring similar diagnostic information considering multiple risk factors of ≥pN2.

Prediction Models—Comparison to Previous Studies

In the multicenter tool for risk prediction of 4 or more ALN metastases in SLN-positive breast cancer developed by Meretoja et al,²⁸ based on retrospective data from 675 patients, the AUC reached 0.77 (95% CI, 0.74-0.81) in the external validation using a retrospective cohort (n = 760). The included variables were site prevalence of ≥pN2, number of metastatic and benign SLNs, tumor size, and extracapsular extension. Murata et al²⁵ presented a model for prediction of high nodal burden based on 804 patients who were cN0 SLN positive. The AUC was 0.89 (95% CI, 0.84-0.93) in the test set, with good discrimination; however, as this was a retrospective single-institution study, external validation is needed. In another prediction model by Kim et al,²⁶ lymphovascular invasion, extranodal extension, T stage, and a high SLN ratio were independently associated with high nodal burden in a retrospective analysis of 1437 patients. The AUC was 0.82 in the test set (no CI provided; no external validation). In a nomogram by Yang et al²⁴ incorporating the number of SLN metastases, extracapsular extension, pT stage, and tumor location in the breast in 1480 patients, the AUC was 0.80 (95% CI, 0.76-0.85) in the external validation set. In a small study by Corsi et al²⁷ (n = 271), SLN ratio, extranodal extension, and multifocal disease were predictors of more than 2 metastatic ALNs, with an AUC of 0.74 (no CI provided; no external validation).

The prevalence of high nodal burden in the above-mentioned studies ranged from 5.7% to 20.7% (compared to 13.7% in this study). While the models based on retrospective cohorts by Meretoja et al and Yang et al were externally validated, none have been evaluated in terms of recurrence or survival analyses.

ILC

ILC is a distinct biological entity which warrants specific focus. The presented prediction model for ILC is, to our knowledge, the first model for high nodal burden in ILC and 1 or 2 SLN macrometastases. Studies are rarely analyzed by histopathological tumor type,³⁷ and treatment recommendations usually address NST and ILC as 1 entity.⁴ Only in recent years, ILC-specific clinical trials have been initiated.¹⁶

Tumor size was selected in the luminal ERBB2-negative cohort but not in the ILC subgroup. Lee et al³⁶ and Rivers et al³⁸ found tumors greater than 20 mm to be associated with high nodal burden. However, in our study larger tumor size was independently associated with high nodal burden in the luminal ERBB2-negative but not the ILC subgroup. Considering the suggested threshold at greater than 20 mm, the significantly larger tumor size in ILC compared to other histopathological tumor types (median, 32 mm vs 18 mm) might explain why tumor size was not a strong independent predictive factor in the ILC subgroup.

Imaging is highly indicative of high nodal burden and can rule out stage ≥pN2 breast cancer with a negative predictive value of 96%.^11,39 The sensitivity of axillary ultrasonography for predicting ≥3 metastatic ALN is, however, lower in ILC than in NST.²⁰ Despite ILC being associated with more ALN metastases,^40,41 nodal burden is more often underestimated preoperatively.⁴² Other imaging modalities, such as magnetic resonance imaging or ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography, are of limited clinical value.^42,43,44 These limitations underline the need for prediction models developed exclusively in ILC.

Altogether, ILC poses diagnostic staging challenges, and the risk of occult ALN involvement remains challenging. The use of a decision support tool might thus be more important in ILC than in NST.

Strengths and Limitations

A major strength of this study is that it is based on meticulously curated prospectively collected data from a large randomized clinical trial. Moreover, we address both global performance measures of discrimination and calibration. In addition, applied sensitivity cutoffs are derived from clinical reasoning rather than being data driven. We exclusively incorporated routinely accessible variables, available at the intended time point for using the nomogram. To further enable clinical use, a nomogram is presented for each target population. While prediction models were not developed to predict RFS, we show clinical validity by differentiation between prognostic strata based on nomogram scores. Longer follow-up will be necessary to monitor separation of RFS curves given the potential of luminal breast cancer to recur late.

The foremost limitation of this study is the lack of validation in an external cohort and the fact that no internal validation was conducted for the ILC subgroup due to limited sample size. Although the inclusion criteria in SENOMAC are considered broad, generalization beyond this population should be used with caution. Another limitation is the absence of some variables of putative relevance for modeling, such as mode of detection (eg, screening or clinically detected), race, ethnicity, and body mass index. Progesterone receptor status and proliferation rate (Ki67) were not considered because of poor standardization and varying availability across centers. The 21-gene Oncotype DX Breast Recurrence Score may inform decisions on adjuvant chemotherapy in postmenopausal luminal ERBB2-negative breast cancer with 1 to 3 metastatic ALNs without CALND. However, the clinical value of Oncotype DX for guidance of adjuvant abemaciclib is not yet determined, and a nomogram can be applied irrespective of the availability of genomic tests.⁴⁵ In the SENOMAC trial, multigene signatures were not registered. While acknowledging this as a limitation, we emphasize that the presented models are clinically feasible also in economically diverse conditions and not dependent on the availability of tissue for analysis. No imaging-derived variables were considered for the models, enabling application in routine clinical settings where axillary imaging is not part of diagnostic workup for patients with cN0 T1-T2 breast cancer eligible for upfront surgery.^11,46

Conclusions

The presented model predicts high nodal burden in patients with luminal ERBB2-negative T1-T3 breast cancer and 1 or 2 SLN macrometastases with good performance measures. Use of the nomogram may facilitate systemic treatment decisions without exposing patients to CALND, thus minimizing the risk of arm morbidity. External validation is necessary.

Supplement 1.

eFigure. Patient Flow Chart

eAppendix 1. Prediction Model – Variable Selection

eAppendix 2. Multivariable Logistic Regression Model

eTable. Baseline Characteristics of the Included Patients Stratified on Invasive Lobular Carcinoma (ILC) vs Other Histopathological Tumor Types

jamasurg-e243944-s001.pdf^{(610.6KB, pdf)}

Supplement 2.

Data sharing statement

jamasurg-e243944-s002.pdf^{(10.5KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure. Patient Flow Chart

eAppendix 1. Prediction Model – Variable Selection

eAppendix 2. Multivariable Logistic Regression Model

eTable. Baseline Characteristics of the Included Patients Stratified on Invasive Lobular Carcinoma (ILC) vs Other Histopathological Tumor Types

jamasurg-e243944-s001.pdf^{(610.6KB, pdf)}

Supplement 2.

Data sharing statement

jamasurg-e243944-s002.pdf^{(10.5KB, pdf)}

[soi240070r1] 1.Giuliano AE, Ballman KV, McCall L, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA. 2017;318(10):918-926. doi: 10.1001/jama.2017.11470 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240070r2] 2.Galimberti V, Cole BF, Zurrida S, et al. ; International Breast Cancer Study Group Trial 23-01 investigators . Axillary dissection versus no axillary dissection in patients with sentinel-node micrometastases (IBCSG 23-01): a phase 3 randomised controlled trial. Lancet Oncol. 2013;14(4):297-305. doi: 10.1016/S1470-2045(13)70035-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240070r3] 3.Donker M, van Tienhoven G, Straver ME, et al. Radiotherapy or surgery of the axilla after a positive sentinel node in breast cancer (EORTC 10981-22023 AMAROS): a randomised, multicentre, open-label, phase 3 non-inferiority trial. Lancet Oncol. 2014;15(12):1303-1310. doi: 10.1016/S1470-2045(14)70460-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240070r4] 4.Burstein HJ, Curigliano G, Loibl S, et al. ; Members of the St. Gallen International Consensus Panel on the Primary Therapy of Early Breast Cancer 2019 . Estimating the benefits of therapy for early-stage breast cancer: the St. Gallen International Consensus Guidelines for the primary therapy of early breast cancer 2019. Ann Oncol. 2019;30(10):1541-1557. doi: 10.1093/annonc/mdz235 [DOI] [PubMed] [Google Scholar]

[soi240070r5] 5.Johnston SRD, Harbeck N, Hegg R, et al. ; MONARCHE Committee Members and Investigators . Abemaciclib combined with endocrine therapy for the adjuvant treatment of HR+, HER2-, node-positive, high-risk, early breast cancer (MONARCHE). J Clin Oncol. 2020;38(34):3987-3998. doi: 10.1200/JCO.20.02514 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240070r6] 6.Rastogi P, O’Shaughnessy J, Martin M, et al. Adjuvant abemaciclib plus endocrine therapy for hormone receptor-positive, human epidermal growth factor receptor 2-negative, high-risk early breast cancer: results from a preplanned MONARCHE overall survival interim analysis, including 5-year efficacy outcomes. J Clin Oncol. 2024;42(9):987-993. doi: 10.1200/JCO.23.01994 [DOI] [PMC free article] [PubMed] [Google Scholar]

[soi240070r7] 7.Cardoso F, van’t Veer LJ, Bogaerts J, et al. ; MINDACT Investigators . 70-Gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med. 2016;375(8):717-729. doi: 10.1056/NEJMoa1602253 [DOI] [PubMed] [Google Scholar]

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PERMALINK

Prediction of High Nodal Burden in Patients With Sentinel Node–Positive Luminal ERBB2-Negative Breast Cancer

Ida Skarping, MD, PhD

Pär-Ola Bendahl, PhD

Robert Szulkin, PhD

Sara Alkner, MD, PhD

Yvette Andersson, MD, PhD

Leif Bergkvist, MD, PhD

Peer Christiansen, MD, DMSc

Tove Filtenborg Tvedskov, MD, PhD

Jan Frisell, MD, PhD

Oreste D Gentilini, MD

Michalis Kontos, MD

Thorsten Kühn, MD, PhD

Dan Lundstedt, MD, PhD

Birgitte Vrou Offersen, MD, PhD

Roger Olofsson Bagge, MD, PhD

Toralf Reimer, MD, PhD

Malin Sund, MD, PhD

Lisa Rydén, MD, PhD

Jana de Boniface, MD, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposure

Main Outcomes and Measures

Results

Conclusions and Relevance

Trial Registration

Introduction

Methods

Patient Characteristics

Important Definitions

Statistical Analysis

Results

Descriptive Results

High vs Low Nodal Burden

Table. Baseline Characteristics of Patients.

Invasive Lobular Cancer

Prediction Models

Luminal ERBB2-Negative Cohort

Figure 1. Discrimination and Calibration of the Prediction Models for High Nodal Burden.

Figure 2. Nomogram for Predicting the Probability of High Nodal Burden.

Lobular Subgroup

RFS

Figure 3. Prognostic Value of the High Nodal Burden Prediction Model.

Discussion

Prediction Models—Comparison to Previous Studies

ILC

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases