A novel model for predicting the risk of non-sentinel lymph node metastasis after positive sentinel lymph node biopsy in Chinese women diagnosed with early breast cancer

doi:10.21203/rs.3.rs-4900297/v1

Background: 30 to 70% of patients with positive sentinel lymph nodes (SLNs) in early breast cancer do not develop non-SLN metastases. They are exposed to the potential complications and sequelae of axillary lymph node dissection (ALND) without gaining additional therapeutic benefit. Therefore, a prediction model for non-SLN metastasis for Chinese breast cancer patients is needed.

Methods:We enrolled 1717 patients with early breast cancer who underwent SLN biopsy, and 481 of these patients underwent further ALND. An additional 113 patients served as a validation cohort. A new predictive model was established using univariate and multivariate Logit regression. The Memorial Sloan Kettering Cancer Center (MSKCC) and Shanghai Cancer Hospital (SCH) models were used for comparison with our new model.

Results: Multivariate regression analysis showed that tumor size, multifocality, lymphovascular invasion, extracapsular extension, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN locationwere independent indicators for non-SLN metastasis. The nomogram established based on these eight variables was well applied in the training cohort (AUC: 0.830) and validation cohort (AUC: 0.785). Moreover, the diagnostic value of our model was superior to that of the MSKCC and SCH models (both P = 0.000). Decision curve analysis showed that the net benefit of our model surpasses that of both the MSKCC and SCH models for the same risk threshold, resulting in greater benefits for patients. With a guaranteed false-negative rate, our model could accurately predict up to 24.5% of patients suitable for exemption from ALND. Meanwhile, our model evaluated the non-SLN status of patients with 3 or more positive SLNs (AUC: 0.843).

Conclusions: We developed a new model to predict non-SLN metastatic status in Chinese patients with early SLN-positive breast cancer. Our model showed good performance in both cohorts and significantly outperforms the MSKCC and SCH models.

early breast cancer

sentinel lymph node

non-sentinel lymph node metastasis

nomogram

Axillary sentinel lymph node biopsy (SLNB) is a proven predictor of axillary lymph node (ALN) metastasis in early breast cancer, and is reliable for guiding the decision for axillary lymph node dissection (ALND). However, in patients with positive SLNB, further ALND has shown that 30 to 70% of non-sentinel lymph nodes (SLNs) are non-metastatic [1–4]. In these cases, patients are exposed to the potential complications and sequelae of ALND, but the procedure for them is without any therapeutic benefit and without gain of any additional prognostic information or change in treatment strategy [5–8]. In this setting, it is possible that removal of only the SLNs can accurately predict the pathological state of the axilla and achieve local control. A meta-analysis has shown that patients with early breast cancer and low SLN metastasis burden have a relatively low rate of axillary non-SLN metastasis [5]. Therefore, a subset of such patients would be able to avoid ALND. At present, for patients with early breast cancer and even 1 or 2 positive SLNs, the results of multiple international clinical prospective studies including Z0011, AMAROS, IBCSG23-01, have shown that additional high-tangential whole-breast irradiation (WBI) or WBI plus axillary regional nodal irradiation (RNI) have no significant difference in recurrence and survival between SLNB and ALND [6, 9–13]. In an estimated 23 to 34% of patients with non-SLN metastases, cancer can be completely controlled by additional axillary RNI without the side effects of ALND [1, 6, 9, 13], although axillary RNI also increases the patient’s risk of lymphedema and lung fibrosis [14, 15]. National Comprehensive Cancer Network guidelines and expert consensus continue to recommend ALND for patients with metastasis detected in 3 or more SLNs, but even in this group, ALND results show that 25% of non-SLNs are non-metastatic [1]. Therefore, in clinical practice, predicting the risk of non-SLN metastasis based on characteristics of the positive SLN and other clinicopathologic features is desirable. If the risk of non-SLN metastasis can be accurately predicted by the status of SLN metastasis, patients with a very low likelihood of non-SLN metastasis can be exempted from ALND and additional axillary RNI.

In recent years, through study design and the use of various graphical and numerical models to predict the non-SLN metastatic status in early SLN-positive breast cancer, several nomograms and scoring systems based on clinicopathological variables have been developed to predict the probability of non-SLN metastasis in SLN-positive patients with early breast cancer. Notable examples are the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram [16], the Cambridge nomogram [17], the Stanford nomogram [3], the Tenon score [18], the MD Anderson Cancer Center Score [19], and the Shanghai Cancer Hospital (SCH) nomogram [20]. The clinical and pathological parameters applied to different prediction models are also different, such as race, and include average age, average body mass index, education level, level of understanding of disease treatment, and surgical methods (i.e., the definition of SLN, the scope of axillary dissection, intraoperative and postoperative pathological detection methods, and others). There are also differences in many influencing factors, so it is difficult to have a prediction model that can be equally applied to various populations. In particular, a prediction model for non-SLN metastasis for individuals diagnosed with breast cancer in the Chinese population is needed.

The purpose of this study is to predict the risk of non-SLN metastasis by utilizing the SLN metastasis status and the clinicopathological characteristics of the tumor in individuals diagnosed with early breast cancer in China. Notably, we first found that the anatomical distribution of positive SLNs in the axilla has significant predictive value for the status of non-SLN metastasis. We aim to establish a prediction model for non-SLN metastasis in early breast cancer in a Chinese population for clinical identification of the subgroup of patients with non-metastatic non-SLNs, which can exempt them from further ALND, avoiding unnecessary overtreatment and, of course, additional axillary RNI. Ultimately, our objective is to contribute a new risk assessment model for breast cancer in China.

2.1 Patients.

From November 2009 to December 2019, 1717 consecutive female patients with diagnoses of primary invasive breast cancer underwent SLNB at the Breast Cancer Center, Cancer Hospital of Shantou University Medical College. Among them, 481 patients were detected with positive SLNs and fulfilled the following criteria. (1) Tumor stage cT1-3N0M0 according to the eighth edition of the American Joint Committee on Cancer (AJCC) staging manual. (2) No prior neoadjuvant therapy. (3) Positive SLNs, including tumor micrometastases or macrometastases, identified after SLNB. (4) SLNB performed by an experienced surgical team. (5) Patients accepted further ALND. (6) Patients had no history of previous malignant. We enrolled an additional 113 patients who met these inclusion criteria at our center from January 2020 to December 2022 as a validation cohort. Patients were excluded if they met any of the following criteria. (1) Breast cancer in situ. (2) Stage IV breast cancer. (3) Isolated tumor cells (ITC) in SLNs. (4) Necessary clinical information unavailable. The ethics committee of the Cancer Hospital of Shantou University Medical College approved the study (No. 2023086). All participants signed an informed consent form, and all the methods were performed in accordance with the approved guidelines..

2.2 Surgery and Pathology.

SLNB was performed using methylene blue (MB) injection (Jumpcan Pharmaceutical Group Co., Ltd., Jiangsu, China) and indocyanine green (ICG) solution (Dandong Yichuang Pharmaceutical Co., Ltd., Jilin, China). First, 2 mL MB was injected subcutaneously into the periareolar area near the outer upper quadrant, and 5 minutes later 1 mL ICG solution (0.5 mg/mL) was injected subcutaneously in the same area. Then, the fluorescence detector (Mingde Pharmaceutical Co., Ltd., Jiangsu, China) was used to observe along the lymphatic vessels and mark the point of fluorescence disappearance as the incision of SLNB. We excised the luminescent lymph nodes (ICG-positive) and/or blue-stained lymph nodes (MB-positive) as SLNs together. During the operation, we searched, dissected, and exposed the course of the second intercostobrachial nerve (ICBN) in the axilla, and the location of the SLNs relative to the ICBN (above or below) was recorded. SLN metastasis was diagnosed by frozen section during operation or by postoperative paraffin section. If tumor macrometastasis or micrometastasis was found in more than 2 metastatic SLNs or in 1 to 2 SLNs and the patients were not willing to receive additional axillary RNI, we routinely performed level I or II ALND. If lymph nodes in level II displayed metastases, we also performed level III ALND [21]. After the operation, all specimens were paraffin-embedded for immunohistochemistry.

2.3 Patient clinicopathological characteristics.

The clinicopathological variables included age, tumor location, tumor size, multifocality, histological type, lymphovascular invasion, extracapsular extension (ECE), histological grade, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) status, Ki-67, molecular subtype, number of SLNs, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location. ER and PR were judged as positive if ≥ 1% of tumor cells showed nuclear staining. HER2-positive status was defined as 3 + score by immunocytochemistry or HER2 gene amplification by fluorescent in situ hybridization (FISH). According to the expression of ER, PR, HER2, and Ki67, the tumors were classified as luminal A-like, luminal B 1/2-like, HER2-positive, and triple negative molecular subtypes. Macrometastasis was defined as metastatic lesions larger than 2 mm in diameter, and micrometastasis was defined as metastatic lesions larger than 0.2 mm and no larger than 2.0 mm in diameter or more than 200 tumor cells in the slice.

2.4 Statistical analysis.

Statistical analysis was performed using SPSS, version 25.0 software (version 25.0; SPSS INC., Chicago, IL) and R software 4.1.0 (r-project.org). Continuous variables were expressed as mean ± standard deviation and tested for normality. Two independent samples were tested using t-test or Mann-Whitney U test. Categorical variables were assessed using the χ² test or Fisher’s exact test. Univariate and multivariate Logit regression was performed to assess independent risk factors for non-SLN involvement. We created a nomogram based on independent risk factors and plotted receiver operating characteristic (ROC) curves and calibration curves to assess model discrimination and calibration. Decision curve analysis (DCA) was used to determine the clinical usefulness of the model. The validation cohort served as an external validation of the model. In addition, the SCH and MSKCC models were evaluated by entering the corresponding clinicopathological indicators in the SCH nomogram and the MSKCC nomogram (https://nomograms.mskcc.org/Breast/BreastAdditionalNonSLNMetastasesPage.aspx) to predict the probability of non-SLN metastasis in Chinese individuals diagnosed with breast cancer. AUC values were compared using the Delong test. For all analyses, P < 0.05 was considered to indicate statistical significance.

3.1 Clinicopathologic features.

This study included 594 women who were diagnosed with breast cancer, 481 in the training cohort and 113 in the validation cohort. In the training cohort, 47.6% of patients had non-SLN metastasis, and in the validation cohort, 45.1% of patients had the same. The training cohort underwent dissection of 1979 SLNs, with an average of 3.31 ± 1.75 SLNs per patient, while the validation cohort underwent dissection of 387 SLNs, with an average of 3.43 ± 1.73 SLNs per patient. As shown in Table 1, all characteristics were similarly distributed between the two cohorts.

Table 1

Comparison of clinicopathological features between the training cohort and the validation cohort
Parameters	Variable	Training n = 481	Validation n = 113	Z /χ²	P value
Age	≤ 50	210	55	2.173	0.140
Age	> 50	271	58
Tumor location	Upper outer	249	54	0.652	0.958
	Lower outer	78	19
	Lower inner	29	7
	Upper inner	90	24
	Central	35	9
Tumor size (cm)	≤ 2	124	38	2.842	0.092
Tumor size (cm)	> 2	357	75
Multifocality	No	464	105	2.04	0.153
Multifocality	Yes	17	8
Histological type	Infiltrating ductal carcinoma	442	102	0.475	0.788
	Invasive lobular carcinoma	13	3
	Other carcinomas	26	8
Lymphovascular invasion	No	435	106	1.278	0.258
Lymphovascular invasion	Yes	46	7
Extracapsular extension	No	379	80	3.333	0.068
Extracapsular extension	Yes	102	33
Histological grade	G1	31	7	2.261	0.520
	G2	145	34
	G3	288	71
	Gx	17	1
Estrogen receptor	Negative	113	31	0.774	0.379
Estrogen receptor	Positive	368	82
Progesterone receptor	Negative	161	38	0.001	0.975
Progesterone receptor	Positive	320	75
HER2	Negative	364	82	0.473	0.492
HER2	Positive	117	31
Ki-67	≤ 20%	193	48	0.210	0.647
Ki-67	> 20%	288	65
Molecular subtype	Luminal A	83	21	1.389	0.846
	Luminal B1	224	46
	Luminal B2	56	14
	HER2-positive	65	17
	Triple negativity	53	15
Number of SLNs	Mean ± SD	3.31 ± 1.75	3.43 ± 1.73	-0.689	0.491^*
Number of SLNs	Median	3	3
Number of negative SLNs	Mean ± SD	1.75 ± 1.65	1.965 ± 1.17	-1.251	0.211^*
Number of negative SLNs	Median	1	2
Number of positive SLNs	Mean ± SD	1.56 ± 0.94	1.46 ± 0.73	-0.548	0.584^*
Number of positive SLNs	Median	1	1
Size of positive SLN	Micrometastasis	32	7	0.031	0.859
Size of positive SLN	Macrometastasis	449	106
Metastatic SLN location	Lower ICBN	278	72	1.325	0.250
Metastatic SLN location	Upper ICBN	203	41
* P value by the Mann-Whitney U test
Abbreviations: HER2, human epidermal growth factor receptor 2; ICBN, intercostobrachial nerve; SD, standard deviation; SLN, sentinel lymph node.

3.2 Univariate and multivariate analysis for non-sentinel lymph node metastasis.

The results of univariate analysis indicated that several factors, including tumor size, multifocality, lymphovascular invasion, ECE, ER status, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location, were significantly associated with non-SLN metastatic status (P < 0.05; Table 2). Multivariate regression analysis showed that tumor size, multifocality, lymphovascular invasion, ECE, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location were independent risk indicators for non-SLN metastasis (Table 3).

Table 2

Univariate analysis of non-SLN metastasis in patients of the training cohort
Parameters	Variable	Total n = 481	Non-SLN metastasis		Z /χ²	P value
Parameters	Variable	Total n = 481	Absent (n = 252)	Present (n = 229)	Z /χ²	P value
Age	≤ 50	210	117 (46.4)	93 (40.61)	1.651	0.199
Age	> 50	271	135 (53.6)	136 (59.39)
Tumor location	Upper outer	249	131 (51.6)	119 (51.9)	1.337	0.855
	Lower outer	78	38 (15.1)	40 (17.5)
	Lower inner	29	16 (6.3)	13 (5.7)
	Upper inner	90	47 (18.7)	43 (18.8)
	Central	35	21 (8.3)	14 (6.1)
Tumor size (cm)	≤ 2	124	81 (32.1)	43 (18.8)	11.201	0.001
Tumor size (cm)	> 2	357	171 (67.9)	186 (81.2)
Multifocality	No	464	250 (99.2)	214 (93.5)	11.661	0.000
Multifocality	Yes	17	2 (0.8)	15 (6.5)
Histological type	Infiltrating ductal carcinoma	442	235 (93.3)	207 (90.4)	2.603	0.272
	Invasive lobular carcinoma	13	4 (1.6)	9 (3.9)
	Other carcinomas	26	13 (5.1)	13 (5.7)
Lymphovascular invasion	No	435	242 (96.0)	193 (84.3)	19.159	0.000
Lymphovascular invasion	Yes	46	10 (4.0)	36 (15.7)
Extracapsular extension	No	379	223 (88.5)	156 (68.1)	29.793	0.000
Extracapsular extension	Yes	102	29 (11.5)	73 (31.9)
Histological grade	G1	31	17 (6.7)	14 (6.1)	0.502	0.918
	G2	145	77 (30.6)	68 (29.7)
	G3	288	148 (58.7)	140 (61.1)
	Gx	17	10 (4.0)	7 (3.1)
Estrogen receptor	Negative	113	69 (27.4)	44 (19.2)	4.452	0.035
Estrogen receptor	Positive	368	183 (72.6)	185 (80.8)
Progesterone receptor	Negative	161	87 (34.5)	74 (32.3)	0.263	0.608
Progesterone receptor	Positive	320	165 (65.5)	155 (67.7)
HER2/neu receptor	Negative	364	186 (73.8)	178 (77.7)	1.001	0.317
HER2/neu receptor	Positive	117	66 (26.2)	51 (22.3)
Ki-67	≤ 20%	193	94 (37.3)	99 (43.2)	1.756	0.185
Ki-67	> 20%	288	158 (62.7)	130 (56.8)
Molecular subtype	Luminal A	83	40 (15.9)	43 (18.8)	6.079	0.193
	Luminal B1	224	110 (43.6)	114 (49.8)
	Luminal B2	56	30 (11.9)	26 (11.3)
	HER2-positive	65	37 (14.7)	28 (12.2)
	Triple negativity	53	35 (13.9)	18 (7.9)
Number of SLNs	Mean	3.31	3.39	3.23	0.755	0.441^*
	SD	1.75	1.81	1.67
	Median	3	3	3
Number of negative SLNs	Mean	1.75	2.13	1.33	5.257	0.000^*
	SD	1.65	1.80	1.36
	Median	1	2	1
Number of positive SLNs	Mean	1.56	1.25	1.90	-6.392	0.000^*
	SD	0.94	0.52	1.15
	Median	1	1	2
Size of the SLN metastasis	Micrometastasis	32	28 (11.1)	4 (1.7)	16.941	0.000
Size of the SLN metastasis	Macrometastasis	449	224 (88.9)	225 (98.3)
Metastatic SLN location	Lower ICBN	278	182 (72.2)	96 (41.9)	45.16	0.000
Metastatic SLN location	Upper ICBN	203	70 (27.8)	133 (58.1)
* P value by the Mann-Whitney U test
Abbreviations: HER2, human epidermal growth factor receptor 2; ICBN, intercostobrachial nerve; SD, standard deviation; SLN, sentinel lymph node.

Table 3

Multivariate regression analysis of non-SLN metastasis in patients of the training cohort
Factors	Coefficient	S.E.	Wald	P	OR	95% CI
Tumor size	0.530	0.264	4.025	0.045	1.698	1.012–2.849
Multifocality	2.390	0.806	8.799	0.003	10.913	2.695–52.925
Lymphovascular invasion	1.597	0.422	14.301	0.000	4.940	2.159–11.304
Extracapsular extension	1.216	0.285	18.160	0.000	3.373	1.928–5.899
Estrogen receptor	0.113	0.268	0.176	0.674	1.119	0.662–1.893
Number of negative SLNs	-0.243	0.079	9.497	0.002	0.784	0.672–0.915
Number of positive SLNs	0.791	0.167	22.435	0.000	2.205	1.590–3.059
Size of the SLN metastasis	2.253	0.696	10.486	0.001	9.515	2.433–37.208
Metastatic SLN location	1.195	0.231	26.862	0.000	3.303	2.102–5.189
Constant	-4.473	0.810	30.527	0.000	0.011
Abbreviations: SLN, sentinel lymph node.

3.3 Construction and validation of the model.

Based on the results of multivariate analysis, Logit(P) = -4.473 + 0.530 × tumor size + 2.390 × multifocality + 1.597 × lymphovascular invasion + 1.216 × ECE − 0.243 × number of negative SLNs + 0.791 × number of positive SLNs + 2.253 × size of the SLN metastasis + 1.195 × metastatic SLN location. The measured values of the number of positive and negative SLNs were substituted into the formula, and tumor size, multifocality, lymphovascular invasion, size of the SLN metastasis, metastatic SLN location, and SLN capsular invasion were assigned a value of 0 or 1. Meanwhile, a nomogram was developed to predict the probability of non-SLN metastasis (Fig. 1). We applied the novel model to the training cohort and the validation cohort and found that the AUC was 0.830 (95% CI: 0.794–0.867) for the training cohort and 0.785 (95% CI: 0.702–0.869) for the validation cohort (Figs. 2A-D). Moreover, the calibration curves demonstrated good concordance between the training and validation cohorts.

3.4 Validation of MSKCC and SCH models.

The MSKCC model was validated on 445 patients in the training cohort (36 patients were not applicable) with an AUC of 0.715 (95% CI: 0.668–0.763) (Fig. 2E). The SCH model was applied to patients with 1 to 2 positive SLNs and was therefore validated in 418 patients of the training cohort with an AUC of 0.755 (95% CI: 0.712–0.797). The AUC values of the new model were significantly higher than those of the MSKCC and SCH models (both P = 0.000). Therefore, the diagnostic value of the new model was superior to that of the MSKCC and SCH models.

3.5 Clinical Use.

The establishment of a conservative risk threshold is imperative in the implementation of predictive models for non-SLN metastases in clinical practice, given the significance of the false negative rate (FNR). Numerous studies have demonstrated that the FNR associated with SLNB ranges from 5 to 10 percent [22–26]. Three cut-off values for FNR were established to assess the clinical utility of the three models (Table 4). At an FNR of 0%, a respective proportion of approximately 0.9%, 1.2%, and 3.3% of patients in MSKCC, SCH, and the new model were deemed suitable for exemption from ALND. At an FNR of 5%, the cutoff value for the new model (< 20.6%) accurately predicted the absence of non-SLN metastasis in approximately 17.7% of patients, while the MSKCC and SCH models predicted 10.11% and 12% of patients, respectively. At an FNR of 10%, the new model’s cutoff value (< 25%) correctly predicted the absence of non-SLN metastasis in 24.5% of patients, while the MSKCC and SCH models predicted 14.6% and 18.4% of patients, respectively. Numerous studies have demonstrated that only 30 to 70% of patients with positive SLNs will develop non-SLN metastases requiring ALND treatment [1–4]. The findings of the DCA indicated that the utilization of the novel model for predicting non-SLN metastases yields greater benefits for patients with or without undergoing ALND if the patient’s risk threshold exceeds 30% (Fig. 2F). Moreover, the net benefit of the new model surpasses that of both the MSKCC and SCH models for the same risk threshold probability (Table 5).

Table 4

The ability of three models to screen for low-risk non-SLN metastases
Model	New model			MSKCC			SCH
Patients screened (n)	481			445			418
Patients with non-SLNs (+)	229			212			176
FNR	0%	< 5%	< 10%	0%	< 5%	< 10%	0%	< 5%	< 10%
CP	4.4%	20.6%	25%	16%	24.9%	27.8%	3.2%	27.7%	31.4%
Patients under CP	17	96	140	5	55	86	6	58	95
Patients under CP with non-SLNs (-)	16 (3.3)	85 (17.7)	118 (24.5)	4 (0.9)	45 (10.11)	65 (14.6)	5 (1.2)	50 (12.0)	78 (18.4)
Abbreviations: CP, cut-off point; FNR, false negative rate; MSKCC, Memorial Sloan Kettering Cancer Center; SCH, Shanghai Cancer Hospital; SLN, sentinel lymph node.

Table 5

Net benefits of the three models at different risk thresholds
Model	New model			MSKCC			SCH
Risk threshold	30%	50%	70%	30%	50%	70%	30%	50%	70%
Net benefit	0.379	0.285	0.154	0.353	0.151	0.030	0.359	0.214	0.061
Abbreviations: MSKCC, Memorial Sloan Kettering Cancer Center; SCH, Shanghai Cancer Hospital.

3.6 Validation of the new model in patients with early breast cancer and 3 or more positive sentinel lymph nodes.

Out of the entire sample size of 594 patients, a total of 73 patients were found to have 3 or more positive SLNs, and 13.7% (10/73) of these patients did not have non-SLN metastases. The new model was utilized to predict the non-SLN status of the subgroup with 3 or more positive SLNs. The results showed an AUC of 0.843 (95% CI: 0.719–0.967) for this subgroup and the calibration curve showed good concordance (Fig. 3A and B).

As a relatively minimally invasive method with both high accuracy and sensitivity, SLNB has increasingly become the preferred method for assessing ALN status in patients diagnosed with breast cancer who have clinically negative ALNs. However not all patients with positive SLNB develop further lymph node metastases. In our study, non-SLN metastasis occurred in only 47.1% (280/594) of patients, which is roughly the same as in other studies [1, 27–30]. The AMAROS and OTOASOR trials showed that these patients had similar local recurrence rates, disease-free survival (DFS), and overall survival (OS) with axillary radiotherapy compared to ALND, with significantly fewer side effects than with ALND, and ultimately enhanced quality of life [11, 12]. Of course, it is important to note that approximately 50% of patients do not have non-SLN metastasis, and additional axillary RNI may also have adverse effects and increase the burden on the patient. Therefore the non-SLN metastasis model is useful to help identify the presence of non-SLN metastasis in individuals diagnosed with early breast cancer. Allowing for a 10% FNR, our new model can predict that 24.5% of SLN-positive patients do not develop further metastases. This subset of patients could avoid further ALND, and additional axillary RNI. This prediction allows patients to avoid superfluous treatment and mitigates the various complications associated with treatment.

Tevis et al. [31] found that the OncotypeDX recurrence score in clinically ALN-negative breast cancer was not predictive of lymph node burden and was not conducive to guiding decisions about the extent of axillary surgery. Currently, the most widely employed and efficacious approach involves modeling to predict non-SLN status, utilizing clinicopathological parameters, including those pertaining to the primary tumor and the SLN. In the present study, the univariate analysis revealed that 9 variables, namely tumor size, multifocality, lymphovascular invasion, ECE, ER status, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location in axilla, were significantly associated with non-SLN metastasis. Subsequent multivariate analysis demonstrated that all parameters, except for ER status, independently contributed to the risk of non-SLN metastasis. The pathological indicators of the primary tumor, including tumor size, multifocality, and lymphovascular invasion, exhibited robust predictive capabilities for non-SLN status, as validated by multiple studies [16, 28–30]. In contrast, tumor location, histological grade, ER status, and molecular subtype were rarely used as significant predictors of non-SLN metastasis, which is consistent with our results. The pathologic indicators of SLNs with significant independent predictive value in our study encompassed ECE, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location in axilla. The prevalence of ECE in SLN-positive patients has been documented to range from 30 to 37.6 percent [32–34]. In contrast, this rate was only 22.7% (135/594) in our study. Nevertheless, our research findings still indicate that ECE has a significant impact on non-SLN metastasis, which is consistent with the results of various prior studies [35–38], despite the contrary view of Wu et al [29].

Multiple studies have revealed that the number of negative SLNs and number of positive SLNs are the most common indicators applied to predict non-SLN metastasis [16, 18, 19, 39–42]. An escalation in the number of positive SLNs has been linked to a heightened risk of non-SLN metastasis, while a rise in the number of negative SLNs implies a diminished probability of non-SLN metastasis. The size of SLN metastasis includes macrometastasis, micrometastases, and ITC. Of those with SLN micrometastasis in our study, only 12.8% (5/39) had non-SLN metastases. The IBCSG 23 − 01 and AATRM trials have both indicated a 13% rate of metastasis in patients with SLN micrometastasis who underwent ALND to identify non-SLN [43, 44]. It is noteworthy that the definition of micrometastasis included ITC in IBCSG 23 − 01. Macrometastasis was found to be associated with a higher incidence of non-SLN metastasis in comparison to micrometastasis [45, 46].

In our new model, the metastatic SLN location is a novel and significant predictor of non-SLN metastasis. The positive SLN is categorized into lower and upper groups based on its relative location to the ICBN. Positive SLNs located both above and below the ICBN are also included in the upper group. Two studies by Li et al. found that MB-stained SLNs were predominantly situated under the ICBN; in 95% (56/59) of patients, positive SLNs were found to be located below the ICBN, and only 3 cases of positive SLN were located on both lower and upper sides [47, 48]. In contrast, our study showed that the lower group accounted for 59% (350/594) of cases. This may be because Li et al. used MB for SLN tracing, whereas we used MB in combination with ICG. Extensive research has demonstrated that the employment of MB alone for SLNB may result in a certain degree of FNR, and a fluorescent combined dye method can improve the detection rate of SLN [49–52]. The results suggested that positive SLN location above the ICBN was a high risk factor for non-SLN metastasis. Therefore, we should not ignore ALND when positive SLNs appear in the upper group. To our knowledge, this is the first study to include the metastatic SLN location in a non-SLN metastasis prediction model.

The MSKCC nomogram is a widely recognized model for predicting non-SLN metastasis. The model includes tumor size, tumor type and grade, number of positive SLNs, number of negative SLNs, lymphovascular invasion, multifocality, and ER status. Numerous studies have validated the MSKCC model, but the AUC varies from 0.6 to 0.8 because of differences in regions and patient populations [3, 4, 17, 20, 29, 53–59]. The SCH nomogram was the first model established using a population of Chinese individuals diagnosed with breast cancer, incorporating variables such as number of positive SLNs, number of negative SLNs, lymphovascular invasion, and size of the SLN metastasis [20], with an original AUC of 0.779. Upon validation using our database, the MSKCC and SCH models demonstrated AUC values of 0.715 and 0.755, suggesting a good discriminatory ability. However, Wu et al. [29] tested the MSKCC and SCH models in 236 Chinese individuals diagnosed with breast cancer and their AUCs were only 0.677 and 0.674. The reason for this discrepancy may be the large difference in the age distribution of our patients and those of Wu et al. In our study, 56.3% (271/481) of the patients were older than 50 years, compared to 37.7% (89/236) in theirs. These findings demonstrate that the efficacy of the models is contingent upon factors such as sample size, parameter definition, parameter inclusion, and ethnic disparities, thereby limiting their applicability to breast cancer in the Chinese population.

Our model was built based on 481 SLN-positive patients with an AUC of 0.830. Our validation cohort had an AUC of 0.785, suggesting favorable performance of the model. Comparative analysis with the conventional MSKCC and SCH models revealed superior diagnostic performance of our model. This can be attributed to our inclusion of a greater number of variables, which effectively mitigated the impact of variable distribution differences. Consequently, our nomogram demonstrated enhanced performance within the patient population. In order to avoid the damage caused by ALND in SLN-positive patients, our model was able to predict 3.3% (0%), 17.7% (< 5%), and 24.5% (< 10%) of patients who could be considered to forgo ALND with a guaranteed FNR. Notably, the MSKCC and SCH models were inferior to our model. However, the focus of the AUC is solely on the predictive accuracy of the model, which does not necessarily imply its clinical applicability. To address this limitation, DCA was employed to evaluate the clinical utility of the model by assessing its impact on clinical decision-making. By setting three risk thresholds based on the clinical non-SLN metastasis rate, we determined that our model yielded a higher net benefit compared to both the MSKCC and SCH models. These findings suggest that our model is more clinically relevant and serves as a valuable reference for determining the need for ALND.

The IBCSG23-01, AATRM, and Z0011 studies revealed no statistically significant disparities in DFS and OS between SLNB-alone and ALND groups [10, 43, 44]. Moreover, the SLNB-alone group exhibited a decrease in the incidence of postoperative upper extremity lymphedema and sensory and motor nerve injury. The AMAROS and OTOASOR studies similarly found no significant difference in prognosis between axillary radiotherapy and ALND groups, and the axillary recurrence rate was extremely low in both groups [11, 12]. Together, these findings imply that the option of disregarding ALND or selecting axillary RNI may be viable in cases of early breast cancer with a limited or moderate tumor burden in the SLNs. Current clinical trials have mostly focused on patients with 1 to 2 positive SLNs. Previous studies and our results also confirm that patients with 3 or more positive SLNs do not necessarily have non-SLN metastases, which would perhaps exempt them from ALND as well [1, 36]. Our new model predicted this subgroup and found an AUC of 0.843, which demonstrates the ability of the new model to accurately evaluate the non-SLN status of individuals diagnosed with breast cancer with 3 or more positive SLNs. Therefore, our model has the ability to precisely forecast the non-SLN status and, consequently, assess the tumor load of ALNs in Chinese women diagnosed with early breast cancer, which can provide valuable information for the subsequent decision-making regarding ALND or additional axillary RNI.

However, our study also has some limitations. Firstly, the study included only Chinese patients from a single hospital, thus lacking the diversity and representation of a larger multicenter cohort. Secondly, the metastatic SLN location has not appeared in previous models, and further validation with a larger sample size is required to establish its reliability. Lastly, patients with ITCs in SLN who do not undergo ALND were excluded from our cohort, thereby limiting the generalizability of our model to this subgroup of patients.

Our study developed a new model to predict non-SLN metastatic status in cases of SLN-positive early breast cancer in Chinese women. The variables of this model include tumor size, multifocality, lymphovascular invasion, ECE, number of negative SLNs, number of positive SLNs, size of the SLN metastasis, and metastatic SLN location. Our model significantly outperformed the MSKCC and SCH models in terms of diagnostic performance and clinical utility in this group, and performed well in the validation cohort. This new predictive model for non-SLN involvement is the most appropriate one for our population.

SLNB: Axillary sentinel lymph node biopsy; ALN: Axillary lymph node; ALND: Axillary lymph node dissection; SLN: Sentinel lymph node; WBI: Whole-breast irradiation; RNI: Regional nodal irradiation; MSKCC: Memorial Sloan Kettering Cancer Center; SCH: Shanghai Cancer Hospital; AJCC: American Joint Committee on Cancer; ITC: Isolated tumor cells; MB: Methylene blue; ICG: Indocyanine green; ICBN: Intercostobrachial nerve; ECE: Extracapsular extension; ER: Estrogen receptor; PR: Progesterone receptor; HER-2: Human epidermal growth factor receptor 2; FISH: Fluorescent in situ hybridization; ROC: Receiver operating characteristic; DCA: Decision curve analysis; FNR: False negative rate; DFS: Disease-free survival; OS: Overall survival

Acknowledgements

We thank BioMed Proofreading for the linguistic editing and proofreading of the manuscript.

Funding

This work was supported by funds from the Foundation of Basic and Applied Basic Research of Guangdong Province, China (No. 2022A1515220202); the 2023 Science and Technology Innovation Strategy Project of Guangdong Province (Big Project + Task List), China (No. STKJ2023009,20230403); the Young Research Fund Project of the Cancer Hospital of Shantou University Medical College (No. 2023A002); and the Science and Technology Project of Shantou, China (No. 200601155261085).

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was approved by the Ethics Committee of the Cancer Hospital of Shantou University Medical College (NO. 2023086), and informed consents were obtained from all patients.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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No competing interests reported.

A novel model for predicting the risk of non-sentinel lymph node metastasis after positive sentinel lymph node biopsy in Chinese women diagnosed with early breast cancer

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Abstract

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Introduction

Patients and Methods

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Discussion

Conclusion

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Version 1