Nomogram for predicting preoperative axillary lymph node status in male breast carcinoma: SEER population-based study

doi:10.21203/rs.3.rs-2098902/v2

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Research Article

Nomogram for predicting preoperative axillary lymph node status in male breast carcinoma: SEER population-based study

https://doi.org/10.21203/rs.3.rs-2098902/v2

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published 01 Apr, 2023

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Background

Sentinel lymph node (SLN) has been recommended as a replacement for axillary lymph node dissection (ALND) in male breast carcinoma (MBC) with clinical axillary lymph node-negative as in women. However, the morbidity after SLN may also have short-term or long-term complications. It is imperative to develop a model to evaluate the risk of lymph node metastasis to avoid unnecessary operations.

Patients and Methods

: A retrospective review of the clinical and pathology records were performed in patients diagnosed with MBC between 2010 and 2018 from Surveillance, Epidemiology, and End Results (SEER) database. The cohort was divided into training and validation cohorts. A logistic regression model was used to construct the nomogram in the training cohort and then verified in the validation cohort.ROC curve, C-index, and calibration were used to assess the predictive ability of the nomogram.

Results

Overall, 2,610 patients diagnosed with MBC were enrolled, of which 1,740 were in the training cohort and 870 were in the validation cohort. Logistic regression analysis indicated ,age at diagnosis, tumor location, tumor stage, pathological type, and histologic grade were significantly related to axillary lymph node metastasis(ALNM). The AUC of the nomogram was 0.846 (95% CI, 0.825–0.867) and C-index was 0.848 (95%CI, 0.807–0.889), demonstrating a good prediction performance. The calibration curve for the nomogram was plotted and the slope was close to 1. The performance of the nomogram was further validated in the validation cohort, with an AUC of 0.848 (95% CI 0.819–0.877).

Conclusion

We successfully established a nomogram to predict LANM, especially for those who were of advanced age at diagnosis,had small tumor size, low malignancy, and clinical axillary lymph node-negative to avoid unnecessary axillary operation —thus improving the quality of life of the patients without adverse effect on their survival rates.

male breast carcinoma

nomogram

axillary lymph node

SEER database

Male breast carcinoma (MBC) is a rare disease that accounts for less than 1% of all cancers in men ^[1]and about < 1% of all breast carcinoma ^[2]. Due to its rarity, the limited information available regarding the epidemiology, treatment, and prognosis of this disease has been extrapolated from the protocols for breast carcinoma in women^[3]. Axillary lymph node(ALN) status is a key step in breast carcinoma because

nodal status has been the standard of care to estimate prognosis and to guide systemic treatment decisions. Many predictors of axillary lymph node metastases(ALNM) have been previously suggested in female breast carcinoma(FBC) including age at diagnosis, tumor size, tumor location, pathological type, histologic grade, estrogen receptor (ER) status, progesterone receptor (PR) status and HER2 status^[4–7].

But, there are few predictive models for ALNM in MBC.Vaysee et al^[8]. used two nomograms (validated in the female population) to demonstrate that the predictive factor of ALNM in FBC was not suitable for MBC. Thus, whether the predictive model for ALNM is suitable for MBC is unclear. We hypothesized that we can build a prediction model of ALNM in MBC to select patients at such a low risk of ALN involvement, thus improving the quality of life of the patients without any adverse effect on their survival rates. To verify this hypothesis, we conducted this research by using the Surveillance, Epidemiology, and End Results (SEER) database to explore the relationship between clinicopathologic features and ALN status.

Data were obtained from (SEER) (www.seer.cancer.gov) with the following search parameters: Incidence - SEER Research Data, 18 Registries, Nov 2020 Sub (2000–2018). This study was exempted by the Ethics Committee of Affiliated Sanming First Hospital of Fujian Medical University. Because our data were from the SEER database, which is open to the public.

We picked up 2,610 male patients with primary nonmetastatic breast cancer between 2010 and 2018 in the study. Patients were included when meeting the following criteria: (1) Male patients; (2) Patients were diagnosed with primary breast cancer (International Classification of Diseases for Oncology, Third Edition [ICD-O-3]); (3) Patients were aged older than 18 years old at diagnosis. Patients were excluded according to the following criteria: (1) Unknown clinical information including tumor size, American Joint Committee on Cancer (AJCC) stage, Tumor location(Primary Site-labeled), Pathological type, Histologic grade, estrogen receptor (ER) status, progesterone receptor (PR) status, and HER2 status; (2) Regional nodes examined Positive ; (3) With metastatic or synchronous tumors.

We randomly divided the included patients into the training cohort and a validation cohort according to the ratio of 2:1. The training cohort was used to build the predictive risk model, and the validation cohort was used for internal validation.

We gathered variables of age at diagnosis, tumor location, AJCC tumor node metastasis (TNM) stage, AJCC T status, AJCC N status, pathological type, histologic grade, ER status, PR status, and HER2 status from the SEER database. Age was classified as ≦ 50, 51–60, 61–70, 71–80, and > 80.Tumor location was classified as Central portion of breast(central), upper outer quadrant (UOQ), lower outer quadrant (LOQ), lower inner lower quadrant (LIQ), and upper inner quadrant (UIQ), nipple, Overlapping lesion of breast(Overlapping), Axillary tail of breast and Breast NOS(other). The pathological type was classified as invasive ductal carcinoma(IDC), invasive lobular carcinoma(ILC), adenocarcinomas(ADENO-CA), Paget's disease(Paget), and Others.ER, PR and HER2 were all classified as Positive and negative (Note: The SEER database began collecting HER-2 information in 2010). Histologic grade was classified as grades I, II, and III. AJCC TNM stage was classified as stages I, II, and III. AJCC Tumor status was classified as T1, T2, T3, and T4. AJCC lymph node status was classified as positive and negative. The molecular subtype was classified as Luminal A, Luminal B, HER2+, and Triple-negative (TN).

Statistical analysis

Baseline characteristics between the training cohort and the validation cohort using chi-square or Fisher’s exact tests for categorical variables and Student’s t-test for continuous variables. Using the clinical and pathological data of the training cohort, a univariate logistic regression analysis was performed to explore ALN metastasis-related variables. To identify factors that were associated with ALN involvement, binary logistic regression analysis was used for multivariable analysis. Odds ratios (OR) were presented with 95% CI. A logistic regression model was used to construct the nomogram. The receiver operating characteristic (ROC) curve, the area under the curve (AUC), and the concordance index (C-index) were used to evaluate the predictive ability of the model. Calibration was assessed graphically by plotting the relationship between actual (observed) probabilities and predicted probabilities in both cohorts. Internal validation of performance was estimated with the bootstrapping method (1000 replications). To further evaluate the clinical value of the model, we considered certain cutoff values for prediction risk in patients in the validation cohort and calculated the corresponding accuracy and false-negative rate of the cutoff values to assess the screening indicators of low-risk patients with ALNM. Statistical analyses were conducted using SPSS for Windows (version 26.0, SPSS Inc., Chicago, IL, USA) and the R programming language and environment version 3.4.1. A two-tailed p-value < 0.05 was considered statistically significant.

Comparison of clinicopathological characteristics between the training cohort and the validation cohort

Based on the inclusion criteria, we identified a total of 2,610 patients who had well-documented patient data for analysis. According to the ratio of 2:1, 1,740 were included in the training cohort and 870 were included in the validation cohort. The clinical and pathological data of the patients between the two groups did not differ significantly (p > 0.05). (Table 1).

Table 1 Comparison of the descriptive characteristics between the training cohort and the validation cohort

Characteristics		Training	%	Validation	%	P -value
Age at diagnosis (years)	N	1740	66.7	870	33.3	0.895
		67.57±11.70		67.64±12.03 10.210.10.2210.4
Age(years)						0.542
	≦ 50	137	7.9	79	9.1
	51-60	336	19.3	147	16.9
	61-70	539	31.0	269	30.9
	71-80	456	26.2	233	26.8
	>80	272	15.6	142	16.3
Tumor location¹						0.716
	Central	744	42.8	403	46.3
	UIQ	82	4.7	35	4.0
	LIQ	32	1.8	14	1.6
	UOQ	222	12.8	100	11.5
	LOQ	65	3.7	35	4.0
	Nipple	79	4.5	39	4.5
	Others	228	13.1	100	11.5
	Overlapping	288	16.6	144	16.6
Tumor stage						0.808
	T1	866	49.8	421	48.4
	T2	704	40.5	360	41.4
	T3	47	2.7	21	2.4
	T4	123	7.1	68	7.8
Pathological type						0.334
	IDC	1582	90.9	789	90.7
	ILC	14	0.8	8	0.9
	ADENO-CA	92	5.3	39	4.5
	Paget	20	1.1	8	0.9
	Others*	32	1.8	26	3.0
Histologic grade						0.613
	Ⅰ	251	14.4	124	14.3
	Ⅱ	936	53.8	453	52.1
	Ⅲ	553	31.8	293	33.7
ER						0.620
	Positive	1707	98.1	851	97.8
	Negative	33	1.9	19	2.2
PR						0.724
	Positive	1595	91.7	801	92.1
	Negative	145	8.3	69	7.9
HER2 receptor status						0.899
	Positive	215	12.4	106	12.2
	Negative	1525	87.6	764	87.8
AJCC stage						0.930
	Ⅰ	837	48.1	412	47.4
	Ⅱ	626	36.0	319	36.7
	Ⅲ	277	15.9	129	16.0
lymph node status						0.976
	Negative	1179	67.8	590	67.8
	Positive	561	32.2	280	32.2
Molecular subtype						0.730
	Luminal A	1502	86.3	748	86.0
	Luminal B	209	12	104	12.0
	HER2+	6	0.3	2	0.2
	TN	23	1.3	16	1.8
In cases of multifocal tumors or unifocal tumors that involved more than 1 quadrant, the tumor location was classified in the following order of priority: Central portion of breast(central), upper outer quadrant (UOQ), lower outer quadrant (LOQ), lower inner lower quadrant (LIQ), and upper inner quadrant (UIQ), nipple, Overlapping lesion of breast(Overlapping), Axillary tail of breast and Breast NOS(other). *others: metaplastic carcinoma, medullary carcinoma, Unspecified malignant neoplasms except for CNS, papillary carcinoma, infiltrating micropapillary carcinoma, tubular carcinoma, etc . (https://seer.cancer.gov/tools/solidtumor/Breast_STM.pdf)

Univariate logistic regression analysis of ALNM in the training cohort

Univariate logistic regression analysis was used to explore ALN metastasis-related variables (Table 2) and showed that age, tumor location, tumor stage, pathological type, histologic grade, ER, PR, Her-2, and molecular subtypes were related to MBC ALN metastasis (p < 0.05).

Table 2 Univariate analysis for factors associated with axillary lymph node metastasis

Variables		OR	OR 95% CI		P -value
Variables		OR	Lower	Upper
Age					<0.001
	≦ 50	1(REF)
	51-60	0.896	0.601	1.337	0.592
	61-70	0.572	0.390	0.838	0.004
	71-80	0.452	0.304	0.670	<0.001
	>80	0.335	0.215	0.522	<0.001
Tumor location					<0.001
	Central	1(REF)
	UIQ	0.380	0.216	0.668	0.001
	LIQ	0.361	0.147	0.888	0.027
	UOQ	0.528	0.377	0.740	<0.001
	LOQ	0.600	0.3411	1.053	0.075
	Nipple	0.859	0.530	1.395	0.540
	Other	0.723	0.527	0.991	0.044
	Overlapping	0.551	0.408	0.745	<0.001
Tumor stage					<0.001
	T1	1(REF)
	T2	6.065	4.703	7.823	<0.001
	T3	24.513	12.099	49.667	<0.001
	T4	53.929	30.248	96.150	<0.001
Pathological type					0.002
	IDC	1(REF)
	ILC	0.801	0.250	2.565	0.708
	ADENO-CA	0.359	0.201	0.641	0.001
	Paget	2.002	0.828	4.840	0.123
	Other	0.462	0.189	1.129	0.090
Histologic grade					<0.001
	I	1(REF)
	II	3.065	1.977	4.752	<0.001
	III	10.642	6.815	16.617	<0.001

ER	ER+	1(REF)
	ER-	2.271	1.139	4.530	0.020
PR	PR+	1(REF)
	PR-	1.541	1.089	2.181	0.015
HER2	HER2+	1(REF)
	HER2-	0.544	0.407	0.727	<0.001
Molecular subtype					<0.001
	LMA	1(REF)
	LMB	1.809	1.348	2.429	<0.001
	HER2	4.602	0.840	25.216	0.079
	TN	1.770	0.771	4.066	0.178

Multivariate logistic regression analysis of ALNM in the training group

After adjusting the significant variables from the univariate analysis, the multivariate analysis found that age at diagnosis, tumor location, tumor stage, pathological type, and histologic grade stayed as independent predictive factors of ALNM. (Table 3)

Table 3 Multivariate logistic regression analysis for factors associated with ALNM

Variables		OR	OR 95% CI		P value
Variables		OR	Lower	Upper	P value
Age					<0.001
	≦ 50	1(REF)
	51-60	0.830	0.503	1.368	0.464
	61-70	0.441	0.275	0.710	0.001
	71-80	0.372	0.228	0.607	<0.001
	>80	0.198	0.113	0.347	<0.001
Tumor location					0.001
	Central	1(REF)
	UIQ	0.508	0.264	0.975	0.042
	LIQ	0.493	0.183	1.332	0.163
	UOQ	0.603	0.400	0.909	0.016
	LOQ	0.615	0.305	1.239	0.174
	Nipple	0.631	0.328	1.212	0.167
	Other	0.522	0.352	0.776	0.001
	Overlapping	0.497	0.342	0.722	<0.001
Tumor stage					<0.001
	T1	1(REF)
	T2	5.877	4.463	7.739	<0.001
	T3	25.211	11.632	54.643	<0.001
	T4	60.418	32.409	112.631	<0.001
Pathological type					0.024
	IDC	1(REF)
	ILC	0.405	0.090	1.832	0.241
	ADENO-CA	0.355	0.176	0.716	0.004
	Paget	1.512	0.457	4.997	0.498
	Other	0.542	0.186	1.577	0.261
Histologic grade					<0.001
	I	1(REF)
	II	2.569	1.555	4.245	<0.001
	III	7.240	4.335	12.093	<0.001
Constant		0.126			<0.001

Establishment of a prediction model for ALNM

A nomogram to predict ALNM was developed in the training cohort (Figure 1). The nomogram that incorporated the following independent predictors were shown to be associated with ALNM including age, pathological type, tumor location, histologic grade, and tumor stage. The weights of each variable in the nomogram corresponded to different points. Points for the following factors were added to the total points, which corresponded to the linear predictors and risk predictors of ALNM: age, histological(pathological type), Site(tumor location), Grade(histologic grade), and T-stage (tumor stage).

ROC curves of the training cohort are plotted in Figure 2A. AUC were 0.846 (95% CI 0.825-0.867).The nomogram demonstrated good accuracy for predicting ALN, with an unadjusted C-index of 0.848 (95% CI, 0.807 - 0.889).To test the performance of the nomogram, 1,000 bootstrap resampling was carried out for verification through the calibration chart in the training cohort (Figure 2B). The calibration curve indicated a good calibration effect of the nomogram. These results indicated that the nomogram is a useful predictor for ALNM.

The predictive ability of nomograms in the validation cohort

This nomogram was prospectively used for patients in the validation cohort. It depicts the ROC curve(Figure 2C), and the AUC value calculated was 0.848(95% CI,0.819-0.877), indicating a good predictive ability. The nomogram was internally verified using the bootstrap (1000 bootstrap resamples) validation method. Good calibration was observed for the probability of ALNM in the validation cohort(Figure 2D).To further evaluate the clinical value of the model under different risks of ALNM. We select 14 cutoffs according to the Jordan index. The number of patients, number of patients with ALNM, sensitivity, specificity, accuracy, and negative predictive value of ALNM were calculated under different predicted risk values. (Table 4). As can be seen from the table, this model is more accurate in predicting patients with negative axillary lymph nodes. There were 204 cases with a predicted risk of <10%, and only 14 (6.9%) had ALNM, indicating that the model was more accurate in predicting patients with a lower risk of ALNM. The reason for this result is related to the study design. Both the training cohort and the validation group were patients with clinically negative axillary lymph nodes, resulting in a more accurate prediction of negative lymph nodes and a poorer prediction of positive lymph nodes.

Table 4 accuracy of the developed model's prediction in the validation cohort

Predicted Risk	No. of patients(% )	No. of patients with ALN metastasis	Sensitivity ( %)	Specificity (% )	Accuracy (% )	Negative predictive value (% )
<0.051	76(8.74)	2	99.30	87.50	97.37	97.37
<0.100	204(23.45)	14	95.00	67.80	93.14	93.14
<0.150	307(35.29)	30	89.30	53.10	90.23	90.23
<0.202	369(42.41)	39	86.10	44.10	89.43	89.43
<0.248	437(50.23)	54	80.70	35.10	87.64	87.64
<0.299	496(57.01)	62	77.90	26.40	87.50	87.50
<0.353	553(63.56)	71	74.60	18.30	87.16	87.16
<0.400	568(65.29)	76	72.90	16.60	86.62	86.62
<0.452	616(70.80)	90	67.90	10.80	85.39	85.39
<0.501	642(73.79)	94	66.40	7.10	85.36	85.36
<0.603	708(81.38)	144	48.60	4.40	84.46	85.36
<0.696	764(87.82)	189	32.50	2.50	84.16	85.36
<0.803	792(91.03)	213	23.90	1.90	84.22	85.36
<0.899	817(93.91)	230	17.90	0.50	83.72	85.36

Sentinel lymph node (SLN) has been recommended as a replacement for axillary dissection in MBC with clinical axillary lymph node-negative, as with women^[9]. However, the morbidity after SLN biopsy is not negligible, short-term or long-term complications such as upper limb edema, numbness, and shoulder pain may also occur in female breast carcinoma patients with SLN^{[10, 11]}.MBC usually has the same common presentation as FBC. Many models for predicting ALNM have been used in women with breast carcinoma^{[12, 13]}. Our study aimed to select patients with low risk of ALNM and to protect them from unnecessary axillary operations as in the case of females.
Because MBC is rare, most studies about MBC only contain small sample sizes, but the SEER database supplies a relatively large sample for the study. In our study, we obtained 2,610 cases from the SEER database. The results provided evidence that analyzing the clinicopathological features can also be used to assist in the judgment of axillary lymph node status in MBC. We successfully established a nomogram model for the prediction of ALNM in MBC. We summarized that age, pathological type, tumor location, histologic grade, and tumor stage associated with ALNM. Applying this model to patients in the training cohort and the validation cohort in this study, the performance of the nomogram in these two groups was similar (AUC = 0.846 versus 0.848), and the nomogram showed good predictive value in both.
Zavagno Gn^[14] findings showed that the youngest patients had the worst prognostic pattern, which improves as age increases and is the best in patients over 75 years of age by looking at the fluency of age on the pathologic and biological features of breast cancer. Many previous studies showed that young age was independently associated with a higher likelihood of one or more positive lymph nodes in FBC^{[15, 16]}. There are few reports on the fluence of age on ALNM in MBC. Our research showed that age at diagnosis was an independent prognostic factor for the risk of ALNM in MBC, and the risk decreased with age.
The pathological types of MBC are mainly IDC, lobular carcinoma is rare, and other types, such as adenocarcinoma, papillary carcinoma, and Paget's disease, have also been reported^[2]. Many researchs^{[12, 15]} have found that favorable histotypes ( medullary, cribriform, tubular, mucinous ) have a significantly lower risk of ALNM than ductal carcinoma in FBC. There is also little research on the relationship between pathological types and ALNM in MBC. Our research also found that other pathological types of MBC, such as lobular carcinoma and adenocarcinoma, were less prone to ALNM than IDC. We found that Paget's disease had the highest risk of ALNM in MBC. Due to the lack of other clinical and pathological data provided by the SEER database, we were unable to further explore the causes, which may be related to delayed treatment due to the patient's misdiagnosed breast cancer as eczema of the nipple and the fact that lymph nodes in the nipple and areola area are abundant and prone to lymphatic metastasis.
MBC is mostly manifested as a mass in the areola area, while the male breast is significantly smaller than the female breast. The tumor is close to the skin, nipple, areola, and chest wall. When the tumor occurs, it is easy to invade the mammary lymphatic drainage system early and lead to lymph node metastasis^[17]. Yang Hongwei et al^[18]confirmed that central location was an independent predictor of ALNM in MBC. Tumors located in the central region are more likely to have ALNM than in other regions. Our results also showed that lymph node metastasis was less likely to occur in tumors located elsewhere than in the central region.
The increasing tumor grade is associated with a higher risk for ALNM in FBC. And this fact has been confirmed in some studies^{[16, 19]}. A few studies ^[18]have found that the risk of ALNM increases with histological grade in MBC. It can be seen from a univariate analysis of our research that Grade 3 was associated with a 6.240-fold greater risk for lymphatic metastasis than grade 1, while grade 2 versus grade 1 had a 1.569-fold risk.
The T-stage is an objective performance criterium of the growth time and growth rate of the tumor. T-stage emerged as the most powerful independent predictor of SLNM. This fact has been confirmed in some research in FBC^{[20, 21]}. Some researchers^{[22, 23]} support the opinion that the advanced tumor stage is associated with a higher risk of death in MBC. Due to the low incidence of MBC, the relationship between T-stage and ALNM is also rarely researched. Our results showed that T-stage played a crucial role in ALNM in MBC. The risk of ALNM increases significantly with T-stage.
Although the nomogram showed good accuracy for predicting ALNM, there were some limitations still existing in the present study. First, data analysis based on SEER, lack of centralized pathology review, and limited information regarding the surgery. In addition, some important clinical information such as preoperative ultrasound and imaging examination, as well as some immunohistochemical index such as Ki67, was lacking. Second, the use of retrospective data introduced the possibility of selection bias. Third, the vast majority of patients in this research were white, so the estimation may be less precise for patients of nonwhite race. Prospective investigations and more in-depth research are needed to help establish the results.
In conclusion, our nomogram could serve as an acceptable and adoptable clinical tool in preoperative evaluation of MBC, especially for those who are of advanced age at diagnosis, have small tumor size, low malignancy, and are radiologically node-negative, to avoid the unnecessary axillary operation before sentinel lymph node biopsy- thus improving the quality of life of the patients without adverse effect on their survival rates.

Ethical Approval

This study was exempted by the Ethics Committee of Affiliated Sanming First Hospital of Fujian Medical University . Because our data were from the SEER database, which is open to the public.

Availability of data and materials

Data supporting this research article are available from the corresponding author upon reasonable request.

Author Contributions

Wenxin Chen wrote the main manuscript text and contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis, and interpretation. Hebing Wang, Binglin Yang, Maoquan Zhang, Jicheng Chen took part in drafting, revising, or critically reviewing the article.

Funding

This work was supported by the Natural Science Foundation of Fujian Province of China( Grant No. 2021J011387 )

Competing interests

The authors declare no conflicts of interest in this study.

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No competing interests reported.

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published 01 Apr, 2023

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Nomogram for predicting preoperative axillary lymph node status in male breast carcinoma: SEER population-based study

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