Pregnancy-associated breast cancer: gestational weight gain impact and nomogram validation

Abstract

Background

As a rare form of breast cancer, the prognosis and influencing factors of pregnancy-associated breast cancer (PABC) remain insufficiently understood. This study aimed to explore the prognostic factors affecting PABC patients, and establish a nomogram for predicting prognosis.

Methods

Patients with PABC and non-PABC, who were treated at three large tertiary institutions, were included in the analysis. The demographic and tumor characteristics of the patients were collected, and their survival were followed up. A Cox proportional hazard regression model was performed to analyze the effects of different variables on the risk of disease-free survival (DFS) in PABC patients. A prognostic nomogram was developed to predict DFS, and its performance was validated using the concordance index, calibration curves, ROC analysis, and decision curve analysis.

Results

Sixty-nine PABC patients and matched non-PABC patients were included in the study. Compared to non-PABC group, PABC patients have less favorable fertility conditions and outcomes, poorer clinicopathological characteristics and worse prognosis. In the analysis of prognostic factors of PABC patients, gestational weight gain (GWG) was identified as an independent factor affecting DFS in PABC patients. A nomogram was subsequent established to predict the possibility of DFS in patients with PABC, and multiple analyses confirmed the robust predictive ability and clinical value of the model.

Conclusions

PABC patients exhibited poorer prognosis compared to non-PABC patients, with GWG identified as a pregnancy-related risk factor influencing their outcomes. The established nomogram showed robust predictive accuracy and holds promise for informing clinical decision-making.

Background

Pregnancy-associated breast cancer (PABC) is typically defined as primary breast cancer diagnosed during pregnancy or within 1 year after delivery [1, 2]. Epidemiological data from Sweden indicate that the incidence of PABC is on the rise, with 13 cases per 100,000 deliveries [3]. Previous studies have identified various factors potentially influencing the onset and progression of PABC, including a family history of breast cancer, a history of alcohol consumption, advanced maternal age at first childbirth, high parity, and early onset of menarche [4–7]. Studies have reported that being overweight is a notable risk factor for breast cancer [8]. Pregnancy-related complications can significantly affect the health of both the mother and the fetus. Specifically, a history of gestational diabetes or preeclampsia has been linked to an increased risk of breast cancer [9, 10]. Given the more advanced stage and poorer prognosis of PABC patients [11], identifying risk factors for PABC remains an urgent priority.

Several studies have demonstrated that the prognosis of PABC is worse than non-PABC [12, 13], with PABC being associated with more aggressive tumor characteristics [14]. Nonetheless, there remains significant debate regarding the prognosis of PABC. Comparative analysis of 5-year and 10-year survival rates reveals that the age-standardized survival rate of women diagnosed with breast cancer during pregnancy (PrBC) is comparable to that of non-PABC cases over time, whereas the survival outcomes for postpartum breast cancer (PPBC) persistently remain inferior [15]. Subgroup analyses of PABC have demonstrated that patients diagnosed in the late stages of pregnancy (second and third trimesters) or the early postpartum period (within 6 months after delivery) experience significantly worse outcomes [16, 17]. Mechanistically, pregnancy exerts a great influence on breast cancer progression through mechanisms involving hormonal fluctuation, immune changes and breast involution [18]. The poor prognosis during pregnancy may be attributable to hormone-driven proliferative promotion and pregnancy-associated immunosuppression [19]. The poor prognosis observed within 5–10 years postpartum has been linked to tumor progression driven by wound-healing responses during breast involution [20].

To further elucidate the clinical characteristics and prognosis of PABC, clinical data and follow-up information from 69 patients across multiple institutions were systematically collected and analyzed. Based on the identified prognostic factors, a nomogram was subsequently developed to estimate the probability of disease-free survival (DFS) in PABC patients, and its predictive accuracy was then validated.

Methods

Patient selection

Between January 1, 2016, and August 1, 2020, a retrospective analysis was conducted on a multi-center cohort of 69 PABC patients from Tongji Hospital affiliated with Tongji Medical College of Huazhong University of Science and Technology, Hubei Cancer Hospital, and Xiangyang Central Hospital. Demographic and clinical data were collected for these patients, and follow-up was conducted. For each PABC case, a non-PABC control case was matched at a 1:1 ratio, based on the following criteria: (1) identical diagnosis and treatment institutions; (2) age within ± 1 year; (3) diagnostic year within ± 1 year. In cases where two or more non-PABC matches were available, the individual with the closest age at diagnosis was prioritized for selection.

Clinical data collection

The following data were recorded: date of diagnosis, age at diagnosis, height, weight (including pre-pregnancy and the last pre-birth weight measurement), age at menarche, level of education, reproductive history, duration of breastfeeding, family history of breast cancer, history of oral contraceptive use, use of assisted reproductive technology (ART), the sex and birth status of the infant corresponding to the parity at the time of diagnosis, pathological characteristics (including tumor type and size, nodal involvement, histological grade, status of estrogen receptors (ER) and progesterone receptors (PR), expression of human epidermal growth factor receptor 2 (HER-2) and Ki-67, and therapeutic strategies (including type of surgery, use of oral capecitabine, chemotherapy, radiotherapy, targeted, endocrine and neoadjuvant therapy). All patients who received neoadjuvant chemotherapy were treated with regimens based on anthracyclines or taxanes. Informed consent was obtained from the patients and their families, and the study was approved by the Human Ethics Committee of Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology (No. TJ-IRB202407109). Informed consent was obtained from all participants included in the study in accordance with the Declaration of Helsinki.

Breast cancer staging was determined according to the 8th edition of the American Joint Committee on Cancer (AJCC). Molecular subtypes were classified based on the 2013 St. Gallen Consensus [21]. A Ki-67 index of ≥ 15% was considered positive. Gestational weight gain (GWG) was represented by the total weight gain during pregnancy, defined as the difference between the most recent weight recorded prior to delivery and the pre-pregnancy weight. To minimize bias, weight records were cross-checked with patient- and family-reported information. For singleton pregnancy, the Institute of Medicine (IOM) gave guidelines for GWG in women with different pre-pregnancy body mass index (BMI) [22]. Patients were categorized into four groups based on their pre-pregnancy BMI: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²) and obese (≥ 30 kg/m²). The guidelines recommend the following GWG ranges for each BMI category: 12.5–18.0 kg for underweight, 11.5–16.0 kg for normal weight, 7.0–11.5 kg for overweight and 5.0–9.0 kg for obese. Patients whose GWG fell within these ranges were classified into the normal GWG group, those below these range were classified as the inadequate GWG group, and those exceeding the range were classified as the excessive GWG group. For full-term twin deliveries, the recommended GWG was 17–25 kg for normal weight women, 14–23 kg for overweight women, and 11–19 kg for obese women [22, 23]. However, the twin data in current cohort did not meet the criteria for full-term delivery, and patients with tumor stage IV (distant metastases) were unsuitable for DFS prognostic analysis. Consequently, 3 cases of preterm twin deliveries, 1 case with incomplete body weight data, 4 cases of induced abortion, and 4 cases with stage IV were excluded from the subsequent analysis. The remaining 57 cases were included in the analysis of prognostic factors.

Follow-up information

The follow-up period extended until August 1, 2021, with no instances of loss to follow-up. DFS was defined as the interval from the first date of initial diagnosis to the first occurrence of any locoregional recurrence, including ipsilateral breast, local/regional lymph nodes, any contralateral breast cancer, distant metastasis or secondary malignancy, whichever event occurred first [24]. DFS analyses included only patients with stage I-III disease. 7 patients in this dataset had stage IV disease (3 in the non-PABC group and 4 in the PABC group). Consequently, these cases and their matched counterparts were excluded, leaving 62 patients in each group for the survival curve comparison.

Statistical analysis

Conditional logistic regression was employed to analyze differences in clinical characteristics and treatment methods between PABC patients and non-PABC controls. The Kolmogorov-Smirnov test, Pearson’s Chi-square test, and Fisher’s exact test were used to compare clinical values between the PrBC group and PPBC group. Survival analysis was performed using the Kaplan-Meier method and the log-rank test. The Cox proportional hazards model was applied for both univariate and multivariate analyses to identify prognostic factors in PABC patients. Variables with p < 0.05 in univariate analysis were included in multivariate analysis, and those with p < 0.05 in multivariate analysis were subjected to nomogram model establishment. Calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) were used to evaluate the calibration, discrimination and clinical utility of the nomogram. Internal validation was performed by calculating the concordance index (C-index) and its 95% confidence interval using the Bootstrap method with 1,000 resamplings.

All statistical analyses were performed using R software version 4.4.1 and SPSS version 26.0. The R packages “caret”, “rms”, “foreign”, “survival”, “forestplot”, “ggplot2”, “ggDCA”, “survminer”, “dplyr”, timeROC” and “dcurves” were employed for data analyses. All tests were two-sided, and a p < 0.05 was considered statistically significant.

Results

Demographic and clinicopathological characteristics of involved patients

A total of 138 patients were enrolled in the study, consisting of 69 PABC cases and 69 age-matched controls. The demographic and gestational characteristics of the patients were summarized in Table 1. Regarding fertility conditions, a significantly higher proportion of multiparous women were diagnosed with PABC compared to the non-PABC group (p < 0.001). Additionally, first-time births at the age of 30 or older were more prevalent in the PABC group than in the non-PABC group (p = 0.022), and PABC patients had a higher incidence of premature births (p = 0.013). Among the PABC patients, there were 13 cases of PrBC and 56 cases of PPBC. Within the subgroup, it was observed that compared to the PPBC group, patients in the PrBC group had a higher incidence of premature births (p < 0.001), fewer breastfeeding attempts (p = 0.041), and a great number of fetuses receiving treatment in the neonatal intensive care unit (NICU) (p = 0.001).

Table 1.

The demographic and gestational characteristics of patients

Variables	No. of patients (PABC)			No. of patients (non-PABC)	p Value (PrBC VS PPBC)	p Value (PABC VS Non-PABC)
	PrBC group	PPBC group	Total
Patients	13	56	69	69	-	-
Age, median (range, year)	33(27–38)	34(25–40)	33(25–40)	32(24–39)	0.197	Matched
Education degree					0.452	0.113
Middle †	2	17	19	31
Senior ‡	8	24	32	26
Undergraduate §	3	15	18	12
Family history of breast cancer					0.226	0.618
Yes	0	10	10	8
No	13	46	59	61
Age at menarche					0.475	0.489
< 12	0	2	2	1
12–13	7	24	31	38
> 13	6	30	36	30
First pregnancy or delivery					0.526	< 0.001
Yes	5	14	19	43
No	8	42	50	26
Age of first delivery					0.455	0.022
< 30	12	44	56	61
≥ 30	1	12	13	3
Unknown	0	0	0	5
Premature					< 0.001	0.013
Yes	8	6	14	1
No	5	50	55	63
Gestational weight gain					0.890	0.144
Normal	3	24	27	25
Inadequate	3	16	19	14
Excessive	2	13	15	25
Unknown	5	3	8	5
Oral contraceptives					0.901	0.853
Yes	7	31	38	40
No	5	24	29	29
Unknown	1	1	2	0
ART ¶					0.586	0.212
Yes	0	6	6	0
No	13	50	63	69
Breastfeeding before diagnosis					0.041	0.796
Yes	8	50	58	59
No	5	6	11	10
Corresponding fetal sex at diagnosis					0.651	0.149
Male	8	31	39	32
Female	3	21	24	32
Unknown	0	4 ††	4 ††	5
NICU ‡‡					0.001	0.022
Yes	7	8	15	4
No	4	48	52	60
Unknown	2	0	2	5

†: Middle school education or below; ‡: Senior high school education, Technical secondary school or Junior college; §: Undergraduate degree or above; ¶: ART, assisted reproductive technology. Among the six PABC patients who underwent ART, two experienced preterm delivery; ††: These 4 cases are those who gave birth to twins; ‡‡: NICU Neonatal Intensive Care Unit, Whether the delivered infant was admitted to the NICU

In terms of clinicopathological characteristics (Table 2), PABC patients presented with more advanced T stage (p = 0.007) and a higher positivity rate of axillary lymph nodes (p = 0.003) compared to non-PABC patients. Additionally, PABC tumors were more frequently negative for PR (p = 0.019), although there was no difference in ER status between the PABC and non-PABC patients (p = 0.467). With regard to treatment modalities, PABC patients were more likely to receive neoadjuvant chemotherapy (p = 0.001) and radiotherapy (p = 0.007), and a greater proportion were treated with oral capecitabine (p = 0.048). However, no statistically significant differences in clinicopathological features or treatment approaches were identified between the PrBC and PPBC subgroups (p > 0.05).

Table 2.

The clinical characteristics of patients

Variables	No. of patients (PABC)				No. of patients (non-PABC)	p Value (PrBC VS PPBC)	p Value (PABC VS Non-PABC)
	PrBC group	PPBC group		Total
Patients	13	56	69		69	-	-
First symptom						1.000	0.730
Mass	13	54	67		66
Skin redness	0	1	1		0
Pain with fever	0	1	1		0
Nipple discharge	0	0	0		3
Initial site						0.525	0.506
Left	5	27	32		36
Right	8	29	37		33
Delayed diagnosis, median (range, day)	60(1–720)	90(3–525)	90(1–720)		60(7–870)	0.468	0.380
Tumor stage						0.939	0.007
T1 + T2	9	42	51		65
T3 + T4	4	14	18		4
Axillary lymph node metastasis						1.000	0.003
Positive	9	39	48		29
Negative	4	17	21		40
Lymphovascular invasion						1.000	0.187
Yes	2	11	13		7
No	11	45	56		62
Stage						0.597	0.019
Ⅰ	1	6	7		19
Ⅱ	5	27	32		37
Ⅲ	7	19	26		10
Ⅳ	0	4	4		3
Histologic grade						0.711	0.991
Ⅰ	0	0	0		2
Ⅱ	5	22	27		29
Ⅲ	3	23	23		22
Unknown	5	14	19		16
Estrogen receptors						0.188	0.467
Negative	3	24	27		23
Positive	10	32	42		46
Progesterone receptors						0.630	0.019
Negative	6	30	36		21
Positive	7	26	33		48
Molecular subtype						0.436	0.653
Luminal †	10	33	43		48
HER-2 ‡ overexpression	0	8	8		7
TNBC §	3	15	18		14
Neoadjuvant chemotherapy						0.172	0.001
Yes	9	27	36		14
No	4	29	33		55
pCR ¶						0.644	0.610
Yes	2	4	6		3
No	7	21	28		10
Unknown	0	2	2		1
Capecitabine						0.856	0.048
Yes	3	17	20		11
No	10	39	49		58
Chemotherapy						-	0.378
Yes	13	56	69		66
No	0	0	0		3
Radiotherapy						1.000	0.007
Yes	10	45	55		30
No	3	11	14		39
Endocrine therapy						0.352	0.227
Yes	9	31	40		47
No	3	25	28		22
Unknown	1	0	1		0
Targeted therapy						1.000	0.097
Trastuzumab	2	9	11		18
Trastuzumab and Pertuzumab	2	10	12		5
No	9	37	46		46
Type of breast surgery						1.000	0.608
Mastectomy	12	47	59		57
Breast conserving surgery	1	7	8		12
No	0	2	2		0
Type of axilla surgery						0.721	0.053
SLNB ††	0	4	4		54
ALND ‡‡	13	50	63		15
No	0	2	2		0

†: luminal, include luminal A and luminal B, the number of Luminal B cases in the PABC group and the non-PABC group was 38 and 39, respectively; ‡: HER-2, human epidermal growth factor receptor 2; §: TNBC, triple negative breast cancer; ¶: pCR, Pathologic complete response; ††: SLNB sentinel lymph node biopsy; ‡‡: ALND, axillary lymph node dissection, one patient in this group underwent a supraclavicular lymph node dissection

Disease-free survival analysis based on follow-up data

The median follow-up duration for DFS was 36 months. A statistically significant disparity in DFS rates was identified between patients with PABC and those with non-PABC (p = 0.031) (Fig. 1). In detail, the 1-year DFS rate of PABC patients was 95%, compared to 98% for the non-PABC group. At 3 years, the DFS rate for PABC patients decreased to 78%, while it remained higher at 95% in the non-PABC group, suggesting that pregnancy may negatively impact breast cancer prognosis.

Fig. 1.

Kaplan-Meier analysis of DFS rate for PABC patients and non-PABC patients

Identification of prognostic factors associated with disease-free survival

Univariate analysis of 57 PABC cases revealed that DFS was significantly associated with age, GWG, lymphovascular invasion (LVI) and oral capecitabine use (all p < 0.05) (Fig. 2). These variables were subsequently included in the multivariate analysis, which demonstrated that age, inadequate or excessive GWG, LVI, and oral capecitabine use were independent predictors of DFS in PABC patients (all p < 0.05) (Fig. 3). Specifically, older patients exhibited a better prognosis than younger ones (HR = 0.787). PABC patients with inadequate or excessive GWG demonstrated nearly nineteen-fold and nine-fold elevated risks of recurrence or metastasis, respectively, when compared to their counterparts with normal GWG (inadequate: HR = 19.290; excessive: HR = 9.008). Similarly, patients with LVI had a six-fold elevated risk of recurrence or metastasis compared with LVI-negative patients (HR = 6.263). Additionally, patients who received oral capecitabine had a four-fold increased risk of recurrence or metastasis compared to those who did not (HR = 4.410). The Kaplan-Meier curves depicting DFS in PABC patients stratified by GWG, LVI and capecitabine use were presented in Fig. 4A-C. Consequently, PABC patients with inadequate GWG, LVI, or capecitabine use exhibited significantly worse DFS compared to their counterparts.

Fig. 2.

Forest plots depicting results of univariate analysis of PABC patients for DFS

Fig. 3.

Forest plots depicting results of multivariate analysis of PABC patients for DFS

Fig. 4.

The DFS curves of PABC patients in different subgroups. A GWG; (B) LVI; (C) capecitabine

Development of a prognostic nomogram for patients with PABC

A Cox proportional hazards regression model was employed to identify determinants influencing DFS in PABC patients, which subsequently informed the development of a prognostic nomogram. The variables incorporated into the nomogram comprised LVI, capecitabine use, GWG, and age. The resulting nomogram was illustrated in Fig. 5. To use the nomogram, one must first locate the patient’s position on respective axes for each variable, determine the corresponding points on the top axis, sums the point values of all variables, and then draw a vertical line downward from this total to predict the risks of 1-year or 3-year DFS in PABC patients.

Fig. 5.

Prognostic nomogram for predicting the 1- and 3‐year probabilities of DFS in patients with PABC

Performance evaluation of the prognostic nomogram

To validate the predictive accuracy of nomogram, the C-index and its 95% confidence interval were calculated using the Bootstrap method with 1,000 resamplings. The graph of the sampling distribution was illustrated in Fig. 6A. The C-index of the nomogram was 0.919 (95%CI: 0.852–0.978), demonstrating excellent discriminative performance in predicting disease prognosis. Additionally, the specificity and sensitivity of the nomogram were evaluated through the construction of ROC curves. The area under the ROC curves (AUC) demonstrated strong prediction accuracy for DFS at 1 and 3 years (1-year: 0.959; 3-year: 0.941) (Fig. 6B). The 45-degree slash in the calibration chart was regarded as a more perfect result. The calibration charts showed that there was good agreement between the 1-year and 3-year DFS prediction probability and the actual probability (Fig. 6C).

Fig. 6.

Internal validation for nomogram. A Sampling distribution plot based on the bootstrap method (resampling = 1000); (B) ROC curves for evaluating the nomogram; (C) Calibration plots of DFS-associated nomograms; (D) The decision curves analysis for assessing clinical utility of the nomogram

DCA curve was illustrated to further evaluate the clinical utility of the nomogram. The DCA curve demonstrated that when the nomogram’s line lies above the two extreme curves (represented by the red, blue, and green segments in Fig. 6D), it signifies clinical utility, with a greater distance from these reference lines corresponding to higher practical value. The nomogram’s dominance in DCA for 1-year and 3-year DFS signifies actionable clinical value and improved patient management in PABC patients (Fig. 6D).

Discussion

Based on epidemiological data from different regions worldwide, the estimated incidence of PABC ranges from 17.5 to 39.9 cases per 100,000 live births between the 1990 s and the early 21 st century. Within this range, the incidence of PrBC is reported to be 3.0 to 7.7 cases per 100,000, whereas the incidence of early PPBC (within 1-year postpartum) is notably higher, ranging from 13.8 to 32.2 cases per 100,000 [25]. Overall, the incidence of early PPBC is consistently higher than that of PrBC [26], a finding that aligns with our data. This phenomenon may be ascribed to the augmentation of breast size and density during pregnancy, which may mask malignancy and delay detection, causing some tumors that arise during pregnancy to be detected later in the early postpartum period [27, 28].

As a distinct form of breast cancer occurring during a specific period, PABC may be influenced by factors such as region, ethnicity, socioeconomic status, and natural environment [29]. Therefore, exploring the prognosis and related factors of PABC patients across different countries and regions is crucial. In comparison to Western countries, there is a paucity of PABC data from Asian populations. To provide a more comprehensive overview of the overall situation of PABC patients in China, we collected data from multi-institutions with complete clinicopathological information. Age is an established prognostic factor in breast cancer, as younger women are predisposed to biologically aggressive tumors that drive poorer clinical outcomes [30]. Given that PABC patients are typically younger [31], we have performed age matching to account for this factor. With regard to reproductive factors, our findings indicated that PABC was more prevalent among multiparous women compared to non-PABC patients, which seemingly contradicted the widely accepted notion that parity conferred a protective effect against breast cancer [32]. A study reported that the effect of parity on breast cancer risk was associated with factors such as age and molecular subtypes [33]. Therefore, this might have been related to the fact that all patients included in our study were under the age of 40. Additionally, the age at first birth was more frequently over 30 years in the PABC group, consistent with epidemiological observations [34]. The present study also revealed that premature births were more common in PABC patients, which may be associated with the higher use of ART in this population (2/14). Furthermore, certain studies have indicated a potential link between ART usage and an elevated risk of breast cancer [35, 36]. It is important to recognize that disease is not solely a consequence of pathological changes; socioeconomic factors and technological innovation also play an undeniable role in its development.

A variety of factors can influence GWG, including physical activity and pre-pregnancy BMI [37, 38]. GWG is associated with several adverse perinatal outcomes, including fetal growth, pre-eclampsia, preterm delivery, cesarean delivery, gestational diabetes and gestational hypertension [23, 39, 40]. A prospective study revealed that GWG was associated with an increased risk of colorectal cancer in offspring [41]. Another study indicated that excessive GWG increased the risk of developing breast cancer later in life [42]. Evidence indicated that excessive GWG in mothers triggered precocious thelarche in daughters, a known risk factor for increased breast cancer incidence [43]. However, to the best of our knowledge, whether GWG affects the prognosis of PABC has not been reported. Our study analyzed the association between inadequate or excessive GWG and PABC outcomes using the IOM guidelines [22]. The results of univariate and multivariate analyses indicated that inadequate or excessive GWG was an independent risk factor for DFS in PABC patients. Given the limited data on PrBC, the extent to which this association is attributable specifically to postpartum-onset disease remains unresolved. Notwithstanding this uncertainty, our findings compellingly establish GWG as a modifiable risk determinant, underscoring the imperative for rigorous weight monitoring and management throughout pregnancy.

At present, the definition of postpartum period in PABC is not fully standardized [44, 45]. Studies that include postpartum periods of 1 year or even 5 years reported that ER- and PR-negative tumors were more prevalent among PABC patients [46, 47]. In current study, the rate of PR negativity was significantly higher in the PABC group compared to the non-PABC group. Similarly, ER negativity was more frequent in the PABC cohort compared to non-PABC controls, albeit without achieving statistical significance. Regarding molecular subtypes, no significant differences were observed between the PABC and non-PABC groups. In both groups, luminal B was the most common subtype, followed by triple negative breast cancer (TNBC). The elevated proportion of PR-negative cases observed in PABC patients may be linked to the predominance of the luminal B subtype. This finding contrasts with previous studies reporting TNBC as the predominant molecular subtype in PABC [48, 49]. These differing conclusions may be related to variations in geographic regions and racial groups.

Regarding treatment, it is well established that capecitabine is commonly used for the treatment of patients with TNBC or recurrent unresectable breast cancer [50]. This study also demonstrated that capecitabine use is an independent predictor of adverse outcomes in PABC. This may be attributed to the fact that capecitabine is used across multiple molecular subtypes in advanced breast cancer. Multiple studies have indicated that LVI is associated with poor prognosis in breast cancer patients [51, 52], and our research supports this as well. Although there was no statistically significant difference in the likelihood of LVI between PABC and non-PABC cases in this study, the probability of LVI remained higher in PABC patients. Compared with non-PABC, those with PABC demonstrated more unfavorable pathological features, including larger tumor size, greater axillary lymph node involvement and higher stage. These findings are consistent with previous studies [53, 54]. Given the observed high rate of lymph node involvement, a greater proportion of PABC patients received neoadjuvant chemotherapy and radiotherapy. The vast majority of patients under 40 years of age included in this study were at medium to high risk of recurrence and metastasis, and nearly all of them received chemotherapy. Consequently, no significant difference was noted between PABC and non-PABC groups in terms of chemotherapy administration.

Nomograms have significant clinical value in predicting survival rates in cancer patients [55, 56]. Here, univariate and multivariate analyses were employed to identify significant prognostic determinants for PABC, including age, GWG, LVI, and capecitabine use. These variables were subsequently integrated into a comprehensive prognostic nomogram. Internal validation demonstrated the superior discriminative capacity and favorable clinical applicability of nomogram, thereby providing a valuable tool to facilitate individualized prognostic stratification and decision-making in clinical practice. These findings hold significant implications for the research on PABC.

Nevertheless, several limitations should be acknowledged in the present study. First, the proposed nomogram has not yet undergone external validation from different research centers. Additionally, the rarity of the condition resulted in a relatively small sample size in this study, which may limit the generalizability of the findings. In the future, we intend to gather additional cases to analyze the prognostic differences between patients diagnosed during pregnancy and at different postpartum periods, and to conduct comprehensive validations of the model with regard to prognosis, accuracy, efficacy, and clinical applicability, thereby enhancing its broader applicability.

Conclusion

PABC patients exhibited more invasive pathological features and a poorer prognosis compared to non-PABC patients. Inappropriate GWG was associated with an increased likelihood of metastasis or recurrence in patients with PABC. The nomogram we constructed may provide a more effective tool for predicting the prognosis of PABC patients.

Abbreviations

ART

Assisted reproductive technology

BMI

Body mass index

DCA

Decision curves analysis

DFS

Disease-free survival

ER

Estrogen receptors

GWG

Gestational weight gain

HER2

Human epidermal growth factor receptor 2

LVI

Lymphovascular invasion

OS

Overall survival

PABC

Pregnancy-associated breast cancer

PrBC

Breast cancer during pregnancy

PPBC

Postpartum breast cancer

PR

Progesterone receptors

TNBC

Triple negative breast cancer

ROC

Receiver operating characteristic

Authors’ contributions

CL performed data curation, formal analysis, investigation, and drafted the original manuscript. RW and SW contributed to methodology, formal analysis, and visualization. SL and WZ were responsible for investigation and resources. XW provided data curation and resources. SL and TY conducted result validation. JH and LZ conceptualized the study and contributed to methodology and manuscript editing. All authors reviewed and approved the final manuscript.

Funding

This work was supported by the Wu Jieping Medical Foundation (Grant No. 320.6750.2024-21-122).

Data availability

All data used or analyzed during this study are included in this article. Readers are encouraged to contact us should they have further inquiries or requirements.

Declarations

Ethics approval and consent to participate

Informed consent was obtained from all participants included in the study in accordance with the Declaration of Helsinki. This study was approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB202407109).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.