Abstract
Objective:
This study investigated the current status of fertility information needs among breast cancer patients of childbearing age and the related influencing factors, to provide a scientific basis for developing targeted interventional strategies.
Method:
This cross-sectional study included 196 women of childbearing age with breast cancer who were undergoing treatment in the breast surgery department. Data were collected via the General Information Questionnaire, Fertility Information Needs Questionnaire, Reproductive Concerns After Cancer Scale, Fertility Intention Scale, and Marriage Adjustment Test.
Results:
Among married women of childbearing age who have breast cancer, the fertility information needs score was found to be 106.68 ± 8.52. Main factors influencing fertility information needs include age (β = − 0.152, P < 0.001), number of children (β = − 0.113, P < 0.001), fertility consultation (β = − 0.239, P < 0.05), tumor stage (β = − 0.436, P < 0.001), level of reproductive concerns (β = 0.182, P < 0.05), fertility intention (β = 0.44, P < 0.001), and marital adjustment (β = 0.151, P < 0.05). Together, these factors explained 40.5% of the variance in fertility information needs among married women with breast cancer (F = 32.199, P <0.001).
Conclusions:
The findings suggest that fertility information needs among breast cancer patients of childbearing age warrant attention, and healthcare providers should develop personalized intervention programs to enhance patients’ knowledge and reduce fertility-related concerns.
Keywords: breast cancer, fertility information needs, fertility intention, marriage adjustment, reproductive concerns
Introduction
According to global cancer statistics, breast cancer ranks second in terms of new cancer cases worldwide[1]. Recent cancer incidence data from China reveal a significant increase among patients aged 35–39. Breast cancer is the leading malignancy among women aged 30–39, indicating a clear trend toward younger onset[2-4]. Breast cancer patients of childbearing age are typically defined as those between 15 and 49 years old. With the trend of breast cancer occurring at a younger age, some patients are diagnosed before having children, making the assessment of fertility potential a crucial component of their long-term quality of life[5,6]. Comprehensive breast cancer treatment options include surgery, chemotherapy, targeted therapy, and endocrine therapy. These treatments can extend patients’ lifespans, but they can also cause ovarian damage, reduced fertility, or even infertility[7]. Young women with breast cancer, particularly those who are childless at diagnosis or desire future children, generally express a strong need for information regarding the impact of cancer treatments on fertility and available fertility preservation options[8]. Data indicate that 75% of young cancer patients are interested in fertility-related information[9]. However, studies suggest that not all patients are informed about the risks and options for fertility preservation, resulting in a lack of information support[10,11]. A deficiency in fertility-related information and uncertainty about one’s fertility potential can adversely affect patients’ mental health and quality of life[12]. Timely and comprehensive information support significantly reduces fertility-related anxiety and decision regret, and enhances the quality of life for cancer patients[13]. Therefore, providing accurate and effective fertility-related information and addressing patients’ fertility information needs are crucial for female breast cancer patients of childbearing age.
HIGHLIGHTS
The “Chinese version of questionnaire on fertility information needs” has good validity and reliability.
Reproductive concerns, fertility intention, and marriage adjustment are the influencing factors of fertility information needs of breast cancer patients.
It is necessary for healthcare professionals to provide targeted guidance to patients based on the influencing factors of their fertility information needs
Information needs arise when patients recognize a gap between their current knowledge and what is required to achieve a specific goal, prompting them to seek information support[14]. Among breast cancer patients of childbearing age, fertility information is one of the most common yet unmet needs. Research indicates that the extent to which these fertility information needs are met significantly influences patients, affecting their reproductive decisions and future fertility potential[15–17]. Therefore, a profound understanding of the current status of these needs is crucial for effective clinical intervention. However, current fertility-related information support for cancer patients often focuses solely on fertility preservation and features relatively limited content and delivery formats[18,19]. Consequently, the information provided by healthcare professionals does not fully align with the actual needs of young breast cancer patients. This study aims to analyze the current status of fertility information needs in this population and explore its influencing factors. The goal is to provide a scientific basis for developing targeted interventional strategies to address these needs effectively.
Methods
Study design
This study employed a cross-sectional design. To ensure methodological rigor and transparency, the research strictly adhered to the STROCSS guidelines for cross-sectional studies[20], as detailed in Supplemental Digital Content Data S1, available at: http://links.lww.com/MS9/B57.
Research design and participants
This study utilized a consecutive convenience sampling method. Between January 2025 and July 2025, all married women of childbearing age diagnosed with breast cancer who were admitted to the department of breast surgery were assessed for eligibility. Inclusion criteria: (1) married patients aged 15–49 years old, defined as the childbearing age group; (2) patients newly diagnosed with breast cancer by pathological or cytological examination, with TNM staging of Stage I, Stage II, or Stage III; (3) with fertility information needs or fertility desires; (4) aware of their diagnosis and having provided informed consent. Exclusion criteria: (1) suffering from other infertility diseases; (2) patients with advanced breast cancer or combined with other malignant tumors; (3) emergency admissions; (4) patients with the presence of mental illness. A total of 250 patients were initially assessed. After applying the inclusion and exclusion criteria, 210 eligible patients were identified. Subsequently, these patients were invited to participate, and 196 valid questionnaires were returned, resulting in a response rate of 93.33%.
Sampling
Based on Kendall’s sample size estimation approach[21], the sample size ought to be a minimum of 5–10 times the count of independent variables. Moreover, to make allowances for possible non-responses, the sample size calculation should be adjusted upward by an additional 20%. The relevant formula is expressed as N = [(5–10) × n]/(1–0.2). Given that this study included 14 variables, when applying the formula N = (14 × 5)/0.8, the minimum necessary sample size was determined to be 88. Ultimately, a total of 196 cases were included in the present study.
Assessment instruments
General information questionnaire
The General Information Questionnaire, designed by the researchers, collected demographic and disease-related data. Demographic data included age, current address, education level, number of children, occupational status, and monthly per capita family income. Disease-related data included fertility consultation, BRCA1/2 gene mutation status, tumor stage, treatment type, and menstrual status.
Chinese version of questionnaire on fertility information needs
This questionnaire was used to assess the fertility information needs of childbearing-age breast cancer patients, thereby providing a basis for developing support strategies[22]. It consisted of six dimensions: disease impact, treatment impact, fertility preservation, fertility management, sexual health, and psychosocial information, totaling 32 items. Higher scores indicated greater fertility information needs. The questionnaire demonstrated a high Cronbach’s α coefficient of 0.951, with subdimension coefficients ranging from 0.862 to 0.923, and a test–retest reliability of 0.948, with subdimension test–retest reliabilities from 0.845 to 0.931.
Reproductive Concerns After Cancer Scale
The Chinese version of the Reproductive Concerns After Cancer Scale is used to assess reproductive worries in young women with breast cancer[23]. It contains six dimensions (ability to conceive, own health, spouse’s knowledge, children’s health, acceptance, and preparation for conception), with 3 items per dimension for a total of 18 items. Items are rated on a 5-point Likert scale (from 1 = “strongly disagree” to 5 = “strongly agree”), yielding a total score from 18 to 90, where a higher score indicates a greater level of worry. The Cronbach’s alpha coefficients for each dimension of the scale ranged from 0.720 to 0.864, the total Cronbach’s alpha coefficient was 0.792, and the retest reliability was 0.956.
Fertility intention scale
The Fertility Intention Scale (FIS) aims to enable cancer patients of childbearing age to self-evaluate their fertility intentions[24]. It includes four dimensions: fertility risks, disease control, social support, and happiness, with a total of 15 items. A 5-point Likert scale was used, with scores ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicated stronger fertility intentions. This study utilized a revised version by Zhu[25]. Compared with the original FIS, this revised version added a “fertility value” dimension, resulting in a total of five dimensions and 21 items. The revised scale demonstrated good reliability and validity, with Cronbach’s α coefficients ranging from 0.775 to 0.929.
Marriage Adjustment Test
The Marriage Adjustment Test (MAT) was originally developed by Locke and then adapted for the Chinese context by Liu and He[26,27]. It is used to assess the intimate relationship between patients and their spouses. This unidimensional scale comprises 15 items, with a maximum possible score of 158. Higher scores indicate better marital adjustment. Specifically, a score below 100 indicates a dysfunctional marital relationship, while a score of 100 or above indicates a well-adjusted marital relationship. The scale demonstrates good reliability, with a Cronbach’s α coefficient of 0.90.
Data collection
Prior to data collection, all research staff received standardized training. During the survey phase, the researcher employed a standardized instructional protocol to convey the study’s purpose, methodology, and necessary precautions to the participants. Subsequently, each participant signed an informed consent form. After the participants completed the questionnaires, the researcher promptly collected them on site and conducted a thorough verification process to ensure there were no omissions or obvious logical errors.
Data analysis
Data were processed and analyzed using IBM SPSS Statistics 23.0 software. Continuous data are presented as mean ± standard deviation, and categorical data are described as numbers and percentages. For univariate analysis, the independent samples t-test or Analysis of Variance (ANOVA) was employed based on data distribution characteristics. Pearson correlation analysis was used to evaluate associations between variables, and multiple linear regression was performed for multifactorial analysis. Multicollinearity was assessed using variance inflation factors (VIF) to ensure the robustness of the regression model. The test level α = 0.05, P <0 .05 indicates that the difference is statistically significant.
Ethical considerations
This study received ethical approval (Ethics Approval No. 2025ky009). The research protocol adhered to the ethical principles outlined in the Declaration of Helsinki and equivalent international ethical guidelines. All participants provided written informed consent after the researchers explained the study’s significance, objectives, and procedures. Participants were informed of their right to withdraw unconditionally at any time without repercussions. All collected data were kept strictly confidential, and any identifying information was anonymized to protect participants’ privacy.
Results
Participants’ general characteristics and differences in fertility information needs
A total of 196 breast cancer patients of childbearing age were investigated in this study, including 132 patients aged 15–40 and 64 patients aged 41–49. At the educational level, there were 52 patients (26.53%) with primary school education or below, 47 patients (23.98%) with junior high school education, 41 patients (20.92%) with high school or college education and 56 patients (28.57%) with college education or above. A total of 48 patients (24.49%) reported being childless, and 57 patients (29.08%) were out of work. Most of the patients had a per capita monthly family income of 3000–5000 yuan. Univariate analysis revealed that the total fertility information needs scores differed significantly based on age, number of children, fertility consultation, and tumor stage (P < 0.05), as shown in Table 1.
Table 1.
Participants’ general characteristics and differences in fertility information needs (n = 196)
| Characteristics | Categories | NO. (%) | Fertility information needs score (M± SD) | F/t-value | P-value |
|---|---|---|---|---|---|
| Age (years) | 15 ~ 40 | 132(67.35) | 104.78 ± 8.59 | 5.528 | 0.003** |
| 41 ~ 49 | 64(32.65) | 96.11 ± 8.51 | |||
| Current address | Municipality | 94(47.96) | 98.87 ± 7.73 | 1.367 | 0.178 |
| Countryside | 102(52.04) | 101.07 ± 8.94 | |||
| Education level | Elementary school and below | 52(26.53) | 99.69 ± 8.46 | 1.232 | 0.308 |
| Middle school | 47(23.98) | 101.00 ± 10.55 | |||
| High school or junior college | 41(20.92) | 100.14 ± 9.42 | |||
| College and above | 56(28.57) | 98.67 ± 6.09 | |||
| Number of children | 0 | 48(24.49) | 102.48 ± 7.21 | 7.166 | 0.001** |
| 1 | 71(36.22) | 99.33 ± 7.57 | |||
| ≥2 | 77(39.29) | 96.86 ± 9.34 | |||
| Occupational status | Be in post | 139(70.92) | 98.13 ± 8.22 | 0.44 | 0.662 |
| Leave work | 57(29.08) | 97.09 ± 9.01 | |||
| Monthly per capita family income (yuan) | < 3000 | 65(33.16) | 97.61 ± 8.56 | 2.715 | 0.151 |
| 3000–5000 | 72(36.73) | 95.25 ± 9.20 | |||
| > 5000 | 59(30.10) | 99.71 ± 3.54 | |||
| Fertility consultation | Yes | 95(48.47) | 98.27 ± 8.11 | 3.404 | 0.032* |
| NO | 101(51.53) | 102.66 ± 8.55 | |||
| BRCA1/2 gene mutation | Yes | 28(14.29) | 95.5 ± 5.96 | 1.793 | 0.061 |
| NO | 168(85.71) | 99.9 ± 8.23 | |||
| Tumor stage | I | 61(31.12) | 105.67 ± 5.94 | 6.438 | 0.001** |
| II | 84(42.86) | 98.2 ± 10.79 | |||
| III | 51(26.02) | 93 ± 5.93 | |||
| Treatment type | Chemotherapy/radiotherapy | 82(41.84) | 99.43 ± 6.47 | 1.501 | 0.226 |
| Surgery + chemotherapy/radiotherapy | 102(52.04) | 100.05 ± 4.35 | |||
| Other treatment | 12(6.12) | 100.29 ± 7.78 | |||
| Menstrual status | Regular | 175(89.29) | 98.5 ± 8.07 | 1.841 | 0.071 |
| Irregular | 21(10.71) | 96.29 ± 9.96 |
*P < 0.05;** P < 0.01.
Fertility information needs of breast cancer patients of childbearing age
In this study, the fertility information needs score of breast cancer patients of childbearing age was 106.68 ± 8.52 points. The scores for each dimension were as follows: disease impact (16.21 ± 3.15), treatment impact (15.21 ± 2.53), fertility preservation (17.09 ± 2.38), fertility management (25.64 ± 4.14), sexual health (13.15 ± 3.67), and psychosocial information (20.38 ± 2.57), as shown in Table 2.
Table 2.
Fertility information needs score for breast cancer patients (n = 196)
| Variables | Score range for each entry | Number of entries | Mean total scores (M ± SD) |
|---|---|---|---|
| Fertility information needs | 1 ~ 5 | 32 | 106.68 ± 8.52 |
| Disease impact information | 1 ~ 5 | 5 | 16.21 ± 3.15 |
| Treatment impact information | 1 ~ 5 | 4 | 15.21 ± 2.53 |
| Fertility preservation information | 1 ~ 5 | 5 | 17.09 ± 2.38 |
| Fertility management information | 1 ~ 5 | 7 | 25.64 ± 4.14 |
| Sexual health information | 1 ~ 5 | 5 | 13.15 ± 3.67 |
| Psychosocial information | 1 ~ 5 | 6 | 20.38 ± 2.57 |
Correlation analysis of reproductive concerns, fertility intention, marriage adjustment, and fertility information needs in breast cancer patients
Pearson correlation analysis indicated that there were significant positive correlations between fertility information needs and reproductive concerns (r = 0.544, P < 0.001), fertility intention (r = 0.627, P < 0.001), and marital adjustment (r = 0.496, P < 0.001), as presented in Table 3.
Table 3.
Correlations between the level of reproductive concern, fertility intentions, marital adjustment levels, and fertility information needs in breast cancer patients (r-value, n = 196)
| Item | Fertility information needs | RCAC | FIS | MAT |
|---|---|---|---|---|
| Fertility information needs | 1.000 | |||
| RCAC | 0.544a | 1.000 | ||
| FIS | 0.627a | 0.515a | 1.000 | |
| MAT | 0.496a | 0.460a | 0.502a | 1.000 |
aP < 0.001. FIS, Fertility Intention Scale; MAT, Marriage Adjustment Test; RCAC, Reproductive Concerns After Cancer Scale.
The r-value represents the relationship between the independent variable and fertility information need.
Multiple linear regression analysis of factors related to fertility information needs of breast cancer patients
Multiple linear regression analysis was conducted (αin = 0.05, αout = 0.10), using the fertility information need scores of breast cancer patients of childbearing age as the dependent variable. Variables statistically significant in univariate analysis and those associated with fertility information needs were selected as independent variables. The distribution of independent variables is shown in Table 4. The results of multiple linear regression analysis showed that age, number of children, fertility consultation, tumor stage, reproductive concerns level, fertility intention, and marriage adjustment were the main factors affecting the fertility information needs of breast cancer patients of childbearing age (P < 0.05), as shown in Table 5.
Table 4.
Independent variable assignment methods
| Independent variable | Assignment method |
|---|---|
| Age (years) | 15–40 = 1,41–49 = 2 |
| Number of children | 0 = 1,1 = 2, ≥ 2 = 3 |
| Fertility consultation | Yes = 1, No = 2 |
| Tumor stage | I = 1,II = 2,III = 3 |
| Total reproductive concerns after cancer score | Substitute the original value |
| Total fertility intentions score | Substitute the original value |
| Total marriage adjustment test score | Substitute the original value |
Table 5.
Multiple linear regression analysis of fertility information needs of breast cancer patients (n = 196)
| Variable | B | SE | β | t | p | 95% CI for B | VIF | |
|---|---|---|---|---|---|---|---|---|
| Lower limit | Upper limit | |||||||
| Constant | 57.274 | 10.445 | 11.77 | <0.001** | 48.662 | 67.072 | ||
| Age | −2.263 | 0.840 | −0.152 | −2.625 | <0.001** | −3.365 | −0.041 | 1.225 |
| Number of children | −2.182 | 0.757 | −0.113 | −2.204 | <0.001** | −2.958 | −0.086 | 1.104 |
| Fertility consultation | −1.477 | 0.602 | −0.239 | −2.132 | 0.035* | −2.484 | −0.105 | 1.048 |
| Tumor stage | −3.981 | 0.949 | −0.436 | −3.700 | <0.001** | −5.902 | −0.082 | 1.155 |
| RCAC | 0.556 | 0.225 | 0.182 | 3.766 | 0.001* | 0.251 | 0.986 | 1.203 |
| FIS | 4.419 | 2.147 | 0.440 | 4.921 | <0.001** | 3.722 | 5.957 | 2.050 |
| MAT | 0.413 | 0.097 | 0.151 | 3.102 | 0.002* | 0.101 | 0.883 | 1.160 |
B, unstandardized coefficient; β, beta or standardized coefficient; FIS, Fertility Intention Scale; MAT, Marriage Adjustment Test; RCAC, Reproductive Concerns After Cancer Scale.
R2 = 42.36%, adjusted R2 = 40.50%; F = 32.199, P < 0.001. * P < 0.05** P <0 .001.
Discussion
Fertility information needs for breast cancer patients of childbearing age to be addressed
Studies have shown that among the unmet needs of cancer patients, the need for fertility-related information ranks first[16]. If the healthcare team can provide patients with the fertility information they need in a timely manner, it will significantly increase their confidence in treatment; conversely, the lack of information may put patients in a difficult decision-making situation, affecting the process and effectiveness of treatment[28]. In this study, the fertility information needs score of breast cancer patients of childbearing age was 106.68 ± 8.52, indicating a moderate level. This finding is consistent with the results of Wang et al[29] but lower than that reported by Liu et al[30] in a study on thyroid cancer patients. This difference may be attributed to the differing severity of the two cancers. Breast cancer usually has a higher degree of malignancy, a more complicated treatment process, and an uncertain prognosis compared with thyroid cancer. The physical trauma and psychological burden brought by its comprehensive treatment force patients to focus mainly on the treatment of the disease and survival issues, objectively weakening their attention to reproductive information. In view of the above, clinical practice needs to build a multi-dimensional support system: (1) Interdisciplinary and inter-hospital resource integration: strengthen cooperation with leading hospitals specializing in reproductive medicine, and establish a fertility committee for oncology patients that integrates multiple disciplines such as reproduction, oncology, nursing, and ethics. (2) Patient-centered: establish an efficient and standardized referral channel between assisted reproduction centers and oncology hospitals to form a service pathway that focuses on patients’ needs. (3) Personalized fertility counseling: fertility counseling is provided through the results of fertility needs assessment.
Fertility information needs are influenced by multiple factors
The results of this study suggest that age is an important factor influencing the fertility information need of breast cancer patients of childbearing age. Breast cancer patients ≤40 years old had significantly higher fertility-related information needs than patients aged 41–49 years old, which is consistent with the findings of Huang et al[31]. This may be because the risk of treatment-induced damage to ovarian function coincides with patients’ peak reproductive years[32]. Furthermore, our study found that among married breast cancer patients of childbearing age, those with fewer children tend to have unmet fertility desires, consequently, they demonstrate a higher demand for fertility-related information. For this reason, healthcare professionals should pay close attention to young breast cancer patients who have not yet had children, initiate fertility counseling within 48 hours of diagnosis, assess patients’ needs through the fertility needs questionnaire, and refer them to reproductive specialists for further detailed counseling and risk assessment. The importance of online platforms should also be emphasized, and relevant information should be pushed out on a regular basis to ensure that patients continue to receive accurate information and support.
The results of this study showed that timely fertility counseling before formally receiving treatment in cancer patients of childbearing age resulted in a significantly lower fertility information need than those who did not, which is consistent with the findings of the study by Shah et al[33]. Young et al concluded that fertility counseling should be conducted in an orderly manner based on the focus of the concerns of cancer patients in different stages of treatment[34]. Fertility counseling can, to some extent, satisfy the patients’ need for fertility information and alleviate fertility concerns[35]. Therefore, healthcare providers should pay particular attention to patients who have not yet received fertility counseling. By delivering critical information in a phased manner – before, during, and after treatment – they can precisely align with the evolving cognitive state and decision-making needs of patients at different stages, ensuring counseling services truly meet patients’ actual requirements.
The results of this study suggest that fertility information needs are higher among early-stage breast cancer patients, which is consistent with the results of the Urech et al study[36]. The reason for this is that patients with early-stage breast cancer have a positive prognosis and tend to focus on their future quality of life, including fertility planning considerations, which in turn leads them to actively seek information about fertility. Clinical data also show that the 5-year survival rate for patients with stage 1 breast cancer is 89.7%[37]. Therefore, healthcare professionals need to provide guidance based on the stage of breast cancer. For patients with early-stage breast cancer, healthcare professionals should proactively provide detailed information on the impact of treatment on fertility, available fertility preservation techniques, and successful cases at the early stage of diagnosis. At the same time, dynamic fertility files should be set up for those who need them, and continuous services should be provided from diagnosis and treatment to pregnancy and delivery. For patients with advanced-stage cancer, their potential fertility needs should not be overlooked. Discussions on this topic can be introduced gradually once the patient’s condition is stable and psychological well-being permits, thereby preserving future options and fostering hope.
Patients with higher levels of fertility concerns have higher information needs
The results of this study showed that the level of fertility concerns was significantly and positively correlated with fertility information needs (P < 0.05), which is consistent with the findings of Ussher et al[38]. In addition, the results of a study on female patients with early-onset colorectal cancer similarly showed that patients with high levels of fertility concerns had unmet fertility information needs[39]. Analyzing the reasons: when an individual feels threatened, they tend to actively seek out information in order to reduce uncertainty[40]. The psychological stress caused by fertility concerns drives patients to actively seek out fertility-related information in order to alleviate their anxiety. However, high levels of fertility concerns can indirectly impair patients’ cognitive functioning, making it difficult for patients to effectively sift through and comprehend complex fertility information, which in turn can exacerbate anxiety due to unmet information needs. Based on the above analysis, healthcare professionals should pay attention to the level of fertility worry in breast cancer patients. Psychological interventions such as positive psychology interventions, group counseling, and self-expression can be used to communicate with and guide patients in a timely manner to reduce their psychological pressure. At the same time, healthcare professionals should fully recognize the relatively high fertility information needs of patients with high levels of fertility concerns. It is recommended to build a joint decision-making platform between doctors and patients, and implement a tiered information support strategy according to the level of fertility concerns of patients, so as to ensure that patients can obtain the fertility information needs matching their needs.
Patients with a higher fertility intention exhibit higher information needs
In this study, patients with higher fertility intention had higher needs for fertility-related information, which is consistent with the findings of Wang et al[29]. Fertility intention, as the core embodiment of an individual’s intrinsic motivation, will directly prompt patients to actively search for fertility-related information[41]. The specific reasons are as follows: (1) Influence of social and cultural expectations: In the Chinese cultural environment, which places a strong emphasis on family continuity, having children is often viewed as essential for lineage and as a source of hope[42], to alleviate the psychological distress associated with potential infertility, patients often turn to peer support groups and reproductive specialists for information on the feasibility of childbearing after breast cancer, thereby increasing their demand for information. (2) Demand for self-identification of roles: Patients with a strong desire to give birth may regard “becoming a parent” as an important role in life and a manifestation of women’s self-worth[43]. They take the initiative to search for relevant information to maintain the consistency of their roles, and regard giving birth as a key point in the continuation of the significance of life. Therefore, we recommend that healthcare providers develop a structured communication framework for fertility-related information education based on the traditional cultural characteristics of their country and apply it to clinical workflow to alleviate barriers caused by sociocultural differences and help patients make appropriate fertility decisions.
Patients with better marital adjustment have higher information needs
In this study, a positive correlation was found between the marital adjustment ability and the information need level of breast cancer patients (P < 0.05), which is consistent with the research results of Xiaotong Yang et al[44]. The reasons are analyzed as follows: When marital adjustment is high, spouses typically collaborate with patients to solve problems and adapt to changes, fostering a strong sense of security and belonging in the patients[45,46]. This positive emotional experience helps enhance patients’ confidence in coping with fertility-related challenges, thereby prompting them to actively explore fertility information. Therefore, it is necessary for medical staff to take measures to improve the marital adjustment status of breast cancer patients and meet their information needs. First, use a marital adjustment scale to conduct an in-depth analysis of the strengths and weaknesses in patients’ marital relationships. For those with low scores, through individual or group counseling sessions, help patients and their spouses recognize their respective roles and responsibilities in the marital relationship. Simultaneously, develop resources such as couple communication guides and family interaction case studies to assist patients in establishing robust family support networks. Enhance patients’ marital adaptation capabilities and address their reproductive health information needs.
Limitations
This study has several limitations. First, the use of convenience sampling may introduce selection bias, potentially limiting the generalizability of the findings. Second, as data collection relied solely on self-report questionnaires, the accuracy and reliability of the data may be influenced by recall bias or social desirability biases. Furthermore, the cross-sectional design precludes the establishment of causal inferences and cannot capture dynamic changes in variables over the disease trajectory. Additionally, this study primarily focused on fertility information needs and their influencing factors but did not further examine the association between these needs and key clinical outcomes, such as treatment adherence. Clarifying whether fertility information support contributes to improved treatment adherence would help comprehensively evaluate its clinical intervention value. Therefore, future longitudinal studies are necessary to explore the dynamic relationships among variables while specifically assessing the potential effects of personalized fertility information support on enhancing patients’ treatment adherence and quality of life.
Conclusions
The fertility information needs of breast cancer patients of childbearing age remain unmet. The main factors influencing these needs include age, number of children, fertility consultation status, tumor stage, level of reproductive concerns, fertility intention, and marriage adjustment. Therefore, healthcare professionals should develop targeted intervention strategies based on these influencing factors. This can help patients form correct views on fertility, enhance their awareness, reduce fertility-related concerns, improve fertility intentions, and promote marital harmony. This study provides a reference for the subsequent development of standardized breast cancer fertility management programs.
Footnotes
YiDan Tang made equal contributions to this work and should therefore be recognized as a co-first author.
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Published online 16 December 2025
Ethical approval
The study was approved by the Ethical Review Committee.
Consent
The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments or similar ethical standards. Participants all signed an informed consent form and were informed that there would be no consequences for opting out of the study at any time.
Sources of funding
This work supported by grants from Jiangxi Provincial Cancer Hospital 2021 Annual Open Research Fund (No.2021J11), National Cancer Center Climbing Fund (No. NCC201914B07) and Jiangxi Provincial Department of Science (No.20203BBGL73167).
Author contributions
Q.W.: Conceptualization, Supervision, Project administration, Writing- review & editing. Y.T.: Methodology, Investigation, Data Curation, Formal analysis, Writing-original draft, Writing-review & editing. X.H.: Investigation, Data curation, Methodology, Formal analysis. Z.W.: Investigation, Data curation, Methodology. All authors have reviewed and approved the manuscript.
Conflicts of interest disclosure
The authors declare no conflict of interest.
Peer and provenance statement
Not commissioned, externally peer-reviewed.
Data availability statement
All data generated or analyzed in this study are included herein. Subject to applicable legal and ethical approvals, the raw data supporting the findings of this study may be obtained from the corresponding author upon reasonable request.
References
- [1].Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024;74:12–49. [DOI] [PubMed] [Google Scholar]
- [2].Zhao WJ, Yin ZY, Wang YX, et al. Interpretation of the 2024 American cancer statistics report and comparison of cancer prevalence between China and the United States. J Cancer Control Res 2024;51:630–41. [Google Scholar]
- [3].Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin 2016;66:115–32. [DOI] [PubMed] [Google Scholar]
- [4].Miller KD, Fidler-Benaoudia M, Keegan TH, et al. Cancer statistics for adolescents and young adults, 2020. CA Cancer J Clin 2020;70:443–59. [DOI] [PubMed] [Google Scholar]
- [5].Liu Q. Reproductive health services in the context of optimizing fertility policies. Chin J Pract Gynecol Obstet 2021;37:1081–83. [Google Scholar]
- [6].Loren AW, Mangu PB, Beck LN, et al. American Society Of Clinical Oncology. Fertility preservation for patients with cancer: american Society of Clinical Oncology clinical practice guideline update. J Clin Oncol 2013;31:2500–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Chemerinski A, Cameron K, Sammel M, et al. Relationship of menopausal symptoms and ovarian reserve in reproductive-aged cancer survivors. J Cancer Surviv 2020;14:607–13. [DOI] [PubMed] [Google Scholar]
- [8].Peate M, Meiser B, Friedlander M, et al. It’s now or never: fertility-related knowledge, decision-making preferences, and treatment intentions in young women with breast cancer—an Australian fertility decision aid collaborative group study. J Clin Oncol 2011;29:1670–77. [DOI] [PubMed] [Google Scholar]
- [9].Schover LR. Patient attitudes toward fertility preservation. Pediatr Blood Cancer 2009;53:281–84. [DOI] [PubMed] [Google Scholar]
- [10].Logan S, Perz J, Ussher JM, et al. A systematic review of patient oncofertility support needs in reproductive cancer patients aged 14 to 45 years of age. Psychooncology 2018;27:401–09. [DOI] [PubMed] [Google Scholar]
- [11].van den Berg M, Ö B, Wldm N, et al. Professionals’ barriers in female oncofertility care and strategies for improvement. Hum Reprod 2019;34:1074–82. [DOI] [PubMed] [Google Scholar]
- [12].Benedict C, Thom B, Friedman DN, et al. Young adult female cancer survivors’ unmet information needs and reproductive concerns contribute to decisional conflict regarding posttreatment fertility preservation. Cancer 2016;122:2101–09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Letourneau JM, Ebbel EE, Katz PP, et al. Pretreatment fertility counseling and fertility preservation improve quality of life in reproductive age women with cancer. Cancer 2012;118:1710–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Liu DL, Zhang J, Gao QQ, et al. Research progress on information needs of thyroid cancer patients. Chin J Nurs Educ 2018;15:703–07. [Google Scholar]
- [15].Bártolo A, Santos IM, Valério E, et al. Depression and health-related quality of life among young adult breast cancer patients: the mediating role of reproductive concerns. J Adolesc Young Adult Oncol 2020;9:431–35. [DOI] [PubMed] [Google Scholar]
- [16].Campbell AG, Hillemeier M. Fertility counseling information adequacy as a moderator of regret among adolescent and young adult breast cancer survivors. Support Care Cancer 2021;29:2689–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Tang C, Qiao CP, Jiang C, et al. Construction and application of an online platform to support fertility information for young breast cancer patients. J Nurs Sci 2022;37:23–27. [Google Scholar]
- [18].Yamauchi K, Nakao M, Nakashima M. Correlates of regret with treatment decision-making among Japanese women with breast cancer: results of an internet-based cross-sectional survey. BMC Womens Health 2019;19:86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Ju J, Zhang LX, Yue J, et al. An investigation of the fertility needs of young patients with breast cancer. Chin Journal of Oncol 2020;42:408–12. [Google Scholar]
- [20].Agha RA, Mathew G, Rashid R, et al. Revised Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) Guideline: an update for the age of Artificial Intelligence. Premier J Sci 2025;10:100081. [Google Scholar]
- [21].Bacchetti P, Leung JM. Sample size calculations in clinical research. Anesthesiology 2002;97:1028–32. [Google Scholar]
- [22].Wang Q, Zhao P, Zhang HM, et al. Development and reliability and validity testing of a fertility information needs assessment questionnaire for breast cancer patients of childbearing age. Chin J Nurs Res 2023;37:3654–58. [Google Scholar]
- [23].Qiao TT, Chen DD, Zheng W, et al. Chinese version of the fertility concerns after cancer scale and its reliability and validity. Chin J Nurs 2016;51:1509–14. [Google Scholar]
- [24].Huang SM, Li CC, Lai JC, et al. Development and validation of a fertility intention scale in breast cancer survivors. J Nurs Res 2018;26:177–84. [DOI] [PubMed] [Google Scholar]
- [25].Zhu F. Revision and validation of the fertility intention scale for breast cancer patients of childbearing age [dissertation]. Hebei University; 2023. [Google Scholar]
- [26].Locke HJ, Wallace KM. Short marital-adjustment and prediction tests: their reliability and validity. Marriage Fam Living 1959;21:251–55. [Google Scholar]
- [27].Liu PY, He MT. Marriage, family, and mental health: a survey analysis of 118 young intellectuals. Chin Ment Health J 1991;5:193-197+238. [Google Scholar]
- [28].Komatsu H, Yagasaki K, Yamauchi H. Fertility decision-making under certainty and uncertainty in cancer patients. Sex Reprod Healthc 2018;15:40–45. [DOI] [PubMed] [Google Scholar]
- [29].Wang Q, Zhao P, Zhang HM, et al. Research on the current status and influencing factors of fertility information needs among breast cancer patients of childbearing age. Chin J Mod Nurs 2025;31:223–27. [Google Scholar]
- [30].Liu QF, Yang XJ, Miao YX, et al. Investigation on the current status of fertility needs among women of childbearing age with thyroid cancer. Contemp Mod Nurse 2020;27:35–37. [Google Scholar]
- [31].Huang SM, Tseng LM, Lai JC, et al. Impact of symptom and social support on fertility intention in reproductive-age women with breast cancer. Clin Nurs Res 2020;29:411–18. [DOI] [PubMed] [Google Scholar]
- [32].Zhang WY, Zhang JJ, Guo C, et al. Meta-synthesis of qualitative research on the decision-making experience of fertility preservation among female cancer patients of childbearing age. Chin J Nurs 2023;58:1504–11. [Google Scholar]
- [33].Shah MS, Letourneau JM, Niemasik EE, et al. The role of in-depth reproductive health counseling in addressing reproductive health concerns in female survivors of nongynecologic cancers. J Psychosoc Oncol 2016;34:305–17. [DOI] [PubMed] [Google Scholar]
- [34].Young K, Shliakhtsitsava K, Natarajan L, et al. Fertility counseling before cancer treatment and subsequent reproductive concerns among female adolescent and young adult cancer survivors. Cancer 2019;125:980–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Fu ZM, Hu Y, Liang AX, et al. Summary of the best evidence for fertility management in testicular cancer patients. Natl J Androl 2025;31:526–34. [Google Scholar]
- [36].Urech C, Ehrbar V, Boivin J, et al. Knowledge about and attitude towards fertility preservation in young female cancer patients: a cross-sectional online survey. Hum Fertil (Camb) 2018;21:45–51. [DOI] [PubMed] [Google Scholar]
- [37].He X, Jiang J, Huang LL, et al. Preliminary study on ultrasound differentiation between triple-negative breast cancer and HER-2 overexpressing breast cancer. J Clin Ultrasound Med 2018;20:165–67. [Google Scholar]
- [38].Ussher JM, Parton C, Perz J. Need for information, honesty and respect: patient perspectives on health care professionals communication about cancer and fertility. Reprod Health 2018;15:2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [39].Wu X, Zhang W, Liu A, et al. Factors associated with reproductive concerns among young female patients with colorectal cancer: a cross-sectional study. J Clin Nurs 2023;32:5274–85. [DOI] [PubMed] [Google Scholar]
- [40].Zhao ZH. The impact of social support on depression in elderly patients with chronic diseases: the mediating role of medical technology anxiety and the moderating role of self-esteem [dissertation]. Nanjing University of Chinese Medicine; 2025. [Google Scholar]
- [41].Zamanian H, Amini-Tehrani M, Jalali Z, et al. Perceived social support, coping strategies, anxiety and depression among women with breast cancer: evaluation of a mediation model. Eur J Oncol Nurs 2021;50:101892. [DOI] [PubMed] [Google Scholar]
- [42].Zhu F, Lu Q, Qi J, et al. Qualitative research on the fertility intention of breast cancer patients of childbearing age. J Nurs 2022;29:23–26. [Google Scholar]
- [43].Connell S, Patterson C, Newman B. A qualitative analysis of reproductive issues raised by young Australian women with breast cancer. Health Care Women Int 2006;27:94–110. [DOI] [PubMed] [Google Scholar]
- [44].Yang X, Zhang W, Huo Q, et al. Dual perspectives on fertility information needs and clinical support barriers in breast cancer care: a qualitative investigation. Preprint (Version 1). Research Square. 2025.
- [45].Yao MX, Li JH, Chen OY, et al. Research progress on the application of marital adjustment in breast cancer patients of childbearing age with menopausal syndrome. Evid Based Nurs 2024;10:4072–76. [Google Scholar]
- [46].Yuan YF, Zheng S. Family cohesion and adaptability and their impact on fertility concerns in female cancer patients of childbearing age. Chin Nurs Manag 2018;18:1719–23. [Google Scholar]
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Data Availability Statement
All data generated or analyzed in this study are included herein. Subject to applicable legal and ethical approvals, the raw data supporting the findings of this study may be obtained from the corresponding author upon reasonable request.