Nomogram for predicting survival in breast cancer with lung metastasis based on SEER data

Introduction

Breast cancer (BC) ranks among the most frequently occurring malignant tumors globally, and it considerably increases morbidity and mortality worldwide (1). Although mortality rates have declined in the recent several years with the development of medical technology, good medical care is not available everywhere. More than 90% of BC-related deaths are associated with metastasis, which mainly occurs in the lungs, bone, liver, and brain (2,3). Isolated mediastinal lymph node metastasis in BC has also been reported (4). Metastatic lung cancer significantly contributes to cancer-related deaths in the United States. Until recent years, the survival outlook over 5 years for individuals with metastatic lung adenocarcinoma has stood at under 5% (5). It is significant to pinpoint the determinants influencing survival in individuals with BC with lung metastasis (BCLM). Wang et al. developed a diagnostic and prognostic prediction model for pulmonary metastasis in triple-negative BC (TNBC). The study encompassed 27,048 TNBC patients, identifying age, tumor size, tumor (T)-stage, and node (N)-stage as independent risk factors for those with lung metastasis. Additionally, histological type, marital status, prior surgical history, chemotherapy, and the presence of bone, brain, and lung metastases were determined to be independent prognostic factors for TNBC patients with lung metastasis (6). van Ommen-Nijhof et al. conducted a survival prognosis analysis of over 200 oligometastatic BC patients using univariate and multivariate analyses (7). An et al. analyzed the relationship between surgical duration and survival time in BC patients using univariate and multivariate analyses (8). Chen et al. found that the long non-coding RNAs (lncRNAs) ZNF582-AS1, MALAT1, and AFAP1-AS1 are involved in influencing lymph node metastasis in BC (9). In the aforementioned studies, univariate analysis considered only the relationship between a single variable and the outcome. Although multivariate analysis can provide estimates of the relative risk or impact of each factor on the outcome, it typically relies on certain statistical assumptions (e.g., linear relationships). In practical applications, many relationships among variables may be nonlinear, and violations of these assumptions can diminish the validity of the analysis results. Furthermore, the estimates of relative risk or impact derived from multivariate analysis tend to be abstract, making them challenging for clinicians or decision-makers to interpret and apply directly. Consequently, it is essential to formulate a stratification framework that integrates both the individual’s health status and the traits of the cancer. The nomogram is extensively employed as a predictive instrument in the realms of medicine; by combining various predictive and influential factors, the nomogram calculates personalized likelihoods of clinical occurrences. This meets the demand for comprehensive biological and clinical frameworks, thereby facilitating the advancement of personalized medicine. Compared with traditional staging, the accuracy is higher, the prognosis is easier to understand, and the prognosis derived from the nomogram is capable of being seamlessly integrated to aid in clinical medicine strategy-making (10-12). The Surveillance, Epidemiology, and End Results (SEER) database serves as a reputable repository for cancer statistics that documents the morbidity, mortality, and health conditions of a large number of individuals diagnosed with cancer. It is frequently utilized to study the epidemiological characteristics of malignant tumors in various populations (13,14). Since there are few studies on patients with BCLM, the intention of our research was to construct a nomogram to forecast the survival prognosis model in patients with BCLM. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1047/rc).

Methods

Patient screening

This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Data were collected from 17 registries within the SEER database, comprising individuals diagnosed with BC from 2010 to 2014. The inclusion criteria were as follows: (I) explicit data contains the following: age, marital status, race, tumor site, laterality, primary surgery or not, surgery distance or not, grade, T stage, N stage, radiation or not, chemotherapy or not; (II) patients diagnosed with lung metastasis; (III) the cancer has a precise left or right primary site; (IV) clarity regarding whether the primary site or the distant site is operated on; and (V) the dates of follow-up, survival duration, and reasons for mortality must be ascertained.

Endpoint definition

The primary endpoints encompassed overall survival (OS) and cancer-specific survival (CSS). OS was determined by assessing the time since diagnosis to the occurrence of death from any reason, whereas CSS was determined by examining the time since diagnosis to the event of death specifically attributed to BC.

Prognostic factors

Xu et al. found that nine variables, including age, marital status, tumor grade, human epidermal growth factor receptor 2 (HER2) status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis, were significantly associated with the OS of BC. Additionally, age, marital status, T-stage, tumor grade, HER2 status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis were identified as independent risk factors for BC-specific survival time (15). Xie et al. found that age, histopathology, tumor grade, marital status, bone metastasis, brain metastasis, liver metastasis, HER2 status, estrogen receptor (ER), progesterone receptor (PR), surgery, neoadjuvant therapy, and chemotherapy are independent factors influencing the prognosis of patients with BCLM (16). As a result, this study encompassed the calculation of various factors including age, marital status, sex, race, tumor site, laterality, primary surgery, surgical distance, breast subtype, grade, T stage, and N stage. Age groups were divided into <59, 59–79, and >79 years. The optimal threshold point was determined using the X-tile program (Yale University, New Haven, CT, USA) (Figure 1) (17).

Figure 1 X-tile analysis determined the optimal threshold for the age of diagnosis. The ideal age thresholds based on OS were 59 and 79 years old. Histograms and Kaplan-Meier analyses were constructed using the specified thresholds. The red scale bar represents the percentage of younger individuals, whereas the green scale bar denotes the percentage of older individuals. The curves in the graph correspond to the columns in the histogram: blue lines indicate low-risk age groups, gray lines denote moderate-risk age groups, and purple lines signify high-risk age groups. Blue bar indicates low-risk age groups, gray bar denotes moderate-risk age groups, and purple bar signifies high-risk age groups. OS, overall survival.

Statistical analysis

Individuals were assigned to a training cohort (n=3,231) and a validation cohort (n=1,078) using a randomized approach for developing nomograms. The Chi-squared test and Fisher’s exact test were employed to compare groups. Univariate and multivariate analyses were utilized to examine factors influencing prognosis independently.

The Akaike Information Criterion (AIC) is a benchmark used to assess the intricacy of statistical models and gauge the appropriateness of the data fit. The lower the AIC value, the more suitable the model (18-20). The stepwise regression method was employed to uncover predictive elements, as confirmed by multivariate analysis, with the aim of achieving the minimum AIC value.

Nomogram establishment

Nomograms were constructed using essential predictive indicators. The prognostic utility of nomograms was evaluated utilizing the concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) (21). An area under the curve (AUC) >0.7 and a C-index >0.7 generally indicate a reasonably good predictive model (22-24). The calibration curve was structured to estimate if the projected life expectancy aligns with the recorded survival. According to the total score assigned to individuals, the overall risk was categorized into two classifications. Kaplan-Meier curves and the log-rank test were employed to assess the differences between two distinct risk cohorts. The X-tile program was utilized to determine the cut-off points for the risk scores, thereby identifying the optimal cut-off points for the entire cohort. In univariate analysis, a P value <0.1 was considered indicative of statistical significance, whereas in multivariate analysis, a P<0.05 was indicative of statistical significance. Statistical software R (version 4.2.2, available at http://www.r-project.org/) was utilized for the analysis of the dataset.

Results

Patients’ baseline profiles

A total of 4,309 cases were counted. The individuals were allocated into a training cohort (n=3,231) and a validation cohort (n=1,078) through a random assignment for the construction of nomograms. Among all the patients, 1,650 (38.29%), 2,101 (48.76%), and 558 (12.95%) were aged <59, 59–79, and >79 years, respectively. Of the total, 1,801 (41.8%) were married, 1,053 (24.44%) were single, and 1,455 (33.77%) had other marital statuses. Furthermore, 65 (1.51%) were male, whereas 4,244 (98.49%) were female. In terms of race, 781 (18.12%) were Black, 3,150 (73.10%) were White, and 378 (8.77%) belonged to other racial groups. Regarding the tumor location, 30 (0.70%) were in the nipple, 406 (9.42%) in the central portion of the breast, 463 (10.74%) in the upper-inner quadrant, 234 (5.43%) in the lower-inner quadrant, 1,382 (32.07%) in the upper-outer quadrant, 344 (7.98%) in the lower-outer quadrant, 45 (1.04%) in the axillary tail of the breast, and 1,405 (32.61%) in overlapping lesions of the breast. Breast distribution included 2,211 (51.31%) in the left breast and 2,098 (48.69%) in the right breast. In terms of surgical intervention, 1,201 (27.87%) underwent primary site tumor surgery, whereas 3,108 (72.13%) did not. Regarding molecular subtypes, 2,333 (54.14%) were luminal A, 739 (17.15%) luminal B, 440 (10.21%) HER2-enriched, and 797 (18.50%) triple-negative. Tumor grading showed 245 (5.69%) as grade I, 1,691 (39.24%) as grade II, and 2,373 (55.07%) as grade III and IV. Tumor size distribution included 464 (10.77%) as T1, 1,200 (27.85%) as T2, 734 (17.03%) as T3, and 1,911 (44.35%) as T4. Lymph node involvement included 1,041 (24.16%) as N0, 2,127 (49.36%) as N1, 521 (12.09%) as N2, and 620 (14.39%) as N3. Treatment details showed that 518 (12.02%) of the patients received radiation, whereas 3,791 (87.98%) did not. Additionally, 2,409 (55.91%) received chemotherapy, whereas 1,900 (44.09%) did not. Demographics and pathology showed no statistically notable distinctions among individuals in the training cohort and the validation cohort (Table 1). The results indicate a median survival time of 1.67 years [95% confidence interval (CI): 1.58–1.83], with a total of 3,640 deaths recorded.

Univariate analyses revealed significant statistical associations between OS and age (P<0.001, P<0.001), marital status (P<0.001, P<0.001), race (P<0.001, P<0.001), surgery of primary (P<0.001), breast subtype (P<0.001, P=0.19, P<0.001), grade (P=0.002, P<0.001), T stage (P=0.03, P=0.01, P<0.001), utilization of radiation (P<0.001), and application of chemotherapy (P<0.001) (Table 2). Similar associations were observed for CSS, where age (P=0.007, P<0.001), marital status (P<0.001, P<0.001), race (P<0.001, P<0.001), surgery of primary (P<0.001), breast subtype (P=0.001, P=0.17, P<0.001), grade (P<0.001, P<0.001), T stage (P=0.01, P=0.003, P<0.001), utilization of radiation (P<0.001), and application of chemotherapy (P<0.001) showed significant statistical connections (Table 3). Age (OS: P=0.050, P<0.001; CSS: P=0.27, P<0.001), marital status (OS: P<0.001, P<0.001; CSS: P<0.001, P<0.001), race (OS: P<0.001, P<0.001; CSS: P=0.001, P<0.001), surgery of primary (OS: P<0.001; CSS: P<0.001), grade (OS: P<0.001, P<0.001; CSS: P<0.001, P<0.001), T stage (OS: P=0.044, P=0.051, P<0.001; CSS: P=0.03, P=0.02, P<0.001), and the decision to use chemotherapy (OS: P<0.001; CSS: P<0.001) were significantly and statistically related to OS as well as CSS according to the results of multivariate Cox analysis (Tables 2,3).

Construction and certification of the nomogram

Factors autonomously prognostic were discerned employing a backward stepwise methodology with AIC, with the objective of mitigating data redundancy. Nomograms contained age, marital status, race, primary site surgery or not, breast subtype, grade, T stage, and whether to use chemotherapy (Figure 2). Each variable was assigned specific points in the nomogram (Table 4). The C-index was 0.77 (95% CI: 0.76–0.78) in OS and 0.77 (95% CI: 0.76–0.78) in CSS for the training set, and 0.78 (95% CI: 0.76–0.8) in OS and 0.78 (95% CI: 0.76–0.8) in CSS in the validation set. Within this training set, the nomogram of OS demonstrated 3- and 5-year AUC values of 0.734 and 0.727, respectively, whereas for CSS, the values were 0.733 and 0.728, respectively (Figure 3). The plots illustrating the alignment of predicted and observed outcomes at 3- and 5-year intervals for OS and CSS demonstrated a favorable concordance (Figures 4,5). The DCA demonstrated that the nomogram exhibited superior predictive capability (Figures 6,7). In the entirety of the study population, Kaplan-Meier curves revealed that the cohort classified as low-risk demonstrated superior survival outcomes in both OS and CSS when contrasted with the high-risk cohort (Figure 8). In the OS cohort, the low-risk cohort demonstrated a higher 5-year OS rate (31.4%) than the high-risk cohort (9.58%, P<0.001). In the CSS cohort, the 5-year CSS rate in the low-risk cohort (35.1%) surpassed that in the high-risk cohort (11.63%, P<0.001).

Figure 2 Nomograms for projecting OS (A) and CSS (B) at 3 and 5 years in individuals diagnosed with BCLM. Connect each factor to its corresponding point on the axis with a vertical line to establish the points for each factor. Predicted survival is then calculated by tracing the vertical line that extends from the accumulated points on the scale to intersect with either the OS or CSS axis. BCLM, breast cancer with lung metastasis; CSS, cancer-specific survival; HER2, human epidermal growth factor receptor 2; OS, overall survival.

Figure 3 The ROC curves were generated for 3- and 5-year OS (A) and CSS (B) within the training cohort. AUC, area under the curve; CSS, cancer-specific survival; OS, overall survival; ROC, receiver operating characteristic; y, year.

Figure 4 Calibration plot in the training set. Nomogram calibration curves are provided for 3-year (A) and 5-year (B) OS, as well as for 3-year (C) and 5-year (D) CSS. The cohort was stratified into triads, with consistent specimens for internal verification. The dotted lines represent a strong correlation between authentic survival outcomes (y-axis) and nomogram forecasts (x-axis). The more closely the dashed line aligns with the plotted values, the greater the precision of the forecasts. CSS, cancer-specific survival; OS, overall survival.

Figure 5 Calibration plots were generated in the validation set. Nomogram calibration curves were constructed for 3-year (A) and 5-year (B) OS, as well as for 3-year (C) and 5-year (D) CSS. The sample was stratified into three groups with equal numbers of participants for external verification. Dashed lines denote optimal alignment between observed survival outcomes (y-axis) and nomogram predictions (x-axis). Proximity of the dashed line to data points indicates elevated forecast precision. CSS, cancer-specific survival; OS, overall survival.

Figure 6 DCA depicting the nomogram’s performance within the training dataset. The decision curves illustrate 3-year (A) and 5-year (B) OS, as well as for 3-year (C) and 5-year (D) CSS. The x-axis denotes different probability thresholds, while the y-axis depicts net proceeds derived from the true positive rate minus the false positive rate. When a line extends horizontally across the chart, it signifies that there were no occurrences of patient mortality, and the yellow line suggests that beneath a particular threshold, all individuals will have succumbed. The black line is on behalf of the net proceeds from employing the nomogram. CSS, cancer-specific survival; DCA, decision curve analysis; OS, overall survival.

Figure 7 DCA for the nomogram were generated in the validation group, illustrating decision curves for 3-year (A) and 5-year (B) OS, as well as for 3-year (C) and 5-year (D) CSS. Threshold probabilities are represented by the x-axis, while the y-axis demonstrates the net benefit counted by subtracting the false positives rate from the true positives rate. A level line adjacent to the x-axis signifies no patient mortality, whereas a yellow line indicates each individual is predicted to decease below a specific level. The black line illustrates the net benefit obtained from utilizing the nomogram. CSS, cancer-specific survival; DCA, decision curve analysis; OS, overall survival.

Figure 8 Kaplan-Meier curves illustrating OS and CSS for high- and low-risk patients. CSS, cancer-specific survival; OS, overall survival.

Discussion

We established a nomogram to evaluate OS and CSS among individuals with BCLM. The C-index and AUC values demonstrated a consistent performance of the nomograms in each of the training and validation sets. The graphical representations of calibration further illustrated the accuracy in predicting OS and CSS. In the context of ROC curve analysis and DCA, we believe that the nomogram has a very strong and accurate ability to predict patient prognosis and survival. This nomogram is invaluable for clinical analysis and prognostic evaluation in BCLM patients, assisting in the development of further individualized treatment plans.

The nomogram consists of several independent general clinical factors. Our study found that age, marital status, race, primary site surgery or not, BC subtype, grade, T stage, and use of chemotherapy can affect the prediction of individuals.

Certain factors are involved in the progression and metastasis of malignant tumors (25). Johnson et al. found that younger age is a prognostic risk factor for BC (26), which is consistent with our findings. Younger bodies are generally healthier and better able to withstand the effects of treatments, including surgery, chemotherapy, radiation, targeted therapy, and immunotherapy. Younger people may be more conscious about BC screening; therefore, the cancer is more inclined to be found at an earlier stage and receive better treatment.

Study has shown that single patients have shorter survival and worse outcomes than married patients (27). Our study also showed that in BCLM, unmarried individuals encountered elevated mortality rates in contrast to their married counterparts. The association between marital status and cancer survival may be affected by several factors, including socioeconomic status, family support, mental health, and lifestyle. Married patients may be more likely to receive family support and care, which can be beneficial in coping with the challenges of cancer treatment and recovery. In addition, marital status could be associated with lifestyle elements, including diet, exercise, and smoking cessation, which can all affect survival rates for cancer patients.

Our study shows that the survival rate of BCLM is lower in people of African descent, and this was also shown by Wang et al. (28). Black BC patients may face fewer treatment options and lower treatment compliance. This may be related to economic factors, access to healthcare, cultural factors, and social support. Sometimes, a lack of appropriate treatment or an inability to afford treatment can affect a patient’s survival rate. BC has a variety of molecular subtypes, some of which have different sensitivity to treatment. Studies have shown that BC in Black women may be predisposed to more aggressive subtypes (29,30), which may have an impact on treatment response and survival.

There may be several reasons why patients with BCLM who have surgery at the primary site have a better survival rate. Surgery can help to control primary BC by reducing its growth and spread in the breast region, thereby reducing metastasis to the lungs. By removing the primary site, the likelihood that the carcinoma will metastasize to distant sites is reduced. Surgery can reduce the tumor load in the body, which may help to slow or stop the progression of metastases in the lungs. Research has demonstrated that in cases of late-stage TNBC, surgical treatment of the primary tumor significantly improves survival in patients with single distant metastases (31). By lowering the number of cancer cells in the body, it may be possible to reduce the impact on additional bodily areas.

TNBC refers to a type of cancer that is not expressed in the ER, PR, and HER2 receptors. This type of cancer limits the applicability of some current treatment options and is generally not sensitive enough to hormone therapy and targeted therapies (32). As a result, patients have relatively few treatment options, which can lead to a relatively poor prognosis. HER2 BC overexpresses HER2, a type of mammary carcinoma that is relatively more aggressive, but the prospects for treatment in these patients have improved due to the availability of drugs targeting HER2, such as herceptin, tyrosine kinase inhibitors, monoclonal antibodies, and antibody-drug conjugates such as DS-8201 (33). Patients with luminal A subtype of BC typically experience a more favorable prognosis compared to those with luminal B subtype, and the favorable outcome of individuals with luminal B type observed within this investigation may be due to the fact that patients with luminal B type have received a more aggressive treatment regimen such as chemotherapy combined with endocrine therapy.

The grade of BC is usually determined by evaluating the cytological and histological characteristics of the tissue sample, including the level of differentiation among the tumor cells, the atypia of the nucleus, and the proliferative activity of the tumor cells. High-grade (grade III and grade IV) BCs usually exhibit the following characteristics: higher cell atypia: cells have a higher degree of abnormalities in morphology and structure; poor cell differentiation: tumor cells are less differentiated than normal breast cells; high proliferative activity: tumor cells proliferate and spread faster (34). These features are often associated with more aggressive tumors, which may lead to a poorer prognosis.

The prognosis deteriorates as the tumor size increases. The larger the primary tumor, the more it indicates that the tumor has been in the breast for a longer time or has developed to a relatively advanced stage.

Research has shown that dose-dense chemotherapy is associated with a more favorable prognosis, indicating its potential preference as a treatment for BC patients; especially for women with lymph node infiltration (35), longer time to adjuvant chemotherapy is probably related to worse survival among patients with BC (36). Research has indicated that supplementary chemotherapy markedly enhances the rates of OS and freedom from distant disease in a broad population (37). Our study shows that patients with BCLM who receive chemotherapy have a better prognosis. Chemotherapy is able to help control the proliferation and spread of cancer cells, slow the deterioration of the disease, and extend the life expectancy of individuals.

Wang et al. found that marital status, previous surgery, and chemotherapy are confirmed to constitute autonomous predictors of prognosis for TNBC patients with BCLM (6). Wang et al. also discovered associations between the forecast of individuals with BCLM and factors such as age, marital status, race, grade, American Joint Committee on Cancer (AJCC) T stage, subtype, surgery, and chemotherapy, but not with radiotherapy (38). Chen et al. documented that factors such as age, race, marital status, pathological grade, molecular subtype, site of extrapulmonary metastasis, as well as whether surgery and chemotherapy were performed, influenced the outcomes of individuals with BCLM (39). Xiao et al. showed that the mortality from all causes and mortality specifically from BC in BCLM were related to molecular subtypes, age, sex, race, marital status, number of distant metastases, and pathological grade (40). These previous findings are generally consistent with this study.

Lymph node staging did not impact the prediction of BC patients experiencing lung metastasis, which may be related to the special condition of lung metastasis. In the case of lung metastasis from BC, it is usually the lungs that have metastases, and the condition of the lymph nodes may have relatively little impact on the development and prognosis of lung metastasis. Lung metastases are usually spread directly from BC cells through the bloodstream to the lungs, rather than through the lymphatic system. Therefore, lymph node involvement often has a limited impact on the treatment and prognosis of lung metastasis. As a consequence, the lymph node stage may not be the main predictor of prognosis of patients with BCLM. Radiation therapy does not directly affect the prognosis of patients with BCLM, possibly because when BC metastases to the lungs, it is usually extensive, meaning that the tumor has metastasized to other areas of the body. In cases of widespread metastasis, radiotherapy targeting a small number of metastases may not be able to fully control the disease. Radiotherapy is recommended for the treatment of lymph node metastasis and palliative care of bone metastasis in BC, but is less effective in patients with lung metastasis (41). Although radiotherapy targeting lung metastases may slow down the advancement of the illness, alleviate symptoms, and enhance overall quality of life to some extent, it may have limited effect on OS extension in patients with BCLM. In addition, Wang et al. discovered that the survival of BCLM individuals was not associated with radiotherapy (42). Zhang et al. even revealed that local radiotherapy can promote lung metastasis of BC in mice to a certain extent (43).

In oncology and medicine, nomograms are frequently used as prognostic devices. Through the integration of diverse predictive and determining factors, the nomogram formulates individualized likelihoods of clinical outcomes, addressing the need for a prognostic model and fulfilling our quest for tailored medical approaches (44,45). Wu et al. used nomograms for predicting the outcome of low-grade endometrial stromal sarcoma (46). A study found that nomograms outperformed AJCC staging in prognosticating individuals’ survival diagnosed with small cell lung cancer (47). Therefore, the nomograms exhibit excellent precision in forecasting survival for malignant tumors. Here are the steps to apply our nomograms. First, connect each clinicopathological feature to the “points” line with vertical lines. Each “point” is then aggregated into the “total points”, and a vertical marker is drawn extending the “total points” to the “OS” and “CSS” rows. In this way, we can predict survival.

Our research has the following advantages: the SEER database has a long data collection time, a large number of cases, and avoids selection bias. The nomograms transform intricate regression data into a visual graph, enhancing the readability and convenience of evaluating BCLM patient predictions. Rapid calculations through a user-friendly digital interface, coupled with improved accuracy, make nomograms easier to understand than traditional staging.

There are still some limitations to our research. In the framework of a retrospective study, the nomogram still requires validation through prospective studies. The detailed operation of the tumor at the primary site, vascular and nerve infiltration, tumor margin after surgery, the site of lymph node metastasis, and the metastatic tumor size could not be classified and expanded. With the improvement of the database, these problems will receive more attention in the future. Despite these limitations, the nomograms we built still have very good predictive value.

Conclusions

We formulated nomograms for forecasting OS as well as CSS in individuals diagnosed with BCLM. The nomogram has a reliable and valid prediction power; it could perhaps assist physicians in calculating patients’ mortality risk. Nevertheless, future clinical studies are needed.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1047/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1047/prf

Funding: This work was supported by the Wenzhou Medical and Health Scientific Research Project (project plan No. 2022012).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1047/coif). All authors report that this study was supported by the Wenzhou Medical and Health Scientific Research Project (project plan No. 2022012). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bakarman M, Kalthoum D, Wahby Salem I, et al. Barriers to Using Breast Cancer Screening Methods Among Adult Females in Jeddah, Saudi Arabia: A Cross-Sectional Study. Cureus 2023;15:e41739. [Crossref] [PubMed]
2. Cheng S, Wan X, Yang L, et al. RGCC-mediated PLK1 activity drives breast cancer lung metastasis by phosphorylating AMPKα2 to activate oxidative phosphorylation and fatty acid oxidation. J Exp Clin Cancer Res 2023;42:342. [Crossref] [PubMed]
3. Liang Y, Zhang H, Song X, et al. Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets. Semin Cancer Biol 2020;60:14-27. [Crossref] [PubMed]
4. Kimura D, Kaneta A, Muto C, et al. Solitary mediastinal metastasis from breast cancer after 14 years: case report. Shanghai Chest 2024;8:19. [Crossref]
5. Araghi M, Mannani R, Heidarnejad Maleki A, et al. Recent advances in non-small cell lung cancer targeted therapy; an update review. Cancer Cell Int 2023;23:162. [Crossref] [PubMed]
6. Wang J, Zhao H, Ye L, et al. Diagnostic and Prognostic Nomograms for Lung Metastasis in Triple-Negative Breast Cancer. Comput Math Methods Med 2022;2022:1750834. [Crossref] [PubMed]
7. van Ommen-Nijhof A, Steenbruggen TG, Capel L, et al. Survival and prognostic factors in oligometastatic breast cancer. Breast 2023;67:14-20. [Crossref] [PubMed]
8. An D, Choi J, Lee J, et al. Time to surgery and survival in breast cancer. BMC Surg 2022;22:388. [Crossref] [PubMed]
9. Chen J, Ling C. Construction of a predictive model for breast cancer metastasis based on lncRNAs. Transl Cancer Res 2023;12:387-97. [Crossref] [PubMed]
10. Tang B, Wu F, Peng L, et al. Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study. Cancer Imaging 2024;24:131. [Crossref] [PubMed]
11. Lv J, Liu YY, Jia YT, et al. A nomogram model for predicting prognosis of obstructive colorectal cancer. World J Surg Oncol 2021;19:337. [Crossref] [PubMed]
12. El Sharouni MA, Ahmed T, Varey AHR, et al. Development and Validation of Nomograms to Predict Local, Regional, and Distant Recurrence in Patients With Thin (T1) Melanomas. J Clin Oncol 2021;39:1243-52. [Crossref] [PubMed]
13. Yu Z, Chen T, Peng H, et al. Trends in incidence, treatment modalities and prognosis of esophageal adenocarcinoma in the US population. Cancer Epidemiol 2024;93:102683. [Crossref] [PubMed]
14. Mao W, Deng F, Wang D, et al. Treatment of advanced gallbladder cancer: A SEER-based study. Cancer Med 2020;9:141-50. [Crossref] [PubMed]
15. Xu Z, Chen Y, Dai Y, et al. Prognostic factors for hormone receptor-positive breast cancer with liver metastasis and establishment of novel nomograms for prediction: a SEER-based study. Transl Cancer Res 2023;12:3672-92. [Crossref] [PubMed]
16. Xie Y, Lei C, Ma Y, et al. Prognostic nomograms for breast cancer with lung metastasis: a SEER-based population study. BMC Womens Health 2024;24:16. [Crossref] [PubMed]
17. Zhou H, Lv D, Cui F, et al. Prognostic value of the geriatric nutritional risk index in patients with non-metastatic clear cell renal cell carcinoma: a propensity score matching analysis. Nutr J 2024;23:114. [Crossref] [PubMed]
18. Portet S. A primer on model selection using the Akaike Information Criterion. Infect Dis Model 2020;5:111-28. [Crossref] [PubMed]
19. Ruan C, Chen X. Development and validation of a prognostic nomogram for predicting liver metastasis in thyroid cancer: a study based on the surveillance, epidemiology, and end results database. Comput Methods Biomech Biomed Engin 2024; Epub ahead of print. [Crossref] [PubMed]
20. Shigematsu H, Fukui K, Kanou A, et al. Diagnostic performance of TILs-US score and LPBC in biopsy specimens for predicting pathological complete response in patients with breast cancer. Int J Clin Oncol 2024;29:1860-9. [Crossref] [PubMed]
21. Lin WP, Xing KL, Fu JC, et al. Development and Validation of a Model Including Distinct Vascular Patterns to Estimate Survival in Hepatocellular Carcinoma. JAMA Netw Open 2021;4:e2125055. [Crossref] [PubMed]
22. Nahm FS. Receiver operating characteristic curve: overview and practical use for clinicians. Korean J Anesthesiol 2022;75:25-36. [Crossref] [PubMed]
23. Wu Z, Shang G, Zhang K, et al. A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study. Int J Surg 2024;110:2178-86. [Crossref] [PubMed]
24. Jin B, Hu W, Su S, et al. The Prognostic Value of Systemic Inflammation Response Index in Cholangiocarcinoma Patients. Cancer Manag Res 2021;13:6263-77. [Crossref] [PubMed]
25. Ivanova M, Evangelista J, Venetis K, et al. Hereditary gastric and breast cancer syndromes with lung metastasis: narrative review of molecular and clinical insights. AME Surg J 2023;3:39. [Crossref]
26. Johnson RH, Anders CK, Litton JK, et al. Breast cancer in adolescents and young adults. Pediatr Blood Cancer 2018;65:e27397. [Crossref] [PubMed]
27. Aizer AA, Chen MH, McCarthy EP, et al. Marital status and survival in patients with cancer. J Clin Oncol 2013;31:3869-76. [Crossref] [PubMed]
28. Wang R, Zhu Y, Liu X, et al. The Clinicopathological features and survival outcomes of patients with different metastatic sites in stage IV breast cancer. BMC Cancer 2019;19:1091. [Crossref] [PubMed]
29. Luningham JM, Seth G, Saini G, et al. Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia. JAMA Netw Open 2022;5:e2238183. [Crossref] [PubMed]
30. Linnenbringer E, Geronimus AT, Davis KL, et al. Associations between breast cancer subtype and neighborhood socioeconomic and racial composition among Black and White women. Breast Cancer Res Treat 2020;180:437-47. [Crossref] [PubMed]
31. Gu Y, Wu G, Zou X, et al. Prognostic Value of Site-Specific Metastases and Surgery in De Novo Stage IV Triple-Negative Breast Cancer: A Population-Based Analysis. Med Sci Monit 2020;26:e920432. [Crossref] [PubMed]
32. Bai X, Ni J, Beretov J, et al. Triple-negative breast cancer therapeutic resistance: Where is the Achilles' heel? Cancer Lett 2021;497:100-11. [Crossref] [PubMed]
33. Swain SM, Shastry M, Hamilton E. Targeting HER2-positive breast cancer: advances and future directions. Nat Rev Drug Discov 2023;22:101-26. [Crossref] [PubMed]
34. Wang Y, Acs B, Robertson S, et al. Improved breast cancer histological grading using deep learning. Ann Oncol 2022;33:89-98. [Crossref] [PubMed]
35. Zhou W, Chen S, Xu F, et al. Survival benefit of pure dose-dense chemotherapy in breast cancer: a meta-analysis of randomized controlled trials. World J Surg Oncol 2018;16:144. [Crossref] [PubMed]
36. Mirza L, Steventon L, Roylance R, et al. Regional differences in neo/adjuvant chemotherapy timing in patients with early-stage triple-negative breast cancer in England. Breast Cancer Res Treat 2024; Epub ahead of print. [Crossref] [PubMed]
37. Rossi L, Stevens D, Pierga JY, et al. Impact of Adjuvant Chemotherapy on Breast Cancer Survival: A Real-World Population. PLoS One 2015;10:e0132853. [Crossref] [PubMed]
38. Wang W, Liu J, Chen Y, et al. An effective tool for predicting survival in breast cancer patients with de novo lung metastasis: Nomograms constructed based on SEER. Front Surg 2023;9:939132. [Crossref] [PubMed]
39. Chen S, Yang J, Liu Y, et al. Prognostic factors and survival outcomes according to tumor subtype in patients with breast cancer lung metastases. PeerJ 2019;7:e8298. [Crossref] [PubMed]
40. Xiao W, Zheng S, Liu P, et al. Risk factors and survival outcomes in patients with breast cancer and lung metastasis: a population-based study. Cancer Med 2018;7:922-30. [Crossref] [PubMed]
41. Gradishar WJ, Moran MS, Abraham J, et al. Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2022;20:691-722. [Crossref] [PubMed]
42. Wang K, Li Y, Wang D, et al. Web-Based Dynamic Nomograms for Predicting Overall Survival and Cancer-Specific Survival in Breast Cancer Patients with Lung Metastases. J Pers Med 2022;13:43. [Crossref] [PubMed]
43. Zhang Z, Yao Z, Zhang Z, et al. Local radiotherapy for murine breast cancer increases risk of metastasis by promoting the recruitment of M-MDSCs in lung. Cancer Cell Int 2023;23:107. [Crossref] [PubMed]
44. Li X, Liu Z, Xu J. Construction and validation of a nomogram of risk factors and prognosis for elderly patients with meningioma. Asian J Surg 2024; Epub ahead of print. [Crossref] [PubMed]
45. Wang R, Dai W, Gong J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol 2022;15:11. [Crossref] [PubMed]
46. Wu J, Zhang H, Li L, et al. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond) 2020;40:301-12. [Crossref] [PubMed]
47. Yang B, Teng M, You H, et al. A Nomogram for Predicting Survival in Advanced Non-Small-Cell Lung Carcinoma Patients: A Population-Based Study. Cancer Invest 2023;41:672-85. [Crossref] [PubMed]