Development and validation of a prognostic nomogram based on the clinical indicators for breast cancer

Introduction

Breast cancer (BC) is one of the most common malignancies in women, characterized by the highest incidence and mortality rates (1). And it is the most common cancer for Chinese women (2). BC has an incidence of approximately 30% and is one of the most common causes of cancer-related deaths, with a mortality rate of 14% (3). Currently, a variety of treatment modalities, including surgery, chemotherapy, hormone therapy, and radiotherapy, are available for patients diagnosed with BC (4,5). However, the prognosis of most patients is still not optimistic (6,7). Age, tumor location, grade, adjuvant therapy and molecular characteristics can affect the prognosis of BC patients (8). Therefore, there is an urgent need for a simple and practical method to evaluate the prognosis of BC (9), to perform individualized risk assessment for clinical BC patients, and to formulate individualized clinical treatment decisions.

Cancer-related inflammation runs through the whole process of the disease (10). Inflammatory cells play an important role in tumor microenvironment (11). Inflammatory cells are mediators involved in tumorigenesis and progression, providing cytokines for cancer cells and enhancing cell survival and proliferation (12). Templeton et al. performed a meta-analysis and found that a high platelet was associated with worse OS in various solid tumors (13). Previous studies have shown that cancer may affect the content of platelets with platelets were involved in angiogenesis and progression of cancer (14). A study of Walraven et al. suggested that the platelets of cancer patients could recognize cancer-related proteins, and anti-tumor treatment could affect the platelet proteome (15). Guthrie et al. indicated that the neutrophil-lymphocyte ratio (NLR) is elevated in patients with advanced or aggressive disease, who exhibit higher tumor staging, lymph node involvement, and metastatic burden, suggesting these patients may represent a high-risk population (16). Cui et al. demonstrated that neutrophils could release neutrophil elastase (Elane) with catalytic activity to kill many types of cancer cells while retaining non cancer cells (17). Therefore, the detection of blood indexes could evaluate the prognosis of cancer patients.

Nomogram is a statistical model that predicts individualized risk by incorporating prognostic factors (18). As a prediction tool, nomogram provides a simple graphical display of statistical prediction model, which generates the numerical probability of clinical events (19,20). For many cancers, nomogram has more advantages than the traditional tumor-node-metastasis (TNM) staging system in predicting outcomes, so it is proposed as an alternative method and even as a new standard (21,22). Prognostic biomarkers or signatures that predict the clinicopathological features and survival rate of BC patients are helpful for the screening, diagnosis, classification and treatment of BC (23).

In this study, we developed and validated a prognostic model based on the clinical indicators related to clinical prognosis of BC patients, for predicting prognosis for clinical BC patients and providing reference for clinical treatment decisions. We present this article in accordance with the TRIPOD reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-aw-454/rc).

Methods

Data collecting and patient characteristics

From April 1, 2012 to April 1, 2015, a retrospective cohort study was conducted at The First Affiliated Hospital of Soochow University and The 904th Hospital of the Joint Logistics Support Force of the People’s Liberation Army, involving 217 BC patients undergoing surgical resection. The follow-up period was March to April 2023. All patients underwent surgical resection and followed the standard treatment guidelines outlined during this period. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Soochow University [No. (2022)340] and the Ethics Committee of The 904th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army (No. 20230222). Individual consent for this retrospective analysis was waived. The sample size of 217 patients was based on data availability during the study period. Following the rule of thumb of at least 10 events per variable (EPV) for Cox regression, with 60 events (deaths) and 10 candidate predictors entering the multivariate analysis, the EPV was 6. While this is below the ideal threshold, potentially increasing the risk of model overfitting, we employed least absolute shrinkage and selection operator (LASSO) regularization for variable selection to mitigate this risk. The final model was constrained to only 6 predictors. For internal validation, a simple split-sample approach was chosen over bootstrapping for this initial report to provide a clear and straightforward assessment of model performance in an independent subset. Bootstrapping techniques will be considered for future analyses to further evaluate model stability. For data missingness, A total of 229 patients were initially identified. Patients were excluded if they were lost to follow-up (n=8) or had missing data for any of the key clinical predictors considered in the final model (n=4). Therefore, 217 patients with complete data for all analysis variables were included in the final cohort. A complete-case analysis was employed. The 12 excluded patients did not differ significantly from the included cohort in terms of age or baseline American Joint Committee on Cancer (AJCC) stage (P>0.05), suggesting that the exclusion is unlikely to have introduced substantial selection bias. Since the analysis was based on complete data, no data filling was required.

The collected clinical data were divided into test set (n=67) and training set (n=150). The following clinical data were collected and grouped separately: age (>50 and ≤50 years), BMI (> median and ≤ median), hypertension (yes and no), diabetes (yes and no), menopause (yes and no), hysterectomy (yes and no), diagnosis (left, right, and bilateral BC), children (>2 and ≤2), histology (other types =0, infiltrative ductal carcinoma was of grade I, II, III on behalf of 1, 2, 3), quadrant (upper-outer quadrant, lower-outer quadrant, lower-inner quadrant and upper-inner quadrant), AJCC stage (I/II = low and III = high), metastasis (yes and no), neoadjuvant therapy (yes and no), surgery type (breast conserving surgery and complete resection surgery), human epidermal growth factor receptor-2 (HER2) state (negative and positive), estrogen receptor (ER) state (negative and positive), progesterone receptor (PR) state (negative and positive), Ki-67 (negative and positive), chemotherapy (yes and no), radiotherapy (yes and no), endocrine therapy (yes and no), targeted therapy (yes and no). All the blood indexes were grouped by the median, as follows: mean platelet volume, percentage of eosinophils, red blood cell (RBC), mean RBC volume, percentage of monocytes, mean corpuscular hemoglobin, eosinophilic cell count, neutrophile granulocyte, haemoglobin, mean corpuscular hemoglobin concentration, plateletcrit, white blood cell, percentage of neutrophils, basophil cells, platelet count, percentage of basophils, erythrocyte hemoglobin distribution width, monocyte count, percentage of lymphocytes, lymphocyte count, platelet distribution width, lithic acid, total protein, glutamyl transpeptidase, lactic dehydrogenase, glycerin trimyristate, carbamide, calcium, natrium, prealbumin, hydroxybutyric dehydrogenase, creatine kinase, low density cholesterol, globulin, glutamic pyruvic transaminase, albumin, C-reactive protein, chlorinum, high density cholesterol, alkaline phosphatase, indirect bilirubin, total cholesterol, glutamic oxaloacetic transaminase, phosphorus, creatinine, potassium, total bilirubin, direct bilirubin. Ki-67 status was dichotomized using the clinically relevant cutoff of 14% (positive: ≥14%; negative: <14%). Other continuous blood indices were dichotomized at their median values for initial exploratory analysis and to facilitate clinical interpretation of the nomogram.

Construction and validation of the nomogram

Based on the results of multivariate Cox regression analysis, after scoring each factor, a nomogram predicting 3-, 5- and 10-year overall survival (OS) was established. The specificity and sensitivity for the studied outcomes were plotted to generate a receiver operating characteristic (ROC) curve, and the area under the curve (AUC) for each marker was calculated. A calibration plot was generated to assess the calibration ability of the nomogram. The calibration plot along the 45° line implicated a perfect model, with great consistency between the predicted and actual outcomes. The test set was used as an internal validation for the nomogram.

Statistical analysis

Statistical comparisons between groups were performed with analysis of Kruskal-Wallis or Wilcox test for continuous variables and the Chi-squared test or Fisher’s exact test for categorical variables. The primary endpoint of the study was OS, which was defined as death from any cause. Univariate Cox regression analysis was used to identify which clinical indicators were significantly correlated with OS. Multivariable Cox regression analysis was used to assess whether the risk score was independent of other clinical feature, and provided the visualized risk prediction, by using rms R package, after each clinical features was assigned a score for the nomogram. According to the risk score formula, each patient was given a risk score and divided into two groups based on their median score, the survival curve was constructed by Kaplan-Meier method and compared by log rank test by survival R package. The LASSO analysis was applied to select potential factors for prediction of prognosis using glmnet R package. The factors selected from the training group were estimated using univariate Cox regression analysis. The clinical indicators with P<0.05 were thought to be associated with prognosis and further analyzed using multivariate Cox regression method. The above analysis was performed by R version 4.1.0 (http://www.r-project.org). All the P values were two-sided, and the results were considered statistically significant when the P values were less than 0.05.

Results

Baseline characteristics

A total of 229 BC patients were initially assessed for eligibility between April 2012 and April 2015. After excluding 12 patients (4 due to incomplete clinical data and 8 lost to follow-up), 217 patients with complete data were included in the final analysis. Baseline characteristics of the patients are summarized in Table S1. Among them, 67 and 150 patients were in the test cohort and train cohort, respectively.

As shown in Table S1, the distributions of important demographic, clinical, and pathological characteristics were well-balanced between the training and validation cohorts. All measured variables showed no statistically significant differences between the two sets (all p values >0.05), including age distribution (P=0.46), hypertension (P=0.72), AJCC stage (P=0.74), metastasis (P=0.82), and key treatment modalities such as chemotherapy (P>0.99) and endocrine therapy (P=0.36). The outcome distribution (death) was also similar between training (27.3%) and validation (28.4%) cohorts (P=0.87). This balanced distribution supports the validity of the random split and the subsequent internal validation.

The median follow-up time of patients was 2,603 days (range, 2,066–2,825 days), and 117 (53.9%) patients were older than 50 years. Of the 217 patients, the median values of weight, height, BMI were 60 kg, 160 cm, 23.44 kg/m², respectively; 48 (22.1%) patients suffered from hypertension, 15 (6.9%) patients were diagnosed with diabetes mellitus, 149 (68.7%) patients had menopause, 16 (7.4%) patients accepted undergone hysterectomy. The proportion of left and right BCs were 47.9% and 51.2%, respectively. The vast majority (95.4%) of patients had 1 or 2 children. The most common primary site was the upper-outer quadrant (67.3%). The most commonly pathological type was invasive ductal carcinoma grade II (54.8%). Most patients (72.4%) had a low AJCC grade at the time of diagnosis. Eventually, there were 25 (11.5%) patients developed recurrence and metastasis.

At diagnosis, 49.8% of the patients presented with stage I BC, followed by 45.2% with stage II, and 5.1% with stage III; 133 (61.3%) patients were free of lymph node metastasis. As for ER, PR and HER2 status, the proportion of positives were 54.8%, 40.1% and 20.7%, respectively. The median value of Ki-67 was 25% in the total cohort. 192 (88.5%) patients accepted the complete resection surgery. The proportion of patients who have received radiotherapy, chemotherapy, endocrine or targeted therapy was 16.6%, 82.9%, 36.9% and 18.0%, respectively. The median values of the blood indicators are shown in the Table S1.

Prognosis and independent prognostic factors for BC

LASSO was undertaken to identify highly relevant variables. We performed the LASSO regression analysis on 72 clinical indicators in the training cohort (Figure 1). With lambda of 0.038, 14 key factors were identified to be of great significance to the prognosis of patients with BC (Figure 2), including age, hypertension, children, AJCC stage, metastasis, neoadjuvant therapy, Ki-67, chemotherapy, endocrine therapy, percentage of eosinophils, RBC, mean corpuscular volume (MCV), percentage of monocytes and neutrophile granulocyte. Then, the univariate Cox regression analysis was applied to determine the clinical signatures associated with the prognosis of BC. The following clinical indicators with P<0.05 were obtained: age, hypertension, children, AJCC stage, metastasis, Ki-67, chemotherapy, endocrine therapy, RBC, and neutrophile granulocyte (Figure 3A). The multivariate Cox regression was performed to further identify independent prognostic factors. The results showed that 6 features (hypertension, AJCC stage, metastasis, Ki-67, endocrine therapy, RBC) were independent prognostic factors for OS of BC patients (Figure 3B). Each variable was assigned a risk score based on the contribution of each variable in the multivariate analysis.

Figure 1 The LASSO regression analysis on 72 clinical indicators in the training cohort. AJCC, American Joint Committee on Cancer; BMI, body mass index; CRP, C-reactive protein; ER, estrogen receptor; HER2, human epidermal growth factor receptor-2; LASSO, least absolute shrinkage and selection operator selection operator; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; PR, progesterone receptor; RBC, red blood cell; WBC, white blood cell.

Figure 2 The LASSO regression analysis with lambda of 0.038, 14 key factors were identified to be of great significance to the prognosis of patients with BC. BC, breast cancer; LASSO, least absolute shrinkage and selection operator selection operator.

Figure 3 The Cox regression model for overall survival in the training cohort. (A) Univariate Cox regression analysis identified 10 clinical indicators that significantly correlated with overall survival. (B) Multivariable Cox regression analysis identified the independent prognostic clinical features. AJCC, American Joint Committee on Cancer; CI, confidence interval; HR, hazard ratio; RBC, red blood cell.

The final Cox model for predicting OS is as follows:
Risk Score = (1.219 × Hypertension_yes) + (1.060 × AJCC_Stage_III) + (2.691 × Metastasis_yes) + (2.465 × Ki-67_positive) + (−1.130 × Endocrine_therapy_yes) + (−0.950 × RBC_high)

where Hypertension_yes =1 if present, 0 otherwise; AJCC_Stage_III =1 if stage III, 0 otherwise; Metastasis_yes =1 if present, 0 otherwise; Ki-67_positive =1 if >14%, 0 otherwise; Endocrine_therapy_yes =1 if received, 0 otherwise; RBC_high =1 if > median, 0 otherwise.

Individual survival probability at time t is calculated as:
S(t) = [S₀(t)]^exp(Risk Score)

The baseline survival estimates S₀(t) at 1, 3, and 5 years were 0.987, 0.945, and 0.898, respectively.

Construction and validation of the nomogram

With the total points of each sample from the training cohort calculated according to this model, all samples were divided into high- or low-risk groups. To evaluate the OS of these low- and high-risk patients, Kaplan-Meier curves were generated, and showed that the OS of low-risk group was higher than that of high-risk group in the training cohort (Figure 4A). A similar result was observed in the test cohort (Figure 4B). In terms of the clinical factors analyzed, no significant difference was observed between the train cohort and the test cohort. Based on the independent prognostic clinical factors selected by the multivariate Cox analysis, the nomograms were built for the training set (Figure 5A). The associated ROC of the train cohort was shown in Figure 5B. The calibration curve of the training set demonstrates the accuracy of the column-line diagram (Figure 5C). The nomogram and ROC curve in the testing set are shown in Figure 6A,6B, respectively. The AUC for 1-year OS was 0.93 in the train cohort and 0.98 in the test cohort. The AUC for 3-year OS was 0.89 in the train cohort and 0.86 in the test cohort. The AUC for 5-year OS was 0.93 in the train cohort and 0.84 in the test cohort. The calibration curve of the testing set is shown in Figure 6C.

Figure 4 Survival analysis for the prognostic model. (A) Kaplan-Meier survival curves of high- and low-risk patients in the training cohorts. (B) Kaplan-Meier survival curves of high- and low-risk patients in the testing cohorts.

Figure 5 Construction and validation of a nomogram for overall survival prediction of BC patients in the training set. (A) The nomogram consisting of clinical prognostic factors predicting 1-, 3-, and 5-year survival for BC patients. (B) The associated ROC of the nomogram in terms of agreement between predicted and observed 1-, 3-, 5-year outcomes. (C) Calibration curves of the nomogram in terms of agreement between predicted and observed 1-, 3-, 5-year outcomes. AJCC, American Joint Committee on Cancer; AUC, area under the curve; BC, breast cancer; OS, overall survival; RBC, red blood cell; ROC, receiver operating characteristic.

Figure 6 Construction and validation of a nomogram for overall survival prediction of BC patients in the testing set. (A) The nomogram consisting of clinical prognostic factors predicting 1-, 3-, and 5-year survival for BC patients. (B) The associated ROC of the nomogram in terms of agreement between predicted and observed 1-, 3-, 5-year outcomes. (C) Calibration curves of the nomogram in terms of agreement between predicted and observed 1-, 3-, 5-year outcomes. *, P<0.05; **, P<0.01; ***, P<0.001. AJCC, American Joint Committee on Cancer; AUC, area under the curve; BC, breast cancer; OS, overall survival; RBC, red blood cell; ROC, receiver operating characteristic.

To illustrate the clinical application of the nomogram, we provide a step-by-step calculation example for a hypothetical patient with the following profile: presence of hypertension, AJCC stage III, no distant metastasis, Ki-67 positive (≥14%), no receipt of endocrine therapy, and RBC count (5.3) above the cohort median.

Using the nomogram (Figure 5A), points are assigned for each predictor: hypertension (yes) ≈45 points, AJCC stage (III) ≈38 points, metastasis (no) ≈0 points, Ki-67 (positive) ≈99 points, endocrine therapy (no) ≈41 points, and RBC (5.3) ≈10 points. The sum of these points is approximately 233. A vertical line drawn from the “total points” axis (at 233) to the survival probability axes indicates estimated 1-, 3-, and 5-year survival probabilities of approximately 92%, 86%, and 75%, respectively.

Alternatively, the same probability can be calculated numerically using the provided formula.

Discussion

In recent years, people have recognized the importance of clinical related factors, especially the importance of blood indexes and clinicopathological features in judging the prognosis of cancer patients (4). It is now clear that the prognosis of cancer patients depends not only on the characteristics of the tumor, but also on many clinical signatures of the patients (24,25). In this study, we collected the clinical indicators of BC patients, including past medical history, marital and reproductive history, pathological characteristics of BC, follow-up treatment and clinical blood index, aiming to establish a prognostic model of BC through comprehensive analysis.

Researchers have shown that the inflammatory indicators in the blood could predict the OS of patients with several types of cancer (26,27). The detection results of clinical blood indexes can play an important role in evaluating the prognosis of BC patients and guiding treatment to a great extent (4). In addition, some commonly used clinical features have clearly confirmed the prognosis of BC patients, including T stage (28), N stage (29), M stage (30) and AJCC stage (31). Based on the clinical indicators from the BC patients in this study, prognostic correlation analysis was carried out to screen out the clinical factors related to prognosis. By using LASSO regression analysis, 72 clinical indicators of BC patients in the training set were analyzed. The results showed that 14 key factors were identified to be of great significance to the prognosis of patients with BC. To identify the prognostic indicators, the Cox-univariate analysis was performed. The results indicated that the following clinical indicators were associated with prognosis of BC patients, including: age, hypertension, children, AJCC stage, metastasis, Ki-67, chemotherapy, endocrine therapy, RBC, neutrophile granulocyte. To further clarify the independently prognostic factors in BC patients, the results of Cox-multivariate regression analysis suggested that 6 features (hypertension, AJCC stage, metastasis, Ki-67, endocrine therapy and RBC) were independent prognostic factors for OS of BC patients. The results indicated that the above characteristics can be used to assess the prognosis of BC patients.

Nomogram is widely used in cancer prognosis assessment, with its core advantage being the ability to simplify complex statistical prediction models into a single numerical evaluation—precisely predicting the probability of clinical events such as death or recurrence through individualized patient data (32). Using simple and understandable graphics to generate these estimates is helpful to guide clinical decision-making through nomogram in clinical diagnosis and treatment (18). Therefore, to better predict the prognosis of BC patients at 1, 3, and 5 years after operation, a concise nomogram was developed by incorporating the characteristics based on these 6 clinical factors into the stepwise Cox model. The AUC for 1-, 3- and 5-year OS was 0.93, 0.89 and 0.93. The calibration plot of 1-, 3- and 5-year survival probability showed that the prediction of nomogram was in good agreement with the actual observation results. Importantly, the results in the validation set were also consistent with the training set. The robust performance in the independent validation cohort can be attributed, in part, to the well-balanced distribution of all important demographic, clinical, and predictor variables between the development and validation datasets (as detailed in Table S1). In addition, the absence of statistically significant differences in key prognostic factors (including age, AJCC stage, metastasis status, and treatment modalities), ensuring comparability between the validation population and the model development population, thereby enhancing the credibility of the internal validation results.

Some limitations exist in this study. First, this report uses retrospective research, leading to some selection bias. Second, the sample size of this study is limited, resulting in a low EPV ratio. This increases the risk of model overfitting and may affect the stability of the estimated coefficients. Therefore, the findings require validation in larger, prospective cohorts. Third, our model was only internally validated using a split-sample approach. External validation using independent cohorts from other institutions is required to confirm the generalizability of our nomogram before clinical application. Fourth, our study used OS as the endpoint. While clinically definitive, breast cancer-specific survival (BCSS) could provide more specific prognostic information and should be considered in future studies. Fifth, dichotomization of continuous blood indices may lead to loss of information. Future studies could explore the use of continuous variables or more nuanced categorization to potentially improve predictive accuracy. In addition, the study did not include genetic biomarkers for BC patients. Therefore, we need to further increase the sample size, add factors closely related to BC, and draw more accurate conclusions.

In conclusion, the purpose of constructing the prognostic model is to apply the collected clinical indicators to clinical practice for guiding patient risk assessment. The model incorporates clinically established prognostic factors for BC as well as newly identified clinical factors for prognostic assessment. While established tools like the Nottingham Prognostic Index exist, our nomogram incorporates readily available clinical and hematological indicators, offering a complementary approach. Future studies directly comparing the performance of our model with these existing tools in the same cohort are warranted. We would like to use the nomogram to conduct prognostic analysis and risk assessment for BC patients and guide clinical diagnosis and treatment decisions.

Conclusions

We developed and validated a novel nomogram based on clinical indicators for predicting OS for BC, which showed good application prospect. This model has the potential to help in clinical decision-making and evaluating patient outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-aw-454/rc

Data Sharing Statement: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-aw-454/dss

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-2025-aw-454/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-2025-aw-454/coif). The authors have no 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 and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Soochow University [No. (2022)340] and the Ethics Committee of The 904th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army (No. 20230222). Individual consent for this retrospective analysis was waived.

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. Wang S, Xiong Y, Zhang Q, et al. Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer. Brief Bioinform 2021;22:bbaa311. [Crossref] [PubMed]
2. Panzarino NJ, Krais JJ, Cong K, et al. Replication Gaps Underlie BRCA Deficiency and Therapy Response. Cancer Res 2021;81:1388-97. [Crossref] [PubMed]
3. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 2011;378:1812-23. [Crossref] [PubMed]
4. Cho U, Park HS, Im SY, et al. Prognostic value of systemic inflammatory markers and development of a nomogram in breast cancer. PLoS One 2018;13:e0200936. [Crossref] [PubMed]
5. Trayes KP, Cokenakes SEH. Breast Cancer Treatment. Am Fam Physician 2021;104:171-8.
6. Ciriello G, Gatza ML, Beck AH, et al. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer. Cell 2015;163:506-19. [Crossref] [PubMed]
7. Bedognetti D, Hendrickx W, Marincola FM, et al. Prognostic and predictive immune gene signatures in breast cancer. Curr Opin Oncol 2015;27:433-44. [Crossref] [PubMed]
8. Yalaza M, İnan A, Bozer M. Male Breast Cancer. J Breast Health 2016;12:1-8. [Crossref] [PubMed]
9. Loi S, Michiels S, Adams S, et al. The journey of tumor-infiltrating lymphocytes as a biomarker in breast cancer: clinical utility in an era of checkpoint inhibition. Ann Oncol 2021;32:1236-44. [Crossref] [PubMed]
10. Davidson S, Coles M, Thomas T, et al. Fibroblasts as immune regulators in infection, inflammation and cancer. Nat Rev Immunol 2021;21:704-17. [Crossref] [PubMed]
11. Mantovani A. The yin-yang of tumor-associated neutrophils. Cancer Cell 2009;16:173-4. [Crossref] [PubMed]
12. Todoric J, Karin M. The Fire within: Cell-Autonomous Mechanisms in Inflammation-Driven Cancer. Cancer Cell 2019;35:714-20. [Crossref] [PubMed]
13. Templeton AJ, Ace O, McNamara MG, et al. Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev 2014;23:1204-12. [Crossref] [PubMed]
14. Lazar S, Goldfinger LE. Platelets and extracellular vesicles and their cross talk with cancer. Blood 2021;137:3192-200. [Crossref] [PubMed]
15. Walraven M, Sabrkhany S, Knol JC, et al. Effects of Cancer Presence and Therapy on the Platelet Proteome. Int J Mol Sci 2021;22:8236. [Crossref] [PubMed]
16. Guthrie GJ, Charles KA, Roxburgh CS, et al. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol 2013;88:218-30. [Crossref] [PubMed]
17. Cui C, Chakraborty K, Tang XA, et al. Neutrophil elastase selectively kills cancer cells and attenuates tumorigenesis. Cell 2021;184:3163-3177.e21. [Crossref] [PubMed]
18. Hu C. Nomogram: A better method for evaluating MVD risk. Int J Cardiol 2024;411:132283. [Crossref] [PubMed]
19. Zhang W, Ji L, Wang X, et al. Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis. Front Endocrinol (Lausanne) 2021;12:752176. [Crossref] [PubMed]
20. Liu L, Xiao Y, Wei D, et al. Development and validation of a nomogram for predicting suicide risk and prognostic factors in bladder cancer patients following diagnosis: A population-based retrospective study. J Affect Disord 2024;347:124-33. [Crossref] [PubMed]
21. Wu C, Zhu S, Wang Q, et al. Development, validation, and visualization of a novel nomogram to predict depression risk in patients with stroke. J Affect Disord 2024;365:351-8. [Crossref] [PubMed]
22. Wong SL, Kattan MW, McMasters KM, et al. A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system. Ann Surg Oncol 2005;12:282-8. [Crossref] [PubMed]
23. Su J, Miao LF, Ye XH, et al. Development of prognostic signature and nomogram for patients with breast cancer. Medicine (Baltimore) 2019;98:e14617. [Crossref] [PubMed]
24. Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov 2022;12:31-46. [Crossref] [PubMed]
25. Varnier R, Sajous C, de Talhouet S, et al. Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives. Cancers (Basel) 2021;13:4840. [Crossref] [PubMed]
26. Biffi G, Tuveson DA. Diversity and Biology of Cancer-Associated Fibroblasts. Physiol Rev 2021;101:147-76. [Crossref] [PubMed]
27. Rui X, Shao S, Wang L, et al. Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram. BMC Cancer 2019;19:1179. [Crossref] [PubMed]
28. Matsuo K, Lee SW, Tanaka R, et al. T stage and venous invasion are crucial prognostic factors for long-term survival of patients with remnant gastric cancer: a cohort study. World J Surg Oncol 2021;19:291. [Crossref] [PubMed]
29. Wen J, Ye F, He X, et al. Development and validation of a prognostic nomogram based on the log odds of positive lymph nodes (LODDS) for breast cancer. Oncotarget 2016;7:21046-53. [Crossref] [PubMed]
30. Scully OJ, Bay BH, Yip G, et al. Breast cancer metastasis. Cancer Genomics Proteomics 2012;9:311-20.
31. Akram M, Iqbal M, Daniyal M, et al. Awareness and current knowledge of breast cancer. Biol Res 2017;50:33. [Crossref] [PubMed]
32. Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol 2015;16:e173-80. [Crossref] [PubMed]