Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

Prognostic factors for hormone receptor-positive breast cancer with liver metastasis and establishment of novel nomograms for prediction: a SEER-based study

Background

The prognosis of patients with hormone receptor (HR)-positive breast cancer with liver metastasis (BCLM) remains dismal and varies widely from person to person. Thus, we sought to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) in patients with HR-positive BCLM using data from the Surveillance, Epidemiology and End Results (SEER) database.

Methods

The data of patients with BCLM, who had received HR-positive diagnoses between 2010 and 2016, were collected from the SEER database. A Cox proportional hazards model was used to evaluate and identify the independent risk factors for OS and BCSS. Subsequently, two new nomograms were developed. Finally, the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) results were evaluated.

Results

The data of 1,780 patients diagnosed between 2010 and 2015 were used to build the nomogram models. Using both univariate and multivariate Cox regression analyses, nine variables, including age, marital status, grade, human epidermal growth factor receptor 2 (HER2) status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis, were found to be significantly associated with OS. Conversely, 10 variables, including age, marital status, T stage, grade, HER2 status, chemotherapy, surgery, bone metastasis, lung metastasis, and brain metastasis, were identified as independent risk factors for BCSS. Using the risk factors listed above, we created 1-, 2-, and 3-year survival nomograms for OS and BCSS, respectively. Subsequently, the data of 312 patients, who had been diagnosed in 2016, were used for the external validation. These results, including the ROC curve, calibration curve, and DCA results, showed that our nomogram had strong predictive power.

Conclusions

Nomograms can effectively and reliably predict a patient's prognosis and could be useful in clinical decision making. The nomograms had strong discrimination, calibration, and clinical values. More aggressive treatment and closer monitoring should be considered when treating high-risk individuals.

Risk stratification system and visualized dynamic nomogram constructed for predicting diagnosis and prognosis in rare male breast cancer patients with bone metastases

Background

Bone metastases (BM) from malignant tumors could disrupt the balance between osteoclasts and osteoblasts and affect bone homeostasis. Malignant breast cancer (BC) is rare in male patients, and co-occurrence of BM is even rarer. Given its low incidence, there is limited research evaluating risk and prognosis. Despite the widespread application of nomograms to predict uncommon malignancies, no studies have constructed predictive models focusing on the diagnosis and prognosis of male breast cancer with bone metastases (MBCBM).

Methods

This study selected all male breast cancer patients (MBC) between 2010 and 2019 in the Surveillance, Epidemiology, and End Results (SEER) database. We used simple and multivariate Logistic regression analyses to identify independent risk factors for BM in MBC patients. Then simple and multivariate Cox regression analyses were employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in MBCBM patients. We established and validated three new nomograms based on these independent factors.

Result

A total of 4187 MBC patients were included, with 191 (4.56%) having bone metastases at the time of diagnosis. The independent risk factors of BM in MBC patients included age, tumor size, marital status, T stage, and N stage. In MBCBM patients, independent prognostic factors for OS and CSS were both age, T stage, ER status, PR status, and surgery. The concordance index (C-index), the area under the curve (AUC) of the receiver operating characteristic curve (ROC), the calibration curve, and the decision curve analysis (DCA) confirmed that these three nomograms could accurately predict the diagnosis and prognosis of MBCBM patients with excellent discrimination and clinical utility superior to the TNM staging system. We then established two prognostic-based risk stratification systems and three visualized dynamic nomograms that could be applied in clinical practice.

Conclusion

In conclusion, this study aimed to establish and validate an accurate novel nomogram to objectively predict the diagnosis and prognosis of MBCBM patients. On this basis, prognostic-based risk stratification systems and visualized dynamic nomograms were constructed to facilitate doctors and patients to quantify individual BM risk probability and survival probability to assist in personalized risk assessment and clinical decision-making.

Machine learning predicts the prognosis of breast cancer patients with initial bone metastases

Background

Bone is the most common metastatic site of patients with advanced breast cancer and the survival time is their primary concern; however, we lack accurate predictive models in clinical practice. In addition to this, primary surgery for breast cancer patients with bone metastases is still controversial.

Method

The data used for analysis in this study were obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of patients with bone metastatic breast cancer (BMBC). Through cross-validation, we constructed an XGBoost model to predicting survival in patients with BMBC. We also investigated the prognosis of patients treated with neoadjuvant chemotherapy plus surgical and chemotherapy alone using propensity score matching and K-M survival analysis.

Results

Our validation results showed that the model has high sensitivity, specificity, and correctness, and it is the most accurate one to predict the survival of patients with BMBC (1-year AUC = 0.818, 3-year AUC = 0.798, and 5-year survival AUC = 0.791). The sensitivity of the 1-year model was higher (0.79), while the specificity of the 5-year model was higher (0.86). Interestingly, we found that if the time from diagnosis to therapy was ≥1 month, patients with BMBC had even better survival than those who started treatment immediately (HR = 0.920, 95%CI 0.869-0.974, P < 0.01). The BMBC patients with an income of more than USD$70,000 had better OS (HR = 0.814, 95%CI 0.745-0.890, P < 0.001) and BCSS (HR = 0.808 95%CI 0.735-0.889, P < 0.001) than who with income of < USD$50,000. We also found that compared with chemotherapy alone, neoadjuvant chemotherapy plus surgical treatment significantly improved OS and BCSS in all molecular subtypes of patients with BMBC, while only the patients with bone metastases only, bone and liver metastases, bone and lung metastases could benefit from neoadjuvant chemotherapy plus surgical treatment.

Conclusion

We constructed an AI model to provide a quantitative method to predict the survival of patients with BMBC, and our validation results indicate that this model should be highly reproducible in a similar patient population. We also identified potential prognostic factors for patients with BMBC and suggested that primary surgery followed by neoadjuvant chemotherapy might increase survival in a selected subgroup of patients.

Survival Analysis in Male Breast Cancer With Bone Metastasis Based on the SEER Database

Purpose

Breast cancer (BC) has been extensively and deeply studied as the number one malignant tumor in women, but its status in male patients, especially in male metastatic patients, is rarely reported. Thus, this study aimed to explore the prognosis and risk factors of male BC with bone metastasis.

Patients and Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify all patients diagnosed with male BC with bone metastasis from 2010 to 2016. Risk factors of overall survival (OS) and cancer-specific survival (CSS) were analyzed by univariable and multivariable Cox analyses. We also drew Kaplan–Meier plots to show the correlation between independent risk factors and survival.

Results

A total of 207 male BC patients with bone metastasis were included for analysis. Approximately one-third of patients also had lung metastasis. Luminal A subtype comprised 58.5% of the overall patient population. These patients had a poor prognosis, with 3-year OS and CSS rates, 36.7% and 39.5%, respectively. Further analysis revealed that age ≤60 years old, luminal A or B, and surgery were independent predictors of prolonged OS and CSS. On Cox multivariable analysis, brain metastasis was associated with OS and not CSS.

Conclusion

We identified four independent factors associated with prognosis in male BC patients with bone metastasis, namely age, tumor subtype, surgery, and brain metastasis. Knowing these risk factors will help clinicians make more appropriate treatment plans.

Development and Validation of Prognostic Nomogram for Elderly Breast Cancer: A Large-Cohort Retrospective Study

Purpose

Our research aims to study the bone metastatic patterns and prognostic outcomes in elderly breast cancer (BC) and to develop elder-specific nomograms.

Methods

We downloaded the data of BC patients between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database. The differences in clinical features and prognosis between young (age < 65) and elderly (age ≥ 65) BC patients were compared. The univariate and multivariate Cox analyses were used to determine the overall survival (OS)- and cancer-specific survival (CSS)-related variables and establish two nomograms of BC patients with bone metastasis (BCBM). The receiver operating characteristic (ROC) curve with area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival curve were selected to evaluate nomograms.

Results

A total of 230,177 BC patients were enrolled in our research, including 142,025 young and 88,152 elderly patients. The prognosis of elderly BCBM patients was significantly worse than young patients. Age, race, breast subtype, tumor size, tumor grade, brain metastasis, liver metastasis, surgery, and chemotherapy were independent prognostic variables for elderly BCBM patients, including OS and CSS. The AUC values at 12, 18, and 24 months were 0.750, 0.751, and 0.739 for OS nomogram and 0.759, 0.762, and 0.752 for CSS nomogram in the training cohort, which were higher than the AUC values of all single independent prognostic variables. The survival curve showed a distinct prognosis between low-, median- and high-risk groups (p < 0.001). Finally, calibration curves and DCA indicated that both nomograms have favorable performance.

Conclusion

Elderly and young patients presented with different bone metastatic frequencies, clinical features, and prognostic outcomes. Two elder-specific nomograms incorporating nine clinical variables were established and validated to be a valuable predictor for elderly BCBM patients.

Clinical Features and Serological Markers Risk Model Predicts Overall Survival in Patients Undergoing Breast Cancer and Bone Metastasis Surgeries

Background

Surgical therapy of breast cancer and bone metastasis can effectively improve the prognosis of breast cancer. However, after the first operation, the relationship between preoperative indicators and outcomes in patients who underwent metastatic bone surgery remained to be studied. Purpose 1. Recognize clinical and laboratory prognosis factors available to clinical doctors before the operation for bone metastatic breast cancer patients. 2. Develop a risk prediction model for 3-year postoperative survival in patients with breast cancer bone metastasis.

Methods

From 2014 to 2020, patients who suffered from breast cancer bone metastasis and received therapeutic procedures in our institution were included for analyses (n=145). For patients who underwent both breast cancer radical surgery and bone metastasis surgery, comprehensive datasets of the parameters of interest (clinical features, laboratory factors, and patient prognoses) were collected (n=69). We performed Multivariate Cox regression to identify factors that were associated with postoperative outcome. 3-year survival prediction model and nomograms were established by 100 bootstrapping. Its benefit was evaluated by calibration plot, C-index, and decision curve analysis. The Surveillance, Epidemiology, and End Results database was also used for external validation.

Results

Radiotherapy for primary cancer, pathological type of metastatic breast cancer, lymph node metastasis, elevated serum alkaline phosphatase, lactate dehydrogenase were associated with postoperative prognosis. Pathological types of metastatic breast cancer, multiple bone metastasis, organ metastases, and elevated serum lactate dehydrogenase were associated with 3-year survival. Then those significant variables and serum alkaline phosphatase counts were integrated to construct nomograms for 3-year survival. The C-statistic of the established predictive model was 0.83. The calibration plot presents a graphical representation of calibration. In the decision curve analysis, the benefits are higher than those of the extreme curve. The receiver operating characteristic of the external validation of the model was 0.82, indicating a favored fitting degree of the two models.

Conclusion

Our study suggests that several clinical features and serological markers can predict the overall survival among the patients who are about to receive bone metastasis surgery after breast cancer surgery. The model can guide the preoperative evaluation and clinical decision-making for patients. Level of evidence Level III, prognostic study.

Application of a Novel Prediction Model for Predicting 2-Year Risk of Non-Alcoholic Fatty Liver Disease in the Non-Obese Population with Normal Blood Lipid Levels: A Large Prospective Cohort Study from China

Purpose

The purpose of this study was to develop and validate a nomogram to better assess the 2-year risk of non-alcoholic fatty liver disease (NAFLD) in non-obese population with normal blood lipid levels.

Patients and Methods

This study was a secondary analysis of a prospective study. We included 3659 non-obese adults with normal blood lipid levels without NAFLD at baseline. A total of 2744 participants were included in the development cohort and 915 participants were included in the validation cohort. The least absolute contraction selection operator (LASSO) regression model was used to identify the best risk factors. Multivariate Cox regression analysis was used to construct the prediction model. The performance of the prediction model was assessed using Harrell’s consistency index (C-index), area under the receiver operating characteristic (AUROC) curve and calibration curve. Decision curve analysis was applied to evaluate the clinical usefulness of the prediction model.

Results

After LASSO regression analysis and multivariate Cox regression analysis on the development cohort, BMI, TG, DBIL, ALT and GGT were found to be risk predictors and were integrated into the nomogram. The C-index of development cohort and validation cohort was 0.819 (95% CI, 0.798 to 0.840) and 0.815 (95% CI, 0.781 to 0.849), respectively. The AUROC of 2-year NAFLD risk in the development cohort and validation cohort was 0.831 (95% CI, 0.811 to 0.851) and 0.797 (95% CI, 0.765 to 0.829), respectively. From calibration curves, the nomogram showed a good agreement between predicted and actual probabilities. The decision curve analysis indicated that application of the nomogram is more effective than the intervention-for-all-patients scheme.

Conclusion

We developed and validated a nomogram for predicting 2-year risk of NAFLD in the non-obese population with normal blood lipid levels.

A nomogram combining PPARγ expression profiles and clinical factors predicts survival in patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with poor prognosis. Peroxisome proliferator-activated receptor γ (PPARγ) is involved in the development of various tumor types. However, its role in hepatocellular carcinoma (HCC) remains unclear. Multiple databases including The Cancer Genome Atlas, Gene Expression Omnibus and Kaplan-Meier plotter were used for bioinformatics analysis of the PPARγ gene or protein. Immunohistochemical labeling of tumor and adjacent normal tissues obtained from 125 patients with HCC was performed to analyze the relationship between PPARγ expression and overall survival (OS) rate. PPARγ was evaluated using functional enrichment analyses and Lasso regression was used to conduct a dimensionality reduction analysis of 43 clinical factors for HCC. An OS prognostic nomogram was then established using seven independent risk factors screened via Lasso regression. PPARγ expression in HCC tumor tissues was higher compared with that in normal liver tissues, and its high expression was associated with poor prognosis, as indicated by bioinformatics analysis. However, opposite results were obtained using the clinical specimens. Functional enrichment analysis indicated that PPARγ was enriched in the 'fatty acid metabolism' pathway. Lasso regression identified seven clinical factors associated with prognosis, including Tumor-Node-Metastasis stage, grade, vascular invasion, α fetoprotein, carbohydrate antigen 199, γ-glutamyl transpeptidase and the PPARγ protein. These seven clinical factors were to construct an OS prognostic nomogram. Overall, PPARγ was highly expressed in the livers of patients with HCC and can be included in an OS prognostic nomogram. However, the factors underlying the differential association of PPARγ expression with HCC prognosis in different datasets should be further investigated.