Score for the Survival Probability in Metastasis Breast Cancer: A Nomogram-Based Risk Assessment Model

Development and validation of nomograms to predict survival in patients with invasive micropapillary carcinoma of the breast

OBJECTIVES

The present study aimed to develop and validate nomograms to predict the survival of patients with breast invasive micropapillary carcinoma (IMPC) to aid objective decision-making.

DESIGN

Prognostic factors were identified using Cox proportional hazards regression analyses and used to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) at 3 and 5 years. Kaplan-Meier analysis, calibration curves, the area under the curve (AUC) and the concordance index (C-index) evaluated the nomograms' performance. Decision curve analysis (DCA), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system.

SETTING

Patient data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. This database holds data related to the incidence of cancer acquired from 18 population-based cancer registries in the US.

PARTICIPANTS

We ruled out 1893 patients and allowed the incorporation of 1340 patients into the present study.

RESULTS

The C-index of the AJCC8 stage was lower than that of the OS nomogram (0.670 vs 0.766) and the OS nomograms had higher AUCs than the AJCC8 stage (3 years: 0.839 vs 0.735, 5 years: 0.787 vs 0.658). On calibration plots, the predicted and actual outcomes agreed well, and DCA revealed that the nomograms had better clinical utility compared with the conventional prognosis tool. In the training cohort, the NRI for OS was 0.227, and for BCSS was 0.182, while the IDI for OS was 0.070, and for BCSS was 0.078 (both p<0.001), confirming its accuracy. The Kaplan-Meier curves for nomogram-based risk stratification showed significant differences (p<0.001).

CONCLUSIONS

The nomograms showed excellent discrimination and clinical utility to predict OS and BCSS at 3 and 5 years, and could identify high-risk patients, thus providing IMPC patients with personalised treatment strategies.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Developing and validating nomograms for predicting the survival in patients with clinical local-advanced gastric cancer

Background

Many patients with gastric cancer are at a locally advanced stage during initial diagnosis. TNM staging is inaccurate in predicting survival. This study aims to develop two more accurate survival prediction models for patients with locally advanced gastric cancer (LAGC) and guide clinical decision-making.

Methods

We recruited 2794 patients diagnosed with LAGC (2010–2015) from the Surveillance, Epidemiology, and End Results (SEER) database and performed external validation using data from 115 patients with LAGC at Yantai Affiliated Hospital of Binzhou Medical University. Univariate and multifactorial survival analyses were screened for meaningful independent prognostic factors and were used to build survival prediction models. Concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were evaluated for nomograms. Finally, the differences and relationships of survival and prognosis between the three different risk groups were described using the Kaplan–Meier method.

Results

Cox proportional risk regression model analysis identified independent prognostic factors for patients with LAGC, and variables associated with overall survival (OS) included age, race, marital status, T-stage, N-stage, grade, histologic type, surgery, and chemotherapy. Variables associated with cancer-specific survival (CSS) included age, race, T-stage, N-stage, grade, histological type, surgery, and chemotherapy. In the training cohort, C-index of nomogram for predicting OS was 0.722 (95% confidence interval [95% CI]: 0.708–0.736] and CSS was 0.728 (95% CI: 0.713–0.743). In the external validation cohort, C-index of nomogram for predicted OS was 0.728 (95% CI:0.672–0.784) and CSS was 0.727 (95% CI:0.668–0.786). The calibration curves showed good concordance between the predicted and actual results. C-index, ROC, and DCA results indicated that our nomograms could more accurately predict OS and CSS than TNM staging and had a higher clinical benefit. Finally, to facilitate clinical use, we set up two web servers based on nomograms.

Conclusion

The nomograms established in this study have better risk assessment ability than the clinical staging system, which can help clinicians predict the individual survival of LAGC patients more accurately and thus develop appropriate treatment strategies.

Prognostic Factors and a Nomogram Predicting Survival in Patients with Breast Ductal Carcinoma in situ with Microinvasion: A Population-Based Study

Purpose

Ductal carcinoma in situ with microinvasion (DCISM) can be challenging to balance the risks of overtreatment versus undertreatment. We aim to identify prognostic factors in patients with DCISM and construct a nomogram to predict breast cancer-specific survival (BCSS).

Materials and Methods

A retrospective cohort study of women diagnosed with DCISM from 1988 to 2015 who were identified in the Surveillance, Epidemiology and End Results database. Clinical variables and tumor characteristics were evaluated, and Cox proportional-hazards regression was performed. A nomogram was constructed from the multivariate logistic regression to combine all the prognostic factors to predict the prognosis of DCISM patients at 5 years, 10 years, and 15 years.

Results

We identified 5438 total eligible breast cancer patients with a median and max survival time of 78 and 227 months, respectively. Here, patients with poorer survival outcomes were those diagnosed between 1988 and 2001, African-American race, under 40 years of age, higher tumor N stage, progesterone receptor-negative tumor, and received no surgery. The nomogram was constructed by the seven variables and passed the calibration and validation steps. The area under the receiver operating characteristic (ROC) curve (AUC) of both the training set and the validating set (5-year AUC: 0.77 and 0.88, 10-year AUC: 0.75 and 0.73, 15-year AUC: 0.72 and 0.65). Receiving chemotherapy was associated with a better BCSS (hazard ratio, HR=0.45, 95% confidence interval, 95% CI = 0.23–0.89), especially in patients with estrogen receptor (ER) negative, progesterone receptor (PR) negative (HR = 0.35, 95% CI = 0.13–0.97) and ER+PR-/ER-PR+ DCISM (HR = 0.07, 95% CI = 0.01–0.59).

Conclusion

Our current study is the first to construct nomograms of patients with DCISM which could help physicians identify breast cancer patients that more likely to benefit from more intensive treatment and follow-up. Chemotherapy might benefit patients with ER-PR- and ER+PR-/ER-PR+ DCISM.

A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study

Objective

The prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat.

Methods

We identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system.

Results

Grade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes.

Conclusion

We have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.

Development and validation of a risk stratification nomogram for predicting prognosis in bone metastatic breast cancer: A population-based study

ABSTRACT

Bone metastasis seriously affects the survival of breast cancer. Therefore, the study aimed to explore the independent prognostic factors in bone metastatic breast cancer (BMBC) and to construct a prognostic nomogram that can accurately predict the survival of BMBC and strictly divide the patients into different risk stratification.Four thousand three hundred seventy six patients with BMBC from the surveillance, epidemiology, and end results database in 2010 to 2015 were collected and randomly divided into training and validation cohort. Multivariate Cox regression identified the independent prognostic factors of BMBC. A nomogram for predicting cancer-specific survival (CSS) in BMBC was created using R software. The predictive performance of the nomogram was evaluated by plotting receiver operating characteristic (ROC) curves and calibration curves.Marital status, race, age, T stage, tumor grade, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, brain metastasis, liver metastasis, lung metastasis, chemotherapy, and breast surgery were identified as independent prognostic factors for CSS of BMBC. The area under the ROC curve at 1-, 3-, and 5-year of the nomogram were 0.775, 0.756, and 0.717 in the internal validation and 0.785, 0.737, and 0.735 in the external validation, respectively. Calibration curves further confirmed the unbiased prediction of the model. Kaplan-Meier analysis verified the excellent risk stratification of our model.The first prognostic nomogram for BMBC constructed in our study can accurately predict the survival of BMBC, which may provide a practical tool to help clinicians evaluate prognosis and stratify the prognostic risk for BMBC, thereby determining which patients should be given intensive treatment and optimizing individual treatment strategies for BMBC.

A nomogram for predicting survival in patients with de novo metastatic breast cancer: a population-based study

BACKGROUND

5-10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) of these patients.

METHODS

de novo MBC patients diagnosed in 2010-2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. They were randomly divided into a training and a validation cohort with a ratio of 2:1. The best subsets of covariates were identified to develop a nomogram predicting OS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The discrimination and calibration of the nomogram were evaluated using the Concordance index, the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curves.

RESULTS

In this study, we included 7986 patients with de novo MBC. The median follow-up time was 36 months (range: 0-83 months). Five thousand three-hundred twenty four patients were allocated into the training cohort while 2662 were allocated into the validation cohort. In the training cohort, age at diagnosis, race, marital status, differentiation grade, subtype, T stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery and chemotherapy were selected to create the nomogram estimating the 1-, 3- and 5- year OS based on the smallest AIC value in the multivariate Cox models. The nomogram achieved a Concordance index of 0.723 (95% CI, 0.713-0.733) in the training cohort and 0.719 (95% CI, 0.705-0.734) in the validation cohort. AUC values of the nomogram indicated good specificity and sensitivity in the training and validation cohort. Calibration curves showed a favorable consistency between the predicted and actual survival probabilities.

CONCLUSION

The developed nomogram reliably predicted OS in patients with de novo MBC and presented a favorable discrimination ability. While further validation is needed, this may be a useful tool in clinical practice.

Independent risk factors evaluation for overall survival and cancer-specific survival in thyroid cancer patients with bone metastasis: A study for construction and validation of the predictive nomogram

Bone is a frequent site for the occurrence of metastasis of thyroid cancer (TC). TC with bone metastasis (TCBM) is associated with skeletal-related events (SREs), with poor prognosis and low overall survival (OS). Therefore, it is necessary to develop a predictive nomogram for prognostic evaluation. This study aimed to construct an effective nomogram for predicting the OS and cancer-specific survival (CSS) of TC patients with BM. Those TC patients with newly diagnosed BM were retrospectively examined over a period of 6 years from 2010 to 2016 using data from the Surveillance, Epidemiology and End Results (SEER) database. Demographics and clinicopathological data were collected for further analysis. Patients were randomly allocated into training and validation cohorts with a ratio of ∼7:3. OS and CSS were retrieved as research endpoints. Univariate and multivariate Cox regression analyses were performed for identifying independent predictors. Overall, 242 patients were enrolled in this study. Age, histologic grade, histological subtype, tumor size, radiotherapy, liver metastatic status, and lung metastatic status were determined as the independent prognostic factors for predicting the OS and CSS in TCBM patients. Based on the results, visual nomograms were separately developed and validated for predicting 1-, 2-, and 3-year OS and CSS in TCBM patients on the ground of above results. The calibration, receiver operating characteristic (ROC) curve and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. Our predictive model is expected to be a personalized and easily applicable tool for evaluating the prognosis of TCBM patients, and may contribute toward making an accurate judgment in clinical practice.

Primary 21-Gene Recurrence Score and Disease Outcome in Loco-Regional and Distant Recurrent Breast Cancer Patients

Background: The 21-gene recurrence score (RS) assay has been proven prognostic and predictive for hormone receptor-positive/HER2-negative, node-negative early breast cancer patients. However, whether primary 21-gene RS can predict prognosis in recurrent breast cancer patients remained unknown.

Patients and Methods: Consecutive breast cancer patients operated in Comprehensive Breast Health Center, Shanghai Ruijin Hospital between January 2009 and December 2018 were retrospectively analyzed. Patients with available 21-gene RS result for the primary tumor and reporting disease recurrence during follow-up were included. Association of 21-gene RS and overall survival (OS), post-recurrence overall survival (PR-OS), post-recurrence progression-free survival (PR-PFS), and first-line systemic treatment after recurrence were compared among different groups.

Results: A total of 74 recurrent patients were included, with 10, 27, 37 patients in the RS <18, 18–30, and ≥ 31 groups, respectively. Recurrent patients with RS ≥ 31 were more likely to receive chemotherapy as their first-line treatment compared to those with RS <31 (P = 0.025). Compared to those with RS <31, patients with RS ≥ 31 had significantly worse OS (P = 0.025), worse PR-OS (P = 0.026), and a trend of inferior PR-PFS (P = 0.106). Multivariate analysis demonstrated that primary ER expression level (OS: P = 0.009; PR-OS: P = 0.017) and histological grade (OS: P = 0.003; PR-OS: P = 0.009), but not primary 21-gene RS (OS: P = 0.706; PR-OS: P = 0.120), were independently associated with worse OS and PR-OS.

Conclusions: High primary 21-gene RS tended to be associated with worse disease outcome in loco-regional and distant recurrent breast cancer patients, which could influence the first-line systemic treatment after relapse, warranting further clinical evaluation.

The E3 Ubiquitin Ligase Cbl-b Predicts Favorable Prognosis in Breast Cancer

Background: Casitas B-lineage lymphoma proto-oncogene-b (Cbl-b) is an E3 ubiquitin-protein ligase and a signal-transducing adaptor protein involved in the development and progression of cancer. Despite the known functions of Cbl-b, its role in breast cancer remains unclear. The aim of this study is to explore the prognostic value of Cbl-b in breast cancer. Methods: Cbl-b expression was analyzed by immunohistochemistry in 292 breast cancer patients from the First Hospital of China Medical University between 1999 and 2008. Kaplan-Meier curve and Cox proportional hazards regression were used to analyze the independent prognostic factors for overall survival (OS) and disease-free survival (DFS). Nomogram was constructed based on these prognostic factors. Results: Cbl-b expression was detected in 54.1% (158/292) breast cancer tissue samples. Cbl-b expression was correlated with DFS (p = 0.033), but was not significantly associated with the known clinic-pathological factors in this study. Log-rank analysis indicated that Cbl-b expression was correlated with better OS (p = 0.013) and DFS (p = 0.016). Multivariate analysis showed that Cbl-b expression was an independent prognostic factor in breast cancer. The nomogram we built for predicting OS was integrated with Cbl-b expression, age, tumor size, lymph node metastasis and histological grade. Except tumor size, all the above factors and date of diagnosis were used to construct the DFS nomogram. The C-indexes of the nomograms were 0.735 and 0.678, respectively. Our new clinical model was superior to the TNM staging for prediction of OS. Conclusion: Cbl-b expression independently predicts favorable prognosis in breast cancer. Cbl-b expression, combined with other variables could be more precise clinical predictive models for predicting OS and DFS in patients with breast cancer.

Establishing a predicted model to evaluate prognosis for initially diagnosed metastatic Her2-positive breast cancer patients and exploring the benefit from local surgery

BACKGROUND

For patients initially diagnosed with metastatic Her2-positive breast cancer (MHBC), we intended to construct a nomogram with risk stratification to predict prognosis and to explore the role of local surgery.

METHODS

We retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier (KM) method and log-rank test were used for the selection of significant variables. Cox regression analysis and Fine-Gray test were utilized to confirm independent prognostic factors of overall survival (OS) and breast cancer-specific survival (BCSS). A nomogram predicting 1-year, 3-year, and 5-year OS was developed and validated. Patients were stratified based on the optimal cut-off values of total personal score. KM method and log-rank test were used to estimate OS prognosis and benefit from local surgery and chemotherapy.

RESULTS

There were 1680 and 717 patients in the training and validation cohort. Age, race, marriage, T stage, estrogen receptor (ER) status, visceral metastasis (bone, brain, liver and lung) were identified as independent prognostic factors for OS and BCSS, while histology was also corelated with OS. C-indexes in the training and validation cohort were 0.70 and 0.68, respectively. Calibration plots indicated precise predictive ability. The total population was divided into low- (<141 points), intermediate- (142-208 points), and high-risk (>208 points) prognostic groups. Local surgery and chemotherapy brought various degrees of survival benefit for patients with diverse-risk prognosis.

CONCLUSIONS

We constructed a model with accurate prediction and discrimination. It would provide a reference for clinicians' decision-making. Surgery on the primary lesion was recommended for patients with good physical performance status, while further study on optimal surgical opportunity was needed.

Supervised Machine Learning Predictive Analytics For Triple-Negative Breast Cancer Death Outcomes

Objective

To use machine learning algorithms to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.

Methods

1570 stage I-III breast cancer patients receiving treatment from Sun Yat-sen Memorial Hospital were analyzed. Machine learning was used to predict the death outcomes of patients with triple-negative breast cancer, 5 years after discharge.

Results

The results showed that platelets, LMR (lymphocyte-to-monocyte ratio), age, PLR (the platelet-to-lymphocyte ratio) and white blood cell counts accounted for a significant weight in the 5-year prognosis of triple-negative breast cancer patients. The results of model prediction indicated that rankings for accuracy among the training group (from high to low) were forest, gbm, and DecisionTree (0.770335, 0.760766, 0.751994, 0.737640 and 0.734450, respectively). For AUC value (high to low), they were forest, Logistic and DecisionTree (0.896673, 0.895408, 0.776836, 0.722799 and 0.702804, respectively). The highest MSE value for DecisionTree was 0.2656, and the lowest MSE value for forest was 0.2297. In the test group, accuracy rankings (from high to low) were DecisionTree, and GradientBoosting (0.748408, 0.738854, 0.738854, 0.732484 and gbm, respectively). For AUC value (high to low), the rankings were GradientBoosting, gbm, and DecisionTree (0.731595, 0.715438, 0.712767, 0.708348 and 0.691960, respectively). The maximum MSE value for gbm was 0.2707, and the minimum MSE value for DecisionTree was 0.2516.

Conclusion

The machine learning algorithm can predict the death outcomes of patients with triple-negative breast cancer 5 years after discharge. This can be used to estimate individual outcomes for patients with triple-negative breast cancer.

B2 Prognostic Score: External Validation of a Clinical Decision-making Tool for Metastatic Breast Cancer

BACKGROUND

The B² Prognostic Score (B²PS) is a clinical decision-making tool in metastatic breast cancer (MBC) that provides risk classification based on routine parameters. This study validates the B²PS in an independent series of MBC for the whole study group and for each intrinsic subtype.

PATIENTS AND METHODS

We analyzed 641 metastasized patients, treated in 17 German certified breast cancer centers between 2001 and 2009. They were classified into low, intermediate, and high-risk groups according to B²PS. Overall survival (OS) curves for the various B²PS groups were compared with Kaplan-Meier method.

RESULTS

According to the B²PS formula, 42.3% of patients were classified as low risk, 25.4% as intermediate risk and 32.3% as high risk. Intermediate- and high-risk patients had a statistically significant decreased OS compared with B²PS low-risk patients: (intermediate-risk: hazard ratio, 1.36; 95% confidence interval, 1.04-1.77; P = .023; high-risk: hazard ratio, 2.62; 95% confidence interval, 2.06-3.32; P < .001). The 5-year survival rates of low-, intermediate-, and high-risk patients were 41.3%, 26.9%, and 10.2%, respectively. The distribution of B²PS risk groups varied significantly within the intrinsic subtypes. For each intrinsic subtype, B²PS gives an additional risk classification.

CONCLUSIONS

This study demonstrates the reproducibility of the B²PS based on routinely assessable parameters and confirms its prognostic value in an independent entire cohort of MBC as well as in the separate intrinsic subtypes. It therefore can help in counseling and individualizing the therapeutic regimens of those patients.

Development and Validation of a Nomogram for Predicting Survival in Male Patients With Breast Cancer

Male breast cancer (MBC) is rare, and most patients are diagnosed at an advanced stage. We aimed to develop a reliable nomogram to predict breast cancer-specific survival (BCSS) for MBC patients, thus helping clinical diagnosis and treatment. Based on data from the Surveillance, Epidemiology, and End Results (SEER) database, 2,451 patients diagnosed with MBC from 2010 to 2015 were selected for this study. They were randomly assigned to either a training cohort (n = 1715) or a validation cohort (n = 736). The Multivariate Cox proportional hazards regression analysis was used to determine the independent prognostic factors, which were then utilized to build a nomogram for predicting 3- and 5-year BCSS. The discrimination and calibration of the new model was evaluated using the Concordance index (C-index) and calibration curves, while its accuracy and benefits were assessed by comparing it to the traditional AJCC staging system using the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and the decision curve analysis (DCA). Multivariate models revealed that age, AJCC stage, ER status, PR status, and surgery all showed a significant association with BCSS. A nomogram based on these variables was constructed to predict survival in MBC patients. Compared to the AJCC stage, the C-index (training group: 0.840 vs. 0.775, validation group: 0.818 vs. 0.768), the areas under the receiver operating characteristic curve of the training set (3-year AUC: 0.852 vs. 0.778, 5-year AUC: 0.841 vs. 0.774) and the validation set (3-year AUC: 0.778 vs. 0.752, 5-year AUC: 0.852 vs. 0.794), and the calibration plots of this model all exhibited better performance. Additionally, the NRI and IDI confirmed that the nomogram was a great prognosis tool. Finally, the 3- and 5-year DCA curves yielded larger net benefits than the traditional AJCC stage. In conclusion, we have successfully established an effective nomogram to predict BCSS in MBC patients, which can assist clinicians in determining the appropriate therapy strategies for individual male patients.

Prognostic nomogram for patients with non-metastatic HER2 positive breast cancer in a prospective cohort

PURPOSE

A nomogram is a reliable tool to generate individualized risk prediction by combining prognostic factors. We aimed to construct a nomogram for predicting the survival in patients with non-metastatic human epidermal growth factor receptor 2 (HER2) positive breast cancer in a prospective cohort.

METHODS

We analyzed 1304 consecutive patients who were diagnosed with non-metastatic HER2 positive breast cancer between January 2008 and December 2016 in our institution. Independent prognostic factors were identified to build a nomogram using the COX proportional hazard regression model. The prediction of the nomogram was evaluated by concordance index (C-index), calibration and subgroup analysis. External validation was performed in a cohort of 6379 patients from the Surveillance, Epidemiology, and End Results (SEER) database.

RESULTS

Through the COX proportional hazard regression model, five independent prognostic factors were identified. The nomogram predicting overall survival achieved a C-index of 0.78 in the training cohort and 0.74 in the SEER cohort. The calibration plot displayed favorable accordance between the nomogram prediction and the actual observation for 3-year overall survival in both cohorts. The quartiles of the nomogram score classified patients into subgroups with distinct overall survival.

CONCLUSION

We developed and validated a novel nomogram for predicting overall survival in patients with non-metastatic HER2 positive breast cancer, which presented a favorable discrimination ability. This model may assist clinical decision making and patient-clinician communication in clinical practice.

Could local surgery improve survival in de novo stage IV breast cancer?

Background

Resection of the primary tumor is recommended for symptom relief in de novo stage IV breast cancer. We explored whether local surgery could provide a survival benefit in these patients and attempted to characterize the population that could benefit from surgery.

Methods

Metastatic Breast cancer patients (N = 313) with intact primary tumor between January 2006 and April 2013 were separated into two groups according to whether or not they had undergone surgery. The difference in characteristics between the two groups was analyzed using chi-square test, Fisher’s exact test and Mann-Whitney test. Univariable and multivariable Cox regression and stratified survival analysis were used to assess the effect of surgery on survival.

Results

Of the 313 patients, 188 (60.1%) underwent local surgery. Patients with local surgery had a 47% reduction in mortality risk vs. those with no surgery (median survival 78 months vs. 37 months; HR = 0.53; 95% CI, 0.36–0.78) after adjustment for clinical and tumor characteristics. Stratified survival analysis showed that patients with bone metastasis alone (and primary tumor ≤5 cm), soft tissue metastasis, or ≤ 3 metastasis sites benefit from surgery.

Conclusion

Surgical resection of the primary tumor can improve survival in selected de novo stage IV breast cancer patients.