scholarly journals Risk Stratification Model for Predicting the Overall Survival of Elderly Triple-Negative Breast Cancer Patients: A Population-Based Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiaozhu Liu ◽  
Song Yue ◽  
Haodong Huang ◽  
Minjie Duan ◽  
Binyi Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Risk Stratification ◽  
Elderly Patients ◽  
Triple Negative ◽  
Training Set ◽  
Medium Risk ◽  
Validation Set ◽  
Stratification System ◽  
Risk Stratification System

Background: The objective of this study was to evaluate the prognostic value of clinical characteristics in elderly patients with triple-negative breast cancer (TNBC).Methods: The cohort was selected from the Surveillance, Epidemiology, and End Results (SEER) program dating from 2010 to 2015. Univariate and multivariate analyses were performed using a Cox proportional risk regression model, and a nomogram was constructed to predict the 1-, 3-, and 5-year prognoses of elderly patients with TNBC. A concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to verify the nomogram.Results: The results of the study identified a total of 5,677 patients who were randomly divided 6:4 into a training set (n = 3,422) and a validation set (n = 2,255). The multivariate analysis showed that age, race, grade, TN stage, chemotherapy status, radiotherapy status, and tumor size at diagnosis were independent factors affecting the prognosis of elderly patients with TNBC. Together, the 1 -, 3 -, and 5-year nomograms were made up of 8 variables. For the verification of these results, the C-index of the training set and validation set were 0.757 (95% CI 0.743–0.772) and 0.750 (95% CI 0.742–0.768), respectively. The calibration curve also showed that the actual observation of overall survival (OS) was in good agreement with the prediction of the nomograms. Additionally, the DCA showed that the nomogram had good clinical application value. According to the score of each patient, the risk stratification system of elderly patients with TNBC was further established by perfectly dividing these patients into three groups, namely, low risk, medium risk, and high risk, in all queues. In addition, the results showed that radiotherapy could improve prognosis in the low-risk group (P = 0.00056), but had no significant effect in the medium-risk (P < 0.4) and high-risk groups (P < 0.71). An online web app was built based on the proposed nomogram for convenient clinical use.Conclusion: This study was the first to construct a nomogram and risk stratification system for elderly patients with TNBC. The well-established nomogram and the important findings from our study could guide follow-up management strategies for elderly patients with TNBC and help clinicians improve individual treatment.

scholarly journals Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shijie Li ◽  
Xuefeng Liu ◽  
Xiaonan Chen
Keyword(s):  
Overall Survival ◽  
Risk Stratification ◽  
Cox Regression ◽  
Staging System ◽  
Tnm Staging ◽  
Calibration Curves ◽  
Tnm Staging System ◽  
Primary Bladder ◽  
Stratification System ◽  
Risk Stratification System

Abstract Background Primary bladder sarcoma (PBS) is a rare malignant tumor of the bladder with a poor prognosis, and its disease course is inadequately understood. Therefore, our study aimed to establish a prognostic model to determine individualized prognosis of patients with PBS. Patients and Methods Data of 866 patients with PBS, registered from 1973 to 2015, were extracted from the surveillance, epidemiology, and end result (SEER) database. The patients included were randomly split into a training (n = 608) and a validation set (n = 258). Univariate and multivariate Cox regression analyses were employed to identify the important independent prognostic factors. A nomogram was then established to predict overall survival (OS). Using calibration curves, receiver operating characteristic curves, concordance index (C-index), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), the performance of the nomogram was internally validated. We compared the nomogram with the TNM staging system. The application of the risk stratification system was tested using Kaplan–Meier survival analysis. Results Age at diagnosis, T-stage, N-stage, M-stage, and tumor size were identified as independent predictors of OS. C-index of the training cohort were 0.675, 0.670, 0.671 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.701, 0.684, 0.679, respectively. Calibration curves also showed great prediction accuracy. In comparison with TNM staging system, improved net benefits in DCA, evaluated NRI and IDI were obtained. The risk stratification system can significantly distinguish the patients with different survival risk. Conclusion A prognostic nomogram was developed and validated in the present study to predict the prognosis of the PBS patients. It may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

scholarly journals QRT-PCR-based DNA Homologous Recombination Associated 4-Gene Score Predicts Pathologic Complete Response to Platinum-Based Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

2021 ◽  
Author(s):  
Ke Zuo ◽  
Xiaoying Yuan ◽  
Xizi Liang ◽  
Xiangjie Sun ◽  
Shujin Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Training Set ◽  
Gene Score ◽  
Qrt Pcr ◽  
Validation Set ◽  
Platinum Agents

Abstract BackgroundCumulative evidences suggested the addition of platinum agents as neoadjuvant chemotherapy (NACT) could improve pathologic complete response (pCR) in triple-negative breast cancers (TNBC). Previous studies showed DNA homologous recombination deficiency (HRD) was a potential biomarker predicting pCR in ER-negative breast cancer. It would be helpful to personalize the use of platinum agents if a predictive biomarker for platinum sensitivity could be developed. Therefore, we tried to develop a HRD gene expression score to predict tumor sensitivity to platinum-based NACT in TNBC.MethodsA retrospective cohort of 127 TNBC patients from 2012 to 2017 was included in this study. All of them were diagnosed and received platinum-based NACT in Fudan University Shanghai Cancer Center. Clinical data and pathological data of the patients were collected and reviewed. By using quantitative reverse transcription-polymerase chain reaction (qRT-PCR), the expression level of eight HRD associated genes was analyzed from the formalin-fixed paraffin-embedded core needle biopsy samples which obtained before NACT. A random forest model was built to estimate the weight of each gene expression level and clinical-pathological factors. Samples were randomized into the training set and validation set with different splitting percentage from 50%:50% to 90%:10%. The training set was used to modulate parameters and select the best model using 5-fold cross validation. The performance of the final model was evaluated in the validation set. ResultsA 4-gene (BRCA1, XRCC5, PARP1, RAD51) expression signature scoring system was developed. TNBC with higher score had nearly quadruple likelihood to achieve pCR to platinum-based NACT compared with a lower score [odds ratio (OR)=3.878; P<0.001]. At the cut-off value of -2.644, the 4-gene score system showed high sensitivity in predicting pCR in breast (93.0%) and pCR in both breast/axilla (91.8%), while, at the cut-off value of -1.969, the 4-gene score showed high specificity for pCR in breast (85.7%) and pCR in both breast/axilla (80.8%). 4-gene score was positively correlated with Ki-67≥40% (P=0.002), but negatively correlated with positive lymph nodes counts (P=0.003). ConclusionThe qRT-PCR-based 4-gene score can be used as an effective predictor of pCR to platinum-based NACT in TNBC.

scholarly journals Prognostic Nomogram and a Risk Classification System for Predicting Overall Survival of Elderly Patients with Fibrosarcoma: A Population-Based Study

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fengkai Yang ◽  
Hangkai Xie ◽  
Yucheng Wang
Keyword(s):  
Overall Survival ◽  
Elderly Patients ◽  
Classification System ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Risk Classification ◽  
Seer Database ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set

Background. The objective of this study was to develop a nomogram model and risk classification system to predict overall survival in elderly patients with fibrosarcoma. Methods. The study retrospectively collected data from the Surveillance, Epidemiology, and End Results (SEER) database relating to elderly patients diagnosed with fibrosarcoma between 1975 and 2015. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses on the training set to construct a nomogram model for predicting the overall survival of patients at 3, 5, and 10 years. The receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the discrimination and predictive accuracy of the model. Decision curve analysis was used for assessing the clinical utility of the model. Result. A total of 357 elderly fibrosarcoma patients from the SEER database were included in our analysis, randomly classified into a training set (252) and a validation set (105). The multivariate Cox regression analysis of the training set demonstrated that age, surgery, grade, chemotherapy, and tumor stage were independent prognostic factors. The ROC showed good model discrimination, with AUC values of 0.837, 0.808, and 0.806 for 3, 5, and 10 years in the training set and 0.769, 0.779, and 0.770 for 3, 5, and 10 years in the validation set, respectively. The calibration curves and decision curve analysis showed that the model has high predictive accuracy and a high clinical application. In addition, a risk classification system was constructed to differentiate patients into three different mortality risk groups accurately. Conclusion. The nomogram model and risk classification system constructed by us help optimize patients’ treatment decisions to improve prognosis.

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

2021 ◽  
Vol 11 ◽  
Author(s):  
Yu Xiong ◽  
Xia Shi ◽  
Qi Hu ◽  
Xingwei Wu ◽  
Enwu Long ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Liver Metastasis ◽  
Risk Stratification ◽  
Staging System ◽  
Tnm Staging ◽  
Tnm Staging System ◽  
Stratification System ◽  
Breast Cancer Liver Metastasis ◽  
Risk Stratification System

ObjectiveThe 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.MethodsWe 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.ResultsGrade, 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.ConclusionWe 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.

A gene signature of chemo-immunization to predict outcome in patients with triple negative breast cancer treated with neoadjuvant chemotherapy.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 575-575 ◽  
Author(s):  
Mohamed-Amine Bayar ◽  
Carmen Criscitiello ◽  
Giuseppe Curigliano ◽  
William Fraser Symmans ◽  
Christine Desmedt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Triple Negative Breast Cancer ◽  
Prognostic Value ◽  
Triple Negative ◽  
Residual Disease ◽  
Gene Signature ◽  
Training Set ◽  
Pre Treatment ◽  
Validation Set

575 Background: In patients with triple-negative breast cancer (TNBC), the extent of tumor-infiltrating lymphocytes (TILs) in the residual disease after anthracycline-based neoadjuvant chemotherapy (NACT) is associated with a better prognosis. We aimed to develop a genomic signature from pre-treatment samples to predict the extent of TILs after NACT, and then to test its prognostic value on survival. Methods: Using 99 pre-treatment samples (training set), we generated a four-gene signature that predicts post-NACT TILs using the LASSO technique. Prognostic value of the signature on survival was assessed on the training set (n=99) and then evaluated on an independent validation set including 185 patients with TNBC treated with NACT. Results: A four-gene signature, assessed on pre-treatment samples and combining the expression levels of HLF, CXCL13, SULT1E1, and GBP1 predicted the extent of lymphocytic infiltration after NACT. In a multivariate analysis performed on the training set, a one-unit increase in the signature value was associated with distant-relapse free survival (DRFS) (HR: 0.28, 95%CI: 0.13-0.63, p=0.002). For the validation set, the four-gene signature was significantly associated with DRFS in the entire set (HR: 0.26, 95%CI: 0.11-0.59, p=0.001) and in the subset of patients with residual disease (HR: 0.23, 95%CI: 0.10-0.55, p< 0·001). Conclusions: We developed a four-gene signature of chemotherapy-induced immune-activation, which predicts outcome in patients with TNBC treated with NACT. [Table: see text]

scholarly journals Prognostic Nomogram and Risk Stratification System for Breast Cancer Bone Metastasis: A SEER-Based Population Study

2021 ◽  
Author(s):  
Hai Lu ◽  
Jinqun Jiang ◽  
YuZhu Zhang ◽  
Rui Xu ◽  
Liping Ren ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Risk Stratification ◽  
External Validation ◽  
Prognostic Indicators ◽  
Tumor Subtype ◽  
Risk Stratification Model ◽  
Breast Cancer Bone Metastasis ◽  
Stratification System ◽  
Risk Stratification System

Abstract Purpose To construct and validate a nomogram and risk stratification model for predicting overall survival (OS) of patients with breast cancer bone metastasis (BCBM).Methods We collected data on BCBM patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were excluded if the data on the follow-up time or clinicopathological information were incomplete. The patients were randomly divided into the training set and validation set. Univariate and multivariate Cox proportional hazard regression models were performed. By integrating these variables, a predictive nomogram and risk stratification model were constructed and assessed using C-indexes and calibration curves.Results Multivariate analysis showed that age, race, marital status, tumor subtype, grade, T classification, surgery, chemotherapy, brain metastasis, liver metastasis, and lung metastasis were independent prognostic indicators of BCBM. These results were reproducible when nomograms were applied to the testing cohort for external validation. The C-index of the nomogram to predict OS was 0.704, which was higher than that of the seventh edition American Joint Committee on Cancer TNM staging system (0.564; P<0.001). A risk stratification model was further generated to accurately differentiate patients into two prognostic groups. The survival rates predicted by the nomogram showed significant distinctions between the Kaplan–Meier curves in the entire cohort and each tumor subtype. Conclusion The nomogram and risk stratification system predicting 1-, 3-, and 5-year OS for patients with newly diagnosed BCBM with satisfactory performance were constructed to help physicians in evaluating the mortality risk in patients with BCBM.

scholarly journals Construction and Validation of a Prognostic Model for the Assessment of Postoperative Overall Survival of Patients with Metaplastic Breast Cancer: Based on a Retrospective Large Data Analysis and Chinese Multicenter Study

2021 ◽  
Author(s):  
Ge Wang ◽  
Xin Ren ◽  
Mengmeng Wang ◽  
Xiaomin Sun ◽  
Yongsheng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Survival Rate ◽  
Prognostic Model ◽  
Seer Database ◽  
Training Set ◽  
Metaplastic Breast Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Purpose: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods: Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans. Abstract Background: Surgery is an important treatment for patients with metaplastic breast cancer (MBC). This study used prognostic clinicopathological factors to establish a model for predicting overall survival (OS) in patients with MBC. Methods： Patients in the Surveillance, Epidemiology, and End Results (SEER) database diagnosed with MBC from 2010–2015 were selected and randomized into a SEER training cohort and an internal validation cohort. We identified independent prognostic factors after MBC surgery based on multivariate Cox regression analysis to construct nomograms. The discriminative and predictive power of the nomogram was assessed using Harrell's consistency index (C-index) and calibration plots. The decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. Results: We divided 1044 patients from the SEER database randomly into a training set (n=732) and validation set (n=312) in a 7:3 ratio. Multifactorial analysis showed that age at diagnosis, T stage, N stage, M stage, tumor size, radiotherapy, and chemotherapy were important prognostic factors affecting OS. The C-index of nomogram was higher than the 7th edition of the AJCC TNM grading system in the SEER training set and validation set. The calibration chart showed that the survival rate predicted by the nomogram is close to the actual survival rate. The DCA showed that the nomogram is more clinically useful and applicable. Conclusions: The prognostic model can accurately predict the post-surgical OS rate of patients with MBC and can provide a reference for doctors and patients to establish treatment plans.

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