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Objective To construct and validate a nomogram model for predicting the relatively long-term survival in patients with metastatic breast cancer based on the SEER database. Methods The clinicopathological data of patients with metastatic breast cancer from 2010 to 2015 in SEER database were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariable and multivariable logistic regression analyses were performed and the nomogram model was constructed in the training set. The receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve were plotted for both the training and validation sets to evaluate the efficacy of the model. Results A total of 6,515 eligible patients with metastatic breast cancer were included, with 4,560 patients in the training set and 1,955 patients in the validation set. In the training set, 2,229 (48.9%) patients were relatively long-term survivors (>24 months), and in the validation set, 970 (49.6%) patients were relatively long-term survivors. Multivariable Logistic regression analysis showed that age, marital status, race, histological grade, T stage, N stage, brain metastasis, liver metastasis, lung metastasis, time from initial diagnosis to treatment, estrogen receptor (ER) status, progesterone receptor (PR) status, surgical approach and molecular subtype were all independent factors which could affect the relatively long-term survival in patients with metastatic breast cancer (all P<0.05). The AUCs of the nomogram model in the training and validation sets were 0.738 (95%CI: 0.724-0.752) and 0.745 (95%CI: 0.723-0.766), respectively. The calibration curve showed a good consistency between the predicted outcomes and actual outcomes of the model, and the clinical decision curve showed a high net benefit of the model. Conclusions The nomogram model constructed in this study can predict the relatively long-term survival of patients with metastatic breast cancer, providing an auxiliary decision-making basis for clinical practice and supporting individualized treatment decisions. [ABSTRACT FROM AUTHOR]

Objective To construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC). Methods A total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA). Results Primary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage. Conclusion The developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment.

Objective To construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC). Methods A total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA). Results Primary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage. Conclusion The developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background and purpose: Guidelines recommend that the axillary lymph node dissection can be omitted for T 1-2 breast cancer patients with 1-2 positive sentinel lymph nodes who undergo breast-conserving mastectomy and whole breast radiation. This study aimed to explore the independent prognostic factors for elderly breast cancer patients with 1-2 positive lymph nodes who underwent mastectomy and construct a nomogram to predict their survival following different axillary surgeries. Methods: T 1-2 invasive breast cancer patients with 1-2 positive nodes and mastectomy from 2010 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) program and divided into the training cohort (n=3 647) and the validation cohort (n=1 216). Univariate and multivariate Cox analyses were used to identify independent risk factors for overall survival (OS). The nomogram was constructed to predict 3- and 5-year OS, which was validated by the concordance index (C-index) and calibration curves. Results: A total of 4 863 patients were included with a 42 months median follow-up time. The nomogram was constructed by incorporating nine independent prognostic factors (age, race, marital status, grade, subtype, T stage, axillary surgery, radiation and chemotherapy) identified by multivariate Cox analysis (P

Objective: To explore the relationship between socioeconomic status (SES) and disease mortality in patients with cecal adenocarcinoma in America through the Surveillance, Epidemiology, and End results (SEER) database., Methods: The SEER database was queried for patients with cecal adenocarcinoma in America diagnosed from 2011 to 2015. Factor analysis, cluster analysis, and univariate and multivariate Cox proportional hazard models were used for data analysis. Five social security factors were identified: factor 1, economic and educational disadvantage; factor 2, characteristics related to immigration (language isolation and foreign birth); factor 3, high relocation rate in the county; factor 4, high intra-state relocation rate; and factor 5, high domestic relocation rate. Five clusters defined by SES were identified., Results: The number of all-cause deaths among 17 185 patients was 5948, and the number of survivors was 11, 237. In the multivariate Cox regression analysis, with cluster 1 (low poverty rate and high education level) as the reference, the hazard ratio (HR) of cluster 3 (high intra-county mobility rate) was 1.13 (95% CI : 1.04-1.21, P < 0.05), and the risk was 13% higher than that of cluster 1. The HR of cluster 4 (low language isolation, foreign birth, housing overcrowding, and intra-country mobility rates) was 1.15 (95% CI : 1.07- 1.24, P < 0.001) with a 15% higher risk than cluster 1. The HR of cluster 5 (economic and educational disadvantages, immigration-related characteristics, and low intra-country mobility) was 1.11 (95% CI : 1.03-1.20, P < 0.01) with a 11% higher risk. The factors related to SES indicators were based on the mortality of patients with cecal adenocarcinoma, indicating that low economic and education levels are risk factors for cecal adenocarcinoma., Conclusion: Low socioeconomic status is associated with an increased risk of death in patients with cecal adenocarcinoma in the United States and show different distribution patterns based on population. Improving health insurance policies and strengthening psychotherapy can provide guidance for improving prognosis f cecal adenocarcinoma patients.

Objective: To explore the prognostic factors of adult ventricle glioma (AVG) and to construct and evaluate a survival-related prognostic nomogram model, which could provide further reference for the clinical management of AVG patients., Methods: The patients covered in the study were selected from the Surveillance Epidemiology and End Results (SEER) database (1973-2016). They all had definite histological diagnosis of AVG. They were assigned randomly to the training cohort and the validation cohort by random number table at a 2/1 ratio. Survival analysis was performed by Kaplan-Meier analysis. Cox regression analysis was employed to determine the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Then, integrating the basic characteristics of patients, the survival-related nomogram predictive model for OS and CSS in the training cohort was constructed, respectively. After that, internal cross validation and external validation of the model were carried out with the training cohort and the validation cohort in succession. The authenticity and reliability of the nomogram model were evaluated by calculating the concordance index (C-index). Calibration plots were constructed to assess the agreement between the predicted values and the observed values in the training cohort and the validation cohort., Results: A total of 369 AVG patients, including 218 males and 151 females, were included. The median age of the patients was 53. According to the WHO classification of gliomas, 66 (17.9%) patients had grade Ⅱ gliomas, 73 (19.8%) had grade Ⅲ gliomas, and 230 (62.3%) had grade Ⅳ gliomas. Regarding the extent of resection (EOR), 59 (16.0%) had gross total resection (GTR) and 145 (39.3%) had subtotal resection (STR) or partial resection (PR). Of all the patients, 167 (45.3%) received postoperative radiotherapy and 143 (38.8%) received postoperative chemotherapy. Patients were randomized into the training cohort ( n =246) and the validation cohort ( n =123), and there was no significant difference ( P >0.05) in the basic clinical characteristics between the training cohort and the validation cohort. In the training cohort, Cox regression analysis revealed that the independent prognostic factors for OS and CSS included age≥65, grades Ⅲ and Ⅳ according to the WHO classification of gliomas, and not receiving radiotherapy. Furthermore, 5 variables, including age, gender, WHO grades, surgery, and radiotherapy, were used to construct the nomogram model for predicting 6-month, 1-year, and 2-year OS and CSS. The results of internal cross validation in the training cohort showed that the C-indexes of OS and CSS were 0.758 and 0.765, respectively. The external validation results of the validation cohort showed that the C-indexes of OS and CSS were 0.733 and 0.719, respectively. Calibration plots for 6-month, 1-year, and 2-year OS in the training cohort showed relatively good agreement, while in the validation cohort the agreement was relatively low. The 6-month, 1-year, and 2-year CSS calibration plots had results similar to the calibration plots of OS., Conclusion: This nomogram predictive model of OS and CSS showed moderately reliable predictive performance, providing helpful reference information for clinicians to make quick and simple assessment of the survival probability of AVG patients., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To retrospectively analyze the clinical characters and prognosis of the patients with Esophageal Squamous Cell Carcinomas as First Primary Malignancy (ESCCFPM), which will help us better understand the relationship between Esophageal Squamous Cell Carcinoma (ESCC) and other cancers, and to provide appropriate research evidence for the clinical diagnosis and treatment. Methods: The clinicopathological and follow-up data of 540 Patients with ESCCFPM between January 1, 2004 and December 31, 2016 were collected from the Surveillance, Epidemiology and End Results (SEER) database of National Cancer Institute. The Kaplan-Meier method was used to determine Overall Survival (OS) curves of ESCC patients, and the Log-Rank test was used to estimate differences in survival. The Cox proportional hazards models were adopted for the prognosis analyses. Results: Regarding the number of multiple primary malignancies (MPMs), 491 had two malignancies, 42 had three malignancies and 7 had four malignancies. ESCCFPM is more common among males. The high incidence age is between 61 and 80 years old. Tumors of the respiratory system (36.9%), were the most common MPMs followed by digestive system (35.2%) and reproductive system (8.9%). The 1-year, 3-year, 5-year OS rates for patients with ESCCFPM were 76.9%, 50.4% and 38.9%, respectively. The age of the ESCC diagnosed, T stage, time of occurrence, carcinoma number, lymph node dissection, surgery, radiotherapy and chemotherapy were the prognostic factor of overall survival for ESCCFPM patients. Age, race, T stage, time of occurrence surgery and radiotherapy were independent prognostic factors for the whole cohort by multivariate survival analysis. Conclusion: ESCCFPM,mainly two-lesion cancer, is most commonly found in respiratory system and digestive systems. Enhanced follow-up of respiratory and digestive tumors in ESCCFPM patients aged 61-80 may help identify multiple primary malignancies. Surgery, radiotherapy and chemotherapy may improve overall survival for ESCCFPM patients.