1.Trends of uterine carcinosarcoma in the United States.
Koji MATSUO ; Malcolm S ROSS ; Hiroko MACHIDA ; Erin A BLAKE ; Lynda D ROMAN
Journal of Gynecologic Oncology 2018;29(2):e22-
OBJECTIVE: Uterine carcinosarcoma (UCS) is a rare type of high-grade endometrial cancer (EC) that has been understudied with population-based statistics due to its rarity. This study examined temporal trends in the proportion of UCS among women with EC. METHODS: This is a retrospective observational study examining The Surveillance, Epidemiology, and End Results program between 1973–2013. Primary EC cases were eligible for analysis, and a time-specific proportion of UCS was examined during the study period. RESULTS: UCS was seen in 11,000 (4.7%) women among 235,849 primary EC cases. Mean age at UCS diagnosis increased from 65.9 to 71.7 years between 1973–1989 and then decreased from 71.7 to 67.0 years between 1989–2013 (both, p < 0.001). Proportion of Black women significantly increased during the study period (11.9%–20.0%, p < 0.001), whereas the proportion of White women decreased from 86.0% to 60.5% between 1987–2013 (p < 0.001). There was a significant increase in the proportion of UCS among primary EC from 1.7% to 5.6% between 1973–2013 (p < 0.001). Among type II ECs (n=76,118), the proportion of UCS also increased significantly from 6.0% to 17.5% between 1973–2013 (p < 0.001). An increasing proportion of UCS was seen in both young and older women but the magnitude of interval increase was larger in the older age group between 1973–2013 ( < 60 years, from 1.3% to 3.3%. p < 0.001; and ≥60 years, from 2.6% to 7.0%, p < 0.001). CONCLUSION: Our study demonstrated that the proportion of UCS has significantly increased among EC, accounting for more than 5% in recent years.
Carcinosarcoma*
;
Diagnosis
;
Endometrial Neoplasms
;
Female
;
Humans
;
Observational Study
;
Retrospective Studies
;
SEER Program
;
United States*
2.Development of a nomogram for predicting survival of patients with ovarian serous cystadenocarcinoma after based on SEER database.
Journal of Zhejiang University. Medical sciences 2021;50(3):369-374
To develop a survival time prediction model for patients with ovarian serous cystadenocarcinoma after surgery. A retrospective analysis of 5906 postoperative patients with ovarian serous cystadenocarcinoma in the surveillance, epidemiology, and end results (SEER) database from 2010 to 2015 was performed. The independent risk factors for long-term survival were analyzed with multivariate Cox proportional hazard regression model. The nomogram of 3-year and 5-year survival was developed by using R language. The receiver operator characteristic (ROC) curve and were used to test the discrimination of the model and the calibration diagram was used to evaluate the degree of calibration of the prediction model. The survival curves was conducted by the risk factors. Cox proportional hazard regression model showed that age, race, histological grade (poorly differentiated and undifferentiated), stage T (T2a, T2b, T2c, T3a, T3b and T3c), and stage M (M1) were independent factors for the prognosis of patients with ovarian serous cystadenocarcinoma after surgery. A nomogram was developed by the R language tool for predicting the 3-year and survival of patients through age, race, histological classification, stage T and stage M. The C-index was 0.688 and the areas under ROC curve of the nomogram for predicting 3-year and 5-year survival were 0.708 and 0.716, respectively. The results of the calibration indicated that the predicted values were consistent with the actual values in the prediction models. The survival time of patients with high-risk factors was shorter than that of patients with low-risk factors (<0.05). The developed nomogram in this study can be used to predict 3-year and 5-year survival of postoperative patients with ovarian serous cystadenocarcinoma, and it may be beneficial to guide clinical treatment.
Cystadenocarcinoma, Serous/surgery*
;
Humans
;
Neoplasm Staging
;
Nomograms
;
Prognosis
;
ROC Curve
;
Retrospective Studies
;
SEER Program
;
Survival Rate
3.Development and validation of a prognostic model based on SEER data for patients with esophageal carcinoma after esophagectomy.
Chao LUO ; Gao Ming WANG ; Li Wen HU ; Yong QIANG ; Chao ZHENG ; Yi SHEN
Journal of Southern Medical University 2022;42(6):794-804
OBJECTIVE:
To develop a nomogram to predict the long-term survival of patients with esophageal cancer following esophagectomy.
METHODS:
We collected the data of 7215 patients with esophageal carcinoma from the Surveillance, Epidemiology, and End Results (SEER) database during the period from 2004 and 2016. Of these patients, 5052 were allocated to the training cohort and the remaining 2163 patients to the internal validation cohort using bootstrap resampling, with another 435 patients treated in the Department of Cardiothoracic Surgery of Jinling Hospital between 2014 and 2016 serving as the external validation cohort.
RESULTS:
In the overall cohort, the 1-, 3-, and 5-year cancer-specific mortality rates were 14.6%, 35.7% and 41.6%, respectively. Age (≥80 years vs < 50 years, P < 0.001), gender (male vs female, P < 0.001), tumor site (lower vs middle segment, P=0.013), histology (EAC vs ESCC, P=0.012), tumor grade (poorly vs well differentiated, P < 0.001), TNM stage (Ⅳ vs Ⅰ, P < 0.001), tumor size (> 50 mm vs 0-20 mm, P < 0.001), chemotherapy (yes vs no, P < 0.001), and LNR (> 0.25 vs 0, P < 0.001) were identified as independent risk factors affecting long-term survival of the patients. The nomograms established based on the model for predicting the survival probability of the patients at 1, 3 and 5 years after operation showed a C-index of 0.726 (95% CI: 0.714-0.738) for predicting the overall survival (OS) and of 0.735 (95% CI: 0.727-0.743) for cancer-specific survival (CSS) in the training cohort. In the internal validation cohort, the C-index of the nomograms was 0.752 (95% CI: 0.738-0.76) for OS and 0.804 (95% CI: 0.790-0.817) for CSS, as compared with 0.749 (95% CI: 0.736-0.767) and 0.788 (95%CI: 0.751-0.808), respectively, in the external validation cohort. The nomograms also showed a higher sensitivity than the TNM staging system for predicting long-term prognosis.
CONCLUSION
This prognostic model has a high prediction efficiency and can help to identify the high-risk patients with esophageal carcinoma after surgery and serve as a supplement for the current TNM staging system.
Aged, 80 and over
;
Esophageal Neoplasms/surgery*
;
Esophagectomy
;
Female
;
Humans
;
Male
;
Prognosis
;
Risk Factors
;
SEER Program
4.Clinicopathological features and prognosis of colorectal stromal tumor.
Wen Peng WANG ; Jie Fu WANG ; Jun HU ; Jun Feng WANG ; Jia LIU ; Da Lu KONG ; Jian LI
Journal of Peking University(Health Sciences) 2020;52(2):353-361
OBJECTIVE:
The incidence of colorectal stromal tumor is low among digestive tract tumors, therefore the literatures about clinicopathological features and prognosis of colorectal stromal tumor are few at home and abroad. In this study, we performed survival analyses for colorectal stromal tumor. The nomogram made by prognostic factors provided basis for evaluation of prognosis.
METHODS:
The clinico-pathological and prognostic data of colorectal stromal tumor between January 1992 and December 2015 were collected from the surveillance, epidemiology, and end results (SEER) database. The survival analyses were made by SPSS 24.0 software. The nomogram and calibration curve were made by RMS package in R 3.5.2 software.
RESULTS:
In the study, 546 patients with colorectal stromal tumor were included. The median age of onset was 64 years. The regional lymph node metastasis (LNM) rate was 9.4%. The multivariate Cox regression analyses of the 546 cases showed that the older age of onset (>64 years), single or divorce, colon tumor (compared with rectal tumor), non-surgery, high histological grade, LNM and distant metastasis were associated with worse cancer specific survival (CSS) and overall survival (OS), P < 0.05 for all. The treatment district was independent prognostic factor of OS (P = 0.027). The C-index of independent prognostic factors predicting CSS and OS probability were 0.76 (95%CI: 0.72-0.80) and 0.75 (95%CI: 0.72-0.78), respectively. Multivariate analyses were further carried out in the 174 patients with definite histological grade and tumor location, which revealed that the age of onset, histological grade, surgery or not were independent prognostic factors of CSS and OS (P < 0.05 for all). Tumor location was associated with CSS (P = 0.041) but not OS (P = 0.057) among the 174 cases. Four independent prognostic factors influencing the 174 patients' prognosis were used to make nomogram for predicting survival probability of 546 cases. The C-index of four prognostic factors predicting probability of CSS and OS of the 546 cases were separately 0.71 (95%CI: 0.66-0.75) and 0.73 (95%CI: 0.70-0.77). The nomogram had more accuracy for predicting OS probability of colorectal stromal tumors.
CONCLUSION
The prognosis of colorectal stromal tumor was affected by multiple clinicopathological factors. The nomogram provided the basis for predicting the survival probability of patients with colorectal stromal tumor.
Aged
;
Colorectal Neoplasms
;
Humans
;
Middle Aged
;
Neoplasm Staging
;
Prognosis
;
SEER Program
5.A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis.
Jong Pill CHOI ; Tae Hwa HAN ; Rae Woong PARK
Journal of Korean Society of Medical Informatics 2009;15(1):49-57
OBJECTIVE: Breast cancer is one of the most common cancers affecting women. Both physicians and patients have concerned about breast cancer survivability. Many researchers have studied the breast cancer survivability applying artificial nerural network model (ANN). Usually ANN model outperformed in classification of breast cancer survivability than other models such as logistic regression, Bayesian network (BN), or decision tree models. However, physicians in the fields hesitate to use ANN model, because ANN is a black-box model, and hard to explain the classification result to patients. In this study, we proposed a hybrid model with a degree of the accuracy and interpretation by combining the ANN for accuracy and BN for interpretation. METHODS: We developed an artificial neural network, a Bayesian network, and a hybrid Bayesian network model to predict breast cancer prognosis. The hybrid model combined the artificial neural network and the Bayesian network to obtain a good estimation of prognosis as well as a good explanation of the results. The National Cancer Institute's SEER program public-use data (1973-2003) were used to construct and evaluate the proposed models. Nine variables, which are clinically acceptable, were selected for input to the proposed models' nodes. A confidence value of the neural network served as an additional input node to the hybrid Bayesian network model. Ten iterations of random subsampling were performed to evaluate performance of the models. RESULTS: The hybrid BN model achieved the highest area under the curve value of 0.935, whereas the corresponding values of the neural network and Bayesian network were 0.930 and 0.813, respectively. The neural network model achieved the highest prediction accuracy of 88.8% with a sensitivity of 93.7% and a specificity of 85.4%. The hybrid Bayesian network model achieved a prediction accuracy of 87.2% with a sensitivity of 93.3% and a specificity of 83.1%. The results of the hybrid Bayesian network model were very similar to the neural network model. CONCLUSION: In the experiments, the hybrid model and the ANN model outperformed the Bayesian network model. The proposed hybrid BN model for breast cancer prognosis predictin may be useful for clinicians in the medical fields, as the model provides both high degree of performance inherited from ANN and good explanation power from BN.
Breast Neoplasms*
;
Classification
;
Decision Trees
;
Female
;
Humans
;
Logistic Models
;
Neural Networks (Computer)
;
Prognosis*
;
SEER Program
;
Sensitivity and Specificity
6.A National Study of Survival Trends and Conditional Survival in Nasopharyngeal Carcinoma: Analysis of the National Population-Based Surveillance Epidemiology and End Results Registry.
Jia Wei LV ; Xiao Dan HUANG ; Yu Pei CHEN ; Guan Qun ZHOU ; Ling Long TANG ; Yan Ping MAO ; Wen Fei LI ; Ai Hua LIN ; Jun MA ; Ying SUN
Cancer Research and Treatment 2018;50(2):324-334
PURPOSE: Conditional survival (CS) provides important information on survival for a period of time after diagnosis. Currently, information on CS patterns of patients with nasopharyngeal carcinoma (NPC) is lacking. We aimed to analyze survival rate over time and estimate CS for NPC patients using a national population-based registry. MATERIALS AND METHODS: Patients diagnosed with NPC between 1973 and 2007 with at least 5-year follow-up were identified from the Surveillance Epidemiology End Results registry. Traditional survival rates and crude CS estimateswere calculated using Kaplan-Meier analysis. Risk-adjusted survival curves were plotted from the proportional hazards model using the correct group prognosis method. RESULTS: For 7,713 patients analyzed, adjusted baseline 5-year overall survival improved significantly from 36.0% in patients diagnosed in 1973-1979, 41.7% in 1980-1989, 46.6% in 1990-1999, to 54.7% in 2000-2007 (p < 0.01). CS analysis demonstrated that for every additional year survived, adjusted probability of surviving the next 5 years increased from 66.7% (localized), 54.0% (regional), and 35.3% (distant) at the time of diagnosis, to 83.7% (localized), 75.0% (regional), and 62.2% (distant) for patients who had survived 5 years. Adjusted 5-year CS differed among age, sex, tumor histology, ethnicity, and stage subgroups initially, but converged with time. CONCLUSION: Treatment outcomes of NPC patients have greatly improved over the decades. Increases in CS become more prominent in patients with distant disease than in those with localized or regional disease as patients survive longer. CS provides more dynamic prognostic information for patients who have survived a period of time after diagnosis.
Diagnosis
;
Epidemiology*
;
Follow-Up Studies
;
Humans
;
Kaplan-Meier Estimate
;
Methods
;
Nasopharyngeal Neoplasms
;
Prognosis
;
Proportional Hazards Models
;
SEER Program
;
Survival Rate
7.Postoperative Radiotherapy Improves Survival in Gastric Signet-Ring Cell Carcinoma: a SEER Database Analysis
Feng WEI ; Hongwei LYU ; Shuoer WANG ; Yan CHU ; Fengyuan CHEN
Journal of Gastric Cancer 2019;19(4):393-407
PURPOSE: To identify the potential therapeutic role of postoperative radiotherapy (RT) in patients with locally advanced (stage II and stage III) gastric signet ring cell carcinoma (SRC).MATERIALS AND METHODS: Patients with locally advanced gastric SRC from the Surveillance, Epidemiology, and End Results program database between 2004 and 2012 were included in our study. Univariate and multivariate Cox proportional models were performed, and survival curves were generated to evaluate the prognostic effect of postoperative RT and surgery alone on SRC patients. Propensity score matching (PSM) was used to avoid selection bias among the study cohorts.RESULTS: We found that patients with postoperative RT had better probability of survival compared with those who did not receive RT (overall survival [OS], P<0.001; cancer-specific survival [CSS], P<0.001). After PSM, analysis of both overall and CSS showed that patients who underwent postoperative RT had better prognosis than those receiving surgery alone in the matched cohort (OS, P=0.00079; CSS, P=0.0036). Multivariate Cox proportional model indicated that postoperative RT had better effect on prognosis compared with surgery alone with respect to both overall (hazard ratio [HR], 0.716; 95% confidence interval [95% CI], 0.590–0.87; P=0.001) and CSS (HR, 0.713; 95% CI, 0.570–0.890; P=0.003).CONCLUSIONS: Postoperative RT had better prognosis compared with surgery alone for both overall and CSS for patients with locally advanced gastric SRC.
Carcinoma, Signet Ring Cell
;
Cohort Studies
;
Humans
;
Nomograms
;
Prognosis
;
Propensity Score
;
Radiotherapy
;
SEER Program
;
Selection Bias
;
Stomach Neoplasms
8.Difference in survival between right- versus left-sided colorectal neuroendocrine neoplasms.
Ge-Han XU ; Hua-Wei ZOU ; Ashley B GROSSMAN
Journal of Zhejiang University. Science. B 2019;20(11):933-939
Neuroendocrine neoplasms (NENs) are a heterogeneous group of tumors that arise from neuroendocrine cells, and in some cases are capable of producing agents that may cause characteristic hormonal syndromes (Cives and Strosberg, 2018). Such tumors were previously thought to be rare, but the rate of detection of NENs, especially from the gastrointestinal tract, is increasing with the widespread use of colonoscopy, cross-sectional imaging, and biomarkers (Gu et al., 2019). A study based on the Surveillance, Epidemiology, and End Results (SEER) database showed that the age-adjusted incidence of NENs increased 6.4-fold from 1973 (1.09 per 100 000) to 2012 (6.98 per 100 000) (Dasari et al., 2017), while there was a progressive increase in the incidence of colorectal NENs (Starzyńska et al., 2017).
Adult
;
Aged
;
Colorectal Neoplasms/mortality*
;
Female
;
Humans
;
Male
;
Middle Aged
;
Neuroendocrine Tumors/mortality*
;
Proportional Hazards Models
;
SEER Program
9.Predicting Breast Cancer Survivability: Comparison of Five Data Mining Techniques.
Arihito ENDO ; Shibata TAKEO ; Hiroshi TANAKA
Journal of Korean Society of Medical Informatics 2007;13(2):177-180
OBJECTIVE: Today in United States, about one in eight women have been affected with breast cancer over their lifetime. Up to today, some various prediction models using SEER (Surveillance Epidemiology and End Results) datasets have been proposed in past studies. However, appropriate methods for predicting the 5 years survival rate of breast cancer have not established. In this study, we evaluate those models to predict the survival rate of breast cancer patients. METHODS: Five data mining algorithms (Artificial Neural Network, Naive Bayes , Decision Trees (ID3) and Decision Trees(J48)) besides a most generally used statistical method (Logistic Regression) were used to evaluate the prediction models using a dataset (37,256 follow-up cases from 1992 to 1997). We also used 10-fold cross-validation methods to assess the unbiased estimate of the five prediction models for comparison of performance of each method. RESULTS: The accuracy was 85.8+/-0.2%, 84.3+/-1.4%, 83.9+/-0.2%, 82.3+/-0.2%, 75.1+/-0.2% for the Logistic Regression, Artificial Neural, Naive Bayes, Decision Trees (ID3), Decision Trees(J48), respectively. Although the accuracy of Logistic Regression showed the highest performances, the Decision Trees (J48) was the lowest one. CONCLUSIONS: The accuracy of Logistic Regression was the best performances, on the other hand Decision Trees (J48) was the worst. Artificial Neural Network indicated relatively high performance.
Bays
;
Breast Neoplasms*
;
Breast*
;
Data Mining*
;
Dataset
;
Decision Trees
;
Epidemiology
;
Female
;
Follow-Up Studies
;
Hand
;
Humans
;
Logistic Models
;
SEER Program
;
Survival Rate
;
United States
10.Clinicopathological Features of Prostate Ductal Carcinoma: Matching Analysis and Comparison with Prostate Acinar Carcinoma.
Aram KIM ; Taekmin KWON ; Dalsan YOU ; In Gab JEONG ; Heounjeong GO ; Yong Mee CHO ; Jun Hyuk HONG ; Hanjong AHN ; Choung Soo KIM
Journal of Korean Medical Science 2015;30(4):385-389
We evaluated the clinicopathological features and prognosis of 29 cases of prostate ductal carcinoma was considered to be an aggressive subtype of prostate acinar carcinoma. We selected 29 cases who were diagnosed prostate ductal carcinoma and had a radical prostatectomy (RP). The acinar group (n = 116) was selected among 3,980 patients who underwent a prostatectomy. The acinar group was matched to the ductal group for prostate specific antigen (PSA), clinical stage, Gleason score, and age. The mean (range) of the follow-up periods for the ductal and acinar group was 23.8 +/- 20.6 and 58 +/- 10.5 months, respectively. The mean age of the prostate ductal and acinar carcinoma patients was 67.3 and 67.0 yr and the mean PSA level was 14.7 and 16.2 ng/mL, respectively. No statistical differences were evident between groups in terms of the final pathologic stage or positive resection margin rate other than the postoperative Gleason score. A greater proportion of the ductal group demonstrated a postoperative Gleason score > or = 8 in comparison with the acinar group (P = 0.024). Additionally, we observed significant prognostic difference in our patient series in biochemical recurrence. The ductal group showed a poorer prognosis than the acinar group (P = 0.016). There were no differences significantly in terms of final pathology and rate of positive resection margin, but a greater proportion of the ductal group demonstrated a Gleason score > or = 8 than the acinar group after matching for PSA, Gleason score in biopsy and clinical stage. The ductal group also showed a poorer prognosis.
Aged
;
Carcinoma, Acinar Cell/*pathology
;
Carcinoma, Ductal/*pathology
;
Humans
;
Male
;
Middle Aged
;
Neoplasm Grading
;
Prostate-Specific Antigen/blood
;
Prostatic Neoplasms/*pathology
;
SEER Program