Incidence and prognostic factors of primary thyroid lymphoma and construction of prognostic models for post-chemotherapy and postoperative patients: a population-based study

Abstract Background Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients. Methods The incidence of PTL in 1975–2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1616 eligible PTL patients diagnosed in 1998–2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS). Results PTL incidence showed a relatively steady increase in 1975–1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994–2017(APC − 2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability. Conclusions The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

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Incidence and Prognostic Factors of Primary Thyroid Lymphoma and Construction of Prognostic Models for Post-chemotherapy and Postoperative Patients: A Population-based Study

10.21203/rs.3.rs-114469/v1 ◽

2020 ◽

Author(s):

Nan Xiang ◽

Fangyuan Dong ◽

Xuebing Zhan ◽

Shuhan Wang ◽

Junjie Wang ◽

...

Keyword(s):

Prognostic Factors ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Models ◽

Steady Increase ◽

Thyroid Lymphoma ◽

Annual Percent Change ◽

Cancer Specific Survival ◽

Good Reliability ◽

Primary Thyroid Lymphoma

Abstract Background: Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. Objective: The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients.Methods: The incidence of PTL in 1975-2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1,616 eligible PTL patients diagnosed in 1998-2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS).Results: PTL incidence showed a relatively steady increase in 1975-1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994-2017(APC -2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. The combination of radiotherapy with chemotherapy or surgery was beneficial to the prognosis of patients. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability.Conclusions: The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

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Identify Prognostic Factors and Establish a Risk Stratification Model to Predict Cancer-Specific Survival for Patients with Pancreatic Cancer Liver Metastasis: A Population-Based Study

10.21203/rs.3.rs-855925/v1 ◽

2021 ◽

Author(s):

Biyang Cao ◽

Chenchen Wu ◽

Letian Zhang ◽

Jing Wang

Keyword(s):

Pancreatic Cancer ◽

Prognostic Factors ◽

Liver Metastasis ◽

Classification System ◽

Cox Regression ◽

Risk Classification ◽

Prognostic Models ◽

Cancer Specific Survival ◽

Discriminative Ability ◽

Cox Regression Analysis

Abstract Background Pancreatic cancer liver metastasis (PCLM) is a commonly fatal disease, but there are few prognostic models for these entities. The purpose of this study is to investigate prognostic factors based on clinicopathological characteristics and establish a prognostic nomogram predicting the cancer-specific survival (CSS) for PCLM patients. Methods The characteristics of 6015 patients with PCLM between 2010 and 2015 from Surveillance, Epidemiology, and End Results (SEER) database were analyzed. Prognostic factors and nomogram predicting CSS were developed by Cox proportional hazard regression model. The predictive accuracy and discriminative ability of nomogram were assessed by concordance index (C-index), calibration curve, decision curve analyses (DCAs) and receiver operating characteristic (ROC) curve. Moreover, a risk classification system was built according to the cut-off values off the nomogram. Results Based on the univariate and multivariate Cox regression analysis, significant prognostic factors were identified and included to establish the nomogram for CSS. The median survival time (MST) for all patients is 4.0 months (95% confidence interval [CI]:3.8–4.2) and CSS at 6, 12 and 18 months was 34.12%, 15.63% and 7.83%, respectively. The C-index of nomogram was 0.693 (95%CI: 0.689–0.697) and all verification results showed an accurate and discriminative ability in predicting CSS. Significant differences in Kaplan–Meier curves were observed in patients stratified into different risk groups (p < 0.001), with MST of 7.0 months (95% CI: 6.7–7.3), 3.0 months (95% CI: 2.7–3.3), and 2.0 months (95% CI: 1.8–2.2), respectively. Conclusions A prognostic nomogram and corresponding risk classification system were proposed to predict CSS for PCLM.

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Development and Validation of Prognostic Nomograms for Patients with Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 To 2016

10.21203/rs.3.rs-915546/v1 ◽

2021 ◽

Author(s):

Zi-Meng Wang ◽

Zuo-Lin Xiang

Keyword(s):

Parotid Gland ◽

Prediction Models ◽

Cox Regression ◽

Clinicopathological Characteristics ◽

Prognostic Indicators ◽

Regression Analyses ◽

Cancer Specific Survival ◽

T Stage ◽

Not Otherwise Specified ◽

N Stage

Abstract Background Parotid gland adenocarcinoma not otherwise specified (PANOS) is a rare malignancy, and the characteristics and prognosis of this disease remain unclear. This study aims to characterize PANOS and establish prognostic prediction models for patients with PANOS. Methods Cases from 2004–2016 were retrieved from the Surveillance, Epidemiology, and End Results Program database (SEER database). Univariate and multivariate Cox regression analyses, Gray's test and propensity score matching (PSM) were conducted to analyze demographics, treatments, and survival outcomes . Results The 446 patients ( 289 men) selected for analysis had a median age of 66 (19–95) years, and 307 patients were diagnosed with stage III/IV disease. The median survival of all patients was 66 months, with a 51.8% 5-year overall survival (OS) rate. Surgical treatment clearly improved survival time (p < 0.001). In the subgroup analysis, radiotherapy showed survival benefits in patients with advanced-stage disease (III/IV) (p < 0.001). Multivariate Cox regression analyses revealed that age, T stage, N stage, M stage and surgery were independent prognostic indicators for OS;T stage, N stage, M stage and surgery were independent risk factors for cancer-specific survival(CSS).In addition, age was independently associated with noncancer-related death. Two nomograms were established based on the results of the multivariate analysis, which was validated by the concordance index (C-index) (0.747 and 0.780 for OS and CSS, respectively) and the area under the time-dependent receiver operating characteristic(ROC) curve(0.756, 0.764 and 0.819 regarding for nomograms predicting 3-, 5- and 10- year OS, respectively and 0.794, 0.789 and 0.806 for CSS, respectively). Conclusions Our study clearly presents the clinicopathological characteristics and survival analysis of patients with PANOS. In addition, our constructed nomogram prediction models may assist physicians in evaluating the individualized prognosis and deciding on treatment for patients.

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Conditional Survival After Surgical Resection of Appendiceal Tumors: a Population-based Study

10.21203/rs.3.rs-403708/v1 ◽

2021 ◽

Author(s):

Shutao Zhao ◽

Chang Lu ◽

Junan Li ◽

Chao Zhang ◽

Xudong Wang

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Population Based ◽

Conditional Survival ◽

Seer Database ◽

Cancer Specific Survival ◽

Population Based Study ◽

Factors Affecting ◽

Probability Of Survival ◽

N Stage

Abstract Background: This study aimed to evaluate the conditional survival (CS) of appendiceal tumors (ATs) after surgery.Methods: A total of 3,031 patients with ATs who underwent surgery were included in the Surveillance Epidemiology and End Results (SEER) database from 2004 to 2016. A multivariate Cox regression model was used to analyze the prognostic factors affecting overall survival (OS) and cancer-specific survival (CSS). CS was used to calculate the probability of survival for another 3 years after the patient had survived x years. The formulas were COS3 = OS (x + 3) /OS (x), and CCS3 = CSS (x + 3)/CSS (x).Results: The 1-year, 3-year, and 5-year OSs for all patients were 95.6%, 83.3%, and 73.9%, respectively, while the 1-year, 3-year, and 5-year CSSs were 97.0%, 87.1%, and 79.9%, respectively. Age, grade, histology, N stage, carcinoembryonic antigen (CEA), and radiation were independent prognostic factors for OS and CSS. For patients that survived for 1 year, 3 years, and 5 years, their COS3s were 81.7%, 83.9%, and 87.0%, respectively. The CCS3s were 85.5%, 88.3%, and 92.0% respectively. In patients with poor clinicopathological factors, COS3 and CCS3 increased significantly, and the survival gap between OS and COS3, CSS and CCS3 was more obvious.Conclusions: CS for appendiceal tumors were dynamic and increased over time, especially in patients with poor prognosis.

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Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

Japanese Journal of Clinical Oncology ◽

10.1093/jjco/hyz182 ◽

2019 ◽

Vol 50 (3) ◽

pp. 261-269

Author(s):

Jieyun Zhang ◽

Yue Yang ◽

Xiaojian Fu ◽

Weijian Guo

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Prognostic Factors ◽

Prognostic Model ◽

Cox Regression ◽

External Validation ◽

Cancer Prognosis ◽

Cancer Specific Survival ◽

Internal Validation ◽

Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

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Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study

Cancer Control ◽

10.1177/10732748211036775 ◽

2021 ◽

Vol 28 ◽

pp. 107327482110367

Author(s):

Fengshuo Xu ◽

Fanfan Zhao ◽

Xiaojie Feng ◽

Chengzhuo Li ◽

Didi Han ◽

...

Keyword(s):

Prognostic Factors ◽

Regression Model ◽

Cox Regression ◽

External Validation ◽

Cancer Specific Survival ◽

Cox Regression Model ◽

Training Cohort ◽

Undifferentiated Pleomorphic Sarcoma ◽

Pleomorphic Sarcoma ◽

End Results

Introduction The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis. Methods Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram’ s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful. Conclusions The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

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Comprehensive lymphadenectomy and survival prediction in uterine serous cancer patients after surgery: a population-based analysis

10.21203/rs.2.24587/v1 ◽

2020 ◽

Author(s):

Hui Li ◽

Xiaofei Xie ◽

Yunyun Liu ◽

Xiaoxin Huang ◽

Haoliang Lin ◽

...

Keyword(s):

Prognostic Factors ◽

Lymph Nodes ◽

Cox Regression ◽

Staging System ◽

Population Based ◽

Pelvic Lymphadenectomy ◽

Survival Prediction ◽

Cancer Specific Survival ◽

Gynecology And Obstetrics ◽

Objective Evidence

Abstract Objective Evidence on uterine serous cancer (USC) prognosis has been limited and inconclusive. We aim to explore the survival benefits of comprehensive lymphadenectomy in USC patients after surgery and develop a prognostic nomogram to predict survival. Materials and Methods USC patients who had undergone hysterectomy between 2010 and 2015 were identified from Surveillance, Epidemiology and End Results (SEER) database. The relationship between the extent of lymphadenectomy and survival, including overall survival (OS) and cancer-specific survival (CSS), was estimated with Kaplan-Meier (K-M) analysis. Univariate and multivariate Cox regression analyses were utilized to determine the independent prognostic factors. A nomogram was then developed, calibrated and internally validated. Results A total of 2853 patients were identified. K-M survival analysis revealed that patients with ≥12 pelvic lymph nodes (PLNs) removed had significantly better OS and CSS than those without (both P <0.001). However, patients with ≥6 para-aortic lymph nodes removed was not associated with similar survival benefits than patients without ( P >0.1). Multivariate analyses for OS and CSS revealed that age, T stage, N stage, tumor size, adjuvant therapy and ≥12 PLNs removed were independent prognostic factors (all P<0.05) and were subsequently incorporated into the nomogram. The Harrell’s C-index of the nomogram was significantly higher than that of the International Federation of Gynecology and Obstetrics staging system (OS: 0.739 vs 0.671, P <0.001; CSS: 0.752 vs 0.695, P <0.001). Furthermore, the nomogram was well calibrated with satisfactory consistency. Conclusions Comprehensive pelvic lymphadenectomy should be recommended to USC patients for its survival benefits. And a nomogram has been developed to predict the survivals of USC patients after surgery.

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Establishment and Validation of Prognostic Nomograms for Patients With Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 to 2016

Frontiers in Surgery ◽

10.3389/fsurg.2021.799452 ◽

2022 ◽

Vol 8 ◽

Author(s):

Zi-Meng Wang ◽

Zuo-Lin Xiang

Keyword(s):

Parotid Gland ◽

Survival Time ◽

Prediction Models ◽

Cox Regression ◽

Stage Iii ◽

Prognostic Indicators ◽

Cancer Specific Survival ◽

Not Otherwise Specified ◽

N Classification ◽

T Classification

Background: Parotid gland adenocarcinoma not otherwise specified (PANOS) is a rare malignant tumor with limited data on its characteristics and prognosis. This research is aimed at characterizing PANOS and developing prognostic prediction models for patients with PANOS.Methods: Cases from 2004–2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program database. Univariate and multivariate Cox regression were applied to ascertain the factors associated with survival. Competing risk analysis and Gray's tests were employed to analyze cancer-specific death. Propensity score matching (1:1) was conducted to reduce the influence of confounding variables.Results: A total of 446 patients with a median age of 66 years were selected, of which 307 were diagnosed with stage III/IV PANOS. The 5-year overall survival (OS) rate of all patients was 51.8%, and the median survival time was 66 months. Surgical treatment clearly improved survival time (p < 0.001). In the subgroup analysis, radiotherapy showed survival benefits in patients with stage III/IV disease (p < 0.001). Multivariate Cox regression analyses showed that age, T classification, N classification, M classification and surgery were independent prognostic indicators for OS; T classification, N classification, M classification and surgery were independent risk factors for cancer-specific survival (CSS). In addition, age was independently associated with other cause-specific death. Based on the results of multivariate analysis, two nomograms were developed and verified by the concordance index (C-index) (0.747 and 0.780 for OS and CSS) and the area under the time-dependent receiver operating characteristic (ROC) curve (0.756, 0.764, and 0.819 regarding for nomograms predicting 3-, 5-, and 10- year OS, respectively and 0.794, 0.789, and 0.806 for CSS, respectively).Conclusions: Our study clearly presents the clinicopathological features and survival analysis of patients with PANOS. In addition, our constructed nomogram prediction models may assist physicians in evaluating the individualized prognosis and deciding on treatment for patients.

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Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2015.33.3_suppl.248 ◽

2015 ◽

Vol 33 (3_suppl) ◽

pp. 248-248

Author(s):

Yu Uneno ◽

Tadayuki Kou ◽

Masashi Kanai ◽

Michio Yamamoto ◽

Peng Xue ◽

...

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Prognostic Factors ◽

Poor Prognosis ◽

Prognostic Model ◽

Palliative Chemotherapy ◽

Cox Regression ◽

Advanced Pancreatic Cancer ◽

Prognostic Index ◽

Prognostic Models

248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.

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Metabolic syndrome and upper tract urothelial carcinoma: A retrospective analysis from a large Chinese cohort.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.452 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 452-452

Author(s):

Hang Xu ◽

Ping Tan ◽

Lu Yang ◽

Qiang Wei

Keyword(s):

Metabolic Syndrome ◽

Urothelial Carcinoma ◽

Prediction Models ◽

Cox Regression ◽

Density Lipoprotein ◽

Upper Tract Urothelial Carcinoma ◽

Survival Outcomes ◽

Cancer Specific Survival ◽

Cox Regression Analysis ◽

Upper Tract

452 Background: Metabolic syndrome (MetS) has been reported to be associated with poor survival outcomes in cancer patients. However, the role of MetS in upper tract urothelial carcinoma (UTUC) has yet to be explored. We aim to investigate the prognostic value of MetS in UTUC after radical nephroureterectomy (RNU). Methods: A total of 644 patients with UTUC after RNU were identified at West China Hospital from May 2003 to December 2016. MetS was defined as the co-existence of three or more of five components (obesity, hypertension, elevated fasting glucose, decreased high-density lipoprotein-cholesterol and hypertriglyceridemia). Logistic and Cox regression analyses was performed to evaluate the associations of MetS with pathological features and survival outcomes. Decision curve analysis was performed to determine the clinical utility of the prediction models. Results: Of 644 patients, 157 (24.4%) had MetS. Over a median follow-up of 39 months, 269 (41.8%) experienced disease recurrence, 233 (36.2%) died and 185 (28.7%) died of UTUC. MetS was independently associated with high-grade disease (odds ratio [OR]: 2.01, P = 0.005), advanced pT stage (≥ pT3, OR: 1.54, P = 0.027) and lymphovascular invasion (OR: 1.71, P = 0.03). Multivariate Cox regression analysis showed that MetS was an independent factor for decreased cancer-specific survival (CSS, HR: 1.38, 95% CI: 1.01-1.89, P = 0.042) but not for RFS (HR: 1.27, 95% CI: 0.97-1.67, P = 0.078) and OS (HR: 1.24, 95% CI: 0.95-1.62, P = 0.121). The estimated c-index of the multivariate models for CSS was 0.763 compared with 0.769 when MetS added. Conclusions: MetS is a negative prognostic factor in UTUC. Further studies of MetS in UTUC are demanded.

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