Population-based SEER analysis of survival in colorectal cancer patients with or without resection of lung and liver metastases.

BACKGROUND: Approximately one third of all patients with CRC present with, or subsequently develop, colorectal liver metastases (CRLM). The objective of this population-based analysis was to assess the impact of resection of liver only, lung only and liver and lung metastases on survival in patients with metastatic colorectal cancer (mCRC) and resected primary tumor. METHODS: Ten thousand three hundred twenty-five patients diagnosed with mCRC between 2010 and 2015 with resected primary were identified in the Surveillance, Epidemiology and End Results (SEER) database. Overall, (OS) and cancer-specific survival (CSS) were analyzed by Cox regression with multivariable, inverse propensity weight, near far matching and propensity score adjustment. RESULTS: The majority (79.4%) of patients had only liver metastases, 7.8% only lung metastases and 12.8% metastases of lung and liver. 3-year OS was 44.5 and 27.5% for patients with and without metastasectomy (HR = 0.62, 95% CI: 0.58-0.65, P < 0.001). Metastasectomy uniformly improved CSS in patients with liver metastases (HR = 0.72, 95% CI: 0.67-0.77, P < 0.001) but not in patients with lung metastases (HR = 0.84, 95% CI: 0.62-1.12, P = 0.232) and combined liver and lung metastases (HR = 0.89, 95% CI: 0.75-1.06, P = 0.196) in multivariable analysis. Adjustment by inverse propensity weight, near far matching and propensity score and analysis of OS yielded similar results. CONCLUSIONS: This is the first SEER analysis assessing the impact of metastasectomy in mCRC patients with removed primary tumor on survival. The analysis provides compelling evidence of a statistically significant and clinically relevant increase in OS and CSS for liver resection but not for metastasectomy of lung or both sites.

Construction and validation of a nomogram for predicting the prognosis of breast cancer patients who received adjuvant therapy: an analysis based on the SEER database.

Background: Breast cancer is the most common malignant tumor in women globally. Despite advances in primary treatment, the role of adjuvant therapy in reducing recurrence and improving survival is critical; however, there is a notable lack of tailored prognostic models for patients receiving adjuvant therapy. This study used the Surveillance, Epidemiology, and End Results (SEER) database to develop a prognostic nomogram for breast cancer patients receiving adjuvant therapy. Methods: The data of breast cancer patients who received adjuvant therapy after surgery in 2014-2015 were extracted from the SEER database. Univariate Cox regression identified significant prognostic variables that were further refined by least absolute shrinkage and selection operator (LASSO) regression and cross-validation analyses. These variables were incorporated into a multivariate Cox regression analysis to establish the predictive model. This model was visualized and validated using various statistical measures. Results: A total of 54,960 patients were included in the study, with 38,472 in the training set and 16,488 in the validation set. Age, sex, race, marital status, grade, tumor (T) stage, lymph node (N) stage, subtype, and radiotherapy were found to be significant independent risk factors of 1-, 3-, and 5-year overall survival (OS). The receiver operating characteristic curve area for 1-, 3-, and 5-year OS was >0.76 in both sets. The consistency index values were 0.768 and 0.763 for the training and validation sets, respectively. The calibration curves showed good fit, and the nomogram exhibited substantial clinical utility. Conclusions: Incorporating various significant factors, the constructed nomogram was able to effectively predict the prognosis of breast cancer patients who received adjuvant therapy. This nomogram extends understandings of complex prognosis scenarios. In addition, it could enhance personalized treatment plans and assist in patient counseling.

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.

Background: The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. Methods: A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. Results: The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. Conclusions: This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

Prognostic nomogram in patients with gastrointestinal stromal tumors: a SEER-based study.

Background: Gastrointestinal stromal tumor (GIST) is a common mesenchymal tumor of the gastrointestinal system. They originate from the interstitial cells of Cajal located within the muscle layer and are characterized by over-expression of the tyrosine kinase receptor KIT. Methods: Data from the Surveillance Epidemiology, and End Results (SEER) database of 1,213 patients diagnosed with GIST between 2010 and 2019 were dichotomized into a modeling set and a validation set at a 2:1 ratio. For the modeling set, both univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. A nomogram was then constructed based on these determinants. Model efficacy was tested using receiver operating characteristic (ROC) curves, calibration curves, clinical decision curves, and risk stratification analysis in both subsets. Results: Identified prognostic determinants included age, sex, pathological differentiation level, tumor-node-metastasis (TNM) stage, surgical intervention, radiotherapy, and marital status. The constructed nomogram showed area under the ROC curve (AUC) values of 0.822, 0.793, and 0.779 for 1-, 3-, and 5-year overall survival (OS) in the modeling set, respectively, while in the validation set, the values were 0.796, 0.823, and 0.806, respectively. Calibration plots from both sets confirmed the concordance between predicted and observed survival. Decision curve analysis (DCA) indicated significant clinical utility for the nomogram. Risk stratification of the patient data revealed distinct survival differences between high-risk and low-risk cohorts in both sets (P<0.001). Conclusions: A novel and potent nomogram for the prognosis of GIST has been introduced. This model's precision offers crucial insights for clinical decisions, yet further external validation remains essential.

Construction of a diagnostic nomogram model for predicting the risk of lymph node metastasis in clinical T1 or T2 colon cancer based on the SEER database.

Background: There are few methods related to predicting lymph node metastasis (LNM) in patients with clinically staged T1 or T2 colon cancer. In this study, we aimed to discover independent risk factors for patients with pathologic T-stage 1 (pT1) or pT2 colon cancer with LNM and to develop a nomogram for predicting the probability of LNM for patients with clinically staged T1 or T2 colon cancer. Methods: All data were drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors for LNM were identified using univariate and multivariate logistic regression analyses, and these factors were used to construct a nomogram. The discriminatory power, accuracy, and clinical utility of the model were evaluated using receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA), respectively. Results: According to the inclusion and exclusion criteria, 32,803 patients with stage pT1 or pT2 colon cancer who had undergone surgery were selected from the SEER database. The data showed that the incidence of LNM in patients with pT1 and pT2 colon cancer was 17.11%. The age, histological grade, histological type, T classification, M classification, and tumour location were independent risk factors identified through univariate and multivariate analyses, and these factors were used to construct a nomogram. The ROC curve analysis showed that the area under the curve (AUC) of the ROC of the predictive nomogram for LNM risk was 0.6714 [95% confidence interval (CI): 0.6621-0.6806] in the training set and 0.6567 (95% CI: 0.6422-0.6712) in the validation set, indicative of good discriminatory power of the model. Calibration curve analysis demonstrated good agreement between the nomogram prediction and actual observation. DCA showed excellent clinical utility of the prediction model. Conclusions: The incidence of LNM was high in patients with pT1 and pT2 colon cancer. The nomogram established in this study can accurately predict the risk of LNM in patients with clinically staged T1 or T2 colon cancer before further clinical intervention, which allows clinicians to develop optimal treatment.

Comparable prognosis of early gastric cancer between intestinal type and diffuse type in patients of age 75 and older: a SEER-based cohort study.

Background: The prognostic significance of Lauren's classification in elderly early gastric cancer (EGC) patients remains largely unknown. We aim to investigate the characteristics and clinical implications of Lauren's classification in elderly EGC patients. Methods: Patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database based on the inclusion and exclusion criteria. Univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment were utilized to evaluate the association between Lauren's classification and cancer-specific survival (CSS) in elderly EGC patients. Stratification and interaction analyses were used to reveal the effects of confounding factors on the association between Lauren's classification and CSS. Results: The diffuse type (median, 41.0 months) showed a similar survival (37.0 months), and was mainly distributed in female group (62.5% vs. 42.2%) with poorly differentiated or undifferentiated components (89.1% vs. 27.0%) compared with intestinal type in elderly EGC patients. Analyses of univariate and multivariate Cox regression, propensity score matching, inverse-probability-weighted analysis, and propensity-score adjustment showed that Lauren's classification was not significantly CSS in elderly EGC patients (P>0.05). Subgroup and interaction analyses confirmed the stability of the results. Conclusions: Diffuse type was mainly distributed in female patients with more poorly differentiated/undifferentiated components and similar prognosis compared with intestinal type in age 75 and older EGC patients. No significant association was observed between diffuse type and CSS of the elderly EGC patients.

Prognostic factors and treatment strategies of limited-stage primary esophageal small-cell carcinoma-a SEER database analysis.

Background: Primary esophageal small-cell carcinoma (PESC) is a rare tumor with poor efficacy, and there is currently no standardized treatment method. Our aim is to explore the prognostic factors and possible optimal treatment modalities for limited-stage PESC. Methods: We retrospectively searched the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2019 for data of patients with limited-stage PESC. Kaplan-Meier method was used to plot survival curves, calculate survival rates, and Log-rank was used to test the differences among survival curves. Prognostic factors were explored through univariate and multivariate Cox regression survival analyses; Cox regression survival analysis was also conducted to analyze the risk of death among treatment groups and compare the survival differences among each treatment group. The non-single treatment (ST) group was defined as the comprehensive treatment (CT) group and it was compared against the ST group. Results: A total of 186 cases of limited-stage PESC were included in the study, there were differences in survival time among different groups due to differences in age, year, median household income, and N stage (P<0.001, P=0.041, P=0.002, P=0.001). The median overall survival (mOS) of the surgical group (19 months) was longer than that of the nonsurgical group (11 months) (P=0.01). The mOS of the chemotherapy group (16 months) was longer than that of the non-chemotherapy group (4 months) (P<0.001). The mOS of the radiotherapy group (16 months) was longer than that of the non-radiotherapy group (8 months) (P<0.001). Univariate analysis showed that age ≥80 years (P=0.006), year (1997-2007) (P=0.01), year (2008-2019) (P=0.01), N2 (P=0.003), surgery (P=0.02), radiotherapy (P<0.001), and chemotherapy (P<0.001) were prognostic factors affecting overall survival (OS) in limited-stage PESC patients. Multivariate analysis showed that SEER stage (P=0.02), age (P=0.007), radiotherapy (P<0.001), surgery (P=0.006), and chemotherapy (P<0.001) were independent prognostic factors affecting OS in patients of limited-stage PESC. Prognosis was better in the non-monotherapy group than in each monotherapy group. The CT group is superior to the ST group (P<0.001). The surgery combined with chemotherapy (SC) group had the longest mOS and the highest reduced risk of death, but there was no statistical difference. Conclusions: SEER stage, age, radiotherapy, chemotherapy, and surgery were independent prognostic factors in limited-stage patients; CT outperformed ST; the SC group had the longest median survival, but showed no statistical difference.

A novel T1a/b classification for testicular cancer: not only seminoma.

Background: The 8th edition of the American Joint Committee on Cancer (AJCC) manual divides T1 stage testicular cancer into T1a and T1b, but it is only applicable to seminoma. The purpose of this observational study is to discuss further the possibility of extending this classification system to any T1 testicular cancer. Methods: Testicular cancer patients from 2000 to 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this analysis. After patient selection, univariate and multivariate Cox regression were used to evaluate the impact of tumor size on survival in patients with T1 testicular cancer. A time-dependent receiver operation curve (ROC) was used to determine the best tumor size cut-off value for further T1 subgroup classification. Restricted cubic splines (RCS) analysis was used to compare different tumor sizes with the best tumor size cut-off value. Propensity score matching (PSM) analysis was conducted to generate baseline balanced data to validate findings. Results: A total of 6,630 patients were included in this study. In the Cox regression model, we found that T1b staged tumor (>34 mm) was an independent risk factor of overall survival [OS, adjusted hazard ratio (HR): 1.57, 95% confidence interval (CI): 1.12-2.21] and cancer-specific survival (CSS, adjusted HR: 5.027, 95% CI: 1.95-12.93). Further PSM analysis consolidated our results. Conclusions: For any T1 testicular cancer, a tumor size of 34 mm could be used as the demarcation point to assess the prognosis. Adopting personalized treatments and follow-up plans may help improve the OS and CSS rate for testicular cancer patients.

Nomogram model for predicting cancer-specific mortality in patients with early-onset colorectal cancer: a competing risk analysis insight from the SEER database and an external validation cohort.

Background: Early-onset colorectal cancer (EOCRC) is increasing in incidence and poses a growing threat. Urgent research is needed, especially in survival analysis, to enhance comprehension and treatment strategies. This study aimed to explore the risk factors associated with cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with EOCRC. Additionally, the study aimed to develop a nomogram predicting CSM using a competitive risk model and validate its accuracy through the use of training, using internal and external cohorts. Methods: Data from EOCRC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database (2008-2017). EOCRC patients who were treated at a tertiary hospital in northeast China between 2014 and 2020 were also included in the study. The SEER data were divided into the training and validation sets at a 7:3 ratio. A univariate Cox regression model was employed to identify prognostic factors. Subsequently, multivariate Cox regression models were applied to ascertain the presence of independent risk factors. A nomogram was generated to visualize the results, which were evaluated using the concordance index (C-index), area under the curve (AUC), and calibration curves. The clinical utility was assessed via decision curve analysis (DCA). Results: Multivariable Cox regression analysis demonstrated that factors such as race, tumor differentiation, levels of carcinoembryonic antigen (CEA), marital status, histological type, American Joint Committee on Cancer (AJCC) stage, and surgical status were independent risk factors for CSM in EOCRC patients. In addition, age, gender, chemotherapy details, CEA levels, marital status, and AJCC stage were established as independent risk factors for OCM in individuals diagnosed with EOCRC. A nomogram was developed using the identified independent risk factors, demonstrating excellent performance with a C-index of 0.806, 0.801, and 0.810 for the training, internal validation, and external validation cohorts, respectively. The calibration curves and AUC further confirmed the accuracy and discriminative ability of the nomogram. Furthermore, the DCA results indicated that the model had good clinical value. Conclusions: In this study, a competing risk model for CSM was developed in EOCRC patients. The model demonstrates a high level of predictive accuracy, providing valuable insights into the treatment decision-making process.

Development and validation of a nomogram to predict cancer-specific survival of patients with large hepatocellular carcinoma accepting surgical resection: a real-world analysis based on the SEER database.

Background: Only a small percentage of patients with large hepatocellular carcinoma (HCC) can undergo surgical resection (SR) therapy while the prognosis of patients with large HCC is poor. However, innovations in surgical techniques have expanded the scope of surgical interventions accessible to patients with large HCC. Currently, most of the existing nomograms are focused on patients with large HCC, and research on patients who undergo surgery is limited. This study aimed to establish a nomogram to predict cancer-specific survival (CSS) in patients with large HCC who will undergo SR. Methods: The study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) database encompassing patients with HCC between 2010 and 2015. Patients with large HCC accepting SR were eligible participants. Patients were randomly divided into the training (70%) and internal validation (30%) groups. Patients from Air Force Medical Center between 2012 and 2019 who met the inclusion and exclusion criteria were used as external datasets. Demographic information such as sex, age, race, etc. and clinical characteristics such as chemotherapy, histological grade, fibrosis score, etc. were analyzed. CSS was the primary endpoint. All-subset regression and Cox regression were used to determine the relevant variables required for constructing the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the nomogram. The area under the receiver operating characteristic curve (AUC) and calibration curve were used to validate the nomogram. The Kaplan-Meier curve was used to assess the CSS of patients with HCC in different risk groups. Results: In total, 1,209 eligible patients from SEER database and 21 eligible patients from Air Force Medical Center were included. Most patients were male and accepted surgery to lymph node. The independent prognostic factors included sex, histological grade, T stage, chemotherapy, α-fetoprotein (AFP) level, and vascular invasion. The CSS rate for training cohort at 12, 24, and 36 months were 0.726, 0.731, and 0.725 respectively. The CSS rate for internal validation cohort at 12, 24, and 36 months were 0.785, 0.752, and 0.734 respectively. The CSS rate for external validation cohort at 12, 24, and 36 months were 0.937, 0.929, and 0.913 respectively. The calibration curve demonstrated good consistency between the newly established nomogram and real-world observations. The Kaplan-Meier curve showed significantly unfavorable CSS in the high-risk group (P<0.001). DCA demonstrated favorable clinical applicability of the nomogram. Conclusions: The nomogram constructed based on sex, histological grade, T stage, chemotherapy and AFP levels can predict the CSS in patients with large HCC accepting SR, which may aid in clinical decision-making and treatment.

A nomogram for predicting overall survival of breast cancer with regional lymph node metastasis in young women.

Background: Young breast cancer (YBC) patients demonstrate a heightened propensity for regional lymph node metastasis (RLNM) in contrast to cohorts across varying age demographics. The aim of our study was to identify clinicopathologic prognostic variables in YBC patients with RLNM and construct a practical and reliable nomogram for the prediction of overall survival (OS) using the Surveillance, Epidemiology, and End Results (SEER) database. Methods: Young individuals (≤40 years) with a diagnosis of breast cancer with RLNM were recognized from the SEER database between 2010 and 2015, and further randomly split into two cohorts: the training set (n=4,497) and the validation set (n=1,927). We first performed univariate and multivariate Cox regression analyses to confirm independent survival predictors of OS. A novel prognostic nomogram was developed and evaluated using Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). To make a clear distinction between high- and low-risk patients in terms of patient survival, Kaplan-Meier survival curves were assessed using the log-rank test. Results: Nine risk factors were found as independent prognostic variables in predicting OS, including race, grade, histology, surgery, radiation, molecular subtype, American Joint Committee on Cancer (AJCC) stage 7th edition, T stage, and N stage. The C-index values of our nomogram were 0.786 [95% confidence interval (CI): 0.767-0.805] and 0.791 (95% CI: 0.760-0.822) in our training and validation groups, respectively. The ROC curves demonstrated sufficient discriminating ability, while the predicted and real survival rates were fairly consistent, as shown by the calibration plots. The prediction model had a higher net benefit and acceptable clinical value, as shown by the DCA curves. Conclusions: In YBC patients with RLNM, we successfully established a unique nomogram to forecast the 2-, 3-, and 5-year OS. Clinicians may utilize this nomogram to pinpoint patients at higher risk and provide them with appropriate customized therapies.

A prognostic nomogram based on least absolute shrinkage and selection operator Cox regression in patients with pulmonary large-cell neuroendocrine carcinoma.

Background: Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is a rare subtype of breast cancer with a poor prognosis. Despite its rarity, it is important to gain a better understanding of the epidemiological, clinical, and prognostic features of pulmonary LCNEC. The purpose of this study was to design, construct, and validate a new nomogram for predicting overall survival (OS) in patients with pulmonary LCNEC. Methods: In total, the data of 1,864 LCNEC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database, which is maintained by the National Cancer Institute in the United States and serves as a comprehensive source of cancer-related information. Of these patients, 556 served as the validation group and 1,308 served as the training cohort. We constructed a new nomogram with the training cohort that included the independent factors for OS as identified by least absolute shrinkage and selection operator Cox regression. Five independent factors were ultimately selected by the stepwise regression. Every factor of the Cox regression was included in the nomogram. Analyses of the calibration curve, decision curve, area under the curve, and concordance index (C-index) values were performed to assess the effectiveness and discriminative ability of the nomogram. Results: Five optimal predictive factors for OS were selected and merged to construct a 3- and 5-year OS nomogram. The nomogram had C-index values of 0.716 and 0.708 in the training cohort and validation cohort, respectively. The actual OS rates and the calibration curves showing the predictions of the nomogram were in good agreement. Conclusions: The prognostic nomogram may be very helpful in estimating the OS of patients with pulmonary LCNEC.

Nomogram predicts risk and prognostic factors for lung metastasis of anaplastic thyroid carcinoma: a retrospective study in the Surveillance Epidemiology and End Results (SEER) database.

Background: Lung metastasis (LM) is a frequent occurrence in patients with anaplastic thyroid cancer (ATC) and is often associated with a poor prognosis. However, there is currently a lack of specific research focusing on the diagnostic and prognostic evaluation of LM in ATC patients using nomograms. Consequently, the establishment of effective predictive models holds significant importance in providing guidance for clinical practice. Methods: We screened patients from Surveillance Epidemiology and End Results (SEER) database between 2000 and 2018. To identify independent risk factors for LM in patients with ATC, we conducted univariate and multivariate logistic regression analyses. We also conducted univariate and multivariate Cox proportional hazards regression analyses to identify independent prognostic factors for ATC patients with LM. Based on these analyses, we developed two novel nomograms. The performance of the nomograms was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A cohort of 540 ATC patients was enrolled in the study, among whom 181 patients (33.5%) were identified with LM at the time of initial diagnosis. The independent risk factors for LM in patients with ATC included tumor size, extent of surgery, lateral cervical lymph node metastasis, and radiotherapy. Furthermore, tumor size, extent of surgery, radiotherapy, and chemotherapy were identified as independent factors influencing the prognosis of ATC patients with LM. The accuracy of the two nomograms in predicting the occurrence and prognosis of LM in ATC patients was confirmed through the analysis of ROC curves, calibration, DCA curves, and Kaplan-Meier (K-M) survival curves on both the training and validation sets. Conclusions: The two nomograms are highly accurate in predicting LM in patients with ATC and in forecasting patient outcomes for patients with lung metastases. Consequently, they offer valuable support for personalized clinical decision-making in future clinical practice.

Predictive nomograms for risk and prognostic factors in metastatic bladder cancer: a population-based study.

Background: Given the poor prognosis of patients with metastatic bladder cancer (MBC), the development of an effective diagnostic and prognostic model is significant in cancer management and for guidance in clinical practice. Methods: We acquired data of 23,180 bladder cancer patients from Surveillance Epidemiology and End Results (SEER) database registered from 2010 to 2019. The optimal cut-off value for patient age and tumor size was determined by x-tile software. Independent risk factors for MBC were identified by univariate and multivariate logistic regression analyses and prognosis factors were identified by univariate and multivariate cox regression analyses, and risk and prognostic nomograms were constructed. The accuracy of the nomograms was verified by receiver operating characteristic (ROC) curves, calibration curves, and its clinical utility was determined by decision curve analysis (DCA) curves and clinical impact curves (CIC). Kaplan-Meier (K-M) survival curves further confirmed the clinical validity of the prognostic model. Results: Through logistic regression analyses, we derived that age, histological type, tumor size, T stage, and N stage were independent risk factors for metastasis in bladder cancer patients. By cox regression analyses, age, chemotherapy, histological type, bone, lung and liver metastases were identified as risk factors influencing prognosis of MBC patients. Area under the curve (AUC) of the risk nomogram was 0.80, the AUC values of 1/2/3 years were 0.74/0.71/0.71 in the training group and 0.81/0.77/0.77 in the validation group. Based on calibration curves, DCA curves, CIC and K-M curves, the nomograms were validated with excellent predictive performance and clinical utility for MBC. Conclusions: The nomograms we constructed have perfect predictive accuracy and clinical practicality for MBC patients, enabling clinicians to provide treatment advice and clinical guidance to patients.

Development and validation of a nomogram for predicting overall survival in patients with stage III-N2 lung adenocarcinoma based on the SEER database.

Background: There is variability in the prognosis of stage III-N2 lung adenocarcinoma (LUAD) patients. The current tumor-node-metastasis (TNM) staging is not sufficient to precisely estimate the prognosis of stage III-N2 LUAD patients. The Surveillance, Epidemiology, and End Results (SEER) database collected first-hand information from a large number of LUAD patients. Based on the SEER database, this study aimed to determine the prognostic factors that affect overall survival (OS) in stage III-N2 LUAD patients and then establish a nomogram for predicting OS in this type of cancer to identify the high-risk population that may require more frequent surveillance or intensive care. Methods: Data for 1,844 stage III-N2 primary LUAD patients who were registered between 2010 and 2015 were obtained from the SEER database. These patients were randomly assigned to either training (n=1,290) or validation (n=554) cohorts at a 7:3 ratio. The univariate and multivariate Cox regression (UCR and MCR) analyses were performed to find the relevant independent prognostic factors. To predict the OS based on these prognostic factors, a nomogram was then developed. The performance of the nomogram was examined based on the calibration curves, and receiver operating characteristic (ROC) curves. The ability of nomogram to stratify patient risk was validated by Kaplan-Meier survival analysis. Results: Age, gender, tumor location, T-stage and treatment modality (chemotherapy, radiation therapy, surgery and scope of lymph node dissection) of stage III-N2 LUAD patients were significantly associated with prognosis. The area under the curve (AUC) values of OS predicted by the nomogram constructed with these factors at 12-, 36- and 60-month were 0.784, 0.762 and 0.763 in the training cohort, whereas 0.707, 0.685 and 0.705 in the validation cohort, respectively. Additionally, calibration curves demonstrated concordance between predicted and observed outcomes. Nomogram risk stratification provides a meaningful distinction between patients with various survival risks. Conclusions: A survival prediction model that may be useful for risk stratification and decision-making is developed and validated for stage III-N2 LUAD patients. A high-risk patient predicted by the prediction model may require more frequent surveillance or intensive care.

Nomogram for predicting preoperative axillary lymph node status in male breast carcinoma: a SEER population-based study.

Background: Sentinel lymph node biopsy (SLNB) has been recommended as a replacement for axillary lymph node dissection (ALND) in male breast carcinoma (MBC) with clinical axillary lymph node-negative (ALN-negative) as in the case of female. However, the morbidity after SLNB may also have short-term or long-term complications. To avoid unnecessary surgery, building a model which is able to assess the risk of lymph node metastasis is vitally significant. Methods: A retrospective review of the clinical and pathology data were carried out for patients diagnosed with MBC between 2010 and 2018 from the Surveillance, Epidemiology, and End Results (SEER) database. The cohort was divided into training and validation cohorts. A logistic regression model was used to construct the nomogram in the training cohort and then verified in the validation cohort. The receiver operating characteristic (ROC) curve, C-index, and calibration were used to evaluate the predictive ability of the nomogram. Results: Overall, 2,610 patients diagnosed with MBC were included in the study, of which 1,740 were in the training cohort and 870 were in the validation cohort. Logistic regression analysis indicated age at diagnosis, tumor location, tumor stage, pathological type, and histologic grade, were significantly related to axillary lymph node metastasis (ALNM). The area under the curve (AUC) of the nomogram was 0.846 (95% CI: 0.825-0.867) and C-index was 0.848 (95% CI: 0.807-0.889), demonstrating a notable prediction performance. The calibration curve for the nomogram was plotted and the slope was close to 1. The prognostic value of the nomogram was further validated in the validation cohort, with an AUC of 0.848 (95% CI: 0.819-0.877). Conclusions: A nomogram to predict ALNM was successfully established, especially for those who were of advanced age at diagnosis, had small tumor size, displayed low malignancy, and showed clinical ALN-negative, to avoid unnecessary axillary operation. The quality of life for patients is enhanced without conceding the overall survival rate.

Survival outcomes in unresectable metastatic rectal cancer patients after both primary site resection and chemoradiotherapy: a SEER-based observational study.

Background: The liver is the most common site for rectal cancer metastasis, and liver resection combined with chemotherapy is the only treatment offering the possibility of long-term survival in patients with metastatic rectal cancer. However, a significant proportion of liver metastases cannot be surgically removed, and very limited data are available regarding the survival outcomes of these patients. This study aimed to investigate the survival pattern of rectal cancer patients with unresectable liver metastases after both chemoradiotherapy and primary tumor resection. Methods: A total of 51,178 rectal cancer patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database, of whom 448 had synchronous liver metastases and underwent both chemoradiotherapy and primary tumor resection. According to different treatment modalities, patients were divided into a hepatic resectable group and an unresectable group. The Kaplan-Meier method was used to estimate patient survival, and differences between the hepatic resectable and unresectable groups were compared using the log-rank test. Univariate and multivariate Cox regression models were used to analyze independent prognostic factors for unresectable tumors. Results: Among the 448 metastatic rectal cancer patients, 60.3% (270) had unresectable liver metastasis. The median survival period, 2-year overall survival (OS) rate, and 5-year OS rate of the unresectable group were 37.0 months, 68.5%, and 32.9%, respectively, compared with 56.0 months, 87.4%, and 48.0%, respectively, in the hepatic resectable group (P<0.001). Multivariate Cox regression analysis suggested that a poor or undifferentiated histological type was independently associated with poor CSS in patients with unresectable liver metastases (P=0.001). Conclusions: Primary tumor resection combined with chemoradiotherapy might be able to yield a satisfactory survival outcome in unresectable metastatic rectal cancer patients. Resection of liver metastases remains the primary treatment for prolonging the OS and CSS time in stage IV patients.

Prognostic risk factors for T1 thoracic esophageal cancer: a retrospective cohort study.

Background: Esophageal cancer has a poor overall prognosis and a high incidence of post-treatment complications. This study aimed to analyze the common surgical methods for treating T1 thoracic esophageal cancer and explore its prognostic risk factors to provide a basis for appropriate treatment selection. Methods: In this population-based retrospective cohort study, data of patients diagnosed with T1 thoracic esophageal cancer from 2010 to 2016 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided according to those who had surgery and those who had radiotherapy. Survival curves were generated using the Kaplan-Meier method and validated by the log-rank test. Cox's regression model was used to analyze the independent prognostic risk factors. Results: Overall, 2,027 eligible patients, including 824 and 1,203 patients in the surgical and non-surgical groups, respectively, were analyzed. There was no significant difference in survival between the surgical and non-surgical groups (P=0.79). In subgroup analysis, the Cox regression analysis showed that radiotherapy was a significant prognostic factor (P=0.00059). Conclusions: The impact of surgery on patients with T1 thoracic esophageal cancer was insignificant; however, radiotherapy was an independent prognostic risk factor. These results provide a reliable basis for clinical treatment of patients with T1 thoracic esophageal cancer.

Survival and prognostic factors for postoperative primary appendiceal cancer: a retrospective cohort study based on the Surveillance, Epidemiology, and End Results database.

Background: The factors affecting the postoperative survival of patients with primary appendiceal cancer (PAC) have yet to be fully explored. And there are no clear guidelines for adjuvant treatment after appendectomy. Whether chemotherapy can prolong patient survival after appendectomy, is critical in guiding postoperative medications. The majority of studies on appendiceal cancer are single case reports, and they focused on the incidence of appendiceal cancer. The present study aimed to investigate the survival characteristics of patients with primary appendiceal cancer after surgery using the Surveillance, Epidemiology, and End Results (SEER) database. Methods: The data of 2,891 cases of primary appendiceal cancer between 2004 to 2015 were obtained from the SEER database and subjected to survival analysis using the Kaplan-Meier method and Cox proportional-hazards model. The annual percentage change (APC) was calculated using the weighted least squares method. Results: The overall age-adjusted incidence rate per 100,000 population steadily increased from 0.58 in 2004 to 1.63 in 2015. For patients who received chemotherapy, the median overall survival (OS) was 65 months and the 5-year OS rate was 51.9%, and for patients who did not receive chemotherapy or whose chemotherapy status was unknown, the median OS was not reached and the 5-year OS rate was 78.9%. Age [35< age <69: hazard radio (HR) =2.147; 95% confidence interval (CI): 1.442-3.197, P<0.001; age >69: HR =5.259; 95% CI: 3.485-7.937, P<0.001], race (White race: HR =0.728; 95% CI: 0.590-0.899, P=0.003), histologic type (mucinous neoplasm: HR =0.690; 95% CI: 0.580-0.821, P<0.001; malignant carcinoid: HR =0.657; 95% CI: 0.536-0.806, P<0.001), grade (II: HR =1.794; 95% CI: 1.471-2.187, P<0.001; III: HR =2.905; 95% CI: 2.318-3.640, P<0.001; IV: HR =3.128; 95% CI: 2.159-4.533, P<0.001), and stage (localized: HR =0.236; 95% CI: 0.194-0.287, P<0.001; regional: HR =0.425; 95% CI: 0.362-0.499, P<0.001) were identified as independent predictors of survival. And after adjusting for known factors (age, sex, race, tumor size, marital status, histologic type, grade, stage), chemotherapy (HR =1.220; 95% CI: 1.050-1.417, P=0.009) was revealed to be an independent indicator of poor prognosis. Conclusions: There was an increasing trend in the incidence of appendiceal cancer in the United States between 2004 and 2015. Chemotherapy was revealed to be an independent indicator of poor prognosis, which provide valuable insight into the therapy of primary appendiceal cancer. Large clinical trials of chemotherapy and targeted therapy for appendiceal cancer are urgently needed.

Development and validation of a prognostic nomogram for Hürthle cell thyroid carcinoma: a SEER-based study.

Background: Hürthle cell carcinoma is a rare subtype of thyroid cancer, and its clinical behavior and biological characteristics remain unclear. This study aimed to establish nomogram models for the prognostic evaluation of Hürthle cell thyroid carcinoma (HCTC) in terms of both cancer-specific survival (CSS) and overall survival (OS). Methods: Data for a total of 3,264 patients with HCTC (2004 to 2018) were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was performed to identify significant predictors of prognosis and develop a prognostic nomogram. The performance of the model was assessed based on the area under the receiver operating characteristic curve (AUC), concordance index (c-index), and calibration curves. Results: Multivariate Cox regression analysis showed that age, sex, summary stage, tumor size, N stage, M stage, and treatment with thyroidectomy were independent predictors of OS. Moreover, age, summary stage, tumor size, N stage, M stage, AJCC stage, and treatment with thyroidectomy were significantly correlated with CSS. The c-index of the OS and CSS nomograms developed based on these factors was 0.822 (95% CI: 0.803-0.841) and 0.893 (95% CI: 0.866-0.920), respectively. The AUC was 0.888, 0.841, and 0.834 for 1-, 3-, and 5-year OS and 0.970, 0.949, and 0.933 for 1-, 3-, and 5-year CSS, respectively. The calibration curves showed good agreement between observed and predicted values. Moreover, decision curve analysis revealed that the nomogram had a better clinical utility than individual clinicopathological markers. Conclusions: A prognostic nomogram that allows the individualized assessment of OS and CSS in HCTC was developed. This nomogram could be used to guide treatment decisions in patients with HCTC.