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Background: The necessity and therapeutic value of lymph node dissection (LND) in early stage T1 MTC patients remain controversial. Methods: Patients with T1MTC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Poisson regression analysis was utilized to investigate promotive factors for lymph node metastasis in T1MTC patients. Fisher's exact test was employed to calculate baseline differences between non-LND and LND groups. Propensity score match (PSM) was used to control baseline bias. Survival outcomes were calculated by KaplanMeier method and log-rank test. Multivariable Cox regression assessed the prognostic impact of LND across subgroups. Results: Of 3298 MTC cases, 50.4% were T1MTC. The lymph node metastasis rate increased along with the T stage (from 22.2% to 90.5%). Among 1231 T1MTC patients included after exclusion criteria, 72.0% underwent LND and 22.0% had lymph node metastasis. Patients aged younger than 44 years (RR=1.700, p<0.001), male (RR=1.832, p<0.001), and with tumor larger than 10mm (RR=2.361, p<0.001) were more likely to have lymph node metastasis, while elderly patients (p<0.001) and those with microcarcinoma (p<0.001) were more likely to undergo non-LND procedures. LND provided no OS or DSS benefit over non-LND before and after propensity score match (matched 10-year OS/DSS: LND 83.8/96.2% vs non-LND 81.9/99.3%, p>0.05). Subgroup analyses revealed no prognostic gain with LND in any subgroup (p>0.05). Conclusion: Nearly half of MTC patients were diagnosed at T1 stage and had low lymph node risk. Different from ATA guidelines, avoiding routine LND conferred similar prognosis to standard procedures while potentially improving quality of life. Large-scale prospective multi-center studies should be conducted to further validate these findings. [ABSTRACT FROM AUTHOR]

Purpose: The study aimed to develop a nomogram model for individual prognosis prediction in patients with hormone receptors positive (HR+) mucinous breast carcinoma (MBC) and assess the value of neoadjuvant chemotherapy (NAC) in this context. Methods: A total of 6,850 HR+ MBC patients from the SEER database were identified and randomly (in a 7:3 ratio) divided into training cohorts and internal validation cohorts. 77 patients were enrolled from the Chongqing University Cancer Hospital as the external validation cohort. Independent risk factors affecting overall survival (OS) were selected using univariate and multivariate Cox regression analysis, and nomogram models were constructed and validated. A propensity score matching (PSM) approach was used in the exploration of the value of NAC versus adjuvant chemocherapy (AC) for long-term prognosis in HR+ MBC patients. Results: Multivariate Cox regression analysis showed 8 independent prognostic factors: age, race, marital status, tumor size, distant metastasis, surgery, radiotherapy, and chemotherapy. The constructed nomogram model based on these 8 factors exhibited good consistency and accuracy. In the training group, internal validation group and external validation group, the high-risk groups demonstrated worse OS (p<0.0001). Subgroup analysis revealed that NAC had no impact on OS (p = 0.18), or cancer specific survival (CSS) (p = 0.26) compared with AC after PSM. Conclusions: The established nomogram model provides an accurate prognostic prediction for HR+ MBC patients. NAC does not confer long-term survival benefits compared to AC. These findings provide a novel approach for prognostic prediction and clinical practice. [ABSTRACT FROM AUTHOR]

Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed. [ABSTRACT FROM AUTHOR]

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Background: Comprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking. Patient and method: The surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration. Result: 577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM. Conclusion: The competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe. [ABSTRACT FROM AUTHOR]

Background: The prevalence of papillary thyroid cancer is gradually increasing and the trend of youthfulness is obvious. Some patients may not be able to undergo surgery, which is the mainstay of treatment, due to physical or financial reasons. Therefore, the prediction of cancer-specific survival (CSS) in patients with non-operated papillary thyroid cancer is necessary. Methods: Patients’ demographic and clinical information was extracted from the Surveillance, Epidemiology, and End Results database. SPSS software was used to perform Cox regression analyses as well as propensity score matching analyses. R software was used to construct and validate the nomogram. X-tile software was used to select the best cutoff point for patient risk stratification. Results: A total of 1319 patients were included in this retrospective study. After Cox regression analysis, age, grade, T stage, M stage, radiotherapy, and chemotherapy were used to construct the nomogram. C-index, calibration curves, and receiver operating characteristic curves all verified the high predictive accuracy of the nomogram. The decision curve analysis demonstrated that patients could gain clinical benefit from this predictive model. Survival curve analysis after propensity score matching demonstrated the positive effects of radiotherapy on CSS in non-operated patients. Conclusion: Our retrospective study successfully established a nomogram that accurately predicts CSS in patients with non-operated papillary thyroid cancer and demonstrated that radiotherapy for operated patients can still help improve prognosis. These findings can help clinicians make better choices. [ABSTRACT FROM AUTHOR]

Background: This retrospective study aims to examine the patient demographics, survival rates, and treatment methods for small-cell neuroendocrine carcinoma (SCNEC) and large-cell neuroendocrine carcinoma (LCNEC) of prostate origin while also identifying the main differences between common types of prostate cancer with comparative analysis for survival. Methods: Our analysis utilized the Surveillance, Epidemiology, and End Results database (SEER), and data was collected from 2000–2020. Cox proportional hazards and chi-squared analysis were used for statistical analysis. Results: A total of 718 cases of prostate small and large neuroendocrine carcinoma were identified. The median age was 71.5 years, and the median follow-up was 11.0 years (95% confidence interval (95% CI) = 9.2–12.8). Most patients were over the age of 80 years (33.8%) and Caucasian (74.4%). The overall 5-year survival was 8.0% (95% CI = 6.8–9.2). The 5-year OS for Caucasians was 7.3% (95% C.I. 6.0–8.3). For Black Americans, the 5-year OS was 11.9% (95% C.I. 7.3–16.5). For Hispanics, the 5-year OS was 12.2% (95% C.I. 7.7–16.7). The 5-year cause-specific survival (CSS) was 16.2% (95% CI = 14.3–18.1). For treatment modality, the five-year survival for each were as follows: chemotherapy, 3.5% (95% CI = 2.1–4.9); surgery, 18.2% (95% CI = 13.6–22.8); multimodality therapy (surgery and chemotherapy), 4.8% (95% CI = 1.7–7.9); and combination (chemoradiation with surgery), 5.0% (95% CI = 1.0–9.0). The prognostic nomogram created to predict patient survivability matched the findings from the statistical analysis with a statistical difference found in race, income, housing, stage, and nodal status. The nomogram also indicated a slight increase in mortality with tumors of greater size. This analysis showed a slight increase in mortality for patients of Asian race. In addition, there was a significant increase in death for patients with stage 3 tumors, as well as patients who underwent surgery and radiation. Furthermore, we performed propensity score matching for survival differences, and no survival difference was found between SCNEC and LCNEC. Conclusions: Asian patients, larger tumor size, and distant disease were associated with worse long-term clinical outcomes. By leveraging insights from registry-based studies, clinicians can better strategize treatment options, improving patient outcomes in this challenging oncology arena. [ABSTRACT FROM AUTHOR]

To examine the potential correlation between chemotherapy and the risk of individual of second primary endometrial cancer (SEC) in patients with rectal cancer (RC) and assess survival outcomes. The study employed the Surveillance, Epidemiology, and End Results database (SEER) as the primary data source, it encompasses a substantial cohort of patients diagnosed with RC between 1975 and 2018. This study involved a total of 30,847 individuals diagnosed with RC, of whom 168 individuals (5.45‰) experienced SEC. Among them, 107 patients (3.47‰) received chemotherapy treatment, while 61 patients (1.98‰) did not receive chemotherapy. The analysis of the overall occurrence of SEC revealed a significant association between SEC and chemotherapy treatment. Univariate and multivariate analyses confirmed a significant association between chemotherapy treatment and an increased risk of developing SEC in RC patients. Upon implementation of a dynamic analysis on the variables of relative risk and standardized incidence ratios, the results revealed that the likelihood of SEC escalated in tandem with advancing age. The examination of patients who developed SEC after receiving and not receiving chemotherapy revealed no substantial disparities in the 10-year overall survival (OS) and (cancer-specific survival) CSS rates. The results were the same after propensity score matching. Nevertheless, a notable discrepancy emerged when comparing the OS and CSS rates at 10 years between patients afflicted with SEC subsequent to chemotherapy and those afflicted with primary endometrial cancer, and the result was the same situation in the no-chemotherapy group. The use of chemotherapy in RC patients has been associated with an increased probability of developing specific SEC. Therefore, it is imperative to prioritize efforts aimed at reducing chemotherapy-related SEC occurrences and improving the prognosis of affected individuals. [ABSTRACT FROM AUTHOR]

The role of neoadjuvant chemotherapy and its benefits in patients with triple-negative breast cancer (TNBC) and small tumors are unclear. This study aims to compare survival differences between clinical T1 TNBC receiving neoadjuvant chemotherapy (NAC) and adjuvant chemotherapy (AC). Data for patients with clinical T1 TNBC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized according to whether they received chemotherapy before or after surgery. Propensity Score Matching (PSM) was used to minimize the influence of confounding factors. OS and BCSS were compared between the two treatment sequences using Kaplan–Meier and univariate and multivariable Cox proportional hazards regression analyses. The study included 6249 women with T1 TNBC. In multivariate analysis, compared with that in the AC group, the hazard ratio for death in the NAC group was 1.54 (95% confidence interval 1.26–1.89, p < 0.001). NAC offers no additional benefits in any age group or T, N subgroups. Our findings suggest that NAC does not provide additional benefit to patients with clinical T1 TNBC, even in the presence of lymph node metastasis, or T1c. [ABSTRACT FROM AUTHOR]

Background: Cancer burden from the elderly has been rising largely due to the aging population. However, research on the long‐term epidemiological trends in cancer of the elderly is lacking. Methods: Registry data of this population‐based cross‐sectional study were from the Surveillance, Epidemiology, and End Results (SEER) database. The study population aged 65 years or more, from geographically distinct regions. Joinpoint regression and JP Surv method were used to analyze cancer trends and survival. Results: Mortality rate during 1975–2020 decreased from 995.20 to 824.99 per 100,000 elderly persons, with an average annual decrease of 0.421% (95% CI, 0.378–0.464). While overall incidence increased with no significance. Prostate (29%) and breast (26%) cancer were the most common malignancies, respectively, in elderly males and females, and the mortality for both of the two (prostate 15%, breast 14%) ranked just behind lung and bronchus cancer, which had the highest mortality rates in males (29%) and females (23%). Many cancers showed adverse trends in the latest follow‐up periods (the last period calculated by the Joinpoint method). For intrahepatic cholangiocarcinoma, incidence (male Annual Percentage Change [APC] = 7.4*; female APC = 6.7*) and mortality (male APC = 3.0*; female APC = 3.3*) increased relatively fast, and its survival was also terrible (3‐year survival only 10%). Other cancers with recent increasing mortality included cancer of anus, anal canal and anorectum, retroperitoneum, pleura, peritoneum, etc. Most cancers had favorable trends of survival during the nearest follow‐up period. Conclusion: Against the background of overall improvement, many cancers showed adverse trends. Further research for the underlying mechanisms and targeted implements towards adverse trends is also urgent. [ABSTRACT FROM AUTHOR]

Currently, there is no consensus regarding the extent of surgery for stage I pulmonary carcinoid (PC) tumors, which encompass typical carcinoid (TC) and atypical carcinoid (AC) tumors. Sublobar resection includes segmental resection and wedge resection; the former is regarded as a type of anatomical resection that is better suited for tumor treatment. Therefore, it needs to be further verified whether differences exist in the effects of the two surgical methods on the survival time of patients. Propensity score matching (PSM) was used. The primary endpoints were cancer-specific survival (CSS) and overall survival (OS) time. Survival differences were analyzed via the Kaplan–Meier method and the log-rank test. There was no significant difference in survival between the sublobar resection and lobectomy groups after PSM in either the TC or AC tumor groups (all p > 0.05). A total of 1680 patients underwent pulmonary wedge resection (TC: n = 1547, AC: n = 133), and 398 patients underwent segmental resection (TC: n = 365, AC: n = 33). After PSM, there were no statistically significant differences in survival, regardless of whether OS or CSS was considered the primary endpoint (OS: p = 0.337; CSS: p = 0.470). Furthermore, segmental resection did not prolong patient survival time compared with wedge resection in different subgroup analyses on the basis of histology, age, and tumor size (all p > 0.05). Finally, the same results were obtained via multivariate Cox analysis (OS: p = 0.153; HR = 1.21; CSS: p = 0.351, HR = 1.32). Sublobar resection could be considered for patients with early-stage typical or atypical pulmonary carcinoid, provided that a rigorous lymph node evaluation is conducted. If the tumor is distant from the pulmonary hilum, either segmentectomy or wedge resection may be performed depending on the specific location of the tumor and the clinical condition of the patient. [ABSTRACT FROM AUTHOR]

We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions. [ABSTRACT FROM AUTHOR]

Few studies have investigated the impact of primary tumor resection (PTR) on patients with distant metastasis medullary thyroid carcinoma (DMMTC). This population-based study aims to assess the application of PTR in DMMTC patients, ascertain its benefits, and identify optimal surgical indications. DMMTC Patients diagnosed between 2010 and 2020 were included through the Surveillance, Epidemiology, and End Results (SEER) program. Logistic regression analysis identified driving factors of surgical decision-making. Propensity score matching (PSM), Kaplan–Meier method, and Cox regression were utilized to compare overall survival (OS) and disease-specific survival (DSS) between surgical and non-surgical groups. Subgroup analyses were performed to determine optimal surgical indications. Of 238 DMMTC patients included, 122 (51.3%) patients underwent PTR. Extrathyroidal extension and N1 stage emerged as independent factors promoting the surgical decision. PSM-adjusted survival analyses revealed significant advantages in both OS and DSS for the surgical group. Moreover, subgroup analyses indicated that except for patients aged ≥ 65 years, tumors ≤ 20 mm, or with multiple metastasized sites (> 1), the others significantly benefit from PTR. PTR significantly improves prognosis in selected DMMTC patients. The decision to undergo PTR in other patients should be based on a comprehensive assessment of the disease, surgeon's experience, and family discussions for potential survival benefits. [ABSTRACT FROM AUTHOR]

Background and Purpose: The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods: Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical. Results: 3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan–Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant. Conclusions: The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans. [ABSTRACT FROM AUTHOR]

Objective: The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified. Methods: From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve. Results: Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P < 0.05), & a nomogram of OS at 1, 3, & 5 years was created using these independent predictors. The nomogram's c-index was 0.750 (95% CI 0.734 ~ 0.766), greater than the TNM staging framework 0.658 (95%CI 0.639 ~ 0.677); the c-index was 0.753 (95% CI 0.737 ~ 0.769) as well as superior to the TNM staging mechanism 0.679 (95% CI 0.503–0.697). According to the calibration curve, the projected survival rate using the nomogram & the actual survival rate are in good agreement. Conclusions: Prognostic nomograms are useful tools for physicians to assess every individual's individualised prognosis & create treatment strategies for those with diffuse gastric cancer. They can reliably predict the prognosis for individuals with diffuse gastrointestinal carcinoma. [ABSTRACT FROM AUTHOR]

Background: Anaplastic thyroid cancer (ATC) is highly invasive, prone to distant metastasis (DM), and has a very poor prognosis. This study aims to construct an accurate survival prediction model for ATC patients with DM, providing reference for comprehensive assessment and treatment planning. Methods: We extracted data of ATC patients with DM diagnosed between 2004 and 2019 from the SEER database, randomly dividing them into a training set and a validation set in a ratio of 7:3. Univariate and multivariate Cox regression analyses were sequentially performed on the training set to identify independent prognostic factors for overall survival (OS) and construct nomograms for 3-month, 6-month, and 8-month OS for ATC patients with DM based on all identified independent prognostic factors. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA) curve analysis, and calibration curves were separately plotted on the training and validation sets to demonstrate the model's performance. Furthermore, patients were stratified into high- and low-risk groups based on their risk scores, and the Kaplan-Meier (KM) survival curves were used to illustrate the survival differences between the two groups. Results: A total of 322 patients were included in this study. Univariate and multivariate Cox regression analyses identified five independent prognostic factors for OS in ATC patients with DM: surgery, tumor size, age, chemotherapy, and radiotherapy. Nomograms for 3-month, 6-month, and 8- month OS were established based on these factors. The training set AUC values (3-month AUC: 0.767, 6-month AUC: 0.789, 8-month AUC: 0.795) and validation set AUC values (3-month AUC: 0.753, 6-month AUC: 0.798, 8-month AUC: 0.806) as well as the calibration curves demonstrated excellent applicability and accuracy of the model. Additionally, the DCA curves indicated substantial clinical net benefit of the model. The KM curves also confirmed the model's excellent stratification ability for patient OS. Conclusion: The nomogram developed in this study accurately predicts OS for ATC patients with DM. It can assist clinicians in formulating appropriate treatment strategies for these patients. [ABSTRACT FROM AUTHOR]

Introduction: Laryngeal cancers account for one-third of all head and neck cancers. We aimed to report the incidence trends of laryngeal cancer over 2000–2020 in the United States (US), by age, sex, race/ethnicity, and histological subtypes. Methods: Data from the Surveillance, Epidemiology, and End Results 22 database were used to identify patients with laryngeal cancer based on the International Classification of Diseases for Oncology, version 3. Age-standardized incidence rates (ASIRs) for laryngeal cancer, adjusted for reporting delays, were calculated. The Joinpoint Regression Program was then utilized to determine annual percent changes (APCs) and average annual percent changes (AAPCs) in the trends. The analysis excluded data from 2020 to prevent potential bias related to the COVID-19 pandemic. Results: A total of 104,991 cases of laryngeal cancer were identified in the US from 2000 to 2019. Squamous cell carcinoma was the predominant subtype, accounting for 94.53% of cases. Above 73.20% occurred among non-Hispanic whites, with the highest incidence observed among individuals aged 55–69 years (46.71%). The ASIRs were 5.98 and 1.25 per 100,000 population for men and women, respectively. Over 2000–2019, there was a significant reduction in ASIRs for laryngeal cancer in both sexes. Non-Hispanic black men exhibited the highest ASIR (9.13 per 100,000) and the largest decline in the ASIRs over 2000–2019 (AAPC: -3.26%). Conclusions: Laryngeal cancer incidence rates showed a decline from 2000 to 2019, in addition to 2020, during the COVID-19 pandemic. Additional research is required to investigate risk factors and their influence on incidence rates of laryngeal cancer. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan–Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients' overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P < 0.001). Competitive risk analysis showed that cancer-specific mortality was significantly higher in the non-radiotherapy group than in the radiotherapy group (P < 0.001). Multivariate Cox analysis showed that the radiotherapy group (relative non-radiotherapy group) showed a significant positive effect on survival outcomes (OS: HR 0.658 95% CI [0.642, 0.675] P < 0.001; CSS: HR 0.662 95% CI [0.645, 0.679], P < 0.001). In addition, age, gender, race, primary tumor site, T stage, N stage, M stage, chemotherapy, and surgery were also considered as important predictors of SCLC outcome. The results of the subgroup analysis showed that the radiotherapy group showed a significant survival advantage regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery (P < 0.001). Radiotherapy may improve both OS and CSS in SCLC patients. Patients with SCLC may benefit from radiotherapy regardless of age, sex, race, primary tumor site, M stage, chemotherapy, and surgery. [ABSTRACT FROM AUTHOR]

Introduction: Right hemicolectomy (RHC) remains the treatment standard for goblet cell adenocarcinoma (GCA), despite limited evidence supporting survival benefit. This study aims to explore factors influencing surgical management and survival outcomes among patients treated with RHC or appendicectomy using NCRAS (UK) and SEER (USA) data. Methods: A retrospective analysis was conducted using 998 (NCRAS) and 1703 (SEER) cases. Factors influencing procedure type were explored using logistic regression analyses. Overall survival (OS) probabilities and Kaplan–Meier (KM) plots were generated using KM analysis and the log-rank test compared survival between groups. Cox regression analyses were performed to assess hazard ratios. Results: The NCRAS analysis revealed that age and regional stage disease were determinants of undergoing RHC, with all age groups showing similar odds of receiving RHC, excluding the 75+ age group. The SEER analysis revealed tumour size > 2 cm, and receipt of chemotherapy were determinants of undergoing RHC, unlike the distant stage, which was associated with appendicectomy. Surgery type was not a significant predictor of OS in both analyses. In NCRAS, age and stage were significant predictors of OS. In SEER, age, stage, and Black race were significant predictors of worse OS. Conclusions: The study shows variations in the surgical management of GCA, with limited evidence to support a widespread recommendation for RHC. [ABSTRACT FROM AUTHOR]

Abstract This study aims to develop and validate a nomogram for predicting overall survival (OS) in Asian patients with Esophageal Cancer (EC). Data from Asian EC patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for initial variable selection, followed by multivariate Cox regression analysis to identify independent prognostic factors. A nomogram was subsequently constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves and calibration curves, while the clinical utility of the nomogram was assessed through decision curve analysis (DCA). The LASSO regression and multivariate Cox regression analysis identified age, sex, marital status, tumor size, M stage, surgery, and chemotherapy as independent prognostic factors. The ROC curve results demonstrated that the area under the curve (AUC) values for predicting 1-year, 3-year, and 5-year OS in the training cohort were 0.770, 0.756, and 0.783, respectively. In the validation cohort, the AUC values were 0.814, 0.763, and 0.771, respectively. Calibration curves indicated a high concordance between predicted and actual OS. The DCA demonstrated that the nomogram has significant clinical applicability. This nomogram provides reliable predictions and valuable guidance for personalized survival estimates and high-risk patient identification.

Abstract Hodgkin lymphoma (HL) is a rare malignancy affecting the lymphatic system. Our study examined the incidence rates of adult HL based on sex, race/ethnicity, age, and histological subgroups in the United States (US) from 2000 to 2020. Data for this study were extracted from the Surveillance, Epidemiology, and End Results 22 database. HL patients were identified utilizing the International Classification of Diseases for Oncology version 3 and categorized as classical HL, lymphocyte-rich/mixed cell/lymphocyte depleted, nodular sclerosis, classical HL, not otherwise specified, and nodular lymphocyte-predominant HL. The study reported average annual percent change (AAPC). All estimates were presented as counts and age-standardized incidence rates (ASIRs) per 100,000 individuals. Between 2000 and 2019, a total of 70,924 cases of HL were reported in the US. Classical HL was the predominant subtype (94.27%), and most incident cases were among non-Hispanic Whites (66.92%) and those aged 20–29 years (24.86%). The ASIR per 100,000 population was 3.83 for men and 2.92 for women. Both sexes showed declines in the AAPCs between 2000 and 2019 (− 0.64% [− 0.99, − 0.28] and − 0.40% [− 0.77, − 0.03] for men and women, respectively). There was a significant decrease in ASIRs after COVID-19 among both sexes (percent change: − 7.49% [− 11.58, − 3.40]). Throughout all age groups, men had a higher incidence rate compared to women, except for those aged 20–29 years. Although the overall HL incidence rate was lowered in the study period from 2000 to 2019, a dramatic decrease in ASIRs of HL patients following COVID-19 pandemic was observed.

Abstract Background Comprehensive investigations of the prognosis factors and treatment strategies with adjustment of competing causes of death for patients with malignant meningioma (MM) is still lacking. Patient and method The surveillance, Epidemiology, and End Results (SEER) database were used to include adult patients with this rare disease between 2004 and 2018. The probability of MM-caused mortality (MMCM) and non-MM-caused mortality (non-MMCM) were presented by cumulative incidence function curves. Then, the association between variates with non-MMCM was evaluated by the cox proportional hazard model, and the prognostic factors of MMCM were identified by Fine-Gray competing risk regression model. Furthermore, a nomogram was developed to predict the 1-year, 2-year, and 5-year MMCM and the performance was tested by a time-dependent area under the receiver operating characteristic (ROC) curve and calibration. Result 577 patients were included, with a median age of 62 (18–100) years old and a median overall survival time of 36 (0–176) months. The percentage of non-MMCM was 15.4% (n = 89) in the entire population and 21.7% (n = 54) in elderly patients. The multivariable Cox proportional hazard regression model revealed that older age and other tumor(s) before or after MM had an independently significant association with higher non-MMCM. After adjustment of competing causes of death, the multivariable Fine-gray regression model identified age group ≥ 65 year, tumor size > 5.3 cm, recurrent MM, and histologic type 9530/3 (Meningioma, malignant) had an independently significant association with higher MMCM. Compared with gross total (GTR) of tumor, subtotal resection of tumor (HR 1.66, 95%CI 1.08–2.56, P = 0.02), partial resection of lobe (HR 2.26, 95%CI 1.32–3.87, P = 0.003), and gross total resection of lobe (HR 1.69, 95%CI 1.12–2.51, P = 0.01) had an independently significant association with higher MMCM. Conclusion The competing risk nomogram including age group, tumor size, initial status, histologic type, and extent of resection is discriminative and clinically useful. This study emphasized the importance of the GTR of tumor in the treatment of MM patients, which had a significantly lower incidence of MMCM compared with biopsy, STR of tumor, partial resection of lobe, and GTR of lobe.

Abstract To examine the potential correlation between chemotherapy and the risk of individual of second primary endometrial cancer (SEC) in patients with rectal cancer (RC) and assess survival outcomes. The study employed the Surveillance, Epidemiology, and End Results database (SEER) as the primary data source, it encompasses a substantial cohort of patients diagnosed with RC between 1975 and 2018. This study involved a total of 30,847 individuals diagnosed with RC, of whom 168 individuals (5.45‰) experienced SEC. Among them, 107 patients (3.47‰) received chemotherapy treatment, while 61 patients (1.98‰) did not receive chemotherapy. The analysis of the overall occurrence of SEC revealed a significant association between SEC and chemotherapy treatment. Univariate and multivariate analyses confirmed a significant association between chemotherapy treatment and an increased risk of developing SEC in RC patients. Upon implementation of a dynamic analysis on the variables of relative risk and standardized incidence ratios, the results revealed that the likelihood of SEC escalated in tandem with advancing age. The examination of patients who developed SEC after receiving and not receiving chemotherapy revealed no substantial disparities in the 10-year overall survival (OS) and (cancer-specific survival) CSS rates. The results were the same after propensity score matching. Nevertheless, a notable discrepancy emerged when comparing the OS and CSS rates at 10 years between patients afflicted with SEC subsequent to chemotherapy and those afflicted with primary endometrial cancer, and the result was the same situation in the no-chemotherapy group. The use of chemotherapy in RC patients has been associated with an increased probability of developing specific SEC. Therefore, it is imperative to prioritize efforts aimed at reducing chemotherapy-related SEC occurrences and improving the prognosis of affected individuals.

Abstract The role of neoadjuvant chemotherapy and its benefits in patients with triple-negative breast cancer (TNBC) and small tumors are unclear. This study aims to compare survival differences between clinical T1 TNBC receiving neoadjuvant chemotherapy (NAC) and adjuvant chemotherapy (AC). Data for patients with clinical T1 TNBC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized according to whether they received chemotherapy before or after surgery. Propensity Score Matching (PSM) was used to minimize the influence of confounding factors. OS and BCSS were compared between the two treatment sequences using Kaplan–Meier and univariate and multivariable Cox proportional hazards regression analyses. The study included 6249 women with T1 TNBC. In multivariate analysis, compared with that in the AC group, the hazard ratio for death in the NAC group was 1.54 (95% confidence interval 1.26–1.89, p

Abstract Background and Purpose The tumour-node metastasis (TNM) classification is a common model for evaluating the prognostic value of tumour patients. However, few models have been used to predict the survival outcomes of patients with colorectal cancer liver metastasis (CRLM) with unresectable metastases who received the primary local surgery. Thus, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to establish novel nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods Extracted primary data on CRLM patients by local surgery from SEER database. All prognostic factors of OS and CSS were determined by Cox regression analysis. The concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were used to further evaluate the accuracy and discrimination of these nomograms. Decision curve analysis (DCA) was executed to evaluate the nomograms for the clinical net benefit. Risk stratification analysis (RSA) was used to evaluate the reliability of them in clinical. Results 3622 eligible patients were screened and assigned to training cohort (1812) or validation cohort (1810). The age, chemotherapy, tumour grade, primary tumour site, tumour size, lymph node positive rate (LNR), marital status, and carcinoembryonic antigen (CEA) were independent prognostic factors of OS. Additionally, the age, chemotherapy, tumour grade, primary tumour site, tumour size, LNR, and CEA were independent prognostic factors of CSS. The results of C-indexes and ROC curves indicated that the established nomograms exhibited better discrimination power than TNM classification. The calibration curves demonstrated excellent agreement between the predicted and actual survival rates for 1-, 3-, and 5 year OS and CSS. Meanwhile, the validation cohort demonstrated similar results. Background the clinic context, the DCA showed that these nomograms have higher net benefits, and the RSA showed that patients were further divided into low risk, medium risk, and high risk groups according to the predicted scores from nomograms. And, the Kaplan–Meier curve and log-rank test showed that the survival differences among the three groups are statistically significant. Conclusions The prognostic nomograms showed very high accuracy, identifiability, and clinical practicality in predicting the OS and CSS of CRLM patients with unresectable metastases treated by local surgery at 1-, 3-, and 5 years, which might improve individualized predictions of survival risks and help clinicians formulate treatment plans.

Abstract Currently, there is no consensus regarding the extent of surgery for stage I pulmonary carcinoid (PC) tumors, which encompass typical carcinoid (TC) and atypical carcinoid (AC) tumors. Sublobar resection includes segmental resection and wedge resection; the former is regarded as a type of anatomical resection that is better suited for tumor treatment. Therefore, it needs to be further verified whether differences exist in the effects of the two surgical methods on the survival time of patients. Propensity score matching (PSM) was used. The primary endpoints were cancer-specific survival (CSS) and overall survival (OS) time. Survival differences were analyzed via the Kaplan–Meier method and the log-rank test. There was no significant difference in survival between the sublobar resection and lobectomy groups after PSM in either the TC or AC tumor groups (all p > 0.05). A total of 1680 patients underwent pulmonary wedge resection (TC: n = 1547, AC: n = 133), and 398 patients underwent segmental resection (TC: n = 365, AC: n = 33). After PSM, there were no statistically significant differences in survival, regardless of whether OS or CSS was considered the primary endpoint (OS: p = 0.337; CSS: p = 0.470). Furthermore, segmental resection did not prolong patient survival time compared with wedge resection in different subgroup analyses on the basis of histology, age, and tumor size (all p > 0.05). Finally, the same results were obtained via multivariate Cox analysis (OS: p = 0.153; HR = 1.21; CSS: p = 0.351, HR = 1.32). Sublobar resection could be considered for patients with early-stage typical or atypical pulmonary carcinoid, provided that a rigorous lymph node evaluation is conducted. If the tumor is distant from the pulmonary hilum, either segmentectomy or wedge resection may be performed depending on the specific location of the tumor and the clinical condition of the patient.

Abstract We aimed to assess the cumulative incidences of cancer-specific mortality (CSM) in non-metastatic patients with non‑muscle invasive urothelial bladder cancer (NMIUBC) and establish competing risk nomograms to predict CSM. Patient data was sourced from the Surveillance, Epidemiology, and End Results database, as well as the electronic medical record system in our institution to form the external validation cohort. Sub-distribution proportional hazards model was utilized to determine independent risk factors influencing CSM in non-metastatic NMIUBC patients. Competitive risk nomograms were constructed to predict 3-year, 5-year, and 8-year cancer-specific survival (CSS) in all patients group, TURBT group and cystectomy group, respectively. The discrimination and accuracy of the model were validated through the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves. Decision curve analysis (DCA) and a risk stratification system was employed to evaluate the clinical utility of the model. Race, age, marital status, surgery in other sites, tumor size, histological type, histological grade, T stage and N stage were identified as independent risk factors to predict CSS in all patients group. The C-index for 3-year CSS was 0.771, 0.770 and 0.846 in the training, testing and external validation sets, respectively. The ROC curves showed well discrimination and the calibration plots were well fitted and consistent. Moreover, DCA demonstrated well clinical effectiveness. Altogether, the competing risk nomogram displayed excellent discrimination and accuracy for predicting CSS in non-metastatic NMIUBC patients, which can be applied in clinical practice to help tailor treatment plans and make clinical decisions.

Abstract Objective The prognostic factors of diffuse GC patients were screened the prognostic nomogram was constructed, and the prediction accuracy was verified. Methods From 2006 to 2018, there were 2877 individuals pathologically diagnosed with diffuse gastric cancer; the clinicopathological features of these patients were obtained from the SEER database & randomly divided into a training cohort (1439) & validation cohort (1438).To create prognostic nomograms & choose independent prognostic indicators to predict the overall survival (OS) of 1, 3, & 5 years, log-rank & multivariate COX analysis were utilized & discrimination ability of nomogram prediction using consistency index and calibration curve. Results Age, T, N, M, TNM, surgical status, chemotherapy status, & all seven markers were independent predictors of OS (P

Abstract Few studies have investigated the impact of primary tumor resection (PTR) on patients with distant metastasis medullary thyroid carcinoma (DMMTC). This population-based study aims to assess the application of PTR in DMMTC patients, ascertain its benefits, and identify optimal surgical indications. DMMTC Patients diagnosed between 2010 and 2020 were included through the Surveillance, Epidemiology, and End Results (SEER) program. Logistic regression analysis identified driving factors of surgical decision-making. Propensity score matching (PSM), Kaplan–Meier method, and Cox regression were utilized to compare overall survival (OS) and disease-specific survival (DSS) between surgical and non-surgical groups. Subgroup analyses were performed to determine optimal surgical indications. Of 238 DMMTC patients included, 122 (51.3%) patients underwent PTR. Extrathyroidal extension and N1 stage emerged as independent factors promoting the surgical decision. PSM-adjusted survival analyses revealed significant advantages in both OS and DSS for the surgical group. Moreover, subgroup analyses indicated that except for patients aged ≥ 65 years, tumors ≤ 20 mm, or with multiple metastasized sites (> 1), the others significantly benefit from PTR. PTR significantly improves prognosis in selected DMMTC patients. The decision to undergo PTR in other patients should be based on a comprehensive assessment of the disease, surgeon’s experience, and family discussions for potential survival benefits.

Introduction: Right hemicolectomy (RHC) remains the treatment standard for goblet cell adenocarcinoma (GCA), despite limited evidence supporting survival benefit. This study aims to explore factors influencing surgical management and survival outcomes among patients treated with RHC or appendicectomy using NCRAS (UK) and SEER (USA) data. Methods: A retrospective analysis was conducted using 998 (NCRAS) and 1703 (SEER) cases. Factors influencing procedure type were explored using logistic regression analyses. Overall survival (OS) probabilities and Kaplan–Meier (KM) plots were generated using KM analysis and the log-rank test compared survival between groups. Cox regression analyses were performed to assess hazard ratios. Results: The NCRAS analysis revealed that age and regional stage disease were determinants of undergoing RHC, with all age groups showing similar odds of receiving RHC, excluding the 75+ age group. The SEER analysis revealed tumour size > 2 cm, and receipt of chemotherapy were determinants of undergoing RHC, unlike the distant stage, which was associated with appendicectomy. Surgery type was not a significant predictor of OS in both analyses. In NCRAS, age and stage were significant predictors of OS. In SEER, age, stage, and Black race were significant predictors of worse OS. Conclusions: The study shows variations in the surgical management of GCA, with limited evidence to support a widespread recommendation for RHC.

Abstract Introduction Laryngeal cancers account for one-third of all head and neck cancers. We aimed to report the incidence trends of laryngeal cancer over 2000–2020 in the United States (US), by age, sex, race/ethnicity, and histological subtypes. Methods Data from the Surveillance, Epidemiology, and End Results 22 database were used to identify patients with laryngeal cancer based on the International Classification of Diseases for Oncology, version 3. Age-standardized incidence rates (ASIRs) for laryngeal cancer, adjusted for reporting delays, were calculated. The Joinpoint Regression Program was then utilized to determine annual percent changes (APCs) and average annual percent changes (AAPCs) in the trends. The analysis excluded data from 2020 to prevent potential bias related to the COVID-19 pandemic. Results A total of 104,991 cases of laryngeal cancer were identified in the US from 2000 to 2019. Squamous cell carcinoma was the predominant subtype, accounting for 94.53% of cases. Above 73.20% occurred among non-Hispanic whites, with the highest incidence observed among individuals aged 55–69 years (46.71%). The ASIRs were 5.98 and 1.25 per 100,000 population for men and women, respectively. Over 2000–2019, there was a significant reduction in ASIRs for laryngeal cancer in both sexes. Non-Hispanic black men exhibited the highest ASIR (9.13 per 100,000) and the largest decline in the ASIRs over 2000–2019 (AAPC: -3.26%). Conclusions Laryngeal cancer incidence rates showed a decline from 2000 to 2019, in addition to 2020, during the COVID-19 pandemic. Additional research is required to investigate risk factors and their influence on incidence rates of laryngeal cancer.

Abstract Purpose To address this evidence gap and validate short-term OS at less than 5 years as a reliable surrogate endpoint for 5-year OS. Methods We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database, focusing on non-metastatic NPC patients diagnosed between 2010 and 2015. Patients were categorized into radiotherapy and chemoradiotherapy groups. Results This retrospective study examined 2,047 non-metastatic NPC patients. Among them, 217 received radiotherapy, and 1,830 received chemoradiotherapy. Our analysis results indicated that the 4-year OS may serve as a reliable surrogate endpoint for patients with AJCC clinical stage I (80 vs. 78%, P = 0.250), regardless of the treatment received. Specifically, in the radiotherapy group, patients with stage I, T0-T1, and N0 NPC showed similar OS rates at 4 and 5 years (83 vs. 82%, P = 1.000; 78 vs. 76%, P = 0.250; 78 vs. 77%, P = 0.500, respectively). Similarly, patients with stage II-IV, T2-T4, and N1-3 NPC showed no significant difference in OS rates between 3 and 5 years (57 vs. 51%, P = 0.063; 52 vs. 46%, P = 0.250; 54 vs. 46%, P = 0.125, respectively) in the radiotherapy group. In the chemoradiotherapy group, only the 3-year OS rate did not significantly differ from that at 5 years in stage I patients (79vs. 72%, P = 0.063). Conclusions Our study suggests that short-term surrogate endpoints may be valuable for evaluating 5-year OS outcomes in NPC patients in non-endemic areas.

Abstract Small cell lung cancer (SCLC) patients exhibit significant heterogeneity in tumor burden, physical condition, and responses to initial treatment. This diversity in treatment responses can result in varying treatment outcomes. The primary objective of this study was to explore the patient demographics associated with improved survival outcomes through radiotherapy. Based on the SEER database, we identified 42,824 SCLC patients enrolled between 2004 and 2015. These patients were stratified into radiotherapy (n = 20,360) and non-radiotherapy groups (n = 22,464). We controlled for confounding factors using propensity score matching (PSM) analysis. Subsequently, Kaplan–Meier (KM) analysis was employed to evaluate the impact of radiotherapy on patients’ overall survival (OS) and cancer-specific survival (CSS). Cancer-specific mortality was further analyzed using competitive risk models. Cox analysis was also conducted to examine additional variables potentially affecting the survival of SCLC patients. We identified a total of 42,824 eligible patients, and following PSM, 13,329 patients were successfully matched in both the radiotherapy and non-radiotherapy groups. The KM analysis showed that the median OS was 9 months in the radiotherapy group and 6 months in the non-radiotherapy group. The median CSS was 10 months in the radiotherapy group and 7 months in the non-radiotherapy group. The 5-year OS and 10-year OS rates were 6.2% versus 1.6% in the radiotherapy group and 2.6% versus 0.8% in the non-radiotherapy group (P

Abstract Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.

Abstract With the rapid development of imaging technology and comprehensive treatment in modern medicine, the early diagnosis rate of breast cancer is constantly improving, and the prognosis is also improving; As breast cancer patients survive longer, the risk of developing second primary cancers increases. Since both breast and thyroid are Hormone receptor sensitive organs, which are regulated by hypothalamus pituitary target gland endocrine axis, changes in body endocrine status may lead to the occurrence of these two diseases in succession or simultaneously. This study extracted clinical data and survival outcomes of breast cancer patients registered in the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2019. After matching the case and controls with propensity scores, the selected patients were randomly split into training and test datasets at a ratio of 7:3. Univariate and multivariate COX proportional regression analysis is used to determine independent risk factors for secondary thyroid cancer and construct a column chart prediction model. Age, ethnicity, whether radiotherapy, tumor primary location, N stage, M stage were identified by Cox regression as independent factors affecting secondary thyroid cancer in patients with breast cancer patients, and a risk factor nomogram was established to predict patients’ 3 and 5 year survival probabilities. The AUC values for 3 and 5 years in the training set were 0.713, 0.707, and the c-index was 0.693 (95% CI 0.67144, 0.71456), and the AUC values for 3 and 5 years in the validation set were 0.681, 0.681, and the c-index was 0.673 (95% CI 0.64164, 0.70436), respectively.

Abstract Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Abstract Background Whether patients with cT1 − 2N1M0 breast cancer can benefit from postoperative radiotherapy (RT) after receiving neoadjuvant chemotherapy (NAC) has been controversial. Therefore, the purpose of this study was to explore whether postoperative RT can benefit this group of patients in terms of survival. Methods We used Surveillance, Epidemiology, and End Results (SEER) data to conduct a retrospective review of women with cT1 − 2N1M0 breast cancer diagnosed between 20 and 80 years of age who received NAC between 2010 and 2015. Our study compared the impact of postoperative RT on overall survival (OS) and cancer-specific survival (CSS) in breast cancer patients using propensity score matching (PSM) and performed subgroup analysis. Results This study finally included 1092 cT1 − 2N1M0 breast cancer patients. Regardless of the patient’s PSM status, postoperative RT was significantly associated with OS of cT1- 2N1M0 breast cancer patients who received NAC. Specifically, the 10-year OS rate was 78.7% before PSM matching, compared with 71.1% in patients who did not receive postoperative RT, and the difference was more significant after PSM matching, which was 83.1% and 71.1% respectively. However, postoperative RT did not significantly benefit CSS in patients with cT1 − 2N1M0 breast cancer who received NAC. The 10-year CSS rate was 81.4% VS 76.2% (P = 0.085) before PSM matching and 85.8% VS 76.2%(P = 0.076) after matching. Due to the intersection of OS and CSS curves, this restricted mean survival time (RMST) method was chosen as a supplement. After 60 months, the OS difference in RMST between the postoperative RT group and the non-radiotherapy (noRT) group was 7.37 months (95%CI: 0.54–14.21; P = 0.034), and the CSS difference was 5.18 months (95%CI: -1.31-11.68; P = 0.118). Subgroup analysis found that in patients with right-sided breast cancer, postoperative RT improved the patient’s OS (HR = 0.45, 95%CI: 0.21–0.95, P = 0.037) and CSS (HR = 0.42, 95%CI: 0.18–0.98, P = 0.045). Conclusions Our results showed that additional postoperative RT improved the OS of cT1 − 2N1M0 breast cancer patients who received NAC, but failed to improve their CSS. It is worth noting that in the subgroup analysis of patients with right-sided breast cancer, we observed significant improvements in OS and CSS. And further prospective studies are still needed to verify the effect of postoperative RT in different subgroups.

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare pathological histotype in ovarian cancer, while the survival rate of advanced OCCC (Stage III-IV) is substantially lower than that of the advanced serous ovarian cancer (OSC), which is the most common histotype. The goal of this study was to identify high-risk OCCC by comparing OSC and OCCC, with investigating potential risk and prognosis markers. Methods Patients diagnosed with ovarian cancer from 2009 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) Program. Logistic and Cox regression models were used to identify risk and prognostic factors in high-risk OCCC patients. Cancer-specific survival (CSS) and overall survival (OS) were assessed using Kaplan-Meier curves. Furthermore, Cox analysis was employed to build a nomogram model. The performance evaluation results were displayed using the C-index, calibration plots, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Immunohistochemically approach was used to identify the expression of the novel target (GPC3). Results In the Cox analysis for advanced OCCC, age (45–65 years), tumor numbers (total number of in situ/malignant tumors for patient), T3-stage, bilateral tumors, and liver metastases could be defined as prognostic variables. Nomogram showed good predictive power and clinical practicality. Compared with OSC, liver metastases had a stronger impact on the prognosis of patients with OCCC. T3-stage, positive distant lymph nodes metastases, and lung metastases were risk factors for developing liver metastases. Chemotherapy was an independent prognostic factor for patient with advanced OCCC, but had no effect on CSS in patients with liver metastases (p = 0.0656), while surgery was significantly related with better CSS in these patients (p

Abstract Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

Abstract Medullary Carcinoma of the Colon (MCC) is a rare histological subtype of colon cancer, and there is currently no recognized optimal treatment plan for it, with its prognosis remaining unclear. The aim of this study is to analyze the independent prognostic factors for MCC patients and develop and validate nomograms to predict overall survival (OS). A total of 760 patients newly diagnosed with MCC from 2004 to 2020 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to a training group and a validation group in a 7:3 ratio. Univariate and multivariable Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The nomogram prediction model was evaluated and validated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The study found that elderly women are more susceptible to MCC, and the ascending colon and cecum are the most common sites of involvement. MCC is poorly differentiated, with stages II and III being the most common. Surgery is the primary treatment for MCC. The prognosis for patients with stage IV MCC is poor, with a median survival time of only 10 months. Independent prognostic factors for MCC include age, N stage, M stage, surgery, chemotherapy, and tumor size. Among them, age 7 cm (HR = 1.66, 95%CI 1.20–2.30) were risk factors for colon medullary carcinoma. The results of ROC, AUC, calibration curves, and DCA demonstrate that the nomogram prediction model exhibits good predictive performance. We have updated the demographic characteristics of colon medullary carcinoma and identified age, N staging, M staging, surgery, chemotherapy and tumor size as independent prognostic factors for colon medullary carcinoma. Additionally, we have established nomograms for prognostic prediction. These nomograms can provide personalized predictions and serve as valuable references for clinical decision-making.

Abstract Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

Abstract Cancers of the kidney and renal pelvis are among the most prevalent types of urinary cancers. We aimed to outline the incidence trends of kidney and renal pelvis cancers by age, sex, race/ethnicity, and histology in the United States (US) from 2000 to 2020. The data was obtained from the Surveillance, Epidemiology, and End Results (SEER) 22 database. The identification of patients with kidney and renal pelvis cancers with morphologies of renal cell carcinoma, nephroblastoma, sarcoma, and neuroendocrine tumor was conducted utilizing the International Classification of Diseases for Oncology version 3. The average annual percent change (AAPC) were presented. All estimates were given in the form of counts and delayed age-standardized incidence rates (ASIRs) per 100,000 people. From 2000 to 2019, a total of 490,481 cases of kidney and renal pelvic cancer were recorded across all age groups in the US. The majority of them were among Non-Hispanic Whites (NHWs) (69.75%) and those aged 55–69 years (39.96%). The ASIRs per 100,000 for kidney and pelvis cancers were 22.03 for men and 11.14 for women. Non-Hispanic Black men had the highest ASIR (24.53 [24.24, 24.81]), and increase in ASIR over the 2000–2019 period (AAPC: 2.19% [1.84, 2.84]). There was a noticeable increase in incidence of kidney and renal pelvis cancers. Individuals aged 70–84 years had the highest ASIR for kidney and renal pelvis cancers. The COVID-19 era has resulted in a significant reduction in incidence rates across all demographics.

Abstract Background Supraglottic squamous cell carcinoma (SGSCC) is characterized by low differentiation, rapid growth, and inconspicuous initial manifestations. Early detection and prompt treatment can significantly improve survival rates. The main focus of treatment is to maintain optimal laryngeal function. Methods Using the Surveillance, Epidemiology, and End Results (SEER) database, we conducted univariate and multivariate Cox regression analyses to identify independent prognostic factors for T1–T2 SGSCC. We also enrolled 109 patients with T1–T2 SGSCC from the First Affiliated Hospital of Xinjiang Medical University as an external validation set. In addition, we developed a nomogram to predict the prognosis of T1–T2 SGSCC, assessed the predictive accuracy and discriminatory ability of the nomogram using the area under the curve (AUC), C-index, receiver operating characteristic (ROC) curve and calibration curve, and confirmed the clinical validity of the nomogram using decision curve analysis (DCA). Results Our investigation identified nine prognostic indicators for T1–T2 SGSCC: age (≥ 65 years), marital status, American Joint Committee on Cancer (AJCC) stage (II–IV), grade (III–IV), M stage (M1), radiotherapy, chemotherapy, sex (female), and surgery. These variables were used to create accurate nomograms that predict overall and specific survival rates at 1, 3, and 5 years. The nomograms demonstrated superior prognostic value and accuracy compared to AJCC staging. Laryngectomy with partial laryngectomy is the preferred treatment option for T1–T2 SGSCC cases, providing superior overall survival (OS) and cancer-specific survival (CSS). Radiotherapy also improves OS and CSS. Our results were based on a comprehensive analysis of various indicators, including the C-index, ROC curve, calibration curve, and DCA curve. Conclusion Nomograms provide significant advantages in treatment decision making and diagnosis. Laryngectomy with partial laryngectomy is the most appropriate method for T1–T2 SGSCC cases. However, radiotherapy can also be used. Thus, patients with T1–T2 SGSCC should be evaluated to determine if combination therapy is the optimal treatment approach. Nevertheless, further research is needed to understand the role of chemotherapy. Overall, this study identified nine key predictors of future outcomes, aiding healthcare professionals in assessing risks and making treatment decisions for T1–T2 SGSCC patients.

Background: Retinoblastoma (RB) is a rare primary malignant tumor primarily affecting children. Our study aims to compare the overall survival (OS) between pediatric and adult RB patients and establish a predictive model for adult RB patients' OS to assist clinical decision-making. Methods: This study retrospectively analyzed data from 1938 RB patients in the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2000 to 2015. Propensity score matching (PSM) ensured balanced characteristics between pediatric and adult groups. A Cox proportional hazards regression model was used to assess prognostic factors, and selected variables were utilized to construct a predictive survival model. The Nomogram model's performance was evaluated through the C-index, time-dependent ROC curves, calibration curves, and decision curve analysis (DCA). Results: Following PSM, adult RB patients had lower OS compared to pediatric RB patients. Independent prognostic factors for adult RB OS included age, gender, disease stage, radiation therapy, income, and diagnosis confirmation. In the training cohort, the Nomogram achieved a C-index for OS of 0.686 and accurately predicted 2-year, 3-year, and 5-year OS with AUC values of 0.672, 0.680, and 0.660, respectively. The C-index, time-dependent ROC curves, calibration curves, and DCA in both training and validation cohorts confirmed the Nomogram's excellent performance. Conclusion: In this study, adult RB patients have worse OS than pediatric RB patients. Consequently, we constructed a Nomogram to predict the risk for adult RB patients. The Nomogram demonstrated good accuracy and reliability, making it suitable for widespread application in clinical practice to assist healthcare professionals in assessing patients’ prognoses.

Abstract Objectives This paper aims to comprehensively analyze trends in gynecological cancers among elderly women in the United States from 1975 to 2020. Methods Surveillance, Epidemiology, and End Results (SEER) population data were utilized for the analysis. Annual Percentage Change (APC) and Average APC were estimated using join‐point regression to assess trends in mortality rates. Results The study reveals an increasing pattern of incidence and mortality in all gynaecological cancer sites except cervical cancer among elderly. The incidence of cervical cancer decreased from 1975 to 2007 and then increased, whereas cancer‐specific mortality decreased from 1977 to 2020, indicating positive advancements in detection and treatment. Conclusions Despite progress in managing certain gynecological cancers, challenges persist, particularly evidenced by increasing mortality rates for cancers in other female genital organs. This underscores the necessity for sustained research efforts and targeted interventions to address these ongoing challenges effectively.

Abstract Cancer-directed surgeries (CDS) play a crucial role in prostate cancer (PCa) management along with possible survival and therapeutic benefits. However, barriers such as socioeconomic factors may affect patients’ decision of refusing recommended CDS. This study aimed to uncover risk factors and the impact on survival associated with CDS refusal. We retrospectively reviewed the Surveillance, Epidemiology, and End Results database for patients diagnosed with PCa between 2000 and 2019. Multiple sociodemographic and clinical characteristics were extracted to assess predictors for physicians’ surgical recommendations and patients’ surgical refusal, respectively. Propensity score matching was performed to balance the covariates. The impact of surgical refusal on mortality risk was also investigated. A total of 185,540 patients were included. The physician’s recommendation of CDS was significantly influenced by the patient’s age, race, income, home location, diagnosis year, Gleason score, prostate-specific antigen (PSA), and TNM stage. About 5.6% PCa patients refused CDS, most of whom were older, non-White race, lack of partners, living outside of metropolitan areas, with higher PSA or lower clinical TNM stage. Patients who refused CDS had an increased risk of cancer-specific mortality and overall mortality than those who performed CDS. Physicians may weigh a host of sociodemographic and clinical factors prior to making a CDS recommendation. Patients’ refusal of recommended CDS affected survival and was potentially modifiable by certain sociodemographic factors. Physicians should fully consider the hindrances behind patients’ CDS refusal to improve patient-doctor shared decision-making, guide patients toward the best alternative and achieve better outcomes.

Abstract The survival significance of the number of positive lymph nodes in salivary gland carcinoma remains unclear. Thus, the current study aimed to determine the effect of the number of positive lymph nodes on disease-specific survival (DSS) and overall survival (OS) in cN0 mucoepidermoid carcinoma (MEC) of the major salivary gland. Patients surgically treated for MEC of the major salivary gland between 1975 and 2019 were retrospectively enrolled from the surveillance, epidemiology, and end results database. The total population was randomly divided into training and test groups (1:1). Primary outcome variables were DSS and OS. Prognostic models were constructed based on the independent prognostic factors determined using univariate and multivariate Cox analyses in the training group and were validated in the test group using C-index. A total of 3317 patients (1624 men and 1693 women) with a mean age of 55 ± 20 years were included. The number of positive lymph nodes was an independent prognostic factor for both DSS and OS, but the effect began when at least two positive lymph nodes for DSS and three positive lymph nodes for OS were found. Predictive models for DSS and OS in the training group had C-indexes of 0.873 (95% confidence interval [CI] 0.853–0.893) and 0.835 (95% CI 0.817–0.853), respectively. The validation of the test group showed C-indexes of 0.877 (95% CI 0.851–0.902) for DSS and 0.820 (95% CI 0.798–0.842) for OS. The number of positive lymph nodes was statistically associated with survival in cN0 major salivary gland MEC. The current prognostic model could provide individualized follow-up strategies for patients with high reliability.

Abstract Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

Abstract To develop and validate a nomograph to predict the long-term survival probability of cervical cancer (CC) patients in Asia, Surveillance, Epidemiology, and End Results (SEER) were used to collect information about CC patients in Asia. The patient data were randomly sampled and divided into a training group and a validation group by 7:3. Least absolute shrinkage and selection operator (LASSO) regression was used to screen key indicators, and multivariate Cox regression model was used to establish a prognostic risk prediction model for CC patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were adopted to comprehensively evaluate the nomogram model. LASSO regression and multivariate Cox proportional hazards model analysis showed that age, American Joint Committee on Cancer (AJCC) Stage, AJCC T, tumor size, and surgery were independent risk factors for prognosis. The ROC curve results proved that the area under curve (AUC) values of the training group in 3 and 5 years were 0.837 and 0.818, The AUC values of the validation group in 3 and 5 years were 0.796 and 0.783. DCA showed that the 3- and 5-year overall survival (OS) nomograms had good clinical potential value. The nomogram model developed in this study can effectively predict the prognosis of Asian patients with CC, and the risk stratification system based on this nomogram prediction model has some clinical value for discriminating high-risk patients.

Huimin Guo,1,&ast; Songkun Gao,2,&ast; Weimin Kong1 1Gynecology Department, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China; 2Gynecologic Oncology Department, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Weimin Kong, Gynecology Department, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing, 100006, People’s Republic of China, Email kwm1967@ccmu.edu.cnObjective: In current most observational studies, the prognosis of cervical adenocarcinoma is worse than that of cervical squamous cell carcinoma. However, most of the current studies are holistic and lack more detailed staging and grouping analysis of the prognosis of the two types of cervical tumors.Patients and Methods: Inclusion from the SEER database of stage IIB-IVA cervical squamous cell carcinoma and cervical adenocarcinoma patients who did not undergo surgery from 2000 to 2019, underwent radiotherapy/chemotherapy/radiotherapy and chemotherapy/no treatment, and then propensity score matching (PSM) was performed to eliminate confounding factors between cervical squamous cell carcinoma and cervical adenocarcinoma patients with the same stage and treatment method. After matching the original data and propensity score, logarithmic rank test and chi square test were used to evaluate the survival benefits of different stages and treatment methods for patients using Kaplan Meier curve. The prognosis of two types of cervical tumors under the same treatment method was compared, and factors that may cause poor prognosis were analyzed, excluding confounding factors.Results: A total of 10,057 patients were included in this study, and survival analysis showed a significant correlation between the treatment method used and patient prognosis (P< 0.05). However, for patients who received radiotherapy or no special treatment, OS and CSS were only related to tumor stage and not to tumor type. In patients undergoing radiotherapy and chemotherapy, the OS and CSS of stage IIIA and IVA patients are not related to tumor pathological characteristics, while the OS of stage IIB patients is not related to tumor properties after PSM.Conclusion: In patients undergoing radiotherapy and chemotherapy, the OS and CSS of stage IIIA and IVA patients were not related to histological type, while the OS of stage IIB patients was not related to histological type after PSM.Keywords: cervical squamous cell carcinoma, cervical adenocarcinoma, staging, prognosis, SEER