Surveillance, Epidemiology, And End Results Research Articles

The Long-Term Results of Proximal Gastrectomy for Proximal Gastric Cancer: A Propensity Score Matching Analysis Based on SEER Database.

Proximal gastrectomy (PG) is one of function-preserving gastrectomy (FPG). In this study, we compared the long-term results of proximal gastric cancer (PGC) patients undergoing proximal gastrectomy and total gastrectomy (TG). Patients diagnosed with PGC and receiving PG or TG between 2004 and 2020 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was applied to minimize confounding factors. Kaplan-Meier analysis and log-rank test were used to compare overall survival (OS) and cancer-specific survival (CSS) between the PG and TG groups. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors affecting OS. A total of 3916 patients were recruited according to the inclusion and exclusion criteria, with 2614 undergoing PG and 1302 undergoing TG. After 1:1 PSM matching, 912 pairs of data were included for analysis. Before PSM matching, PG group tended to have better OS and CSS outcomes. However, after PSM matching, both surgical approaches showed similar long-term results. PG for PGC yields comparable long-term outcomes to TG and demonstrates safety in terms of oncologic outcomes.

Survival Benefits of Postoperative Chemotherapy in Patients With Colorectal Mucinous Adenocarcinoma: An Analysis Utilizing Propensity Score Matching From the Surveillance, Epidemiology, and End Results Database.

This study aimed to investigate the characteristics of patients with colorectal mucinous adenocarcinoma (MAC) who benefit from postoperative chemotherapy (POCT) and to develop effective postoperative survival nomograms for predicting overall survival (OS) in colorectal MAC patients. Data of colorectal MAC patients who underwent surgery from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020 were collected. Patients were grouped based on POCT, and intergroup analysis was performed using 1:1 propensity score matching (PSM). Kaplan-Meier (K-M) curves were used to compare the prognosis between the 2 groups. Cox analysis was employed to identify factors associated with OS in patients with colorectal MAC who underwent POCT. The variance inflation factor (VIF) and bilateral stepwise regression were used to determine factors included in the model. Additionally, a nomogram was constructed to predict postoperative survival outcomes for patients. The discriminative ability of the nomograms was evaluated using the C-index and calibration curve analysis, the decision curve analysis (DCA) assessed the clinical utility of the nomogram, and the receiver operating characteristic (ROC) curve evaluated the nomograms' performance. This study encompassed 6829 patients with colorectal MAC, among whom 2258 received POCT, and 4571 did not. Whether pre or post PSM, patients in the POCT group consistently exhibited a superior median OS compared to those in the postoperative non-chemotherapy group (P < .0001). For colorectal MAC patients undergoing POCT, OS was correlated with factors such as patient age, carcinoembryonic antigen levels, tumor deposits, perineural invasion (PNI), lymph node examination count, T staging, and Grade staging. Notably, a significant chemotherapy advantage was observed in patients without perineural invasion, those with lymph node examination counts exceeding 12, and patients with moderately differentiated tumors. The overall colorectal MAC patient postoperative OS predictive nomogram demonstrated a C-index of .74, with a calibration curve near the diagonal and a DCA curve indicating positive net benefits. In comparison to TNM staging, the ROC curves of the nomogram at 1 year, 3 years, and 5 years demonstrated superior predictive capabilities (AUC: .80 vs .71, .78 vs .71, .77 vs .70). This study revealed the characteristics of colorectal MAC patients who benefit from POCT and established effective prognostic nomograms, which can aid clinicians in designing personalized treatment plans for individual patients and promote precision medicine.

Survival Improvement of StageIV Non-small Cell Lung Cancer in the Immunotherapy Era: A Retrospective Cohort Study in a US Population.

Immune checkpoint inhibitors (ICIs) greatly improved outcomes of stageIV non-small cell lung cancer (NSCLC) in randomized clinical trials. Limited data exists regarding the survival improvement of ICI use at the population level. Clinical data of patients with pathologically confirmed stageIV NSCLC diagnosed in 2013 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Survival outcomes were compared before and after ICI use to assess the survival improvement in the immunotherapy era in the real world. A total of 19,433 patients with stageIV NSCLC were included. Being female, age < 60years, of American Indian and Asian or Pacific Islander race, married, non-squamous histology, without bone, brain, or liver metastases, and receiving chemotherapy were significantly associated with better prognosis in multivariable analyses. Propensity score matching (PSM) based on associated factors resulted in 8743 patients each in the non-immunotherapy and immunotherapy groups (1:1 ratio). After PSM, both overall survival (OS) (p < 0.001) and cancer-specific survival (CSS) (p < 0.001) were significantly improved in the immunotherapy group. The median OS (mOS) was 6.0months in the non-immunotherapy group and 8.0months in the immunotherapy group. The 1-, 2-, and 3-year OS rate was 29.0%, 14.2%, and 8.5% in the non-immunotherapy group, and 37.8%, 23.5%, and 16.7% in the immunotherapy group, respectively. Patients who were male, under 60years old, married, white, had adenocarcinoma, had no bone or liver metastases, and received chemotherapy or radiation therapy were more likely to benefit from immunotherapy. Survival outcomes of patients with stageIV NSCLC were significantly improved in the immunotherapy era. Population-level benefits of survival varied among subgroups, and not all the increase in OS meant a clinically meaningful benefit.

Rurality and outcomes in prostate cancer: role of socioeconomic status and educational attainment

Abstract Background/Introduction Patients diagnosed with prostate cancer (PC) have a higher cardiovascular disease (CVD) prevalence and mortality risk. Rurality have been associated with up to 33% higher CVD risk and 44% higher all-cause mortality risk in patients with PC. Purpose Analyze the impact of rurality (from both patient and provider perspectives) on the development of adverse outcomes after a PC diagnosis according to socioeconomic and educational status. Methods We included men aged ≥66 with a new primary PC diagnosis (defined as ICD-10 = C61) from 2009-2017 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. The primary outcomes were CVD (comprising heart failure, atrial fibrillation, acute myocardial infarction, peripheral artery disease, and ischemic stroke), PC-specific mortality (PCsm), cardiovascular disease mortality (CVDm), and all-cause mortality. Patients living in areas with a population of &amp;lt;2,500 or &amp;lt;20,000, not adjacent to a metropolitan area, were considered to have a positive rural status. The provider status (metropolitan vs. non-metropolitan) was defined according to the 2013 National Center for Health Statistics' Urban-Rural Classification Scheme for Counties. Low socioeconomic status (SES) was defined according to Yost Index quintiles. Low educational status was defined according to percentiles of people &amp;gt;25 years old with less than a high school diploma. Multivariable Fine-Gray models, accounting for competing mortality/type of mortality, and Cox regression models were applied. Results We included 75,221 patients with PC (Picture 1), of which 1,828 were from rural areas and 362 were from rural areas with providers from non-metropolitan areas. Among patients from rural areas under the care of non-metropolitan providers, 82.6% had low SES (Yost Index ≤ 2). In this subgroup, patients from rural areas with non-metropolitan providers had 30% higher CVD risk (sHR 1.30; 95% CI 1.04-1.62) and 44% higher all-cause mortality risk (sHR 1.44; 95% CI 1.14-1.83; Picture 2) when compared to patients with low SES from urban or rural areas with providers from metropolitan areas. Additionally, within patients from rural areas with non-metropolitan providers, the median % of people with only high school diploma was 38.3 (IQR 36.2-40.9). Among those with low educational status (≤41% of high school county-rate), patients from rural areas with non-metropolitan providers had a 35% higher associated CVD risk (sHR 1.35; 95% CI 1.06-1.71), 54% higher associated CVDm risk (sHR 1.54; 95% CI 1.01-2.36), and a 32% higher associated all-cause mortality risk (sHR 1.32; 95% CI 1.04-1.66). Conclusion Patients with PC from rural areas with non-metropolitan providers have higher rates of low SES and low educational status and suffer from up to 54% higher associated risks of adverse outcomes, emphasizing the role of multiple domains of the Social Determinants of Health in health outcomes.

Prognostic factors and predictive models for primary pulmonary diffuse large B-cell lymphoma: a population-based analysis

ABSTRACT Background Primary pulmonary diffuse large B-cell lymphoma (PP-DLBCL) is a rare extranodal non-Hodgkin's lymphoma (EN-NHL). Its prognosis as an aggressive lymphoma is abysmal, and predictive models are still lacking. Methods We screened patients diagnosed with PP-DLBCL between 2010 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. Then, univariate and multivariate COX regression analyses were used to identify independent risk factors affecting patient prognosis. Finally, a novel nomogram was constructed and the model was evaluated by looking at three dimensions. Results A total of 831 patients were included in this study. Most of the patients were elderly (526 (63.8%)) and female (428 (51.9%)). The included patients were randomized in a 7:3 ratio into a training group (577 (70%)) and a validation group (248 (30%)). We concluded that the independent risk factors of prognosis were age, extrapulmonary metastasis, radiotherapy, chemotherapy, and surgical intervention. The results of receiver operating characteristic curves, calibration curves, and decision curve analysis in the training and validation groups confirmed that the risk prediction nomogram could accurately predict the survival of PP-DLBCL. Conclusion This study is the first large population-based clinical data study on PP-DLBCL. A novel predictive model about prognosis has been developed to help clinical decision-making.

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases.

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients. Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90). We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group. Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Assessing the Effect of the COVID-19 Pandemic on 1-Year Cancer Survival in the United States.

The COVID-19 pandemic had a significant impact on health care delivery. We used the Surveillance, Epidemiology, and End Results (SEER) data to assess changes in 1-year relative survival and competing risk probabilities of cancer and non-cancer death for patients diagnosed in 2018 Q2 (pre-pandemic) and 2020 Q2 (pandemic). For all cancer sites combined, 1-year relative survival declined from 82.3% in 2018 Q2 to 77.5% in 2020 Q2, with the steepest declines seen in stomach, leukemia, and liver cancers. However, survival improved nearing pre-pandemic levels during 2020 Q3. Competing risk survival measures revealed that the decline in 1-year survival was driven by increases in both the probability of dying of cancer (rising from 15.4% to 19.2%) and of other causes, including COVID (rising from 3.8% to 5.2%). The pandemic led to significant declines in survival and increased mortality from both cancer and other causes for patients diagnosed in 2020 Q2.

Intraoperative radiotherapy might not serve as a standard therapy for retroperitoneal liposarcoma: insights from a population-based propensity score-matched study

BackgroundDifficulty in achieving complete resection leads to a poor prognosis for retroperitoneal soft tissue sarcoma, hence emphasizing the significance of adjuvant treatment. The benefit of preoperative radiotherapy for retroperitoneal liposarcoma was initially demonstrated by the STRASS trial. However, the impact of intraoperative radiotherapy (IORT) on retroperitoneal liposarcoma remains unexplored.MethodPatients with retroperitoneal liposarcoma were identified in the Surveillance, Epidemiology, and End Results (SEER) database, treated between 2000 and 2019. Subsequently, a 1:1 propensity score-matched (PSM) analysis was conducted based on variables identified from a multivariate analysis. T-tests were used to assess differences in normally distributed continuous variables, while the rank-sum test was applied to variables that did not follow a normal distribution. The chi-squared test was utilized to evaluate differences in categorical variables. Ultimately, survival analysis was performed using SPSS to evaluate patient prognosis.ResultA total of 2129 patients with retroperitoneal liposarcoma were included in our study. Age, sex, histology, grading, chemotherapy, and tumor size as independent prognostic risk factors for these patients through multivariate Cox regression analysis. Subsequently, 66 patients were included in the survival analysis through PSM, with 33 patients receiving IORT. Finally, the survival analysis revealed that there was no difference in overall survival among patients with retroperitoneal liposarcoma, regardless of whether they received IORT or not (p= 0.711).ConclusionAs an exploratory study, our findings suggest that patients may not derive benefit from intraoperative radiotherapy. These observations are intended to lay the groundwork for future prospective clinical studies.

Primary diffuse large B-cell lymphoma of bone in adults: A SEER population-based study.

Primary diffuse large B-cell lymphoma of the bone (PB-DLBCL) is an extremely rare type of extra-nodal lymphoma. The clinical characteristics, management, and survival outcomes of adult PB-DLBCL patients remain poorly defined. To explore the clinical manifestations, staging, therapeutic options, prognostic factors and outcomes of adult patients with PB-DLBCL and to create a model to predict survival outcomes. Data of adult PB-DLBCL patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program 18 registries database from 2000 to 2018. The Kaplan-Meier survival analysis was conducted to calculate survival rates. Univariate Cox regression, best subset selection (BESS), and least absolute shrinkage and selection operator (LASSO), followed by backward stepwise multivariable Cox regression, were used to construct the nomogram. The nomograms were evaluated using the concordance index (C-index), calibration curves and decision curve analysis (DCA). Diffuse large B-cell lymphoma (DLBCL) (67.51%) was the most frequent type of primary bone lymphoma. The most involved sites were the spine and lower-limb long bones. For the whole cohort, the 3-, 5-, 10- and 15-year overall survival (OS) rates were 74.9%, 70.5%, 60.0%, and 49.9%, and corresponding disease-specific survival (DSS) rates were 79.7%, 77.8%, 75.1%, and 71.4%, respectively. For OS, age, Ann Arbor stage, primary site and therapy were confirmed as final factors to develop the nomogram in adult PB-DLBCL patients, whereas for DSS, Age, marital status, Ann Arbor stage, number of bone lesions, therapy and year of diagnosis were confirmed as final factors in developing the nomogram. The nomograms demonstrated good accuracy and clinical utility. Established nomograms can accurately predict the survival of patients with PB-DLBCL and help clinicians optimize treatment.

Life Expectancy in High-Grade Incidental Prostate Cancer Patients Versus Population-Based Controls According to Treatment Type.

To quantify the differences in 5-year overall survival (OS) between high-grade (Gleason sum 8-10) incidental prostate cancer (IPCa) patients and age-matched male population-based controls, according to treatment type: no active versus active treatment. We relied on the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) to identify not actively treated and actively treated high-grade IPCa patients. For each case, we simulated an age-matched male control (Monte Carlo simulation), relying on Social Security Administration Life Tables (2004-2020) with 5 years of follow-up. Additionally, we relied on Kaplan-Meier plots to display OS for each treatment type. Multivariable Cox regression models were fitted to predict overall mortality (OM). Of 564 high-grade IPCa patients, 345 (61%) were not actively treated versus 219 (39%) were actively treated, either with radical prostatectomy or radiotherapy. Median OS was 3 years for not actively treated high-grade IPCa patients, with OS difference at 5 years follow-up of 27% relative to their age-matched male population-based controls (37% vs. 64%). Median OS was 8 years for actively treated high-grade IPCa patients, with OS difference at 5 years follow-up of 6% relative to their age-matched male population-based controls (68% vs. 74%). In the multivariable Cox regression model, active treatment independently predicted lower OM (hazard ratio = 0.6; 95% confidence interval = 0.4-0.8; p < 0.001). Relative to Life Tables' derived age-matched male controls, not actively treated high-grade IPCa patients exhibit drastically worse OS than their actively treated counterparts. These observations may encourage clinicians to consider active treatment in newly diagnosed high-grade IPCa patients.

Association Between Insurance Status and Nonelderly Penile Squamous Cell Carcinoma Survivorship: A National Retrospective Analysis

Background: Penile squamous cell carcinoma is an aggressive malignancy with significant physical and psychological impacts. Socioeconomic factors influence prognosis in genitourinary cancers, making the investigation of insurance status critical for reducing cancer burden and promoting health equity. Materials and Methods: Men diagnosed with primary penile squamous cell carcinoma from 2007 to 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) national database. Participants were categorized based on insurance status: privately insured, Medicaid, and uninsured. Pearson’s chi-squared test assessed the distribution of observed frequencies between the patient demographics, socioeconomic status, tumor characteristics, and surgical variables across the insurance groups. Overall and cancer-specific survival was estimated using a multivariate Cox hazards proportional model analysis. Results: The multivariate Cox hazards proportional model showed that, compared to privately insured patients, Medicaid patients had an increased risk for overall death (hazard ratio [HR] = HR 1.54; 95% CI, 1.12–2.07). For cancer-specific mortality, Medicaid patients had an increased risk of death compared to privately insured patients (HR 1.58; 95% CI, 1.11–2.25). Conclusions: Medicaid does not mitigate the differences caused by health insurance status due to health insurance disparities for overall or cancer-specific mortality. Lower Medicaid reimbursements and out-of-pocket costs lead to a narrow network of physicians, hospitals, and treatment modalities that compromise health equity. Increasing awareness of health insurance disparities and improving access to care via a clinician–community–governmental partnership can potentially lead to improved predictive outcomes.

Machine learning predictive models and risk factors for lymph node metastasis in non-small cell lung cancer

BackgroundThe prognosis of non-small cell lung cancer (NSCLC) is substantially affected by lymph node metastasis (LNM), but there are no noninvasive, inexpensive methods of relatively high accuracy available to predict LNM in NSCLC patients.MethodsClinical data on NSCLC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for LNM were recognized LASSO and multivariate logistic regression. Six predictive models were constructed with machine learning based on risk factors. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Subgroup analysis with different T-stages was performed on an optimal model. A webpage LNM risk calculator for optimal model was built using the Shinyapps.io platform.ResultsWe enrolled 64,012 NSCLC patients, of whom 26,611 (41.57%) had LNM. Using multivariate logistic regression, we finally identified 10 independent risk factors for LNM: age, sex, race, histology, primary site, grade, T stage, M stage, tumor size, and bone metastases. GLM is the optimal model among all six machine learning models in both the training and validation cohorts. Subgroup analyses revealed that GLM has good predictability for populations with different T staging. A webpage LNM risk calculator based on GLM was posted on the shinyapps.io platform (https://wubopredict.shinyapps.io/dynnomapp/).ConclusionThe predictive model based on GLM can be used to precisely predict the probability of LNM in NSCLC patients, which was proven effective in all subgroup analyses according to T staging.

Long-term Survival Outcomes in Locally Advanced Breast Cancer after Mastectomy with or without Breast Reconstruction.

There is ongoing debate about the safety of breast reconstruction for patients with locally advanced breast cancer (LABC) who have undergone total mastectomy (TM). More and more LABC patients are undergoing breast reconstruction after TM, but its long-term survival outcomes remain unclear. This study aims to compare the survival outcomes of LABC patients who underwent breast reconstruction after TM with those who did not, based on a large sample. We collected data for all LABC patients who underwent TM with or without breast reconstruction in the Surveillance, Epidemiology, and End Results (SEER) database. We divided patients into two groups: TM group and total mastectomy with reconstruction (TM+R) group. The primary outcomes were overall survival (OS) and breast cancer-specific survival (BCSS). Propensity score matching (PSM) analysis was used to eliminate imbalances of baseline data between the in tow groups. Data were analyzed using chi-square tests, Kaplan-Meier methods, and univariate and multivariate cox regression analyses. We identified 39,112 eligible patients (33,169 patients received TM and 5,943 received TM+R), and 8,680 patients were matched after PSM (4,340 patients received TM and 4,340 received TM+R). Patients with middle age, white, married, lived in urban, IIB-IIIA stage, invasive ductal carcinoma (IDC), pathological grade II-III, hormone receptor positive, and undergone chemotherapy were more likely to receive breast reconstruction. After PSM, Kaplan-Meier survival analysis showed better OS and BCSS in the TM+R group versus the TM group (OS:P&lt;0.001; BCSS: P=0.008). Multivariate cox regression analysis showed that TM+R significantly improved OS and BCSS (OS: hazard ratio (HR) 0.73, 95% CI [0.68,0.79], P&lt;0.001; BCSS: 95% CI [0.79,0.94], P=0.001). Subgroup analysis showed that patients with old age, white, and hormone receptor positive had better OS and BCSS by TM+R compared to TM. Breast reconstruction after total mastectomy is associated with better OS and BCSS in patients with LABC.

Impact of distant metastasis on overall survival and cancer specific survival of elderly patients with differentiated thyroid cancer

Distant metastases are common in most elderly patients, because elderly patients are diagnosed with advanced thyroid cancer due to delayed diagnosis. There is still few specific real-world data regarding prognosis in the elderly with differentiated thyroid cancer (DTC). The purpose of this study is to evaluate the prognostic factors and survival rate of elderly DTC patients with metastasis. This retrospective study included 14,603 elderly patients diagnosed with DTC from 2010 to 2015, including 447 patients with distant metastasis via the Surveillance, Epidemiology and End Results (SEER) database. The prognostic factors of overall survival (OS) and cancer-specific survival (CSS) in elderly DTC patients with metastasis or non-metastasis were determined by univariate and multivariate analysis. Age, primary site operation, radiotherapy, chemotherapy and tumor size are associated with OS and CSS in elderly DTC patients with distant metastasis. Compared with the patients without surgery, patients with total thyroidectomy showed significantly better OS. For the elderly DTC patients, radiotherapy was associated with improving OS and CSS. Chemotherapy increased the risk of death. For elderly DTC patients, early identification of distant metastasis, total thyroidectomy and radiotherapy are associated with better prognosis.

Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation

PurposeThe overall survival of patients with pancreatic cancer is extremely low. We aimed to establish machine learning (ML) based model to accurately predict three-year survival and prognosis of pancreatic cancer patients.MethodsWe analyzed pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic analysis were employed to select variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were evaluated by area under the curve (AUC), area under precision-recall curve (PRAUC), accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores and Balanced Accuracy (bacc) and F Beta Score (fbeta). An optimal ML model was constructed to predict three-year survival, and the predictive results were explained by SHapley Additive exPlanations (SHAP) framework. Meanwhile, 101 ML algorithm combinations were developed to select the best model with highest C-index to predict prognosis of pancreatic cancer patients.ResultsA total of 20,064 pancreatic cancer patients from SEER database was consecutively enrolled. We utilized eight clinical variables to establish prediction model for three-year survival. CatBoost model was selected as the best prediction model, and AUC was 0.932 [0.924, 0.939], 0.899 [0.873, 0.934] and 0.826 [0.735, 0.919] in training, internal test and external test sets, with 0.839 [0.831, 0.847] accuracy, 0.872 [0.858, 0.887] sensitivity, 0.803 [0.784, 0.825] specificity and 0.832 [0.821, 0.853] precision. Surgery type had the greatest effects on three-year survival according to SHAP results. For prognosis prediction, “RSF+GBM” algorithm was the best prognostic model with C-index of 0.774, 0.722 and 0.674 in training, internal test and external test sets.ConclusionsOur ML models demonstrate excellent accuracy and reliability, offering more precise personalized prognostic prediction to pancreatic cancer patients.

Income disparities have a significant impact on thyroid cancer recurrence and survival

PurposeIncome inequality profoundly impacts cancer outcomes, yet its specific effects on thyroid cancer remain unclear. Elucidating the influence of socioeconomic disparities is imperative to advance health equity and optimize patient care. This study evaluates associations between median household income and thyroid cancer recurrence and survival using national cancer registry data. Methods139,302 thyroid cancer patients undergoing surgery from 2000 to 2019 were analyzed from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were categorized by median annual household income at the county level (> or <$75,000). Multivariable regression determined the impact of income on recurrence and overall mortality. ResultsHigher-income patients had 26 % lower recurrence odds (OR 0.74, 95 %CI 0.55–0.99, p=0.042) and longer median survival (18.1 vs 17.7 years, p<0.001) compared to lower-income patients. On multivariate analysis, high income remained an independent predictor of reduced mortality after adjusting for demographics, tumor factors, and treatment (adjusted HR=0.84, 95 %CI=0.81–0.87, p<0.001). Cancer-directed surgery (HR=0.28, 95 %CI=0.26–0.30, p<0.001) and radioactive iodine (HR=0.69, 95 %CI=0.66–0.71, p<0.001) were associated with lower mortality risk. ConclusionsIncome disparities have a significant influence on thyroid cancer outcomes, including lower recurrence and reduced mortality. Targeting socioeconomic inequity could substantially reduce recurrence, improve survival, and promote health equity for all patients.

Prognostic prediction and treatment options for gastric signet ring cell carcinoma: a SEER database analysis

BackgroundIn recent years, the overall incidence of gastric cancer has decreased. However, the incidence of gastric signet ring cell carcinoma (SRCC) is still increasing year by year. Compared with other subtypes (non-SRCC) such as adenocarcinoma, SRCC usually exhibits a more aggressive biological behavior. Therefore, studying the prognostic differences and factors associated with SRCC is essential to improve the accuracy of diagnosis and prognosis. The purpose of this study was to investigate the prognostic factors influencing the prognosis of patients with SRCC and to develop personalized treatments for different subgroups of patients.MethodsThe data on gastric SRCC patients and gastric adenocarcinoma (AC) patients from 1992 to 2020 was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The data of gastric SRCC as the external validation group was reviewed from the First Affiliated Hospital of Soochow University. The overall survival (OS) and cancer specific survival (CSS) at 1 and 2 years were predicted for SRCC patients by constructing prognostic nomograms. A series of validation methods, including Akaike information criterion (AIC), decision curve analysis (DCA), calibration curve analysis, the concordance index (C-index) and the area under the receiver operating characteristic (AUC) curve, were used to verify the accuracy and reliability of the models.ResultsIn all, 549 patients with SRCC were included after propensity score matching (PSM). Multivariate Cox regression analysis showed that T stage, N stage, M stage and surgical approach were independent risk factors affecting the prognosis of SRCC patients. A prognostic nomogram was constructed and validated as an accurate model for SRCC patients after scoring by receiver operating characteristic curve (ROC) curves and calibration plots. The patients were further divided into high-risk and low-risk groups, and the Kaplan-Meier curves showed that SRCC patients in the low-risk group could receive only surgery without chemotherapy, while chemotherapy plus surgery was a better option for SRCC patients in the high-risk group.ConclusionThe prognosis for SRCC was less favorable than that of AC in terms of CSS. The nomograms were developed and validated to predict OS and CSS in patients with SRCC, helping in developing appropriate individualized treatment schedules.

Demographic Characteristics and Survival in Young-Onset Colorectal Neuroendocrine Neoplasms.

Recent epidemiological studies have revealed an upward trend in young-onset colorectal cancer (YOCRC) overall, whereas specific data on young-onset colorectal neuroendocrine neoplasms (YONEN) remain limited. This study investigated the demographic characteristics and survival trends in YONEN and compared these with those of young-onset colorectal adenocarcinoma (YOADC), the most common histologic subtype of YOCRC. A retrospective analysis was conducted from 2000 to 2019 using the Surveillance, Epidemiology, and End Results (SEER) database. Survival outcomes were assessed using univariate and multivariable Cox proportional models, with demographic differences evaluated via Wilcoxon rank sum and Chi-square tests. Out of 61,705 patients aged 20-49 with colorectal cancer, 8% had NEN, and 92% had adenocarcinoma. The YONEN cohort had a higher proportion of Black patients and a lower proportion of White patients than the YOADC cohort (21% vs. 13% and 44% vs. 57%, respectively). NEN was more commonly found in the rectum (79%), and adenocarcinoma was mostly colonic (57%) in origin. YONEN patients had better survival than YOADC patients. Multivariate analysis in YONEN patients revealed that Hispanic patients had better overall survival compared to White patients (HR 0.67, 95% CI 0.47-0.95, p = 0.024). Racial disparities should be investigated further to aid in policymaking and targeted interventions.

Histological variants of pancreatic ductal adenocarcinoma: a survival analysis.

Pancreatic ductal adenocarcinoma (PDAC) can be classified into distinct histological subtypes based on the WHO nomenclature. The aim of this study was to compare the prognosis of conventional PDAC (cPDAC) against the other histological variants at the population level. The Surveillance, Epidemiology and End Results (SEER) database was used to identify patients with microscopically confirmed PDAC. These patients were divided into 9 histological subgroups. Overall survival was assessed using the Kaplan-Meier method and Cox regression models stratified by tumor histology. A total of 159,548 patients with PDAC were identified, of whom 95.9% had cPDAC, followed by colloid carcinoma (CC) (2.6%), adenosquamous carcinoma (ASqC) (0.8%), signet ring cell carcinoma (SRCC) (0.5%), undifferentiated carcinoma (UC) (0.1%), undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) (0.1%), hepatoid carcinoma (HC) (0.01%), medullary carcinoma of the pancreas (MCP) (0.006%) and pancreatic undifferentiated carcinoma with rhabdoid phenotype (PUCR) (0.003%). Kaplan-Meier curves showed that PUCR had the worst prognosis (median survival: 2 months; 5-year survival: 0%), while MCP had the best prognosis (median survival: 41 months; 5-year survival: 33.3%). In a multivariable Cox model, several histological subtypes (i.e. CC, ASqC, SRCC, UCOGC) were identified as independent predictors of overall survival when compared to cPDAC. PDAC is a heterogenous disease and accurate identification of variant histology is important for risk stratification, as these variants may have different biological behavior.

A Predictive Model for Gastric Cancer-Specific Death after Gastrectomy: A Competing-Risk Nomogram

Background: We aimed to assess the likelihood of cause-specific death and other causes of death after gastrectomy for gastric cancer (GC). Additionally, a competing-risk nomogram was developed for patient counseling and decision-making. Methods: Eligible GC patients who had gastrectomy between 2007 and 2015 were included in the study from the Surveillance, Epidemiology, and End Results (SEER) database. Death from gastric cancer and death from other causes were considered as separate competing events. Cumulative incidence functions (CIF) were calculated for each event, and a competing-risk nomogram was developed. Results: Overall, 8,808 patients who underwent gastrectomy were analyzed. Among them, 4,659 (52.90%) died from gastric cancer and 1,284 (14.58%) died from other causes. The five-year cumulative incidence of cause-specific death for gastric cancer was 50.4%, and 10.2% for deaths from other causes. Several independent factors, such as age at diagnosis, tumor site, grade, size, lymph node examination results, pathological T status, pathological N status, metastatic status, Lauren classification, radiation, and chemotherapy, were found to be associated with gastric cancer-specific death. The nomogram, based on results from the competing risk regression model, demonstrated good performance. Conclusion: We have developed a nomogram aimed at predicting gastric cancer-specific mortality in patients following gastrectomy. The model has undergone internal validation, demonstrating good accuracy and reliability. It serves as useful tool that can assist physicians and patients in making more informed clinical decisions.