SEER Database Research Articles

Unlocking survival benefits: primary tumor resection in de novo stage IV breast cancer patients

ABSTRACT Background For patients with de novo stage IV breast cancer (BC), the conditions under which the primary tumor resection (PTR) may offer benefit remain unclear. Methods The SEER database provides treatment data for patients with de novo stage IV BC. We screened cases of metastatic BC diagnosed from 2010 to 2015, with primary endpoints of overall survival (OS) and cancer-specific survival (CSS). Results 9252 patients with stage IV de novo BC were enrolled. For OS, median survival time (MST) was 38 months with systematic treatment (ST) compared to 52 months with ST plus PTR (p < 0.001). For CSS, MST was 38 months for ST versus 54 months for ST plus PTR (p < 0.001). The results of the Cox proportional hazards regression analysis regarding PTR, for OS: bone metastasis (aHR 0.664, 95%CI 0.583–0.756, p < 0.001); liver-lung metastasis (aHR 0.528, 95%CI 0.327–0.853, p = 0.009). For CSS: bone metastasis (aHR 0.655, 95%CI 0.571–0.751, p < 0.001); liver-lung metastasis (aHR 0.549, 95%CI 0.336–0.889, p = 0.017). Kaplan-Meier analysis indicated that in patients with bone metastases and liver-lung metastases, PTR could improve survival outcomes. Conclusion Liver-lung metastases and bone metastases in patients with de novo stage IV BC could enhance both OS and CSS through PTR.

A predictive and prognostic model for metastasis risk and prognostic factors in gastrointestinal signet ring cell carcinoma.

This study aimed to predict metastasis risk and identify prognostic factors of gastrointestinal signet ring cell carcinoma (SRCC) using data from the SEER database, the largest cancer dataset in North America. Data were obtained from the SEER database, covering 17 cancer registries from 2004 to 2020. Demographic and clinical data included sex, age, race, tumor location, size, pathological grade, stage, overall survival time, and treatment modalities. Statistical analyses were conducted using SPSS and R software. Propensity Score Matching (PSM) ensured comparable baseline characteristics between gastric cancer (GC) and colorectal cancer (CRC) groups. LASSO regression analysis identified predictors of metastasis, leading to the construction of predictive models using the lrm function in R. Nomograms were visualized with the "rms" package and assessed via ROC curves, calibration curves, and decision curve analysis (DCA). Cox regression analyses identified prognostic indicators for overall survival (OS), and Kaplan-Meier curves compared OS between high-risk and low-risk groups. From 2004 to 2020, 7680 SRCC patients were identified, including 4980 GC and 2700 CRC patients. CRC patients were older and had larger tumors, higher staging, and worse differentiation. Nomograms demonstrated good discriminative ability, with AUCs of 0.704 and 0.694 for GC, and 0.694 and 0.701 for CRC in training and validation cohorts, respectively. The DCA curve indicates that this predictive model has a high gain in predicting metastasis and OS. The nomograms effectively predicted metastasis risk and OS in metastatic SRCC patients, offering clinical utility in stratifying patients and guiding treatment decisions, thereby enhancing personalized treatment approaches.

Risk factors of surgical compliance and impact on survival in patients with kidney cancer: a population-based, propensity score matching study.

To identify the impact of surgical compliance on survival in patients with kidney cancer and to explore the factors that influence surgical compliance. Clinical date of kidney cancer patients were identified from the SEER databases, and the patients were divided into surgical compliance group and surgical noncompliance group. Cox survival analysis and Kaplan-Meier curves were used to evaluate the effect of surgical compliance on overall survival (OS) and cancer-specific survival (CSS). A diagnostic nomogram was constructed to quantify individual differences in compliance, and receiver operating characteristics (ROC) curves and calibration curves were used to assess the accuracy of the nomogram. Propensity score matching (PSM) was performed to balance potential baseline confounding factors. Of the 133,950 patients eligible for surgical resection, 2,814 (2.1%) patients did not opt for surgery ultimately. Surgical noncompliance was associated with poor prognosis. In all patients, Cox regression analysis showed that surgical noncompliance was an independent predictor for OS [before: HR = 2.490, 95% CI 2.374-2.612, p < 0.001; after: HR = 2.380, 95% CI 2.202-2.573, p < 0.001] and CSS [before: HR = 2.490, 95% CI 2.318-2.675, p < 0.001; after: HR = 2.035, 95% CI 1.813-2.285, p < 0.001] of kidney cancer patients before and after PSM. Multivariable logistic regression revealed that older age, afro-american origin, lower household income, and advanced tumor grade were associated with surgical noncompliance. The ROC and calibration curves showed that the diagnostic nomogram had high predictive accuracy. Surgical compliance was an independent prognostic factor for OS and CSS in patients with kidney cancer, and surgical noncompliance was associated with poor survival.

The effect of thyroid cancer on the survival of patients with digestive system tumors using SEER database.

The number of cancer survivors worldwide is increasing every year, making secondary primary cancers (SPC) is a growing health threat. Studies have been conducted to investigate the risk of occurrence between digestive system tumors (DST) and thyroid cancer (TC). However, existing studies have tended to focus more on the risk of developing SPC and less on the impact of SPC on the survival of cancer survivors. In this study, using the Surveillance, Epidemiology, and End Results database, we aimed to explore the impact of TC on the survival of patients with DST by data between 2000 and 2018. The study employed the standardized incidence ratio to assess the relative risk of SPC, propensity score matching was conducted to mitigate confounding effects, Kaplan-Meier, Cox proportional risk model and competitive risk model were used to analyze the factors affecting the overall survival and cancer-specific survival. We furthermore explored the influence of pathological types and radiotherapy of TC on the survival of DST patients. 518,901 patients with DST only, 801 patients with TC occurring earlier than DST (TC-1st), and 744 patients with DST occurring earlier than TC (DST-1st) were included. The total incidence rate of small intestine cancer after TC was higher than that of the general population, and the incidence of TC after DST was higher than in the general population. DST patients with a history of TC had better overall survival and lower cancer-specific mortality and this difference was particularly significant in patients with DST-1st. In addition, radiotherapy for TC had no effect on cancer-specific mortality in patients with DST.

Investigating the impact of tumor size on survival outcomes in thymoma and thymic carcinoma patients using the SEER database.

This study aims to clarify the impact of tumor size on the prognosis of patients diagnosed with thymoma and thymic carcinoma, leveraging data from a population-based registry. Utilizing the SEER database, this retrospective analysis identified patients diagnosed with thymoma and thymic carcinoma from 2000 to 2020. Propensity score matching was employed to mitigate potential statistical biases between groups categorized by tumor size (≤ 6.5cm and > 6.5cm). The study included a total of 3857 patients, comprising 2688 with thymoma and 1169 with thymic carcinoma. Multivariate analysis demonstrated that tumors ≤ 6.5cm independently correlated with improved Cancer-Specific Survival (CSS) (p = 0.001; p < 0.001) and Overall Survival (OS) (p < 0 .001; p < 0.001) in both thymoma and thymic carcinoma cohorts. Subgroup analysis revealed that smaller tumors (≤ 6.5cm) conferred survival benefits in patients with Masaoka-Koga stage IIB thymomas and stage III/IV thymic carcinomas (thymoma: CSS: p < 0.0001; OS: p = 0.00045; thymic carcinoma: CSS: p = 0.028; OS: p = 0.014). Additionally, WHO type A/AB/B1 and type B2/B3 thymomas with tumors ≤ 6.5cm exhibited superior CSS (p = 0.005; p < 0.00018) and OS (p = 0.015; p = 0.0021). Through propensity matching analysis utilizing the SEER database, this study underscores the prognostic significance of tumor size in both early-stage thymoma and advanced-stage thymic carcinoma, identifying a critical threshold of 6.5cm. In the WHO classification, tumor size based on the cut-off value of 6.5cm has a greater impact on the prognosis of type B2/B3 (high-risk group) than A/AB/B1 (low-risk group).

Prognosis analysis and nomogram for predicting lateral lymph node metastasis in Medullary Thyroid Microcarcinoma.

Currently, the incidence rate of Medullary Thyroid Microcarcinoma (micro-MTC) has an increasing trend, but the incidence of LNM and prognosis were still ambiguous. We analyzed the status of neck LNM of micro-MTC patients and created a prognostic nomogram to predict the probability of lateral lymph node metastasis (LLNM) for clinical practice. This is a retrospective study included patients with micro-MTC from SEER database for the period from 2004 to 2017 and patients from our medical center for the period from 2011 to 2019. A nomogram was constructed and the accuracy and clinical practicability were separately tested by Harrell's C-indexes, calibration plots, Receiver operating characteristic curve (ROC) and decision curve analyses (DCA). A total of 413 patients with micro-MTC from SEER database and 64 patients with micro-MTC from our department enrolled in the study. There were 16.0% and 9.4% cases in SEER database and 39.1% and 25.0% cases in our department appeared LNM and LLNM, respectively. Besides, a nomogram was constructed to assess the incidence of LLNM with good C-index, which was 0.850 in training cohort and 0.856 in validation cohort. The results of the area under the curve (AUC) were 0.830 in training cohort, 0.801 in validation cohort and 0.832 in external testing cohort, respectively. A relatively high rate of LLNM than expected was found, which should be emphasized. The prediction model could facilitate clinicians to assess the probability of LLNM and make a personalized treatment strategy.

Development and validation of a nomogram to predict the survival and estimate surgical benefits for gastric cancer with liver metastasis receiving primary tumor resection

BackgroundSurgical treatment has been widely controversial for gastric cancer accompanied by liver metastasis (GCLM). This paper aims to develop and validate a nomogram to predict the survival and estimate surgical benefits for GCLM patients.MethodsA total of 616 GCLM patients from the Surveillance, Epidemiology, and End Results Program (SEER) database and 74 GCLM patients receiving primary tumor resection (PTR) from the Chinese center were included in this study. Patients from the SEER database were divided into training set (with PTR) (n=493) and non-operative set (without PTR) (n=123). Patients undergoing PTR from China were included as external validation set. Independent risk factors associated with the overall survival of GCLM patients undergoing PTR were identified in the training set via log-rank test and Cox regression analysis. Afterwards, a comprehensive model and corresponding nomogram were constructed and validated by validation set.ResultsThe survival of patients undergoing PTR (n=493) was longer than that without PTR (n=123) (log-rank test, p&amp;lt;0.0001) in SEER cohort. T stage (HR=1.40, 95% CI=1.14, 1.73), differentiation grade (HR=1.47, 95% CI=1.17, 1.85), non-hepatic metastases (HR=1.69, 95% CI=1.29, 2.21), and adjuvant therapy (HR=0.34, 95% CI= 0.28, 0.42) were closely related with the survival of GCLM with PTR, and thus, a four-factor nomogram was established. However, GCLM patients receiving PTR in the high-risk subgroup (n=255) screened out by the nomogram did not have better survival outcomes compared with patients without PTR (n=123) (log-rank test, p=0.25).ConclusionsThe nomogram could predict survival of GCLM patients receiving PTR with acceptable accuracy. In addition, although PTR did improve the survival of whole GCLM patients, patients in the high-risk subgroup were unable to benefit from PTR, which could assist clinicians to make decisions for the treatment of GCLM.

Clinical features and prognostic factors of IV combined small cell lung cancer: A propensity score matching analysis.

Nowadays, the characteristics and treatment of combined small-cell lung carcinoma (CSCLC) remain controversial. This study aimed to analyze the features of clinical demographics, survival outcomes and treatment modalities among IV CSCLC, IV SCLC and IV NSCLC, to provide more evidence for the study of IV CSCLC. All CSCLC, SCLC and NSCLC patient data were obtained from the SEER database (2010-2020). Pearson's χ2 test was used to compare the differences in clinical characteristics. Propensity score matching (PSM) was utilized to balance the bias of the variables between patients. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify prognostic factors. KM analysis was used to calculate survival. Adjusted analyses for the primary outcome of different treatment modalities of IV CSCLC, IV SCLC and IV NSCLC were performed using Cox regression models. A total of 493 patients with IV CSCLC, 35503 patients with SCLC, 122807 patients with IV NSCLC were included in this study. The demographic characteristics and tumor characteristics of the three groups were different. Before PSM, there were significant differences in OS and CSS among IV CSCLC, IV SCLC and IV NSCLC, After PSM, there was a significant difference in OS and CSS between the IV CSCLC and IV NSCLC. Risk/protective factors for OS and CSS were different in three groups. Chemotherapy, radiotherapy, and surgery can improve IV CSCLC's survival time. The combination of surgery and chemoradiotherapy treatment group for patients with IV CSCLC demonstrated best OS compared to control treatment groups, and the surgery combined chemotherapy treatment group exhibited the best CSS. Additionally, for select patients with stage IV CSCLC who have missed the window for surgical intervention at the time of initial diagnosis, chemoradiotherapy presents a viable and effective treatment option. The clinical characteristics IV CSCLC, IV SCLC and IV NSCLC were significantly different. The prognosis for IV CSCLC is notably poorer than IV NSCLC, similar to IV SCLC. Surgery combined therapy emerged as the preferred treatment modalities and chemoradiotherapy was a good choice for patients who have lost the indication of surgery for patients diagnosed with IV CSCLC.

Prognostic nomograms for locally advanced cervical cancer based on the SEER database: Integrating Cox regression and competing risk analysis.

Locally advanced cervical carcinoma (LACC) remains a significant global health challenge owing to its high recurrence rates and poor outcomes, despite current treatments. This study aimed to develop a comprehensive risk stratification model for LACC by integrating Cox regression and competing risk analyses. This was done to improve clinical decision making. We analyzed data from 3428 patients with LACC registered in the Surveillance, Epidemiology, and End Results program and diagnosed them between 2010 and 2015. Cox regression and competing risk analyses were used to identify the prognostic factors. We constructed and validated nomograms for overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression identified key prognostic factors for OS, including advanced International Federation of Gynecology and Obstetrics stage, age, marital status, ethnicity, and tumor size. Notably, International Federation of Gynecology and Obstetrics stages IIIA, IIIB, and IVA had hazard ratios of 2.227, 2.451, and 4.852, respectively, significantly increasing the mortality risk compared to stage IB2. Ethnic disparities were evident, with African Americans facing a 39.8% higher risk than Caucasians did. Competing risk analyses confirmed the significance of these factors in DSS, particularly tumor size. Our nomogram demonstrated high predictive accuracy, with area under the curve values ranging from 0.706 to 0.784 for DSS and 0.717 to 0.781 for OS. Calibration plots and decision curve analyses further validated the clinical utility of this nomogram. We present effective nomograms for LACC risk stratification that incorporate multiple prognostic factors. These models provide a refined approach for individualized patient management and have the potential to significantly enhance therapeutic strategies for LACC.

Development and validation of a risk predictive nomogram for colon cancer-specific mortality: a competing risk model based on the SEER database

BackgroundUtilizing the SEER database, we developed a competing risk model along with a nomogram designed for the early identification of colon cancer-specific mortality (CSM) risk.MethodsClinical and pathological information, along with other significant data, were obtained from the SEER database. Patients were randomly divided into a training set and a validation set. We investigated the independent factors affecting CSM among colon cancer patients using univariate and multivariate analyses within a competing risk framework, ultimately developing a predictive tool for CSM in colon cancer.ResultsInvolving 40,261 individuals diagnosed with colon cancer, our study included 10,397 deaths directly due to the disease and an additional 5,828 from other causes. We used a competing risk model to predict cancer-specific mortality (CSM) in these patients. For the training dataset, the model’s area under the curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific survival (CSS) was 0.835 (95% confidence interval [CI] 0.826 to 0.844), 0.849 (95% CI 0.843 to 0.855), and 0.843 (95% CI 0.836 to 0.850), respectively. In the validation group, the AUC values for the same time periods were 0.846 (95% CI 0.833 to 0.860), 0.853 (95% CI 0.843 to 0.862), and 0.846 (95% CI 0.835 to 0.856), respectively. In comparison, traditional survival analysis yielded higher cumulative CSM rates over time than those provided by our competing risk approach.ConclusionWe created a competitive risk assessment model along with a predictive tool designed to estimate CSM in patients with colon cancer. This nomogram demonstrates high accuracy and reliability, aiding medical professionals in making clinical decisions and developing patient follow-up plans.

Prediction of Early Mortality in Esophageal Cancer Patients with Liver Metastasis Using Machine Learning Approaches

Patients with esophageal cancer liver metastasis face a high risk of early mortality, making accurate prediction crucial for guiding clinical decisions. However, effective predictive tools are currently limited. In this study, we used clinicopathological data from 1897 patients diagnosed with esophageal cancer liver metastasis between 2010 and 2020, which were sourced from the SEER database. Prognostic factors were identified using univariate and multivariate logistic regression, and seven machine learning models, including extreme gradient boosting (XGBoost) and support vector machine (SVM), were developed to predict early mortality. The models were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and F1 scores. Results showed that 40% of patients experienced all-cause early mortality and 38% had cancer-specific early mortality. Key predictors of early mortality included age, location, chemotherapy, and lung metastasis. Among the models, XGBoost performed best in predicting all-cause early mortality, while SVM excelled in predicting cancer-specific early mortality. These findings demonstrate that machine learning models, particularly XGBoost and SVM, can serve as valuable tools for predicting early mortality in patients with esophageal cancer liver metastasis, aiding clinical decision making.

Comparative Cardiovascular Risks of Radical Prostatectomy and External Beam Radiation Therapy in Early-Stage Prostate Cancer: A Comprehensive Retrospective Analysis.

The risk of cardiac disease mortality has recently become a focal point of concern within the medical community for patients with prostate cancer (PCa). Given that radical prostatectomy (RP) and external beam radiation therapy (EBRT) are the main treatment modalities for localized PCa, their specific impact on cardiovascular-specific mortality (CSM) remains unclear. This study explored the specific effects of RP and EBRT on CSM risk to guide clinical treatment decisions. Data from patients aged 45-74years, who were diagnosed with T1-2N0M0 stage PCa from the SEER database (2010-2015), were used. Multivariate statistical methods, including propensity score matching (PSM), competing risk regression, COX regression analysis, and Fine-Gray testing, were applied to assess the impact of RP and EBRT on CSM risk. Among 146,082 T1-2 stage PCa patients, cardiac disease emerged as the primary cause of death, surpassing PCa itself. Multifactorial COX regression and competing risk regression analyses indicated that local treatments do not increase CSM risk. Further analysis revealed a significant increase in CSM risk for patients undergoing only EBRT compared with those undergoing only RP (hazard ratio [HR] = 2.71, 95% confidence interval [CI] 1.96-3.74, P < 0.001), with subsequent PSM adjustment, further confirming a significantly reduced risk in the RP treatment group (HR 0.23, 95% CI 0.13-0.40, P < 0.001). T1-2 stage PCa patients face a significant risk of CSM, with RP offering a potential advantage over EBRT in reducing this risk. These findings encourage clinicians to comprehensively consider the potential impact on cardiac health when formulating treatment plans, providing crucial guidance for optimizing treatment strategies.

Machine Learning Model for Early Prediction of Survival in Gallbladder Adenocarcinoma: A comparison Study

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70%) and validation (30%) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

Survival Outcomes of Radiofrequency Ablation for Intrahepatic Cholangiocarcinoma from the SEER Database: Comparison with Radiotherapy and Resection

To determine effectiveness of radiofrequency ablation for treatment of intrahepatic cholangiocarcinoma (iCCA) using a population-based database. Data was extracted from Surveillance, Epidemiology, and End Results database from 2000 to 2020 to include 194 patients who underwent ablation for iCCA. Patient demographics, overall survival (OS), and cancer-specific survival (CSS) were retrieved. Factors associated with survival were evaluated. Comparison between ablation and surgical resection (n=2653) or external beam radiotherapy (n=1068) were performed. In the ablation group, atients diagnosed and treated after 2010 demonstrated improved OS than the 2000-2009 subgroup (mOS 32 versus 21 months, HR: 0.50 [95%CI: 0.33-0.75], p=0.001). Additional factors associated with OS included tumor size (≤3cm versus >3cm, p=0.049) and tumor stage (p<0.001). For patients diagnosed after 2010, the 1-, 3-, and 5-year OS were 82.8% (95%CI: 74.8-88.4%), 43.5% (95%CI: 33.5-53.1%), and 23.7% (95%CI: 15.3-33.5%), respectively. Patients with local disease (1-year OS: 87.8% [95%CI: 78.6-93.3%]) demonstrated improved OS than regional (1-year OS: 81.3% [95%CI: 52.5-93.5%]) and distant disease (50.2% [95%CI: 34.0-78.8%], p<0.001). For tumors ≤3cm, ablation and surgical resection offered comparable survival benefits (p=0.561), although both were better than radiotherapy (p<0.0001). Survival of iCCA patients who underwent thermal ablation has improved over the last 10 years. For tumors ≤3cm, ablation could be as effective as resection with careful candidate selection, and may be considered as front line compared to radiotherapy in certain patient populations. Patient selection based on tumor size and disease stage could improve survival outcomes.

Incidence and gender difference of brain metastases in newly diagnosed follicular thyroid cancer patients.

Population-based estimates of brain metastases in follicular thyroid cancer (FTC) patients with or without distant metastases (DMs) at diagnosis are lacking. To study the prevalence of brain metastases in FTC patients and compare gender disparity. DMs are defined as bone, lung, and brain metastases. Using the SEER database, we identified 5116 patients diagnosed with FTC between 2010 and 2019. The incidences of brain metastases were calculated for the entire cohort and among patients with bone/lung metastases. Cohorts were stratified by gender and age. 4.8% (245) had DMs at diagnosis, primarily in the form of bone metastases (3.6%), followed by lung metastases (2.4%). The incidence of brain metastases at initial diagnosis was only 0.37% (17 females and 2 males), but occurred in 8.2% and 6.1% of patients with bone metastases and lung metastases, respectively. Median survival for patients with brain metastases was only 8.0 months (95% CI, 4.1-11.9). Interestingly, female patients with bone metastases exhibited a significantly higher incidence of brain metastases compared to males (12.0% vs. 1.5%), with a notable odds ratio of 8.971 (95% CI:1.152-69.835) in univariate analysis. Multivariate logistic regression analysis confirmed that being female (odds ratio, 10.08; 95% CI:1.243-81.748) was the sole statistically significant risk factor for brain metastases in FTC patients with bone metastases at diagnosis. An incidence of brain metastases is observed in newly diagnosed FTC patients with DMs, especially in females with bone involvement. Our findings advocate for the early detection of brain metastases in female FTC patients with concurrent bone metastases at diagnosis.

Representativeness of Blood and Marrow Transplant Clinical Trials Network (BMT CTN) trial participants

Underrepresentation by race and ethnicity in oncology clinical trials, including hematopoietic cell transplantation (HCT), is a known challenge. This analysis studied accrual on Blood and Marrow Transplant Clinical Trials Network (BMT CTN) trials conducted in 2014-20 by race/ethnicity, age, and sex comparing these characteristics with those of potentially eligible patients identified from SEER and CIBMTR databases for the disease, age, and years of interest of BMT CTN studies. Five BMT CTN trials met inclusion criteria, including one autologous and four allogeneic HCT trials. Two studies focused on multiple myeloma (BMT CTN 1302 and 1401), two studies on graft versus host disease (GVHD) treatment (BMT CTN 1301 and 1501), and one study on post-HCT maintenance therapy in FLT3+ acute myelogenous leukemia (BMT CTN 1506). A decline in the proportion of patients from minority racial and ethnic groups was seen from the SEER population to trial enrollees, with the largest drop seen between the SEER population and all patients transplanted (on or off trial) in US transplant centers. Allogeneic HCT trials which allowed alternate donor graft sources had less decrease from the SEER population. No decrease was seen in clinical trial enrollment with respect to older age and female HCT recipients. This study provides insights into the underrepresentation of racial and ethnic minority patients in BMT CTN clinical trials, largely due to lack of access to HCT in general. Pathways expanding access to donors and improving the outreach of HCT programs to underserved populations are needed to improve access to clinical trials.

Differential Analysis of Surgical Treatment Modalities in T2N0M0 Bladder Cancer Patients: A Novel Propensity Score-Based Cohort Study

ObjectiveThis study explored prognostic differences between radical cystectomy (RC), tri-modality treatment (TURBt combined with radiotherapy and chemotherapy, TMT) and electrocautery (EC) and partial cystectomy (PC) for T2N0M0 MIBC. Materials and MethodsIn the SEER database (2004-2015, 2018-2020), we identified patients with T2N0M0 bladder cancer receiving RC, TMT, EC or PC treatment. To minimize confounding factors, 1:1 propensity score matching (PSM) with a 0.1 caliper was conducted. Kaplan-Meier analysis and log-rank tests were used to compare survival rates. Cox analysis identified independent prognostic factors, tumor size, and age, stratified treatment groups, and further Kaplan-Meier analysis of CSS was performed in matched cohorts. ResultThis study included 6526 patients with T2N0M0 MIBC. Among them, 348 (5.33%) were in the PC group, 309 (4.73%) were in the EC group, 1833 (28.09%) were in the TMT group, and 4036 (61.84%) were in the RC group. After 1:1 propensity score matching, RC showed improved CSS (HR=0.67, 95%CI 0.47-0.95, and PC also benefited (HR=0.97, 95%CI 0.69-1.36) compared to EC. While TMT showed a worse end (HR=1.41, 95%Cl 1.03-1.92) compared to EC. Cox analysis was used to stratify tumor size and age for subgroup analysis. Results for tumor size subgroups were aligned with PSM findings. In the age-stratified subgroups, patients aged <67 years, both RC (HR=0.54, P=0.107) and TMT(HR=0.91, P=0.785) showed better prognoses compared to EC treatment, while PC treatment showed worse prognoses compared to EC treatment (HR=1.23, P=0.542).; for 68-77 years, RC (0.64, P=0.1436) and PC (HR=0.46, P=0.0283)had advantages, and PC is more recommended. For >78 years, RC had superior CSS over EC and PC, whereas TMT had the poorest prognosis. ConclusionIn clinical T2N0M0 MIBC, overall, RC outperformed focal-tumor therapy and PC, irrespective of tumor size. However, considering age, we recommend PC treatment for patients aged 68-77 and EC for those aged >78 years. Micro-AbstractThis study compared radical cystectomy (RC), focal-tumor therapy combined with radiotherapy and chemotherapy (TMT and EC), and partial cystectomy (PC) for non-metastatic muscle-invasive bladder cancer. Using SEER data and a series of statistical analyses, overall, RC outperformed focal-tumor therapy and PC, irrespective of tumor size. However, considering age, we recommend PC treatment for patients aged 68-77 and EC for those aged >78 years. In contrast, for patients younger than 67 years of age, we recommend RC for those who can tolerate surgical treatment and TMT for those who can't.

Integrating Machine Learning with Web-Based Tools for Personalized Prognosis in Oral Adenoid Cystic Carcinoma

BackgroundAdenoid cystic carcinoma (ACC) of the oral cavity is a rare head and neck cancer. This rarity contributes to the paucity of comprehensive research on this cancer thereby complicating the development of evidence-based treatment strategies. This study aims to use machine learning (ML) techniques to analyze survival outcomes and optimize treatment approaches of ACC. MethodsThe SEER database (2000-2020) was used in this study. Cox regression analysis was used to identify the prognostic variables; prognostic models using five ML algorithms were constructed to predict the 5-year survival rates. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. Also, Kaplan-Meier survival analysis was performed. ResultsThis study's sample included 645 patients. The most common primary site for ACC was the hard palate, followed by the cheek mucosa. Survival rates varied across treatment groups, with the highest rates observed in patients who underwent surgery only. ML models revealed that the most significant prognostic factors were age, metastasis, and surgery. ConclusionsThis study contributes evidence and knowledge to the limited literature on ACC and emphasizes the importance of adjuvant radiotherapy. This study highlights that metastasis and age are key prognostic factors. Furthermore, the developed ML-based web tool offers a novel approach for the personalized prognosis of these rare cancer types.

Risk and survival outcomes of secondary pelvic neoplasm after radiotherapy in female patients with genital neoplasms: A large Population-Based cohort study

Background and purposeTo investigate the impact of radiotherapy (RT) on the risk of secondary pelvic neoplasms (SPN) and the survival outcomes of patients following a diagnosis of female patients with genital neoplasm(FGN). Materials and MethodsUtilizing SEER databases, this study involved 102,895 patients from nine oncology centers, spanning 1990 to 2015. We employed the Fine-Gray competing risks regression methodology to chart the trajectory of SPN development and used the Kaplan–Meier method to calculate the 10-year overall survival rates. ResultsThis study included 25,774 patients in the RT group and 77,121 in the non-radiotherapy (NRT) group. The cumulative incidence rate of SPN was 5.10 % in the RT group and 3.42 % in the NRT group. The RT group showed a significantly higher incidence of bladder cancer (adjusted hazard ratio [HR]: 1.75; 95 % confidence interval [CI]: 1.43–2.14; P < 0.05), colon cancer (adjusted HR: 1.32; 95 % CI: 1.16–1.49; P < 0.05), and rectal cancer (adjusted HR: 1.34; 95 % CI: 1.10–1.65; P < 0.05) compared to the NRT group. After propensity score matching, patients in the RT group who developed bladder cancer had significantly reduced 10-year survival rates compared to patients with primary pelvic tumors (P = 0.01). ConclusionRT is identified as an independent risk factor for the development of SPN in patients with FGN. Patients with FGN who undergo RT demonstrate a significant increase in the risk of developing secondary neoplasms, specifically bladder cancers, and experience a reduction in 10-year survival rates.

Diagnosis-to-treatment interval in cervical cancer: An exploratory analysis of the SEER database

Diagnosis-to-treatment interval in cervical cancer: An exploratory analysis of the SEER database