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ABSTRACT Background This study aimed to identify a specific SCLC population that would benefit from surgery. Methods This study utilized patient data retrieved from the Surveillance, Epidemiology, and End Results (SEER) database spanning 2010 to 2017. To mitigate clinical biases, the propensity score matching (PSM) technique was employed. Separate cohorts were aligned using PSM according to the AJCC 8th edition TNM classification. The Kaplan–Meier method and a competing risk model were applied to evaluate overall survival (OS) and lung cancer–specific survival (LCSS), respectively. Outcomes Among the 3394 patients with potentially resectable SCLC included in the study, 3062 underwent chemoradiotherapy and 332 underwent surgical treatment with adjuvant chemotherapy. Surgery was associated with better OS (median OS: 49 months; 95% CI: 35–63 months vs. 27 months; 95% CI: 21–33 months, p

Abstract Background The effectiveness of sentinel lymph node biopsy (SLNB) versus axillary lymph node dissection (ALND) in managing early‐stage male breast cancer (MBC) patients with T1‐2 tumors and limited lymph node metastasis, all receiving radiotherapy, remains uncertain. This study examines trends and survival outcomes for SLNB and ALND in the United States. Methods We conducted a retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) data from 2010 to 2020 for MBC patients with T1‐2 tumors and 1–2 positive lymph nodes undergoing radiotherapy. Patients were classified by nodes removed (SLNB ≤5, ALND ≥10), comparing overall survival (OS) and breast cancer‐specific survival (BCSS) between the groups before and after propensity score matching. Results Of 299 MBC patients analyzed, SLNB usage increased from 18.8% in 2010 to 61.0% in 2020. Multivariable logistic regression highlighted significant associations of SLNB use with diagnosis year, race, surgery type, positive lymph node count, and metastasis size. No significant differences in 5‐year OS (77.98% SLNB vs. 85.85% ALND, p = 0.337) or BCSS (91.54% SLNB vs. 94.97% ALND, p = 0.214) were observed. Propensity score matching (96 patients per group) confirmed similar 5‐year OS (83.9% for SLNB vs. 82.0% for ALND, p = 0.925) and BCSS (90.1% for SLNB vs. 96.9% for ALND, p = 0.167). Conclusion SLNB and ALND provide comparable survival outcomes in early‐stage MBC patients with limited lymph node metastasis undergoing radiotherapy. The increased utilization of SLNB supports its consideration to reduce surgical morbidity in selected MBC patients despite limited direct evidence.

ABSTRACT Aims Surgical resection is the primary treatment option for patients diagnosed with nonfunctional pancreatic neuroendocrine tumors (NF‐Pan‐NETs). However, the postoperative prognostic evaluation for NF‐Pan‐NET patients remains obscure. This study aimed to construct an efficient model to predict the prognosis of NF‐Pan‐NET patients who have received surgical resection. Methods NF‐Pan‐NET patients after pancreatectomy were retrieved from the SEER database for the period of 2010 to 2019. A total of 2844 patients with NF‐Pan‐NET from SEER database were included in our study. After careful screening, six clinicopathological variables including age, grade, AJCC T stage, AJCC N stage, AJCC M stage, and chemotherapy were selected to develop nomograms to predict overall survival (OS) and cancer‐specific survival (CSS) respectively of the patients. Results The novel models demonstrated high accuracy and discrimination in prognosticating resected NF‐Pan‐NET through various validation methods. Furthermore, the risk subgroups classified by the newly developed risk stratification systems based on the nomograms exhibited significant differences in both OS and CSS, surpassing the efficacy of the AJCC 8th TNM staging system. Novel nomograms and corresponding risk classification systems were developed to predict OS and CSS in patients with NF‐Pan‐NET after pancreatectomy. Conclusion The models demonstrated superior performance compared to traditional staging systems, providing clinicians with more accurate and personalized guidance for postoperative surveillance and treatment.

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 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 Objective Mucinous breast cancer (MBC) is a kind of breast cancer (BC), which is rare in clinic, mainly for women, because of the low incidence rate, so there is no unified standard treatment protocol. Elderly patients have a poor prognosis due to their combined comorbidities. This study aims to investigate the effect of surgery and chemoradiotherapy on the prognosis of elderly female MBC patients and construct nomograms for predicting the OS and CSS in elderly female MBC patients. Methods Data for female MBC patients over 65 years are obtained from the Surveillance, Epidemiology and End Results (SEER) database, patients were divided into two groups: the training set and the validation set. External validation data of the prediction model were provided by Kunming Hospital of Traditional Chinese Medicine. We used Cox regression modeling, which was used to identify independent risk factors affecting patient prognosis. After avoiding confounding bias according to the multifactorial Cox regression model, we used these screened statistically significant results to construct column‐line plots. The performance of the model was tested using the consistency index (c‐index), the calibration curve, and the area under the operating characteristic curve of the receiver (AUC). Subsequently, we used decision curve analysis (DCA) to examine the potential clinical value of our nomograms. Results A total of 8103 elderly MBC female patients were extracted from the database SEER and were assigned to the training and validation set, randomly. A total of 83 patients from Kunming Hospital of Traditional Chinese Medicine were used in the external verification set. After multifactorial Cox regression analysis, we found that age, race, T‐stage, M‐stage, surgical approach, radiotherapy, and tumor size were independent risk factors for OS in elderly MBC patients. Similarly, independent risk factors of CSS included age, marital status, N stage, M stage, surgical approach, chemotherapy, and tumor size. The C‐index for the OS training, validation, and external verification set were 0.731 (95%CI 0.715–0.747), 0.738 (95%CI 0.724–0.752), and 0.809 (95%CI 0.731–0.8874). The C‐index of the training set, the validation set, and external verification set for CSS were 0.786 (95%CI 0.747–0.825), 0.776 (95%CI 0.737–0.815), and 0.84 (95%CI0.754–0.926), respectively. The AUC, calibration curves and DCA also showed good accuracy. Conclusions In this study, we construct a new nomogram to predict the prognosis of elderly patients with MBC. The nomograms have undergone internal and external validation and have been confirmed to have good clinical applicability. At the same time, we found that for elderly female MBC patients, surgery and radiotherapy significantly benefit their survival, but chemotherapy is not conducive to patient survival.

ABSTRACT Background Despite being uncommon, vulvar cancer is a serious health concern with a 5‐year relative survival rate of 71%. Aims The objective was to investigate the incidence rates of this disease across different racial, ethnic, and histological subgroups in the United States, as well as the effects of the COVID‐19 pandemic on this cancer. Methods We used the Surveillance, Epidemiology, and End Results (SEER) 22 database. The International Classification of Diseases for Oncology Version 3 topologic code C51 was assigned for vulvar cancer. Average annual percent change (AAPC) and the pairwise comparison with the parallelism and coincidence were reported. Counts and age‐adjusted incidence rates per 100 000 individuals with their corresponding 95% confidence intervals (CIs) were reported. Results The age‐adjusted incidence rate of vulvar cancer was 2.40 (95% CI, 2.38–2.43) over the period 2000–2019, with an AAPC of 0.80 (95% CI, 0.63–0.99). By race/ethnicity, only non‐Hispanic Whites had an increasing trend over 2000–2019 (AAPC: 1.30; 95% CI, 1.07–1.54). The highest age‐adjusted incidence rate of vulvar cancer in the United States was for squamous cell carcinoma (SCC). There was a significant decrease in the age‐standardized incidence rate of vulvar cancer in all races/ethnicities in all age groups (AAPC: −10.15; 95% CI, −15.35 to −4.94) over 2019–2020. Also, the incidence rates and incident numbers of vulvar cancer increased with aging and peaked at the 85+ age group. Conclusion There was an increase in the incidence rate of vulvar cancer in all races, with a significantly increased trend in non‐Hispanic White women from 2000 to 2019. SCC displayed the highest incidence rate among vulvar cancer histological types. It is recommended to conduct further research to identify the relevant risk factors of vulvar cancer in the United States.

Abstract Purpose The observation‐based prognosis, rather than resection, for small carcinoid tumors is still unclear. This lack of clarity has important implications for counseling elderly patients or patients for whom surgical resection poses a high risk. This study compared the outcomes of observation and surgical resection in patients with pulmonary carcinoid (PC) tumors ≤3 cm in size without metastasis. Methods Data of patients with PC tumors with ≤3 cm in diameter and without lymph node and distant metastases were retrieved from Surveillance, Epidemiology, and End Results (SEER) registry. To reduce the inherent bias of retrospective studies, propensity score matching analysis was performed. Overall survival (OS) and lung carcinoid‐specific survival (LCSS) were analyzed using Kaplan–Meier plots. Multivariate analysis was used to determine predictors of LCSS in different size subgroups. Results In total, 4552 patients with early‐stage PCs ≤3 cm in diameter, including 435 (9.56%) who were observed and 4117 (90.44%) treated by surgery, were recruited. Patients with surgery had significantly better OS and LCSS than those who were observed. However, patients with observation had comparable LCSS to those with surgery for PCs with tumor diameters ≤1 cm. Multivariate analysis indicated that surgical resection was an independent prognostic factor for LCSS in 1 cm < tumors ≤2 cm, and 2 cm < tumors ≤3 cm groups, but not for tumors ≤1 cm in diameter. Conclusion Surgical resection of small PCs is associated with a survival advantage over observation. However, for early PCs ≤1 cm in diameter, observation may be considered in patients with high risk for surgical resection.

Abstract Background Despite the growth in primary cancer (PC) survivors, the trends and disparities in this population have yet to be comprehensively examined using competing risk analysis. The objective is to examine trends in time to second primary cancer (SPC) and to characterize age, sex, and racial disparities in time‐to‐SPC. Methods A retrospective analysis was conducted based on Surveillance, Epidemiology, and End Results (SEER). Two datasets for this study are (1) the discovery dataset with patients from SEER‐8 (1990–2019) and (2) the validation dataset with patients from SEER‐17 (2000–2019), excluding those in the discovery dataset. Patients were survivors of lung, colorectal, breast (female only), and prostate PCs. Results The 5‐year SPC cumulative incidences of lung PC increased from 1990 to 2019, with the cumulative incidence ratio being 1.73 (95% confidence intervals [CI], 1.64–1.82; p

Abstract Background Histological transformation (HT) to diffuse large B‐cell lymphoma (DLBCL) is a common complication of follicular lymphoma (FL) and is usually associated with a dismal outcome. However, the survival rate of these patients has improved over the last 20 years with the introduction of rituximab. This study aimed to access the outcome of transformation to DLBCL (t‐DLBCL) from FL in a retrospective series that began after the widespread use of rituximab use. In addition, we also compared survival between t‐DLBCL and primary DLBCL (p‐DLBCL) in the same timeframe. Methods We utilized the Surveillance, Epidemiology, and End Results (SEER) database to identify patients with primary FL and patients with p‐DLBCL between 2000 and 2020. Patients who had a subsequent diagnosis of DLBCL at least 2 months after FL diagnosis were identified as t‐DLBCL. Results Finally, we identified 50,332 FL and 95,933 p‐DLBCL. With a median follow‐up of 119 months, 1631 patients developed t‐DLBCL. The median time from FL diagnosis to t‐DLBCL was approximately 4 years. The post‐transformation survival (PTS) rate at 5 years was 49.6%, with a median PTS of 56 months. Older age, advanced stage, and early transformation were associated with worse PTS. Furthermore, t‐DLBCL receiving chemotherapy or combined modality as initial therapy before HT was also associated with worse PTS, while the result was inverse when taking the impact of initial management strategy at HT into account. Taking t‐DLBCL and p‐DLBCL as a whole, comparable survival was observed between p‐DLBCL and t‐DLBCL receiving radiation or watch‐and‐wait as initial therapy prior to HT. Conclusion The outcome of t‐DLBCL in the rituximab era was better than historical series before the rituximab era. Due to the good prognosis, we did not recommend autologous stem cell transplantation for t‐DLBCL receiving watch‐and‐wait or radiation as initial therapy before HT.

Abstract Objective The primary aim of this study was to create a nomogram for predicting survival outcomes in penile cancer patients, utilizing data from the Surveillance, Epidemiology, and End Results (SEER) and a Chinese organization. Methods Our study involved a cohort of 5744 patients diagnosed with penile cancer from the SEER database, spanning from 2004 to 2019. In addition, 103 patients with penile cancer from Sun Yat‐sen Memorial Hospital of Sun Yat‐sen University were included during the same period. Based on the results of regression analysis, a nomogram is constructed and validated internally and externally. The predictive performance of the model was evaluated by concordance index (c‐index), area under the curve, decision curve analysis, and calibration curve, in internal and external datasets. Finally, the prediction efficiency is compared with the TNM staging model. Results A total of 3154 penile patients were randomly divided into the training group and the internal validation group at a ratio of 2:1. Nine independent risk factors were identified, including age, race, marital status, tumor grade, histology, TNM stage, and the surgical approach. Based on these factors, a nomogram was constructed to predict OS. The nomogram demonstrated relatively better consistency, predictive accuracy, and clinical relevance, with a c‐index over 0.73 (in the training cohort, the validation cohort, and externally validation cohort.) These evaluation indexes are far better than the TNM staging system. Conclusion Penile cancer, often overlooked in research, has lacked detailed investigative focus and guidelines. This study stands as the first to validate penile cancer prognosis using extensive data from the SEER database, supplemented by data from our own institution. Our findings equip surgeons with an essential tool to predict the prognosis of penile cancer better suited than TNM, thereby enhancing clinical decision‐making processes.

Abstract Background Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. Methods Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). Results In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762–0.823), 0.904 (0.835–0.973), 0.759 (0.731–0.787), 0.221 (0.186–0.256), 0.974 (0.967–0.982), 0.353 (0.306–0.399), and 0.834 (0.696–0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753–0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. Conclusions This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

Abstract Background Identifying the risk factors for distant metastasis in early‐onset colorectal cancer (EOCRC) is crucial for elucidating its etiology and facilitating preventive treatment. This study aims to characterize the variability in EOCRC incidence and discern both heterogeneous and homogeneous risk factors associated with synchronous liver, lung, and hepato‐lung metastases. Methods This study included patients with EOCRC enrolled in the SEER database between 2010 and 2015 and divided patients into three groups by synchronous liver, lung, and hepato‐lung metastases. Each group of patients with different metastasis types was randomly assigned to the development and validation cohort in a ratio of 7:3. Logistic regression was used to analyze the heterogeneous and homogenous risk factors for synchronous liver, lung, and hepato‐lung metastases in the development cohort of patients. Nomograms were built to calculate the risk of metastasis, and the receiver operating characteristic (ROC) curve and calibration curve were used to quantitatively evaluate their performance. Results A total of 16,336 eligible patients with EOCRC were included in this study, of which 17.90% (2924/16,336) had distant metastases. The overall incidences of synchronous liver, lung, and hepato‐lung metastases were 11.90% (1921/16,146), 2.42% (390/16,126), and 1.50% (241/16,108), respectively. Positive CEA values before treatment, increased lymphatic metastases, and deeper invasion of intestinal wall were positively correlated with three distant types of metastases. On the contrary, the correlation of age, ethnicity, location of primary tumor, and histologic grade among the three types was inconsistent. The ROC curve and calibration curve proved to have fine performance in predicting distant metastases of EOCRC. Conclusions There are significant differences in the incidence of distant metastases in EOCRC, and related risk factors are heterogeneous and homogenous. Although limited risk factors were incorporated in this study, the established nomograms indicated good predictive performance.

Abstract Background Lymph node (LN) metastasis is crucial in non‐small cell lung cancer (NSCLC) prognosis and treatment, but the TNM system lacks LN quantity consideration. Our goal is to investigate the role of positive LNs (nPLN) and positive LN rate (LNR) in overall survival (OS) and assess whether they offer higher value in prognostic assessment of NSCLC than N‐stage. Methods Patients were stratified into four subgroups using X‐Tile software. Statistical analysis was conducted using the Kaplan–Meier method, univariate analysis, and multivariate Cox regression analysis. Model performance was evaluated using the Harrell consistency index (C‐index), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The prognostic performance of the nodal classification was validated using overall survival as the endpoint. Results The survival curves demonstrate distinct disparities between each nPLN and LNR category. A pronounced trend toward deteriorating overall survival from N‐PLN 1 to N‐PLN 2+ was observed across all tumor size categories. However, the differences between each LNR category were only significant for tumors ≤3 cm and 5–7 cm. Notably, both nPLN and LNR classifications displayed a higher C‐index, lower AIC, and lower BIC compared with the N staging. Furthermore, the LNR classification provided superior prognostic stratification when compared with the nPLN classification. Conclusions Our results demonstrate that nPLN and LNR classifications may offer improved prognostic performance compared with the current N classification for LN‐positive NSCLC patients. Nonetheless, more studies are needed to assess the feasibility of incorporating these classifications into the next TNM staging system.

Abstract Background The efficacy of breast reconstruction for patients with N2‐3M0 stage female breast cancer (FBC) remained unclear due to the lack of randomized clinical trials. This retrospective study aimed to explore the efficacy of breast reconstruction for patients with N2‐3M0 stage FBC. Methods Two thousand five hundred forty‐five subjects with FBC staged by N2‐3M0 from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results database. Generalized boosted model (GBM) and propensity score matching (PSM) analyses and multivariable Cox analyses were employed to assess the clinical prognostic effect of postmastectomy reconstruction for patients with N2‐3M0 stage FBC in breast cancer‐specific survival (BCSS). Results Totally, 1784 candidates underwent mastectomy alone (mastectomy group), and 761 candidates underwent postmastectomy reconstruction (PMbR group), with 418 breast‐specific deaths after a median follow‐up time of 57 months (ranging from 7 to 227 months). BCSS in the mastectomy group showed no statistical difference from that in the PMbR group in the PSM cohort (HR = 0.93, 95% CI: 0.70–1.25, p = 0.400) and GBM cohort (HR = 0.75, 95% CI: 0.56–1.01, p = 0.057). In the multivariate analyses, there was no difference in the effect of PMbR and mastectomy on BCSS in the original cohort (HR = 0.85, 95% CI: 0.66–1.09, p = 0.197), PSM cohort (HR = 0.86, 95% CI: 0.64–1.15, p = 0.310), and GBM cohort (HR = 0.84, 95% CI: 0.61–1.17, p = 0.298). Triple‐negative breast cancer (TNBC) was a detrimental factor affecting BCSS for patients in the PMbR group. Conclusions Our study demonstrated that PMbR is an oncologically safe surgical treatment and can be widely recommended in clinics for females with non‐TNBC staged by T0‐3N2‐3M0.

Abstract This study provided systematic insights into the patterns of triple‐negative breast cancer (TNBC) distant metastases (DM) and investigated the related elements for the prognosis prediction of TNBC patients based on a large sample. We reviewed eligible patients with TNBC from the SEER database between 2010 and 2015. We also analyzed differences in baseline characteristics among patients with diverse modes of metastasis. Meanwhile, we calculated proportional mortality ratio and expression of proportional trends in different patients. Subsequently, KM analysis was employed to investigate the survival outcomes. Finally, the predictive and prognostic factors of DM were identified. In this study, we included 24 822 TNBC patients, including 1026 DM patients and 23 796 non‐DM patients. At the time of initial diagnosis, 4.1% of patients had DM, and 36.9% had multiple metastases. According to the study, the most common sites of metastasis in DM patients were bone (251 cases) and lung (244 cases), while the least common organ of metastasis was brain (37 cases). Age, grade, T, N, and marital status were deemed as risk elements of DM. T stage, insurance status, marital status, surgery treatment, chemotherapy, number of metastatic sites, and metastatic sites also significantly affected the diagnosis of DM. Our study showed that the most common site of metastasis in TNBC patients with DM was bone and the least common site was brain. Different modes of metastasis have different survival and prognostic characteristics. Thus, our research may have important implications for the clinical practice of TNBC patients in the future.

Abstract Background No well‐performing nomogram has been developed specifically to predict individual‐patient cancer‐specific survival (CSS) and overall survival (OS) among patients with resectable colorectal liver metastasis (CRLM) who undergo simultaneous resection of primary and hepatic lesions without neoadjuvant chemotherapy (NAC). We aim to investigate the prognosis of patients with resectable CRLM undergoing simultaneous resection of primary and hepatic lesions without NAC. Methods Data of patients with CRLM in the Surveillance, Epidemiology and End Results Program (cohort, n = 225) were collected as the training set, and data of patients with CRLM treated at the National Cancer Center (cohort, n = 180) were collected as the validation set. The prognostic value of the clinicopathological parameters in the training cohort was assessed using Kaplan‒Meier curves and univariate and multivariate Cox proportional hazards models, and OS and CSS nomograms integrated with the prognostic variables were constructed. Calibration analyses, receiver operating characteristic (ROC) curves, and decision curve analyses (DCAs) were then performed to evaluate the performance of the nomograms. Results There was no collinearity among the collected variables. Three factors were associated with OS and CSS: the pretreatment carcinoembryonic antigen (CEA) concentration, pathologic N (pN) stage, and adjuvant chemotherapy (each p

Abstract Background The purpose of this study was to develop and validate a nomogram to predict survival in testicular cancer patients. Methods Testicular cancer patients diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were selected for this study. A random sampling method was used to divide patients into training and validation cohorts, which accounted for 30% and 70% of the total sample, respectively. The nomogram was developed using the training cohort and evaluated using the C index, calibration chart, and area under the receiver operating characteristic curve (AUC). Results Seven risk factors that affect the survival of testicular cancer patients (AJCC stage, marital status, age at diagnosis, race, SEER historic stage A, surgery status, and origin) were identified using Cox proportional hazard regression analysis. The nomogram has a higher C index (0.897) and AUC when compared with the AJCC staging system. The results of the calibration chart of the nomogram show that the predicted survival of testicular cancer patients at 3, 5, and 10 years after diagnosis is very close to their actual survival. Conclusions We developed and validated a nomogram for predicting the survival rate of testicular cancer patients at 3, 5, and 10 years after diagnosis. This nomogram has better discrimination, calibration, and clinical validity than the AJCC staging system. This indicates that the nomogram can be used to predict the survival of testicular cancer patients effectively, and provide a reference for patient treatment strategies.

Abstract Background Malignant hemangioendothelioma is an endothelial cancer with heterogeneous clinical behavior that can range from indolent to aggressive, of which the majority are epithelioid (EHE). Its incidence and demographics have not been previously well defined in a large cohort. Methods This retrospective analysis used the US Cancer Statistics National Program of Cancer Registries – Surveillance Epidemiology End Results (SEER) combined database to identify patients in the US newly diagnosed with hemangioendothelioma between the years of 2001 and 2017 (n = 1986). Survival analyses were performed on a subset of patients within the SEER‐18 database with survival information available (n = 417). Outcomes included incidence, demographics of patients newly diagnosed with hemangioendothelioma, extent of disease at presentation, and overall survival. Results The incidence of hemangioendothelioma in the US is 0.4 cases per million person‐years. Although cases rose to 122 newly diagnosed in the year 2017 (90 EHE, 32 other hemangioendothelioma), incidence rates were stable. Skin and connective tissues were the most common presenting sites (33.4%), followed by liver (24.5%), lung (17.6%), and bone (12.5%). Median age at diagnosis was 55 years; 27.2% of patients were pediatric, adolescent, or young adult (

Abstract Background There are well‐established disparities in colorectal cancer (CRC) outcomes between White and Black patients; however, assessments of CRC disparities for other racial/ethnic groups are limited. Methods The Surveillance, Epidemiology, and End Results database identified patients aged 50–74 years with CRC adenocarcinoma from 2000 to 2019. Trends in age‐adjusted incidence rates were computed by stage at diagnosis and subsite across five broad race/ethnic groups (White, Black, Asian/Pacific Islander [API], American Indian/Alaskan Native [AIAN], and Hispanic) and four API subgroups (East Asian, Southeast Asian, South Asian, and Pacific Islander) Multivariable logistic regression evaluated associations between race/ethnicity and diagnosis stage. Multivariable Cox proportional hazards models assessed differences in cause‐specific survival (CSS). Results Hispanic, AIAN, Southeast Asian, Pacific Islander, and Black patients were 3% to 28% more likely than Whites to be diagnosed with distant stage CRC, whereas East Asian and South Asians had similar or lower risk of distant stage CRC. From Cox regression analysis, Black, AIAN, and Pacific Islanders also experienced worse CSS, while East Asian and South Asian patient groups experienced better CSS. No significant differences in CSS were observed among Hispanic, Southeast Asian, and White patients. When stratified by stage, Black patients had worse CSS across all stages (early, hazard ratio (HR) = 1.38; regional, HR = 1.22; distant, HR: 1.07, p

Abstract Background The gains in survival outcomes of US patients with hepatocellular carcinoma (HCC) have come at the expense of developing non‐cancer‐related morbidities, such as cardiovascular diseases (CVDs) and infections. However, population‐based data on causes of death (CODs) in patients with HCC are scarce. Methods A cancer registry database in the United States was used to analyze the CODs among patients with HCC. Death cause distribution and standardized mortality ratios were calculated to quantify the disease‐specific death burden. Results A total of 40,094 patients with a histological diagnosis of HCC were identified from the SEER‐18 database between 2000 and 2018, of which 30,796 (76.8%) died during the follow‐up period. The majority of these deaths (25,153, 81.7%) occurred within 2 years after diagnosis, 13.2% (4075) occurred within 2–5 years, and 5.1% (1568) occurred after 5 years. All age groups had a lower burden of female deaths than of male deaths during the study period. With respect to CODs, 23,824 (77.4%), 2289 (7.4%), and 4683 (15.2%) were due to HCC, other cancers, and non‐cancer causes, respectively. Non‐cancer‐related deaths were more common among older patients and those with longer latency periods since diagnosis. The major causes of non‐cancer‐related deaths are other infectious and parasitic diseases, including HIV and CVDs. Conclusions CODs during HCC survivorship varied, and a growing number of survivors tended to die from causes other than HCC, with an increasing latency period since diagnosis. Comprehensive analyses of mortality patterns and temporal trends could underpin strategies to reduce these risks.

Abstract Objectives The role of postoperative radiotherapy (PORT) in malignant pleural mesothelioma (MPM) remains controversial and the eighth edition TNM staging scheme for MPM has not been fully verified. We aimed to develop an individualized prediction model for identifying optimal candidates for PORT among MPM patients who received surgery plus chemotherapy and externally validate the performance of the new TNM staging scheme. Materials and Methods Detailed characteristics of MPM patients during 2004–2015 were retrieved from SEER registries. Propensity score matching (PSM) was conducted to reduce disparities of baseline characteristics (age, sex, histologic type, stage, and type of surgery) between the PORT group and no‐PORT group. A novel nomogram was constructed based on independent prognosticators identified by multivariate Cox regression model. The discriminatory performance and degree of calibration were evaluated. We stratified patients into different risk groups according to nomogram total scores and estimated the survival benefit of PORT in different subgroups in order to identify the optimal candidates. Results We identified 596 MPM patients, among which 190 patients (31.9%) received PORT. PORT conferred significant survival benefit in the unmatched population, while there was no significant survival difference favoring PORT in the matched population. The C‐index of the new TNM staging scheme was closed to 0.5, which represented a poor discriminatory ability. A novel nomogram was constructed based on clinicopathological factors, including age, sex, histology, and N stage. We stratified patients into three risk groups. Subgroup analyses indicated that PORT was beneficial for high‐risk group (p = 0.003) rather than low‐risk group (p = 0.965) and intermediate‐risk group (p = 0.661). Conclusion We established a novel predictive model, which could make individualized prediction of survival benefit of PORT for MPM and could compensate for weakness in TNM staging system.

Abstract Introduction Patients presenting with brain metastases (BMs) from lung squamous cell carcinoma (LUSC) often encounter an extremely poor prognosis. A well‐developed prognostic model would assist physicians in patient counseling and therapeutic decision‐making. Methods Patients with LUSC who were diagnosed with BMs between 2000 and 2018 were reviewed in the Surveillance, Epidemiology, and End Results (SEER) database. Using the multivariate Cox regression approach, significant prognostic factors were identified and integrated. Bootstrap resampling was used to internally validate the model. An evaluation of the performance of the model was conducted by analyzing the area under the curve (AUC) and calibration curve. Results A total of 1812 eligible patients' clinical data was retrieved from the database. Patients' overall survival (OS) was significantly prognosticated by five clinical parameters. The nomogram achieved satisfactory discrimination capacity, with 3‐, 6‐, and 9‐month AUC values of 0.803, 0.779, and 0.760 in the training cohort and 0.796, 0.769, and 0.743 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. There was also a substantial difference in survival curves between the different prognostic groups stratified by prognostic scores. For ease of access, the model was deployed on a web‐based server. Conclusions In this study, a nomogram and a web‐based predictor were developed to assist physicians with personalized clinical decisions and treatment of patients who presented with BMs from LUSC.

Abstract To investigate the misclassification rates of Asian‐American patients with low‐risk prostate cancer (PCa). Patients diagnosed with low‐risk PCa treated with radical prostatectomy between 2010 and 2015 in the Surveillance, Epidemiology, and End Results database were included in this study. Then, basic characteristics and pathological outcomes of the enrolled patients were retrospectively extracted. We compared the rates of upgrading and/or upstaging between Asian‐American patients and White/Black patients. Moreover, temporal trend analyses were performed to explore the changes in upgrading and upstaging rates in each race over time. Finally, logistic regression models were constructed to explore the role of Asian race in upgrading and upstaging and to screen out potential risk factors for predicting upgrading and upstaging in Asian‐American patients. Asian‐Americans had a significantly higher rate of upgrading than Whites (P

Abstract Background Despite the importance of regular dental visits for detecting oral cancer, millions of low‐income adults lack access to dental services. In July 2009, California eliminated adult Medicaid dental benefits. We tested if this impacted oral cancer detection for Medicaid enrollees. Methods We analyzed Surveillance, Epidemiology, and End Results‐Medicaid data, which contains verified Medicaid enrollment status, to estimate a difference‐in‐differences model. Our design compares the change in early‐stage (Stages 0–II) diagnoses before and after dropping dental benefits in California with the change in early‐stage diagnoses among eight states that did not change Medicaid dental benefits. Patients were grouped by oropharyngeal cancer (OPC) and non‐OPC (oral cavity cancer), type, and the length of Medicaid enrollment. We also assessed if the effect of dropping dental benefits varied by the number of dentists per capita. Results Dropping Medicaid dental benefits was associated with a 6.5%‐point decline in early‐stage diagnoses of non‐OPC (95% CI = −14.5, −3.2, p = 0.008). This represented a 20% relative reduction from baseline rates. The effect was highest among beneficiaries with 3 months of continuous Medicaid enrollment prior to diagnosis who resided in counties with more dentists per capita. Specifically, dropping dental coverage was associated with a 1.25%‐point decline in the probability of early‐stage non‐OPC diagnoses for every additional dentist per 5000 population (p = 0.006). Conclusions Eliminating Medicaid dental benefits negatively impacted early detection of cancers of the oral cavity. Continued volatility of Medicaid dental coverage and provider shortages may be further delaying oral cancer diagnoses. Alternative approaches are needed to prevent advanced stage OPC.

Abstract Background Studies on the epidemiological information and prognosis of primary malignant lacrimal gland tumors (MLGTs) are rare for its low occurrence. The goal of our research was to investigate the epidemiological characteristics and survival outcomes of patients with MLGTs. Methods Incidence and demographic information of patients with MLGTs were collected from the Surveillance, Epidemiology, and End Results (SEER) database. To identify independent prognostic factors for disease‐specific survival (DSS) and overall survival (OS), univariate and multivariate Cox regression analysis were performed. Results The overall incidence of primary MLGTs from 1975 to 2020 was 0.413/1,000,000 (according to the 2000 American standard population), with a steadily increasing incidence over years. A total of 964 patients with primary MLGTs were diagnosed, with an average age of 59.3 years. Of these, 53.2% were aged ≥60 years, 57.4% were female, and 77.1% were whites. Multivariate Cox regression analysis demonstrated that year of diagnosis, age, sex, histological type, SEER stage, surgery, and chemotherapy were independent prognostic factors of DSS or OS. Conclusions Although primary MLGT is rare, its incidence has steadily increased in the past 46 years, and surgery was related to a better prognosis.

Abstract Importance Accurate representation of racial and ethnic population subgroups in clinical trials is fundamental to ensure universal effectiveness of new therapies as well as to decrease disparities in oncology care. Objective To determine whether Hispanic people are underrepresented in Phase I and II clinical trials for liver cancer in the United States. Participants A database search was performed in clinicaltrials.gov for interventional liver cancer studies based only in the US with reported results from September 1, 2002, to February 1, 2023. A total of 37 trials with 963 total patients met inclusion criteria and were included for further analysis. Proportion of total patients by race/ethnicity was calculated for non‐Hispanic white, non‐Hispanic black, Asian, Hispanic, and American Indian/Alaska Native subpopulations. The age‐adjusted incidence rates of liver and intrahepatic bile duct were acquired from the National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Results Liver cancer incidence rates (per 100,000 people) were highest in Indians/Alaska Native people (18.8 cases) followed by Hispanic people (15.1 cases), then Asian people (12.5 cases), then non‐Hispanic black people (11 cases), and non‐Hispanic white people (7.5 cases). From a total of 91 phase I or II clinical trials for liver cancer in the US, 41% reported race/ethnicity enrollment data; among these, 62.8% of patients were non‐Hispanic White, 15.9% were non‐Hispanic black, 8.8% were Hispanic, 12.7% Asian, and 0.5% American Indian/Alaska Native. Conclusions and Relevance Less than half of phase I or II clinical trials for liver cancer in the US in the last 20 years reported race/ethnicity data to clinicaltrials.gov. Compared to the relative incidence rate of liver cancer, non‐Hispanic black people and Hispanic people are underrepresented in these clinical trials.

Abstract Background The use of systematic treatment for tubular carcinoma (TC) of the breast remained controversial. This study aimed to explore the efficacy of chemotherapy on TC to develop individualized treatment strategies. Methods Using the Surveillance, Epidemiology, and End Results (SEER) database, 6486 eligible cases with TC and 309,304 with invasive ductal carcinoma (IDC) were collected. Breast cancer‐specific survival (BCSS) was assessed through multivariable Cox analyses and Kaplan–Meier analyses. Differences between groups were balanced using propensity score matching (PSM) and inverse probability of treatment weighting (IPTW). Results Compared with IDC patients, TC patients had a more favorable long‐term BCSS after PSM (hazard ratio = 0.62, p = 0.004) and IPTW (hazard ratio = 0.61, p

Abstract Background Malignant myofibroblastic tumors are a rare group of soft tissue sarcomas, for which a prognosis prediction model is lacking. Based on the Surveillance, Epidemiology, and End Results (SEER) database and cases from Nanjing Drum Tower Hospital, the current study constructed and validated a nomogram to assess overall survival of patients with malignant myofibroblastic tumors. Methods Data of patients with myofibroblastic tumors diagnosed between 2000 and 2018 were extracted from the SEER database. Similarly, data of patients with myofibroblastic tumor in Nanjing Drum Tower Hospital between May 2016 and March 2022 were collected. Then, we conducted univariate and multivariate Cox analyses to identify independent prognostic parameters to develop the nomogram. The model was evaluated by concordance index (C‐index), calibration curve, the area under the curve (AUC), decision curve analysis (DCA), Kaplan–Meier analysis, and subgroup analyses. Results Seven variables were selected to construct the nomogram. The results of the C‐index (0.783), calibration curve, the AUCs, and subgroup analyses demonstrated the accurate predictive capacity and excellent discriminative ability of the nomogram. The DCA of the model indicated its better clinical net benefit than that of the traditional system. Conclusion Evaluation of the predictive performance of the nomogram revealed the superior sensitivity and specificity of the model and the higher prediction accuracy of the outcomes compared with those of the traditional system. The established nomogram may assist patients in consultation and help physicians in clinical decision‐making.

Abstract Background The incidence and mortality of pancreatic adenosquamous carcinoma (PASC) have received little attention. The goal of our study was to explore the overall epidemiological trend of PASC at the population level. Methods The Surveillance, Epidemiology, and End Results database was used to collect the incidence, incidence‐based (IB) mortality, and patient details for PASC from 2000 to 2017. The Joinpoint regression tool was used to examine the trends in incidence and IB mortality. The Kaplan–Meier approach was used for survival analysis. Univariate and multivariate Cox regression analyses were used to determine the independent prognostic factors. Results We included 815 patients with PASC in the study. The incidence of PASC continuously increased from 2000 to 2017, with an annual percentage change (APC) of 3.9% (95% CI: 2.2%–5.7%, p

Abstract Objectives The CSCO and ASCO guidelines in 2021 recommend chemotherapy for stage III–IVA (8th edition of AJCC staging) nasopharyngeal carcinoma (NPC). Actually, patients with stage T3–4N0M0 are often excluded from various clinical trials of the locoregionally advanced NPC, and the survival benefit of chemotherapy in such patients has always been controversial. This study aims to explore the benefit of chemotherapy in patients with locoregionally advanced NPC, especially those with negative lymph nodes. Methods A total of 2741 patients were extracted from the SEER database. After a 1:1 PSM analysis, 272 patients were obtained to further explore whether the addition of chemotherapy would achieve survival benefits. Results After PSM, Kaplan–Meier curves showed that the overall survival (OS) of patients receiving chemoradiotherapy (p = 0.031) was higher than those receiving radiotherapy alone. Similar results were observed for cancer‐specific survival (CSS). We further stratified the patients according to lymph node status and found that the addition of chemotherapy in patients with positive lymph nodes could significantly improve 5‐year OS rates (58.08% vs. 43.95%; p = 0.025) and 5‐year CSS rates (67.42% vs. 51.95%; p = 0.015) compared with radiotherapy alone, but there was no additional benefit of chemotherapy in patients with negative lymph nodes. For all 449 cases of T3–4N0M0 NPC, radiotherapy improved the OS rates (HR 0.293, 95% CI 0.203–0.424) and the CSS rates (HR 0.252, 95% CI 0.171–0.371) compared with no radiotherapy, while chemotherapy did not show significant survival benefit compared with no chemotherapy. Conclusion Our results reveal that stage T3–4N0M0 NPC may be exempted from chemotherapy, and use radiotherapy alone to reduce toxic and side effects. These results still need to be verified by future prospective trials.

Abstract Purpose This study aimed to compare the prognostic value of multiple lymph node metastasis (LNM) indicators and to develop optimal prognostic nomograms for bladder cancer (BC) patients. Methods BC patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly partitioned into training and internal validation cohorts. Genomic and clinical data were collected from The Cancer Genome Atlas (TCGA) as external validation cohort. The predictive efficiency of LNM indicators was compared by constructing multivariate Cox regression models. We constructed nomograms on basis of the optimal models selected for overall survival (OS) and cause‐specific survival (CSS). The performance of nomograms was evaluated with calibration plot, time‐dependent area under the curve (AUC) and decision curve analysis (DCA) in three cohorts. We subsequently estimated the difference of biological function and tumor immunity between two risk groups stratified by nomograms in TCGA cohort. Results Totally, 10,093 and 107 BC patients were screened from the SEER and TCGA databases. N classification, positive lymph nodes (PLNs), lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) were all independent predictors for OS and CSS. The filtered models containing LODDS had minimal Akaike Information Criterion, maximal concordance indexes and AUCs. Age, LODDS, T and M classification were integrated into nomogram for OS, while nomogram for CSS included gender, tumor grade, LODDS, T and M classification. The nomograms were successfully validated in predictive accuracy and clinical utility in three cohorts. Additionally, the tumor microenvironment was different between two risk groups. Conclusions LODDS demonstrated superior prognostic performance over N classification, PLN and LNR for OS and CSS of BC patients. The nomograms incorporating LODDS provided appropriate prediction of BC, which could contribute to the tumor assessment and clinical decision‐making.

Abstract Background Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5‐year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. Methods The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver‐operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. Results A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p

Abstract Background Patients with early esophageal cancer (EC) receive individualized therapy based on their lymph node metastasis (LNM) and distant metastasis (DM) status; however, deficiencies in current clinical staging techniques and the issue of cost‐effectiveness mean LNM and DM often go undetected preoperatively. We aimed to develop three clinical models to predict the likelihood of LNM, DM, and prognosis in patients with early EC. Method The Surveillance, Epidemiology, and End Results database was queried for T1 EC patients from 2004 to 2015. Multivariable logistic regression and Cox proportional hazards models were used to recognize the risk factors of LNM and DM, predict overall survival (OS), and develop relevant nomograms. Receiver operating characteristic (ROC)/concordance index and calibration curves were used to evaluate the discrimination and accuracy of the three nomograms. Decision curve analyses (DCAs), clinical impact curves, and subgroups based on model scores were used to determine clinical practicability. Results The area under the curve of the LNM and DM nomograms were 0.668 and 0.807, respectively. The corresponding C‐index of OS nomogram was 0.752. Calibration curves and DCA showed an effective predictive accuracy and clinical applicability. In patients with T1N0M0 EC, surgery alone (p

Abstract Background This study aims to determine the independent prognostic predictors of cancer‐specific survival (CSS) in patients with primary Ewing sarcoma (ES) that underwent chemotherapy and create a novel prognostic nomogram and risk stratification system. Methods Demographic and clinicopathologic characteristics related to patients with primary ES that underwent chemotherapy between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. CSS was the primary endpoint of this study. First, independent prognostic predictors of CSS identified from univariate and multivariate Cox regression analyses were used to construct a prognostic nomogram for predicting 1‐, 3‐, and 5‐year CSS of patients with primary ES that underwent chemotherapy. Then, calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the nomogram's prediction accuracy, while decision curve analysis (DCA) was used to evaluate the nomogram's clinical utility. Finally, a mortality risk stratification system was constructed for this subpopulation. Results A total of 393 patients were included in this study. Age, tumor size, bone metastasis, and surgery were independent prognostic predictors of CSS. The calibration curves, ROC, and DCA showed that the nomogram had excellent discrimination and clinical value, with the 1‐, 3‐, and 5‐year AUCs higher than 0.700. Moreover, the mortality risk stratification system could effectively divide all patients into three risk subgroups and achieve targeted patient management. Conclusions Based on the SEER database, a novel prognostic nomogram for predicting 1‐, 3‐, and 5‐ year CSS in patients with primary ES that underwent chemotherapy has been constructed and validated. The nomogram showed relatively good performance, which could be used in clinical practice to assist clinicians in individualized treatment strategies.

Abstract Background Adults aged 65 years and above account for over half of all cancer diagnoses in the United States, but little is known about trend of elderly cancer survival in the United States. We aimed to assess the survival trend for elderly cancer in the United States from 1995 to 2014. Methods We used data from Surveillance, Epidemiology, and End Results 12 registries and included 1,112,441 eligible patients aged 65 years or older who were diagnosed between 1995 and 2014 with cancer and followed up until December 2019. Overall and stage‐specific 5‐year relative survival, ratio of observed survival to expected survival, with 95% confidence intervals (CIs) of elderly cancer patients stratified by age were estimated during four periods (1995–1999, 2000–2004, 2005–2009, and 2010–2014). Cox proportional hazards models were used to estimate hazard ratios for cancer‐specific death among patients diagnosed during 2000–2004, 2005–2009, 2010–2014, compared diagnoses in 1995–1999. We also calculated stage distribution and treatment rate during four periods. Results In the United States, 5‐year relative survival for elderly cancer patients improved from 57.3% (95% CIs 57.0–57.5) in 1995–1999 to 60.7% (60.5–60.9) in 2010–2014. After controlling for sociodemographic and tumor characteristics, about a 19% reduction in cancer‐specific deaths among diagnoses in 2010–2014 compared with 1995–1999. Cancer survival improved for elderly patients in all age groups, with exception of stable survival for patients aged 85 and above. Comparing 1995–1999 with 2010–2014, relative survival improved from 84.7% (84.3–85.1) to 86.7% (86.3–87.0) for localized stage and from 12.4% (12.1–12.7) to 18.7% (18.4–19.0) for distant stage for all cancers combined. The trends in stage distribution and treatment rate for all cancers combined were relatively stable. Conclusions In the United States, survival for elderly cancer patients has improved slightly from 1995 to 2014, possibly mainly due to advances in treatment. Further studies are warranted to explore interventions to improve elderly cancer survival.

Abstract Background Retinoblastoma is a rare malignancy involving the retina, although, more common among children, with genetic inheritance explaining the incidence as well as acquired forms. The incidence varies among race and sex as well as mortality and survival. The current study aimed to assess retinoblastoma cumulative incidence (CMI), mortality, and survival by sex. Methods A retrospective cohort design was used to assess the CMI, mortality, and survival in this pediatric malignancy based on the Surveillance Epidemiology and End Results (SEER) data 2000–2017. The binomial regression model was used to examine sex differentials in mortality, as well as other study variables, while Cox proportional hazard model was used for the survival variability by sex. Results The CMI during this period was higher among males relative to females (males n = 249, 56.7%; females n = 190, 43.3%, χ2 = 2.90, df = 1, p = 0.089). There were sex differences in mortality, with excess mortality observed among males compared to females, risk ratio = 3.40, 95% CI [1.0–15.72]. The survival differences by sex indicated decreased survival among males relative to females, hazard ratio (HR) = 3.39, 95% CI [1.0–15.72]. After controlling for the potential confoundings, namely tumor grade, urbanity, and median income the survival disadvantage of males persisted. Compared to females’, males were more than three times as likely to die, adjusted HR = 3.42, 99% CI [0.37–31.60]. Conclusion In a representative sample of pediatric retinoblastoma, there was a sex differential in survival with excess risk of dying identified among males relative to females, which may be explained in part by male X‐linkage.

Abstract Background Osteosarcoma is well‐established as the most common bone cancer in children and adolescents. Patients with localized disease have different prognoses and management than those with metastasis at the time of diagnosis. The purpose of this study was to explore potential risk factors for metastatic disease. Methods The Surveillance, Epidemiology, and End Results (SEER) Program database was used to identify patients diagnosed with osteosarcoma between 2004 and 2015. We developed prediction models for distant metastasis using six machine learning (ML) techniques, including logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GaussianNB), Extreme Gradient Boosting (XGBoost), random forest (RF), and k‐nearest neighbor algorithm (kNN). The adaptive synthetic (ADASYN) technique was used to deal with imbalanced data. The Shapley Additive Explanation (SHAP) analysis generated visualized explanations for each patient. Finally, the average precision (AP), sensitivity, specificity, accuracy, F1 score, precision‐recall curves, calibration plots, and decision curve analysis (DCA) were conducted to evaluate the models' effectiveness. Results The six machine learning algorithms achieved AP of 0.661–0.781 for predicting distant metastasis. The RF model yielded the best performance with an accuracy of 71.8 percent and an AP of 0.781 and was highly dependent on tumor size, primary surgery, and age. SHAP analysis provided model‐independent interpretation, highlighting significant clinical factors associated with the risk of metastasis in osteosarcoma patients. Conclusions An accurate machine learning‐based prediction model was established for metastasis in osteosarcoma patients to help clinicians during clinical decision‐making.

Abstract Background Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa. Methods We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1‐, 3‐, 5‐, and 10‐year overall survival (OS) and cancer‐specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C‐index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival. Results The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C‐index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772–0.802, CSS = 0.892–0.936) and external validation (C‐index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1‐, 3‐, 5‐, and 10‐year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS. Conclusion The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions.

Abstract Objective The prognostic factors for patients with epithelial sarcoma remain unclear. The study aims to develop a practical clinical nomogram that predicts prognosis in patients with ES using the Surveillance, Epidemiology, and End Results (SEER) database. Methods We extracted clinical data from 2004 to 2015 from the SEER database about patients with ES. All patients were randomly divided into training cohort and validation cohort. Kaplan–Meier analyses were used to compare outcomes between different subgroups. In order to estimate the chance of survival for patients with ES, we developed a nomogram. Nomogram performance was evaluated by discrimination and calibration. Additionally, an analysis of decision curves was conducted to evaluate the clinical usefulness of this newly developed model. Results In the primary cohort,320 met the inclusion criteria to be entered into this study. The median OS was 66.000 months (range 34.704 to 94.296 months), and the 1‐, 3‐, and 5‐year OS rates were 70.7%, 56.1%, and 50.4%, respectively. For the validation cohort, we studied 136 consecutive patients. Age, primary site, grade, AJCC (American Joint Committee on Cancer) T, AJCC M, and surgery were included in the nomogram. The C‐index values for the training set and validation set were 0.817 and 0.832, respectively. The calibration plots showed good agreement between the prediction and the observation. Based on the clinical decision curve, the model has a good clinical net benefit for ES patients. Conclusions It is the first study that developed an effective survival prediction model for patients with ES. Using this nomogram can assist in clinical decision‐making as it has satisfactory accuracy. Even so, additional external validation is needed.

Abstract Background The impact of prior cancer history on survival of hypopharyngeal cancer patients remains unknown. The present study assessed the impact of prior cancer history on survival of patients with hypopharyngeal cancer. Methods Patients with primary hypopharyngeal cancer diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was conducted to balance baseline characteristics. One‐to‐one PSM, Kaplan–Meier method, and log‐rank test were performed for survival analysis. Results We included 5017 patients with hypopharyngeal cancer. Prior cancer history had no significant impact on overall survival of hypopharyngeal cancer patients in comparison with those without prior cancer history (p = 0.845, after PSM). Subgroup analysis showed that prior cancer history had no significant effect on overall survival of hypopharyngeal cancer patients. Conclusion More hypopharyngeal cancer patients with prior cancer history should be considered for clinical trials. However, further prospective studies are needed.

Abstract Background Evidence about the prognostic value of primary tumor surgery (PTS) in patients with brain metastatic malignancies is ambiguous and controversial. This study assessed the survival benefits of primary tumor surgery in patients with brain metastases (BMs). Methods Adults patients with BMs that originated from lung, breast, kidney, skin, colon, and liver diagnosed between 2010 and 2018 were derived from the Surveillance, Epidemiology, and End Results database (SEER). Propensity score matching (PSM) was used to balance the bias between patients with or without PTS. Then the prognostic value of PTS was estimated by Kaplan–Meier analysis and Cox proportional hazard regression models. Results A total of 32,760 patients with BMs secondary to non‐small cell lung cancer (NSCLC), small cell lung cancer (SCLC), breast cancer, renal cancer, melanoma, colorectal cancer, and liver cancer were identified from the database. After PSM at 1:1 ratio, PTS appeared to significantly prolong cause‐specific survival (CSS) time for patients with BMs secondary to NSCLC, breast cancer, renal cancer, and colorectal cancer (hazard ratio [HR] = 0.60 [0.53–0.68], 0.56 [0.43–0.73], 0.47 [0.37–0.60], and 0.59 [0.37–0.95], respectively, all p

Abstract Background Tumor‐size‐stratified analysis on the prognosis of uterine sarcoma is insufficient. This study aimed to establish the tumor‐size‐stratified nomograms to predict the 3‐ and 5‐year overall survival (OS) of patients with uterine sarcoma. Methods The data analyzed in this study were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. We collected data from patients with uterine sarcoma diagnosed between 2004 and 2015. According to the median tumor size of 7.8 cm, the enrolled patients were divided into two tumor size (TS) groups: TS

Abstract Background Patients with small‐cell lung cancer (SCLC) have a high incidence of synchronous brain metastases (SBM) and a poor prognosis, which causes a heavy burden of morbidity and mortality. A better understanding of the demographic and tumor‐specific characteristics of these patients is critical to guiding clinical practice. The purpose of this study was to investigate the predictive and prognostic value of the clinical characteristics of SCLC patients with SBM at initial diagnosis. Methods This is a retrospective study based on the data in the latest Surveillance, Epidemiology, and End Results (SEER) version which was released in 2021 for patients diagnosed with SCLC in the presence or absence of SBM from 2010 to 2018. Multivariable logistic regression was performed to identify predictors of the presence of SBM at the initial diagnosis. Kaplan–Meier curves and multivariable Cox regression models were built to compare the prognosis of patients with different clinical characteristics and treatments. Results A total of 33,169 SCLC patients were enrolled in this study, including 5711 (17.2%) patients with SBM and 27,458 (82.8%) patients without SBM. Patients who are black(HR = 1.313, 95% CI = 1.167–1.478, p

Abstract Background Primary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer‐specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3‐, 5‐, and 10‐year probability of CSS in these patients. Methods The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3‐, 5‐, and 10‐year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed. Results Tumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3‐, 5‐, and 10‐year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3‐, 5‐, and 10‐year areas under ROC curve all higher than 0.800. Furthermore, the nomogram‐based mortality risk stratification system could effectively divide these patients into three risk subgroups. Conclusions The nomogram constructed in this study could accurately predict 3‐, 5‐, and 10‐year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.

Abstract Background Intraductal papillary carcinoma (IPC) with invasion is a rare type of breast cancer. There have been few studies on its prognosis, and a nomogram that predicts the prognosis of the disease has not been described to date. Methods Patients who were diagnosed with invasive IPC were screened from the Surveillance, Epidemiology, and End Results (SEER) database. The screened patients were randomly divided into a training cohort and a verification cohort at 7:3. A Cox proportional hazard regression model was performed to analyze the effects of different variables on the risk of death in invasive IPC. A nomogram was constructed to quantify the possibility of death. The concordance index (C‐index), calibration plots, receiver operating characteristic (ROC) curves, and decision curves analysis (DCA) were used to verify the proposed model. Results We included a total of 803 patients diagnosed with invasive IPC, including 563 patients in the training cohort and 240 patients in the validation cohort. The median follow‐up times in the training cohort and validation cohort were 63 months (range, 2–155 months) and 61 months (range, 1–154 months), respectively. For all patients, the probability of death with invasive IPC was 1.4% within 5 years and 5.4% within 10 years. In multivariate analysis, sex, race, tumor size, lymph node status, type of treatment, and chemotherapy were related to the prognosis of invasive IPC. We constructed a nomogram to predict the possibility of death in patients with invasive IPC. Conclusion Patients with invasive IPC had a high survival rate. The proven nomogram was helpful to both patients and clinical decision makers.

Abstract Background Primary gastric T‐cell lymphoma (PG‐TCL) is a rare hematological malignancy with few data reported. The objective of this study is to investigate the epidemiology, clinical characteristics, and survivals of PG‐TCL. Methods Totally, 164 patients with PG‐TCL from 1975 to 2016 extracted from the Surveillance, Epidemiology, and End Results Program (SEER) database were analyzed. Kaplan–Meier method was applied to plot overall survival (OS) and cancer‐specific survival (CSS). The prognostic factors of OS and CSS were explored by Cox proportional hazard regression. Nomograms were constructed to predict survival possibilities. Results The age‐adjusted incidence rate of PG‐TCL was 0.0091 per 100,000 person‐years and increased with age. The median age at onset was 65 years old with male predominance. The major histological type was peripheral T‐cell lymphoma, NOS (63.4%). The 1‐, 2‐, and 5‐year OS were 45.5%, 34.7%, and 23.5%, respectively while the 1‐, 2‐, and 5‐year CSS were 47.4%, 37.3%, and 29.6%, respectively. Multivariate Cox analysis demonstrated that age at diagnosis, use of chemotherapy, and radiotherapy were the independent prognostic factors for OS. Chemotherapy combined with radiotherapy could significantly improve patients' OS compared with chemotherapy alone. Moreover, age at diagnosis and use of chemotherapy were also the independent prognostic factors for CSS. Nomograms for PG‐TCL were developed to predict 1‐, 2‐, and 5‐year OS possibilities. The predictability of nomograms was verified by high concordance index and good agreement with the predicted value in calibration plots. Conclusion PG‐TCL is a rare neoplasm with low incidence. Patients with PG‐TCL generally exhibited poor prognosis. Use of chemotherapy plus radiotherapy was associated with favorable OS.

Abstract Background The American Joint Committee on Cancer (AJCC) tumor‐node‐metastasis (TNM) staging system focuses on traditional biological factors (BFs). The present study incorporates nonbiological factors (NBFs) into the AJCC‐TNM staging system in terms of the advanced clinical management and prognostic‐prediction accuracy of pancreatic ductal adenocarcinoma (PDAC). Methods Eight thousand three hundred and thirty eligible patients with PDAC were obtained from Surveillance, Epidemiology, and End Results database between January 1, 2011, and December 31, 2015. Multivariate Cox proportional hazards regression analysis and Kaplan–Meier curves were used to testify the feasibility of cancer‐specific survival (CSS) prediction based on TNM‐NBF stages. Results The large population‐based study demonstrated that NBFs (insurance status, marital status, county‐level median household income, and unemployment) were significant prognostic indicators (p

Abstract Background Esophageal neuroendocrine carcinoma (ENEC) is an extremely rare type of malignancy. Clinical data of ENEC are limited to case reports and case series. More information is needed on its clinical feature, management, and prognosis. Methods This study collected information of ENEC patients diagnosed pathologically from 2010 to 2018. Data including demographic information, clinical features, and survival trends were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Statistical analyses were performed with STATA/SE 15.1, SPSS 25.0, and GraphPad Prism 8. Results A total of 283 ENEC patients were included in this study. The small‐cell and large‐cell subtypes of ENEC possess similar clinical features. The lower third of the esophagus (58%) was the most common location of ENEC. At the time of diagnosis, most ENEC patients were AJCC 7th stage IV (48.1%). Metastasis occurred in more than half of the ENEC patients (53.4%), and the most common metastatic site was the liver (37.1%). Compared with poorly differentiated esophageal squamous cell carcinoma (ESCC), another aggressive malignancy of the esophagus sometimes confused with ENEC because of similar histological features, our study showed differences in tumor location and metastatic rate, but similar poor survival rates. Multivariate survival analysis showed that ENEC located at the middle third of esophagus (p = 0.013), “Brain metastasis” (p = 0.019), and “Liver metastasis” (p

Abstract Background After radical resection of a nonmetastatic Merkel cell carcinoma (M0 MCC), postoperative radiation therapy (RT) is recommended as it improves survival. However, the role of RT in specific subgroups of M0 MCC is unclear. We sought to identify whether there is a differential survival benefit from RT in specific M0 MCC patient subgroups. Methods M0 MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database registry were collected. The best prognostic age, tumor size, and lymph node ratio (LNR, ratio between positive lymph nodes and resected lymph nodes) cutoffs were calculated. The primary endpoint was overall survival (OS). Results A total of 5644 M0 MCC patients (median age 77 years, 62% male) were included: 4022 (71%) node‐negative (N0) and 1551 (28%) node‐positive (N+). Overall, 2682 patients (48%) received RT. Age > 76.5 years, tumor size >13.5 mm, and LNR >0.215 were associated with worse OS. RT was associated with longer OS in the M0 MCC, N0, and N+ group and independently associated with a 25%, 27%, and 26% reduction in the risk for death, respectively. RT benefit on survival was increased in tumor size >13.5 mm in the N0 group and LNR >0.215 in the N+ group. No OS benefit from RT was observed in T4 tumors (N0 and N+ groups). Conclusions RT was associated with improved survival in M0 MCC, irrespective of the nodal status. LNR >0.215 is a useful prognostic factor for clinical decision‐making and for stratification and interpretation of clinical trials.