Trends and efficacy of omitting axillary lymph node dissection in early-stage male breast cancer with limited nodal involvement: A population-based cohort study.

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.

Establishment and Validation of Prognostic Nomograms for Nonmetastatic Melanoma of the Limbs-A SEER-Based Study.

BACKGROUND: Malignant melanoma, a highly aggressive skin cancer, has remarkable incidence and mortality nowadays. This study aims to explore prognostic factors associated with nonmetastatic cutaneous melanoma of the limbs and to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS). METHODS: The study cohort was derived from the Surveillance, Epidemiology, and End Results database. Univariate Cox regression, Lasso regression, and multivariate Cox regression analyses were conducted to identify prognostic factors and construct nomograms. The receiver operating characteristic (ROC) curve, time-dependent C-index, calibration curve, decision curve analysis (DCA) and Kaplan-Meier method were used to evaluate the accuracy and clinical applicability of the nomograms. RESULTS: A total of 15,606 patients were enrolled. Multivariate analysis identified several prognostic factors for OS and CSS including age, sex, histologic type, N stage, tumor thickness, depth of invasion, mitotic rate, ulceration, surgery of primary site, systemic therapy, race, and number of lymph nodes examined. A nomogram incorporating 12 independent predictors for OS was developed, with a C-index of 0.866 (95% confidence interval [CI]: 0.858-0.874) in the training cohort and 0.853 (95% CI: 0.839-0.867) in validation. For CSS, 10 independent predictors and one related factor were included, yielding a C-index of 0.913 (95% CI: 0.903-0.923) in the training cohort and 0.922 (95% CI: 0.908-0.936) in validation. The ROC curve, time-dependent C-index, calibration curve, DCA, and K-M plot demonstrated favorable discrimination, calibration, and clinical utility. CONCLUSION: The developed nomograms provide a precise and personalized predictive tool for risk management of patients with nonmetastatic limb melanoma.

Immunotherapy may be more appropriate for ERBB2 low-expressing extramammary paget's disease patients: a prognosis analysis and exploration of targeted therapy and immunotherapy of extramammary paget's disease patients.

Extramammary Paget's disease (EMPD) is a rare cutaneous malignancy characterized by its uncertain etiology and metastatic potential. Surgery remains the first-line clinical treatment for EMPD, but the efficacy of radiotherapy and chemotherapy remains to be fully evaluated, and new therapies for EMPD are urgently needed. In this study, we initially screened 815 EMPD patients in the Surveillance, Epidemiology, and End Results (SEER) database and analyzed their clinical features and prognostic factors. Using the dataset from the Genome Sequence Archive (GSA) database, we subsequently conducted weighted gene coexpression network analysis (WGCNA), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analyses, grouping the samples based on EMPD disease status and the levels of ERBB2 expression. The prognostic analysis based on the SEER database identified increased age at diagnosis, distant metastasis, and receipt of radiotherapy as independent risk factors for EMPD. Moreover, our results indicated that patients who received chemotherapy had worse prognoses than those who did not, highlighting the urgent need for novel treatment approaches for EMPD. Functional analysis of the GSA-derived dataset revealed that EMPD tissues were significantly enriched in immune-related pathways compared with normal skin tissues. Compared with those with high ERBB2 expression, tissues with low ERBB2 expression displayed greater immunogenicity and enrichment of immune pathways, particularly those related to B cells. These findings suggest that patients with low ERBB2 expression are likely to benefit from immunotherapy, especially B-cell-related immunotherapy.

Screening of prognostic factors and survival analysis based on histological type for perimenopausal endometrial carcinoma treated with hysterectomy.

PURPOSE: This study aimed to explore the prognostic factors and survival patterns based on the histological type for the perimenopausal endometrial carcinoma (PIPEC) patients treated with hysterectomy. METHODS: The PIPEC patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Methods of random survival forest (RSF) and Cox regression were used to identify the possible prognostic factors of PIPEC patients. Then overall survival (OS) and cancer-specific survival (CSS) of PIPEC data were analyzed by histological types with regional lymph nodes status and SEER-stage to investigate the survival patterns of the PIPEC patients. RESULTS: A total of 14,178 PIPEC patients were included in the study. We found tumor size, grade, histology, SEER-stage, AJCC-stage, AJCC-T stage, metastasis to distant organs and regional lymph nodes status had a significant survival outcome for PIPEC both for OS and CSS (all p < 0.05). Regardless of regional lymph nodes status and SEER-stage for OS and CSS, the low-grade endometrioid carcinoma had the best prognosis outcome, followed by the mix cell adenocarcinoma and high-grade endometrioid carcinoma, while the carcinosarcoma and undifferentiated carcinoma had relatively poor prognosis outcome. And the survival patterns of different histological types of PIPEC were diverse and changed along with the time. CONCLUSION: We identified the possible prognostic factors of PIPEC patients treated with hysterectomy. And survival analysis based on the regional lymph nodes status and SEER-stage revealed the different histological types of PIPEC had diverse survival patterns, which will be helpful for guiding clinical practice.

A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.

Background: Patients with distant metastases from neuroblastoma (NB) usually have a poorer prognosis, and early diagnosis is essential to prevent distant metastases. The aim was to develop a machine-learning model for predicting the risk of distant metastasis in patients with neuroblastoma to aid clinical diagnosis and treatment decisions. Methods: We built a predictive model using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018 on 1,542 patients with neuroblastoma. Seven machine-learning methods were employed to forecast the likelihood of neuroblastoma distant metastases. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for building machine learning models. Secondly, the subject operating characteristic area under the curve (AUC), Precision-Recall (PR) curves, decision curve analysis (DCA), and calibration curves were used to assess model performance. To further explain the optimal model, the Shapley summation interpretation method (SHAP) was applied. Ultimately, the best model was used to create an online calculator that estimates the likelihood of neuroblastoma distant metastases. Results: The study included 1,542 patients with neuroblastoma, multifactorial logistic regression analysis showed that age, histology, tumor size, tumor grade, primary site, surgery, chemotherapy, and radiotherapy were independent risk factors for distant metastasis of neuroblastoma (P < 0.05). Logistic regression (LR) was found to be the optimal algorithm among the seven constructed, with the highest AUC values of 0.835 and 0.850 in the training and validation sets, respectively. Finally, we used the logistic regression model to build a network calculator for distant metastasis of neuroblastoma. Conclusion: The study developed and validated a machine learning model based on clinical and pathological information for predicting the risk of distant metastasis in patients with neuroblastoma, which may help physicians make clinical decisions.

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

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

Primary pulmonary diffuse large B-cell lymphoma (PP-DLBCL), a rare extranodal non-Hodgkin's lymphoma (EN-NHL), has a very poor prognosis as an aggressive lymphoma.We screened individuals from the Surveillance, Epidemiology, and End Results (SEER) database who were diagnosed with PP-DLBCL between 2010 and 2019. Then, univariate and multivariate COX regression analyses were used to identify independent risk factors affecting patient prognosis.Finally, we built a new predictive model to aid in clinical decision making.

AI Survival Prediction Modeling: The Importance of Considering Treatments and Changes in Health Status over Time.

BACKGROUND AND OBJECTIVES: Deep learning (DL)-based models for predicting the survival of patients with local stages of breast cancer only use time-fixed covariates, i.e., patient and cancer data at the time of diagnosis. These predictions are inherently error-prone because they do not consider time-varying events that occur after initial diagnosis. Our objective is to improve the predictive modeling of survival of patients with localized breast cancer to consider both time-fixed and time-varying events; thus, we take into account the progression of a patient's health status over time. METHODS: We extended four DL-based predictive survival models (DeepSurv, DeepHit, Nnet-survival, and Cox-Time) that deal with right-censored time-to-event data to consider not only a patient's time-fixed covariates (patient and cancer data at diagnosis) but also a patient's time-varying covariates (e.g., treatments, comorbidities, progressive age, frailty index, adverse events from treatment). We utilized, as our study data, the SEER-Medicare linked dataset from 1991 to 2016 to study a population of women diagnosed with stage I-III breast cancer (BC) enrolled in Medicare at 65 years or older as qualified by age. We delineated time-fixed variables recorded at the time of diagnosis, including age, race, marital status, breast cancer stage, tumor grade, laterality, estrogen receptor (ER), progesterone receptor (PR), and human epidermal receptor 2 (HER2) status, and comorbidity index. We analyzed six distinct prognostic categories, cancer stages I-III BC, and each stage's ER/PR+ or ER/PR- status. At each visit, we delineated the time-varying covariates of administered treatments, induced adverse events, comorbidity index, and age. We predicted the survival of three hypothetical patients to demonstrate the model's utility. MAIN OUTCOMES AND MEASURES: The primary outcomes of the modeling were the measures of the model's prediction error, as measured by the concordance index, the most commonly applied evaluation metric in survival analysis, and the integrated Brier score, a metric of the model's discrimination and calibration. RESULTS: The proposed extended patients' covariates that include both time-fixed and time-varying covariates significantly improved the deep learning models' prediction error and the discrimination and calibration of a model's estimates. The prediction of the four DL models using time-fixed covariates in six different prognostic categories all resulted in approximately a 30% error in all six categories. When applying the proposed extension to include time-varying covariates, the accuracy of all four predictive models improved significantly, with the error decreasing to approximately 10%. The models' predictive accuracy was independent of the differing published survival predictions from time-fixed covariates in the six prognostic categories. We demonstrate the utility of the model in three hypothetical patients with unique patient, cancer, and treatment variables. The model predicted survival based on the patient's individual time-fixed and time-varying features, which varied considerably from Social Security age-based, and stage and race-based breast cancer survival predictions. CONCLUSIONS: The predictive modeling of the survival of patients with early-stage breast cancer using DL models has a prediction error of around 30% when considering only time-fixed covariates at the time of diagnosis and decreases to values under 10% when time-varying covariates are added as input to the models, regardless of the prognostic category of the patient groups. These models can be used to predict individual patients' survival probabilities based on their unique repertoire of time-fixed and time-varying features. They will provide guidance for patients and their caregivers to assist in decision making.

Epidemiology and survival of primary intracranial malignant tumor patients with drop metastasis: a population-based analysis.

BACKGROUND: Drop metastasis significantly impacts the survival of patients with primary intracranial malignant tumors. Using the information of collaborative stage from the SEER database, we aim to analyze the epidemiology and prognosis of primary intracranial malignant tumor patients with drop metastasis. METHODS: We analyzed the distribution of patients and the frequency according to the demography and clinical characteristics of patients with drop metastasis. We also analyzed the survival of these patients with drop metastasis. Multivariate Cox proportional hazards models were used to analyze possible prognostic indicators. RESULTS: A total of 56,839 cases with primary intracranial malignant tumors were ultimately included in this cohort study. A total of 792 cases were confirmed to have drop metastasis. The average rate of drop metastasis was 1.4%. Most of the patients with drop metastases were diagnosed before ten years old. The three most common primary intracranial malignant tumors with drop metastasis were glioblastoma, embryonal/primitive/medulloblastoma, and anaplastic astrocytoma. Embryonal/primitive/medulloblastoma had the highest drop metastasis rate, at 11.6%. Tumors located in the infratentorial space and ventricles had a higher rate of drop metastasis than tumors in other locations. The prognosis for patients with drop metastasis is poor. Routine complete treatment (surgery of the primary tumor plus chemoradiotherapy) can significantly improve overall survival. CONCLUSION: We conducted a population-based analysis of primary intracranial malignant tumor patients with drop metastasis. Our study can help clinicians acquire general information on the epidemiology and survival of primary intracranial malignant tumor patients with drop metastasis.

The role of wedge resection and lymph node examination in stage IA lung carcinoid tumors.

Background: Current guidelines recommend anatomical resection and mediastinal lymph node resection for stage I to IIIA pulmonary carcinoids (PCs). The role of wedge resection in stage IA PCs remains controversial, previous studies focused on typical carcinoids (TCs) while differentiating histological subtypes preoperatively is not easy. We aimed to study the effect of wedge resection and lymph node examination (LNE) in patients with stage IA PCs. Methods: Patients who underwent anatomical and wedge resection for stage T1N0M0 lung carcinoid tumors between 2004 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were also divided into a non-LNE group and an LNE group. Kaplan-Meier analysis and the log-rank test were used to calculate and compare overall survival (OS). Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to balance the variables between groups. Univariate and multivariate Cox proportional hazard models were developed to determine prognostic factors. Results: A total of 2,029 patients with bronchopulmonary carcinoid tumors were included in this study, 1,450 underwent lobectomy, 147 underwent segmentectomy and 432 underwent wedge resection. Initially, 5-year survival differed marginally between wedge and anatomical resection (91% vs. 95%, P=0.051), but lost significance after adjustment. LNE improved 5-year survival (95% vs. 89%, P=0.003), and this remained significant after adjustment. In multivariate cox analysis, LNE remained a significant variable while extent of resection was not. This result also remained consistent after adjustment. OS was comparable between wedge resection and anatomical resection when at least 1 lymph node was examined. Conclusions: For early-stage PC, wedge resection was not inferior to anatomical resection in terms of OS, while LNE significantly increased the survival in both multivariate and matched studies. The relationship between surgical extent and survival in the unadjusted study may be attributed to the lower rate of LNE in wedge resection. Our findings support wedge resection with emphasis on LNE in early-stage PCs.

Nomogram predicts cervical lymph node metastasis of pathological subtypes of papillary thyroid carcinoma.

BACKGROUND: Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice. METHODS: Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation. RESULTS: A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability. CONCLUSION: In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis.

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

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

Efficacy of various extent of resection on survival rates of patients with pilocytic astrocytoma: based on a large population.

Pilocytic astrocytoma (PA) is classified as a Grade I benign neuroglial tumor. The extent of surgical resection is a critical factor influencing the prognosis for patients with PA. In prior researches of PA, the extent of surgical resection is generally categorized into GTR, STR and biopsy. In some researches on brain tumor surgeries, the extent of resection also includes GTL. There is no existing research specifically comparing the efficacy of GTR versus GTL in PA treatment. In this study, the data we used are from the SEER database. We categorized the extent of resection into GTL, GTR, STL, STR, biopsy, and no surgery based on SEER classification of surgical procedures, to investigate the impact of extent of resection on PA patient survival. A multivariate logistic regression model was utilized to acquire odds ratios (OR) for different extent of resection. Survival outcomes across different extent of resection (GTL, GTR, STL, STR, biopsy, no surgery) were assessed using Kaplan-Meier survival curve analysis, with curve comparisons conducted via log-rank tests. The impact of various risk factors on survival was assessed using the Cox proportional hazards model. The hazard ratio (HR) was employed to quantify the influence of one or more factors on overall survival throughout the follow-up period. Multivariate Cox analysis revealed that age, tumor location, extent of resection, as well as the application of radiotherapy and chemotherapy, all significantly impacted prognosis. Compared to GTL, GTR did not significantly increase the risk of mortality (HR 1.17; 95% CI 0.73-1.86, p = 0.5). Furthermore, there was no statistically significant difference between the Kaplan-Meier survival curves of the two groups (p = 0.18). We employed propensity score matching (PSM) to balance the differences in baseline characteristics of patients receiving chemotherapy or radiotherapy. A total of 4429 patients were included in this study. Age, diagnosis period, race, tumor size, and tumor location as influential on the extent of resection. Age, tumor location, extent of resection, and application of radiotherapy and chemotherapy influenced the survival of PA patients. The Kaplan-Meier survival curves revealed that the long-term survival rate for GTR is slightly higher than that for GTL. The PSM analysis revealed that the application of radiotherapy and chemotherapy was associated with the reduction of overall survival in PA patients. In conclusion, there was no significant difference in survival between GTR and GTL, so GTR with less damage was preferred. The application of radiotherapy and chemotherapy can reduce overall survival of patients with PA.

The prognostic significance of synchronous metastasis in glioblastoma multiforme patients: a propensity score-matched analysis using SEER data.

Background: Glioblastoma multiforme (GBM) with synchronous metastasis(SM) is a rare occurrence. We extracted the data of GBM patients from the SEER database to look into the incidence of SM in GBM, determine the prognostic significance of SM in GBM, and assess therapeutic options for patients presenting with SM. Methods: From 2004 to 2015, information on GBM patients was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The propensity score matching (PSM) method was employed to mitigate confounding factors between SM and non-SM groups, subsequently investigating the prognostic significance of SM in patients with GBM. Multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic variables for GBM patients with SM. A forest plot was used to visualize the results. Results: A cohort of 19,708 patients was obtained from the database, among which 272 (1.4%) had SM at the time of diagnosis. Following PSM at a 3:1 ratio, in both univariate and multivariate cox regression analysis, SM (HR = 1.27, 95% CI: 1.09-1.46) was found to be an independent predictive predictor for GBM patients. Furthermore, the Cox proportional hazard forest plot demonstrated that independent risk variables for GBM patients with SM included age (Old vs. Young, HR = 1.44, 95% CI: 1.11-1.88), surgery (biopsy vs. no surgery, HR = 0.67, 95% CI: 0.46-0.96;Subtotal resection vs. no surgery, HR = 0.47, 95% CI: 0.32-0.68;Gross total resection vs. no surgery, HR = 0.44, 95% CI: 0.31-0.62), radiotherapy (HR = 0.58, 95% CI: 0.41-0.83), and chemotherapy (HR = 0.51, 95% CI: 0.36-0.72). Conclusion: The predictive value of SM in GBM was determined by this propensity-matched analysis using data from the SEER database. Radiotherapy, chemotherapy, and surgery constitute an effective treatment regimen for patients with SM. A more positive approach toward the use of aggressive treatment for GBM patients with SM may be warranted.

Pediatric non-nasopharyngeal head and neck squamous cell carcinoma: Analysis of the Surveillance, Epidemiology, and End Results (SEER) program with review of the literature.

BACKGROUND: Despite its prevalence in adults, head and neck squamous cell carcinoma (HNSCC) is considered a rare entity in pediatrics where lymphomas, neural tumors, and soft tissue sarcomas predominate in the head and neck. Given the association of squamous cell carcinoma with the human papillomavirus, a risk factor that may be present from birth, and the difficulties in staging this disease for prognostication in children, it is important to revisit nationally collected data for prevalence and outcomes assessments. OBJECTIVE: To examine a publicly available national database to describe the incidence, pathology, treatment, and survival of pediatric HNSCC. To review the available literature regarding management, outcomes, and risk factors for this disease process. METHODS: The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) tumor database was queried to identify pediatric subjects ages 0 to 19 diagnosed HNSCC between 1973 and 2019. RESULTS: Two-hundred ninety-two cases were identified. Subjects were 62.7 % male (n = 183) and the average age was 15.4 years (range 2-19, median 16). Subjects were 65.8 % white (n = 192), 22.9 % black (n = 67), 8.9 % Asian/Pacific Islander (n = 26), 1 % American Indian (n = 3), and 1.4 % unknown (n = 4). The most common primary sites were nasopharynx (45.9 %), oral cavity (30.5 %), larynx (8.6 %), salivary gland (4.1 %), nasal cavity & paranasal sinus (3.4 %), and lip (2.7 %). There was no statistically significant difference between primary subsite and age, race, histologic grade, or extent of disease. The 5-year overall survival was 83.6 %. DISCUSSION: Head and neck squamous cell carcinoma is more likely to present in older children and is more prevalent in White populations. The nasopharynx is the most common subsite involved, which differs from adult populations in which non-nasopharyngeal subsites including the larynx, oral cavity, and oropharynx are most frequently affected. CONCLUSION: Head and neck squamous cell carcinoma is rare in pediatric patients but should not be overlooked by physicians in the differential diagnosis, particularly in teenagers. Further study is needed to determine whether this represents a unique entity or can be staged and treated according to adult guidelines.

Histological variants of pancreatic ductal adenocarcinoma: a survival analysis.

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

Association between radiation therapy for primary endometrial cancer and risk of second primary malignancies: a retrospective cohort study.

Our objective was to evaluate the association of adjuvant radiation therapy (RT) to subsequent second primary malignancies (SPMs) in endometrial cancer survivors. Patients with endometrial cancer as their first malignancy were identified from 8 registries of the Surveillance, Epidemiology, and End Results (SEER) database. SPMs were defined as any type of primary malignancy that occurred more than 12 months after the diagnosis of endometrial cancer. Fine-Gray competing risk regression and Poisson regression were used to evaluate the radiotherapy-associated risk (RR) for SPMs. The Kaplan-Meier method was applied to assess the survival outcomes of endometrial cancer patients. Of 62,108 endometrial cancer patients,16,846 patients (27.12%) were in the RT group, and 45,262 patients (72.88%) were in the no-RT group. During the 30-year follow-up period, the cumulative incidence of SPMs was 20.9% and 19.7% in each group, respectively. In both multivariable competing risk regression analysis and Poisson regression analysis, adjuvant RT was found to be associated with a higher risk of developing colon and rectum cancer (adjusted hazard ratio (HR), 1.29; 95% confidence interval (CI), 1.12-1.50; P < 0.001; adjusted RR, 1.29; 95% CI, 1.11-1.49; P < 0.001), lung and bronchus cancer (adjusted HR, 1.27; 95% CI, 1.08-1.50; P = 0.004; adjusted RR, 1.26; 95% CI, 1.07-1.49; P = 0.005), vulva cancer (adjusted HR, 1.72; 95% CI, 1.04-2.85; P = 0.036; adjusted RR, 1.74; 95% CI, 1.03-2.88; P = 0.035), urinary bladder cancer (adjusted HR, 1.86; 95% CI, 1.41-2.46; P < 0.001; adjusted RR, 1.85; 95% CI, 1.40-2.44; P < 0.001), and non-Hodgkin lymphoma (adjusted HR, 1.37; 95% CI, 1.06-1.77; P = 0.016; adjusted RR, 1.37; 95% CI, 1.05-1.76; P = 0.017). However, a slightly decreased risk of breast cancer was observed in patients who underwent adjuvant RT (adjusted HR, 0.89; 95% CI, 0.80-0.98; P = 0.021; adjusted RR, 0.88; 95% CI, 0.80-0.98; P = 0.020). The RR for colon and rectum cancer decreased with age and elevated with increasing latency since endometrial cancer diagnosis, and the RR for urinary bladder cancer showed a similar tendency with latency. SPMs can significantly impair the survival outcomes of primary endometrial cancer survivors. Our findings suggest that adjuvant RT for endometrial cancer patients increases the risk of non-Hodgkin lymphoma and several types of solid cancer. Long-term surveillance of these patients should be recommended for detecting SPMs.

Role of Surgery in Potentially Resectable Small-Cell Lung Cancer Based on the Eighth Edition of the TNM Classification: A Population Study of the US SEER Database.

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 < 0.001) and LCSS (SHR, 0.578; 95% CI: 0.411-0.815, p < 0.001) in stage I patients after PSM. However, there was no significant difference in OS and LCSS between the surgery and nonsurgery groups in stage II and III patients after PSM. In the entire cohort, lobectomy was associated with improved OS (median OS: 48.6 vs. 28.7 months, p < 0.0001), but not LCSS (SHR, 0.696; 95% CI: 0.466-1.040, p = 0.078) compared with sublobar resection after PSM. CONCLUSION: Surgery with adjuvant chemotherapy significantly improved the survival prognosis of patients with early-stage SCLC. However, surgical treatment should be carefully considered in patients with stage II/III disease. Lobectomy is oncologically equal to sublobar resection.

Socioeconomic disparities in reception of limb-sparing surgery versus amputation for lower extremity sarcoma.

BACKGROUND: In lower extremity sarcoma treatment, limb salvage approaches present superior alternatives to amputation due to reduced postoperative morbidity and improved quality of life. This study provides a novel analysis of socioeconomic disparities that may affect reception of limb-sparing surgery. METHODS: Patients with lower extremity bone or soft tissue sarcoma who received either limb-sparing surgery or amputation from 2007 to 2021 were identified in the Surveillance, Epidemiology and End Results (SEER) database. Demographic, socioeconomic, and oncologic variables were collected for each patient. Multivariate binary logistic regression was conducted to assess preoperative demographic and oncologic risk factors for amputation (p < 0.05). RESULTS: A total of 6465 patients were identified in the final cohort, 586 (9.1%) of whom received amputation. After controlling for tumor size, stage, and neoadjuvant therapy administration, non-Hispanic American Indian/Alaskan Native race/ethnicity predicted the highest odds of amputation (OR: 1.78, 95% CI: 1.12-2.85, p = 0.015). Nonmetropolitan residence (OR: 1.69, 95% CI: 1.43-2.00, p < 0.001) also conferred higher risk of amputation compared with residence in a large metropolitan area. Overall, amputation was associated with a higher risk of ten-year cancer-specific mortality (p < 0.001) even when controlled by sociodemographic and clinical characteristics. CONCLUSIONS: There are significant disparities in limb-sparing surgery and amputation rates in lower extremity sarcoma management, even when accounting for differences in baseline oncologic characteristics. Further study into socioeconomic drivers of these trends will allow the development of initiatives that improve disparities in reconstructive outcomes.

Risk of subsequent primary cancers in bladder cancer survivors.

We aimed to investigate the risk of bladder cancer (BCa) survivors developing or dying from 15 specific-subsequent primary cancers (SPCs). A total of 229,554 BCa survivors were identified from the Surveillance, Epidemiology, and End Results database. Incidence and mortality per 10,000 person-years, absolute excess risk (AER) per 10,000 person-years, standardized incidence ratios, and standardized mortality ratios were calculated. Among BCa survivors, 38,207 developed SPCs and 17,546 died of SPCs. The risk of developing and dying from SPCs was significantly high for 10 and 6 of the 12 common SPCs in men, respectively, while for 6 and 5 of the 14 common SPCs in women, respectively. The SPCs with high risk of development in men were colorectal, breast, liver, and pancreatic cancer, and the ones with high risk of death were liver and pancreatic cancer. Moreover, SPCs with a high risk of development or death among young BCa survivors include ureter, kidney and renal pelvis, and lung cancer. In addition, BCa survivors within 1 year of diagnosis have a significantly higher risk of development and death from ureter, kidney and renal pelvis, prostate, and cervix cancer, but a lower risk of prostate cancer than the general population after 5 years of diagnosis. Lung cancer had a significantly high risk of development but a low risk of death. Among BCa survivors, the risk of developing or dying from few SPCs is significantly high. These findings may provide an important basis for clinical follow-up of BCa survivors.

Population-based long-term prognosis analysis of subcutaneous gastrointestinal stromal tumors.

BACKGROUND: Subcutaneous gastrointestinal stromal tumors (scGISTs) are extremely rare tumors, and the analysis of their long-term prognosis remains unreported. Therefore, our objective is to analyze the long-term prognosis of patients with scGISTs using the Surveillance, Epidemiology, and End Results database. METHODS: Patients diagnosed with GISTs between 2000 and 2019 were included in the study. To handle missing data, multiple imputation techniques were employed. Kaplan-Meier analysis and Cox proportional hazards models were used to evaluate overall survival (OS) and cancer-specific survival (CSS), and subgroup analyses were conducted for various variables. RESULTS: A total of 12,882 patients were enrolled, with 12,636 diagnosed with GISTs and 246 with scGISTs. In comparison to GISTs patients, scGISTs patients exhibited inferior OS [hazard ratio (HR) 1.69, 95% confidence interval (CI) 1.45-1.98, P < 0.001] and CSS (HR 2.16, 95% CI 1.78-2.61, P < 0.001). Across various subgroups, including age, sex, surgical intervention, marital status, and chemotherapy, scGISTs patients consistently demonstrated significantly poorer OS and CSS outcomes compared to GISTs patients (P < 0.05). The 1-, 3-, and 5-year OS rates for scGISTs patients were 78.4%, 60.3%, and 49.3%, respectively, with corresponding CSS rates of 83.3%, 67.8%, and 57.4%. Notably, scGISTs patients who received surgical treatment had significantly higher 5-year OS rates (62.1% vs 30.9%, P < 0.001) and CSS rates (67.8% vs 40.0%, P < 0.001) compared to those who did not undergo surgery. Multivariate Cox regression analysis identified age, surgical status, and mitotic rate as risk factors influencing OS in scGISTs patients, while surgical status and mitotic rate were identified as risk factors affecting CSS. CONCLUSIONS: Compared to GISTs patients, scGIST patients exhibit a less favorable prognosis; nonetheless, surgical intervention has been demonstrated to enhance their prognosis.