End Results Database Research Articles

Machine learning-driven survival prediction in gestational trophoblastic neoplasms: a focus on PSTT and ETT prognosis.

The clinicopathological characteristics and prognosis of placental site trophoblastic tumor (PSTT) and epithelioid trophoblastic tumor (ETT) have not been well summarized. Consequently, we conducted the largest to date series of samples of both types and employed machine learning (ML) to assess treatment effectiveness and develop accurate prognostic models for patients with GTN. Gestational choriocarcinoma (GCC) was used as the control group to show the clinical features of PTSS and ETT. The Surveillance, Epidemiology, and End Results (SEER) database provided the data used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of the ML models. We also investigated the role of multiple therapeutic options using the Kaplan-Meier survival analysis. The study population comprised 725 patients. Among them, 139 patients had ETT, 107 had PSTT, and 479 had GCC. There were no significant differences in survival between the different tumor groups. Multivariate Cox regression analysis revealed that metastasis was a significant prognostic factor for GCC, while older age and radiotherapy were significant prognostic factors for PTSS and ETT. ML models revealed that the Gradient Boosting classifier accurately predicted the outcomes, followed by the random forest classifier, K-Nearest Neighbors, Logistic Regression, and multilayer perceptron models. The most significant contributing factors were tumor size, year of diagnosis, age, and race. Our study provides a method for treatment and prognostic assessment of patients with GTN. The ML we developed can be used as a convenient individualized tool to facilitate clinical decision making.

Impact of Postoperative Chemotherapy on Survival in Patients with Primary Central Nervous System Lymphoma: A Study Based on the SEER Database.

Aims/Background We aimed to investigate the impact of postoperative chemotherapy (POCT) on survival in patients with primary central nervous system lymphoma (PCNSL) using data from the Surveillance, Epidemiology, and End Results (SEER) database. Methods This study included 786 PCNSL patients, of which 605 received chemotherapy after surgery, and 181 did not. Data from the SEER registry database (2007-2020) were used to analyze PCNSL. Baseline information, including age, sex, race, marital status, primary tumour site, histological type, summary stage, surgical procedures, chemotherapy, and radiotherapy, was analyzed. Propensity Score Matching (PSM) (1:1) was employed to balance the effects of confounding variables between the two groups. Subsequently, Cox regression and bidirectional stepwise regression were used to identify independent prognostic factors. Kaplan-Meier (K-M) survival curves were constructed to assess the impact of POCT on patient prognosis. Additionally, two cases of PCNSL with typical magnetic resonance imaging appearances were presented. Results Multivariate Cox regression results revealed that age older than 60 years (hazard ratio [HR] = 1.786; 95% confidence interval [CI]: 1.272-2.509; p = 0.001) and absence of POCT (HR = 2.841; 95% CI: 2.159-3.738; p < 0.001) were independent prognostic risk factors, while primary tumour locations in the meninges (HR = 0.136; 95% CI: 0.032-0.569; p = 0.006) and other nervous system regions (HR = 0.552; 95% CI: 0.326-0.936; p = 0.027), as well as histological morphologies such as diffuse large B-cell lymphoma (HR = 0.233; 95% CI: 0.128-0.425; p < 0.001) and non-Hodgkin lymphoma (HR = 0.559; 95% CI: 0.356-0.876; p = 0.011), were associated with favourable patient outcomes. K-M curves demonstrated that the group undergoing POCT had a significantly more favourable prognosis compared to the non-POCT group, before (HR = 0.454; 95% CI: 0.343-0.600; p < 0.0001) or after PSM (HR = 0.580; 95% CI: 0.431-0.780; p < 0.0001). For patients with PCNSL, those with tumours located in the infratentorial region (HR = 0.231; 95% CI: 0.078-0.682; p = 0.046), supratentorial region (HR = 0.250; 95% CI: 0.163-0.383; p < 0.0001), overlapping brain regions (HR = 0.201; 95% CI: 0.056-0.727; p = 0.0058), and those who underwent biopsy (HR = 0.740; 95% CI: 0.463-1.182; p = 0.003), subtotal resection (STR) (HR = 0.490; 95% CI: 0.265-0.906; p = 0.0064), or gross total resection (GTR) (HR = 0.613; 95% CI: 0.292-1.287; p = 0.0003) had better prognoses in the postoperative chemotherapy group compared to the non-chemotherapy group. Conclusion POCT significantly improves the prognosis of PCNSL patients and identifies the characteristics of the benefiting population. This information aids clinical practitioners in designing personalized treatment plans for individuals and advancing precise treatment.

Rethinking the prognosis model of differentiated thyroid carcinoma.

The prediction efficiency of long-term cancer-specific survival (CSS) in guiding the treatment of differentiated thyroid carcinoma (DTC) patients is still unsatisfactory. We need to refine the system so that it more accurately correlates with survival. This is a retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database, and included patients who underwent surgical treatment and were diagnosed with DTC from 2004 to 2020. Patients were divided into a training cohort (2004-2015) and validation cohort (2016-2020). Decision tree methodology was used to build the model in the training cohort. The newly identified groups were verified in the validation cohort. DTC patient totals of 52,917 and 48,896 were included in the training and validation cohorts, respectively. Decision tree classification of DTC patients consisted of five categorical variables, which in order of importance were as follows: M categories, age, extrathyroidal extension, tumor size, and N categories. Then, we identified five TNM groups with similar within-group CSS. More patients were classified as stage I, and the number of stage IV patients decreased significantly. The new system had a higher proportion of variance explained (PVE) (5.04%) and lower Akaike information criterion (AIC) (18,331.906) than the 8th TNM staging system (a PVE of 4.11% and AIC of 18,692.282). In the validation cohort, the new system also showed better discrimination for survival. The new system for DTC appeared to be more accurate in distinguishing stages according to the risk of mortality and provided more accurate risk stratifications and potential treatment selections.

Effect of laterality on the postoperative survival of non-small cell lung cancer patients undergoing pneumonectomy.

Pneumonectomy is one of the important surgical methods for non-small cell lung cancer (NSCLC). This study evaluated the effects of laterality on the short- and long-term survival of NSCLC patients undergoing pneumonectomy. We reviewed the Surveillance, Epidemiology, and End Results database to retrieve the data of patients who underwent pneumonectomy for stage I-III NSCLC from 2004 to 2015. Propensity score matching (PSM) was used to reduce the selection bias. Logistic regression was used to analyze the correlation between laterality and mortality at 3, 6, and 9 months. The Kaplan-Meier curve was used to further assess the effect of laterality on overall survival (OS). A total of 4,763 patients met the enrollment criteria [right-sided, 1,988 (41.7%); left-sided, 2,775 (58.3%)]. After PSM, 1,911 patients for each side were included in the further analysis. The first 6 months following pneumonectomy was the main period of death, with 32.0% (428/1,336) and 19.9% (250/1,258) of right- and left-sided deaths occurring during this period. The logistic regression analysis showed that right-sided pneumonectomy was an independent risk factor for 3- (P<0.001) and 6-month (P<0.001) mortality. However, laterality had no significant effect on postoperative death at 7-9 months (P=0.82). In the total cohort, right-sided patients had worse OS (P<0.001), but the subgroup survival analysis of patients with a follow-up period >6 months revealed that laterality had no statistically significant effect on OS (P=0.75). Right-sided pneumonectomy was associated with a higher perioperative mortality risk that lasted about 6 months. After that period, laterality was not observed to have a significant prognostic effect on the OS of patients undergoing pneumonectomy.

Is radiotherapy still the optimal initial choice for patients with early-stage low-grade follicular lymphoma in the modern era? A population-based study.

Despite radiotherapy (RT) is recognized as preferred initial therapy for early-stage low-grade follicular lymphoma (FL) by many international practice guidelines, the medical oncologist has improperly underutilized RT, and diverse management strategies, including systemic therapy (ST), combined modality (CM) and watch and wait (WW), are still used. Except survival outcomes, previous studies concerned little about the treatment-related toxicity, which is also important factor in choosing initial management strategy, especially second primary malignancies (SPMs). The aim of this study was to compare the overall survival (OS) and the SPMs risk between different management strategies, which can provide guidance for the choice of optimal initial management strategy. Data was acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Finally, A total 10,900 patients were identified, in which 930 cases developed SPMs. The use of radiotherapy (RT) has remained consistently low, with a utilization rate of around 20%, while most patients have received watchful waiting (WW) and systemic therapy (ST). In the rituximab era, multivariate analysis indicated that RT exhibited significantly superior OS and did not increase SPMs risk in comparison with ST and WW. At the same time, although there were no significant differences in OS between CM and RT, RT had significantly lower SPMs risk in comparison with CM. The use of RT improved the OS and did not increase the SPMs risk in comparison with other management strategies. Considering the low application rate of RT, oncologists should emphasize and increase the use of RT as an initial management strategy in patients with early-stage low-grade FL.

The Effect of Surgical Resection on Cancer-Specific Mortality in Pelvic Soft Tissue Sarcoma According to Histologic Subtype and Stage

Background/Objectives: The impact of surgical resection versus non-resection on cancer-specific mortality (CSM) in soft tissue pelvic sarcoma remains largely unclear, particularly when considering histologic subtypes such as liposarcoma, leiomyosarcoma, and sarcoma NOS. The objective of the present study was to first report data regarding the association between surgical resection status and CSM in soft tissue pelvic sarcoma. Methods: Using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019, we identified 2491 patients diagnosed with pelvic soft tissue sarcoma. Cumulative incidence plots were used to illustrate CSM and other-cause mortality rates based on the histologic subtype and surgical resection status. Competing risk regression models were employed to assess whether surgical resection was an independent predictor of CSM in both non-metastatic and metastatic patients. Results: Among the 2491 patients with soft tissue pelvic sarcoma, liposarcoma was the most common subtype (41%), followed by leiomyosarcoma (39%) and sarcoma NOS (20%). Surgical resection rates were 92% for liposarcoma, 91% for leiomyosarcoma, and 58% for sarcoma NOS in non-metastatic patients, while for metastatic patients, the rates were 55%, 49%, and 23%, respectively. In non-metastatic patients who underwent surgical resection, five-year CSM rates by histologic subtype were 10% for liposarcoma, 32% for leiomyosarcoma, and 27% for sarcoma NOS. The multivariable competing risk regression analysis showed that surgical resection provided a protective effect across all histologic subtypes in non-metastatic patients (liposarcoma HR: 0.2, leiomyosarcoma HR: 0.5, sarcoma NOS HR: 0.4). In metastatic patients, surgical resection had a protective effect for those with leiomyosarcoma (HR: 0.6) but not for those with sarcoma NOS. An analysis for metastatic liposarcoma was not possible due to insufficient data. Conclusions: In non-metastatic soft tissue pelvic sarcoma, surgical resection may be linked to a reduction in CSM. However, in metastatic patients, this protective effect appears to be limited primarily to those with leiomyosarcoma.

Distant metastasis patterns among lung cancer subtypes and impact of primary tumor resection on survival in metastatic lung cancer using SEER database.

This research aimed to systematically uncover the metastatic characteristics and survival rates of lung cancer subtypes and to evaluate the impact of surgery at the primary tumor site on cancer-specific survival in DM lung cancer. We used the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019) to identify primary lung cancers with DM at presentation (M1). Kaplan-Meier (KM) survival curves were generated and compared utilizing log-rank tests. Cox regression methods were employed to determine hazard ratios (HR) and 95% confidence intervals related to CSS factors. Inverse probability of treatment weighting (IPTW) was applied to reduce bias. We analyzed 77,827 M1 lung cancer cases, with 41.22% having DM at presentation. Bone metastasis was most common in ADC, ASC, SCC, LCC; brain in LCNEC; liver in SCLC. Lung was common in TC + AC and SCC. Long-term survival was best in TC + AC and worst in SCLC (p < 0.001). Male gender, age < 50, primary tumor site (main bronchus, lower lobe), large tumor diameter, ADC/SCLC/SCC pathology, and regional lymph node involvement were significant risk factors for multiorgan metastasis. Age ≥ 50, male, large tumor diameter, positive lymph nodes, and multiorgan metastases were associated with lower CSS. In contrast, radiotherapy, chemotherapy, systemic therapy, and surgery were associated with higher CSS rates. Primary tumor resection improved survival in lung cancer patients (excluding small cell lung cancer, SCLC) with single organ metastases (KM log rank p < 0.001, HR = 0.6165; 95% CI (0.5468-0.6951)), especially in brain (p < 0.001, HR = 0.6467; 95% CI (0.5505-0.7596)) and bone (p = 0.182, HR = 0.6289; p < 0.01), but not in liver or intrapulmonary metastases after IPTW. Significant differences in DM patterns and corresponding survival rates exist among lung cancer subtypes. Primary tumor resection improves survival in lung cancer patients (excluding small cell lung cancer, SCLC) with single organ metastases, with better outcomes in patients with brain and bone metastases, while no significant benefit was seen in patients with liver and intrapulmonary metastases.

A Population-Level Validation of the New 9th Edition of the American Joint Commission on Cancer Staging System for Anal Squamous Cell Carcinoma.

The staging system for anal squamous cell carcinoma (ASCC) was recently revised, downstaging selected node-positive patients and upstaging some with larger tumors. We aimed to validate this staging system using a population-based cohort. The Surveillance, Epidemiology, and End Results database was analyzed to identify adult ASCC patients. Patients were staged according to the American Joint Committee on Cancer (AJCC) 8th and 9th edition systems. Survival probabilities were estimated using Kaplan-Meier curves. Cox regression models were utilized to estimate the effect of stage on overall (OS) and cancer-specific survival (CSS). A total of 2117 patients were identified and staged based on the two AJCC classifications. At 24 months when comparing stage IIB versus stage IIIA, the 8th edition revealed an improved OS (79% vs. 88%) and CSS (84% vs. 91%) for stage IIIA disease, while the 9th edition restored the hierarchical OS (IIB 88% vs. IIIA 78%) and CSS (91% vs. 82%) order. The hierarchical increase of the OS HRs, and nearly all CSS HRs, across disease stages validated the updated edition of the AJCC classification. Although this change may not have significant implication on the management of ASCC, it does provide better prognostication.

Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database

This study aimed to compare the long-term outcomes of breast-conserving surgery plus radiotherapy (BCS + RT) and mastectomy in early breast cancer (EBC) patients who received neoadjuvant systemic therapy (NST), and sought to construct and authenticate a machine learning algorithm that could assist healthcare professionals in formulating personalized treatment strategies for this patient population. We analyzed data from the Surveillance, Epidemiology, and End Results database on EBC patients undergoing BCS + RT or mastectomy post-NST (2010–2018). Employing propensity score matching (PSM) to minimize potential biases, we compared breast cancer-specific survival (BCSS) and overall survival (OS) between the two surgical groups. Additionally, we trained and validated six machine learning survival models and developed a cloud-based recommendation system for surgical treatment based on the optimal model. Among the 13,958 patients, 9028 (64.7%) underwent BCS + RT and 4930 (35.3%) underwent mastectomy. After PSM, there were 3715 patients in each group. Compared to mastectomy, BCS + RT significantly improved BCSS (p < 0.001) and OS (p < 0.001). Prognostic variables associated with BCSS were utilized to develop machine learning models. In both the training and validation cohorts, the random survival forest (RSF) model demonstrated superior predictive performance (0.847 and 0.795), not only outperforming other machine learning models, including Rpart (0.725 and 0.707), Xgboost (0.762 and 0.727), Glmboost (0.748 and 0.788), Survctree (0.764 and 0.766), and Survsvm (0.777 and 0.790), but also outperforming the classical COX model (0.749 and 0.782). Lastly, a web-based prediction tool was built to facilitate clinical application [https://jhren.shinyapps.io/shinyapp1]. After adjusting other confounders, BCS + RT was associated with improved outcomes in patients with EBC after NST, compared to those who underwent mastectomy. Moreover, the RSF model, a reliable tool, can predict long-term outcomes for patients, providing valuable guidance for operative methods and postoperative follow-up.

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Predictive model of prognosis index for invasive micropapillary carcinoma of the breast based on machine learning: a SEER population-based study

BackgroundInvasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial.ObjectiveThis study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma.MethodsA total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients.ResultsCox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863.ConclusionsA machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.

Nomogram predicting overall and cancer specific prognosis for poorly differentiated lung adenocarcinoma after resection based on SEER cohort analysis

The prognosis of poorly differentiated lung adenocarcinoma (PDLA) is determined by many clinicopathological factors. The aim of this study is identifying prognostic factors and developing reliable nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with PDLA. Patient data from the Surveillance, Epidemiology and End Results (SEER) database was collected and analyzed. The SEER database was used to screen 1059 eligible patients as the study cohort. The whole cohort was randomly divided into a training cohort (n = 530) and a test cohort (n = 529). Cox proportional hazards analysis was used to identify variables and construct a nomogram based on the training cohort. C-index and calibration curves were performed to evaluate the performance of the model in the training cohort and test cohorts. For patients with PDLA, age at diagnosis, gender, tumor size were independent prognostic factors both for overall survival (OS) and cancer-specific survival (CSS), while race and number of nodes were specifically related to OS. The calibration curves presented excellent consistency between the actual and nomogram-predict survival probabilities in the training and test cohorts. The C-index values of the nomogram were 0.700 and 0.730 for OS and CSS, respectively. The novel nomogram provides new insights of the risk of each prognostic factor and can assist doctors in predicting the 1-year, 3-year and 5-year OS and CSS in patients with PDLA.

Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer. Women with cervical cancer diagnosed between 2013 and 2015 were included in our study using data from the Surveillance, Epidemiology, and End Results (SEER) database. Using this dataset, the study assesses the performance of Weibull, Cox proportional hazards models, and Random Survival Forests in terms of predictive accuracy and risk factor identification. The findings reveal that machine learning models, particularly Random Survival Forests (RSF), outperform traditional statistical methods in both predictive accuracy and the discernment of crucial prognostic factors, underscoring the advantages of machine learning in handling complex survival data. However, for a survival dataset with a small number of predictors, statistical models should be used first. The study finds that RSF models enhance survival analysis with more accurate predictions and insights into survival risk factors but highlights the need for larger datasets and further research on model interpretability and clinical applicability.

Is chemotherapy beneficial? A retrospective study of chemotherapy in patients with invasive intraductal papillary-mucinous carcinoma

Background and aimWhether chemotherapy can improve the prognosis of invasive intraductal papillary-mucinous carcinoma (IPMC) still remains unclear. The aim of this study is to observe the difference in survival time of patients with invasive IPMC receiving or not receiving chemotherapy. Methods117 patients with invasive IPMC were included in The Surveillance, Epidemiology, and End Results (SEER) database. These patients were subsequently divided into two subgroups according to whether they received chemotherapy or not: the non-chemotherapy group (patients who did not receivechemotherapy, N = 58), the chemotherapy group (patients who received chemotherapy, N = 59). The overall survival (OS) and cancer specific survival (CSS) of two treatment groups were evaluated. ResultsBefore adjusting for pathology grade, the Kaplan-Meier analysis showed that the difference of survival time is not significant between non-chemotherapy group and chemotherapy group (P > 0.05), but the land-mark analysis showed that short-term death risk of the chemotherapy group is significantly lower than non-chemotherapy group (P < 0.05). After adjust the pathology grade, survival time of the chemotherapy group is significantly longer than non-chemotherapy group (P < 0.05). Univariate and multivariate Cox regression showed that chemotherapy was an independent prognostic protective factor for invasive IPMC (P < 0.05). Land-mark analysis showed that short-term death risk of the chemotherapy group is significantly lower than non-chemotherapy group in N1-N2 subgroup (P < 0.05). ConclusionChemotherapy is an independent protective factor IPMC, especially reducing the risk of short-term death for IPMC patients with lymph node metastasis.

Individualized evaluation of risk and prognosis in uterine leiomyosarcoma patients with synchronous distant metastases: a real-world retrospective study.

Uterine leiomyosarcoma (uLMS) accounts for roughly 70% of all uterine sarcomas, with recurrence and mortality rates notably higher than those of other uterine tumors. The prognosis of uLMS patients who have distant metastases remains poor. The objective of this study was to determine independent risk variables related to distant metastases in patients with uLMS and prognostic factors for those with distant metastases. Subsequently, two practical nomograms were developed and validated to assess the probability of distant metastases and predict survival outcomes for these with distant metastases, respectively. A real-world retrospective study was carried out using data from patients diagnosed with primary uLMS in the Surveillance, Epidemiology, and End Results (SEER) database spanning the years 2010 to 2015. Univariate andmultivariate logistic regression analyses were utilized to identify clinicopathological characteristics related to the risk of distant metastases, while univariate and multivariate Cox regressions were employed to determine prognostic factors. Then, a risk nomogram incorporating independent risk variables and a prognostic nomogram integrating independent prognostic factors were established in the training cohort and validated for accuracy in the validation cohort, respectively. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and calibration curves were utilized to measure the accuracy of nomograms, while decision curve analysis (DCA) curves were employed to assess their clinical benefit capacity. Based on the median total point derived from the prognostic nomogram, patients were stratified into high- and low-risk groups. The differentiation ability of the prognostic nomogram was evaluated using Kaplan-Meier survival analysis with the log-rank test. The study encompassed 1,362 patients diagnosed with uLMS, among whom 337 cases (24.7%) manifested synchronous distant metastases at the initial diagnosis. Univariate and multivariate logistic regression analyses identified race, histological grade, T stage, N stage, tumor size, surgery, and chemotherapy as independent risk factors for distant metastases in uLMS patients. The outcomes of both univariate and multivariate Cox analyses indicated that surgery and chemotherapy emerged as independent protective factors for prognosis in uLMS patients with distant metastases, whereas higher histological grade and T stage were identified as independent risk factors. The risk nomogram incorporating independent risk variables and the prognostic nomogram integrating independent prognostic factors could respectively predict the risk of metastases and the prognosis very effectively in both training and validation cohorts. In summary, we developed the novel well-validated risk nomogram to precisely assess the probability of metastases in uLMS patients and prognostic nomogram to predict the prognosis of those with distant metastases, providing decision-making guidance for tailoring individualized clinical management of these patients.

Sex-Based Differences in Lung Cancer Incidence: A Retrospective Analysis of Two Large US-Based Cancer Databases

Background/Objectives: Non-small cell lung cancer (NSCLC) has seen a relative rise in incidence among females versus males in recent years, although males still have a higher overall incidence. However, it is unclear whether this trend is consistent across all populations. Therefore, we retrospectively examined this relationship in two large high-risk clinical cohorts. Methods: First, we analyzed lung cancer incidence among individuals with a smoking history of over 40 pack-years in the National Lung Screening Trial (NLST). Then, we investigated the incidence of second primary NSCLC in patients who underwent lobectomy for previous stage I lung cancer using the Surveillance, Epidemiology, and End Results (SEER) database. We performed both univariate and multivariable time-to-event analyses to investigate the relationship between sex and lung cancer incidence. Results: In the NLST cohort (n = 37,627), females had a higher risk of developing primary NSCLC than males (HR = 1.11 [1.007–1.222], p = 0.035) after adjusting for age and pack-year history. In the SEER cohort (n = 19,327), females again exhibited an increased risk of developing a second primary lung cancer (HR = 1.138 [1.02–1.269], p = 0.021), after adjusting for age, race, grade, and histology. Conclusions: Our analysis reveals that females have a modestly higher lung cancer incidence than males in high-risk populations. These findings underscore the importance of further researching the underlying cellular processes that may cause sex-specific differences in lung cancer incidence.

Adult Prostate Sarcoma: Demographics, Treatment Patterns, and Survival

BackgroundThis study aimed to examine clinicopathologic characteristics, treatment patterns, and survival rates in a contemporary population-based cohort of adult prostate sarcoma patients.MethodsIn the Surveillance, Epidemiology, and End Results database (2004–2020), adult patients with prostate sarcoma were identified. Descriptive statistics, Kaplan–Meier analyses, smoothed cumulative incidence plots, and Cox regression models were used.ResultsOf 125 patients, 45 (36%) harbored leiomyosarcoma, 17 (14%) had rhabdomyosarcoma, 15 (12%) had stromal sarcoma, 17 (14%) had sarcoma not otherwise specified (NOS), and 31 (25%) had other sarcoma subtypes. Metastatic stage was most common in the rhabdomyosarcoma patients (44%) and least common in the leiomyosarcoma (21%) and stromal sarcoma (20%) patients. Most of the rhabdomyosarcoma patients received the combination of systemic and radiation therapy with (24%) or without radical surgery (35%), whereas most of the leiomyosarcoma and stromal sarcoma patients underwent radical surgery with (22 and 13%) or without (22 and 47%) radiation. In the overall population, the median overall survival was 27 months. The 5-years overall versus cancer-specific versus other-cause mortality rates were respectively 71 versus 58 versus 13%. In the multivariable Cox regression models, the highest overall mortality was exhibited by the patients with metastatic disease (hazard ratio [HR] 2.87; 95% confidence interval [CI] 1.55–5.31; p < 0.001) or unknown disease stage (HR 2.94; 95% CI 2.20–7.21; p = 0.019). Conversely, of all the histologic subtypes, only stromal sarcoma distinguished itself by lower overall mortality (HR 0.41; 95% CI 0.18–0.96; p = 0.039).ConclusionsFour major histologic subtypes were identified. Among most adult sarcoma patients, treatment patterns vary according to histology, from multimodal therapy to radical prostatectomy alone. These treatment differences reflect equally important heterogeneity in survival patterns.

Dynamic estimates of survival in patients with follicular thyroid cancer: a retrospective cohort study.

Few studies have been conducted on the dynamic survival rates of follicular thyroid cancer (FTC). This study aimed to ascertain how the survival probability of patients with FTC changes over time. In this retrospective analysis, 10,617 patients diagnosed with FTC between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database were included. Actuarial disease-specific survival (DSS) was estimated using the Kaplan-Meier method, and the log-rank test was used for comparisons. The annual hazard of mortality was determined using the hazard function, and the conditional survival (CS) was calculated using the life table method. A total of 459 (4.3%) patients died of FTC, and the 5-year and 10-year DSS rates were 96.6 ± 0.2% and 94.6 ± 0.3%, respectively. There was a statistically significant difference in the DSS rate between patients with different SEER combined summary stages (P < 0.001). The annual hazard curve for cancer mortality in the entire study cohort displayed a steep downward trend with a slight peak at 2.5 years after diagnosis, followed by a gradual decline. Patients with distant metastases exhibited a higher mortality hazard curve and more notable declining trend. CS demonstrated an upward trend across the entire study population, with the most pronounced trend in patients with distant metastases. Prognosis improved over time in a stage-dependent manner in patients with FTC after diagnosis. The most significant improvement was observed in the patients with distant metastases. Notably, dynamic survival estimations, such as death hazard and conditional survival analysis, provide more precise survival projections than traditional survival analysis for FTC survivors.

The prognostic impact of tumor deposits in colorectal cancer: More than just N1c.

The identification of tumor deposits (TD) currently plays a limited role in staging for colorectal cancer (CRC) aside from N1c lymph node designation. The objective of this study was to determine the prognostic impact, beyond American Joint Committee on Cancer N1c designation, of TDs among patients with primary CRC. Patients who had resected stage I-III primary CRC diagnosed between 2010 and 2019 were identified from the National Cancer Institute's Surveillance, Epidemiology, and End Results database. Cancer-specific survival (CSS) stratified by TD status and lymph node (N) status was calculated using the Kaplan-Meier method and multivariable Cox proportional hazards regression analyses. In total, 147,783 patients with primary CRC were identified. TDs were present in 15,444 patients (10.5%). The presence of TDs was significantly associated with adverse tumor characteristics, including advanced pathologic stage, nodal status, and metastasis status. The presence of TDs was associated with worse CSS (hazard ratio [HR], 3.12; 95% confidence interval [CI], 3.02-3.22), as it was for each given N category (e.g., N2a and TD-negative [HR, 2.50; 95% CI, 2.37-2.64] vs. N2a and TD-positive [HR, 3.75; 95% CI, 3.49-4.03]). The presence of multiple TDs was also associated with decreased CSS for each given N category compared with a single TD (e.g. N2a with one TD [HR, 3.09; 95% CI, 2.65-3.61] vs. N2a with two or more TDs [HR, 4.32; 95% CI, 3.87-4.82]). TDs were identified as an independent predictor of a worse outcome in patients with CRC. The presence of TDs confers distinctly different CSS and provides important prognostic information among patients with CRC and warrants further investigation as a unique variable in future iterations of CRC staging.

Survival outcomes of neoadjuvant versus adjuvant therapy in patients with T1c, node‐negative, human epidermal growth factor receptor 2–positive breast cancer: A Surveillance, Epidemiology, and End Results population‐based study

AbstractBackgroundPersistent debates exist regarding the superiority of neoadjuvant therapy (NAT) over adjuvant therapy (AT) for patients with T1c, node‐negative, human epidermal growth factor receptor 2–positive (HER2+) breast cancer, and relevant guidelines for these patients are lacking.MethodsData on patients with T1cN0M0‐stage HER2+ breast cancer who received chemotherapy and surgery were extracted from 2010 to 2020 from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was used to create well‐balanced cohorts for the NAT and AT groups. Kaplan–Meier (KM) analysis and Cox proportional hazards models were used to assess the differences between NAT and AT in terms of overall survival (OS) and breast cancer–specific survival (BCSS). Additionally, logistic regression models were used to explore factors associated with response to NAT.ResultsAfter PSM, 2140 patient pairs were successfully matched, which achieved a balanced distribution between the NAT and AT groups. KM curves revealed similar OS and BCSS between patients receiving NAT and those undergoing AT. A multivariate Cox model identified achieving pathological complete response (pCR) after NAT, compared with AT, as a protective prognostic factor for OS (hazard ratio, 0.52; 95% CI, 0.35–0.77; p &lt; .001) and BCSS (hazard ratio, 0.60; 95% CI, 0.37–0.98; p = .041). A logistic regression model revealed that White race and hormone receptor–negative status independently predicted pCR.ConclusionsFor patients with T1cN0M0‐stage HER2+ breast cancer, NAT demonstrated comparable OS and BCSS to AT. Patients who achieved pCR after NAT exhibited significantly better survival outcomes compared with those who received AT.