Summary Stage Research Articles

Competitive Risk Analysis of Thymic Carcinoma Based on the Surveillance, Epidemiology, and End Results Database

In general, we use the classical Cox proportional hazards model to derive factors that affect the prediction of patients diagnosed with thymic carcinoma (TC); however, when competing risks exist, the results can be biased. This study aimed to build a competing risk model for patients with TC to explore a more accurate method for assessing the relevant factors affecting patient prognosis. We obtained data on patients with TC who met the inclusion criteria between 2004 and 2016 (with additional treatment fields) in the Surveillance Epidemiology, and End Results database. The cumulative incidence function and Gray’s test were used for univariate analysis, followed by the fine-Gray and Cox proportional hazards models for multivariate analysis. Of the 478 subjects with TC who were finally included, 284 (170 died from TC, and 114 died from other causes) (59.41%) died, and 194 (40.59%) patients were alive. Univariate Gray’s test results indicated that age, marital status, tumor size, summary stage (localized, regional, or distant), chemotherapy status, and surgery status significantly affected the cumulative incidence of the target event (P < 0.05). Multivariate competing risk analyses indicated that tumor size, marital status, summary stage, and surgery status were independent risk factors for the prediction of subjects (P < 0.05). This study explored a more accurate method to assess the prognostic factors of patients with TC. Our findings can contribute to the clinical development of more scientific and accurate treatment methods, providing benefits to the majority of patients with TC.

Epidemiology and survival outcomes of patients with orbital region non-cutaneous squamous cell carcinoma: a population-based analysis.

Non-cutaneous squamous cell carcinoma (ncSCC) of the orbital region is very rare. Thus, its epidemiological characteristics and prognosis are poorly understood. The aim of the study was to assess the epidemiological characteristics and survival outcomes of ncSCC of the orbital region. Incidence and demographic data on ncSCC of the orbital region were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and analyzed. The chi-square test was used to calculate the differences between groups. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors for disease-specific survival (DSS) and overall survival (OS). The overall incidence of ncSCC in the orbital region from 1975 to 2019 was 0.68/1,000,000, and the incidence showed an increasing trend during this period. A total of 1,265 patients with ncSCC of the orbital region (mean age, 65.3 years) were identified in the SEER database. Of these, 65.1% were aged ≥60 years, 87.4% were White, and 73.5% were male. The conjunctiva (74.5%) was the most common primary site, followed by the orbit (12.1%), lacrimal apparatus (10.8%), and overlapping lesion of the eye and adnexa (2.7%). Multivariate Cox regression analysis revealed that age, primary site, SEER summary stage, and surgery were independent prognostic factors for DSS, whereas age, sex, marital status, primary site, SEER summary stage, and surgery were independent prognostic factors for OS. The incidence of ncSCC in the orbital region has increased over the past 40 years. It usually affects White people, men, and people aged ≥60 years, and its most common site is the conjunctiva. Orbital SCC has worse survival outcomes than SCC of other sites in the orbital region. Surgery is the independent protective treatment for ncSCC of the orbital region.

Competing risk nomogram for predicting cancer-specific mortality in patients with non-melanoma skin cancer.

This study aimed to assess the cumulative incidences of Non-melanoma skin cancer (NMSC)-specific mortality (NMSC-SM) and develop a competing risk nomogram for NMSC-SM. Data on patients diagnosed with NMSC between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. To identify the independent prognostic factors, univariate and multivariate competing risk models were used, and a competing risk model was constructed. Based on the model, we developed a competing risk nomogram to predict the 1-, 3-, 5-, and 8-year cumulative probabilities of NMSC-SM. The precision and ability to discriminate of the nomogram were evaluated through the utilization of metrics, such as receiver-operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve. Decision curve analysis (DCA) was employed to assess the clinical usefulness of the nomogram. Race, age, the primary site of the tumor, tumor grade, size, histological type, summary stage, stage group, order of radiation and surgery, and bone metastases were identified as independent risk factors. The prediction nomogram was constructed using the variables mentioned above. The ROC curves implied the good discrimination ability of the predictive model. The nomogram's C-index was 0.840 and 0.843 in the training and validation sets, respectively, and the calibration plots were well fitted. In addition, the competing risk nomogram demonstrated good clinical usefulness. The competing risk nomogram displayed excellent discrimination and calibration for predicting NMSC-SM, which can be used in clinical contexts to help guide treatment decisions.

Development and validation of web-based prognostic nomograms for massive hepatocellular carcinoma (≥10 cm): A retrospective study based on the SEER database.

Massive hepatocellular carcinoma (MHCC, a maximum tumor size of at least 10 cm) tends to have a poor prognosis. Therefore, this study aims to construct and validate prognostic nomograms for MHCC. Clinic data of 1292 MHCC patients between 2010 and 2015 were got from the surveillance, epidemiology, and end results (SEER) cancer registration database. The whole set was separated into the training and validation sets at a ratio of 2:1 randomly. Variables, significantly associated with cancer-specific (CSS) and overall survival (OS) of MHCC were figured out by multivariate Cox regression analysis and were taken to develop nomograms. The concordance index (C-index), calibration curve, and decision curve analysis (DCA) were taken to validate the predictive abilities and accuracy of the nomograms. Race, alpha-fetoprotein (AFP), grade, combined summary stage, and surgery were identified as independent factors of CSS, and fibrosis score, AFP, grade, combined summary stage, and surgery significantly correlated with OS in the training cohort. They then were taken to construct prognostic nomograms. The constructed model for predicting CSS exhibited satisfactory performance with a C-index of 0.727 (95% CI: 0.746-0.708) in the training group and 0. 672 (95% CI: 0.703-0.641) in the validation group. Besides, the model for predicting OS of MHCC also showed strong performance both in the training group (C-index: 0.722, 95% CI: 0.741-0.704) and the validation (C-index: 0.667, 95% CI: 0.696-0.638) group. All calibration curves and decision curves performed satisfactory predictive accuracy and clinic application values of the nomograms. The web-based nomograms for CSS and OS of MHCC were developed and validated in this study, which prospectively could be tested and may serve as additional tools to assess patient's individualized prognosis and make precise therapeutic selection to improve the poor outcome of MHCC.

Abstract 4217: Cumulative environmental quality is associated with breast cancer incidence differentially by summary stage and urbanicity

Abstract Individual environmental contaminants have been associated with breast cancer (BCa); however, simultaneous evaluations of multiple exposures are limited. The USEPA has constructed an environmental quality index (EQI) that includes different exposures across air, water, land, sociodemographic, and built environments. As links between BCa and the EQI have so far been confined to total BCa incidence, herein we investigated if these multiple exposures in broad EQI domains were associated with BCa incidence stratified by stage of disease. Incidence rates of total, in situ, localized, regional, and distant breast cancer were assessed by linking the EQI dataset to county-level age-standardized incidence rates obtained from the North Carolina Central Cancer Registry (2010-2014), which reports all cancer cases diagnosed in NC residents. Generalized linear models (SAS 9.3) were constructed to determine the associations between breast cancer incidence rates by summary stage and the EQI in total and by domain, compared across rural versus urban counties. Models were adjusted for percent African American (AA), percent smokers, and mammography screening rates in each county. Mann-Whitney rank tests and D'Agostino-Pearson normality tests were used to determine statistical significance. Results showed that the EQI is variable across NC, akin to variability across the U.S. (interquartile range 25th-75th% for total EQI in NC: -0.187 to 0.734 vs in the U.S.: –0.606 to 0.706). In counties with poor total EQI scores, and thus poor overall environmental quality, total BCa incidence increased by 10.82 cases per 100,000 persons (95%CI: 2.04, 19.60, p=0.016). This association was most pronounced for localized BCa (β=5.59, 95%CI: 0.59, 10.58, p=0.029). Higher incidence of early-stage disease (carcinoma in situ β=5.25, 95%CI: 2.34, 8.16, p=0.001 and localized BCa β=6.98, 95%CI: 2.24, 11.73, p=0.004) and total breast cancer (β=11.44, 95%CI: 3.01, 19.87, p=0.008) also occurred in counties with poor land quality, especially urban counties. The county AA percentage was associated with increased incidence of regional (0.12 cases per % increase, 95% CI 0.01, 0.22, p=0.022) and distant BCa (0.06 per % increase, 95% CI 0.02, 0.10, p=0.003). Associations persisted and were strengthened in urban counties for regional BCa, and in rural counties for distant BCa. In sum, we found that BCa is associated with environmental quality differentially by disease stage, environmental domain, and urbanicity. Although what drives poor EQI varies by region, our analyses are generalizable to other states and counties across the U.S. and suggest that cumulative environmental exposures should be assessed in the context of cancer stage. Funding: Duke Cancer Institute pilot (GRD, KPH) in P30 Cancer Ctr grant; NIEHS T32-ESO21432-05; NCI-3P20CA202925-04S2 Diversity Supplement (LGS). Citation Format: Larisa M. Gearhart-Serna, Kate Hoffman, Hillary Hsu, Gayathri R. Devi. Cumulative environmental quality is associated with breast cancer incidence differentially by summary stage and urbanicity. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4217.

Delay in Surgery and Papillary Thyroid Cancer Survival in the United States: a SEER-Medicare Analysis.

Delays in surgery and their impact on survival in papillary thyroid cancer (PTC) is unclear. We sought to investigate the association between time to surgery and survival in patients with PTC. A total of 8,170 Medicare beneficiaries with PTC who underwent thyroidectomy were identified within the SEER-Medicare linked data files between 1999-2018. Disease-specific survival (DSS) and overall survival (OS) were estimated using Kaplan-Meir analysis, and Cox proportional hazards models were specified to estimate the association between time to surgery and survival. Among 8,170 patients with PTC, mean age 69.3 (SD+/- 11.4), 89.8% had surgery within the first 90 days, 7.8% had surgery 91-180 days from diagnosis, and 2.4% had surgery after 180 days. Increasing time to surgery was associated with increased mortality for OS in the >180-day group (aHR 1.24, 95% CI 1.01-1.53). Moreover, on stratification by summary stage, those with localized disease in the 91-180-day group increased risk by 25% (aHR 1.25 95%CI 1.05-1.51) and delaying over 180 days increased risk by 61% (aHR 1.61 95%CI 1.19-2.18) in OS. Those with localized disease in the >180-day group had almost four times the estimated rate of DSS mortality (aHR3.51 95%CI 1.68-7.32). When stratified by T stage, those with T2 disease in the >180 days group had double the estimated rate of all-cause mortality (aHR 2.0, 95% CI 1.1-3.3) and almost triple the estimated rate of disease-specific mortality (aHR 2.7, 95% CI 1.05-6.8). Delays in surgery for PTC may impact OS and DSS in localized disease, prior to nodal metastasis.

Deep-learning-based survival prediction of patients with cutaneous malignant melanoma.

This study obtained data on patients with cutaneous malignant melanoma (CMM) from the Surveillance, Epidemiology, and End Results (SEER) database, and used a deep learning and neural network (DeepSurv) model to predict the survival rate of patients with CMM and evaluate its effectiveness. We collected information on patients with CMM between 2004 and 2015 from the SEER database. We then randomly divided the patients into training and testing cohorts at a 7:3 ratio. The likelihood that patients with CMM will survive was forecasted using the DeepSurv model, and its results were compared with those of the Cox proportional-hazards (CoxPH) model. The calibration curves, time-dependent area under the receiver operating characteristic curve (AUC), and concordance index (C-index) were used to assess the prediction abilities of the model. This study comprised 37,758 patients with CMM: 26,430 in the training cohort and 11,329 in the testing cohort. The CoxPH model demonstrated that the survival of patients with CMM was significantly influenced by age, sex, marital status, summary stage, surgery, radiotherapy, chemotherapy, postoperative lymph node dissection, tumor size, and tumor extension. The C-index of the CoxPH model was 0.875. We also constructed the DeepSurv model using the data from the training cohort, and its C-index was 0.910. We examined how well the aforementioned two models predicted outcomes. The 1-, 3-, and 5-year AUCs were 0.928, 0.837, and 0.855, respectively, for the CoxPH model, and 0.971, 0.947, and 0.942 for the DeepSurv model. The DeepSurv model presented a greater predictive effect on patients with CMM, and its reliability was better than that of the CoxPH model according to both the AUC value and the calibration curve. The DeepSurv model, which we developed based on the data of patients with CMM in the SEER database, was found to be more effective than the CoxPH model in predicting the survival time of patients with CMM.

Thyroid cancer pathologic upstaging: Frequency and related factors

IntroductionHistopathologic assessment of thyroid tumors can lead to stage migration. We assessed frequency of pathologic upstaging, and associations with patient and tumor factors. MethodsPrimary thyroid cancers treated between 2013 and 2015 were included from our institutional cancer registry. For tumor, nodal, and summary stage, upstaging was present when final pathologic stage was greater than clinical staging. Multivariate logistic regression and Chi-squared tests were performed. Results5,351 resected thyroid tumors were identified. Upstaging rates for tumor, nodal, and summary stage were 17.5% (n = 553/3156), 18.0% (n = 488/2705), and 10.9% (n = 285/2607), respectively. Age, Asian race, days to surgery, lymphovascular invasion, and follicular histology were significantly associated. Upstaging was significantly more common after total vs partial thyroidectomy, for tumor (19.4% vs 6.2%, p < 0.001), nodal (19.3% vs 6.4%, p < 0.001), and summary stages (12.3% vs 0.7%, p < 0.001). ConclusionsPathologic upstaging occurs in a considerable proportion of thyroid tumors, most commonly after total thyroidectomy. These findings can inform patient counseling.

A Clinical Prediction Model of Overall Survival for Patients with Cervical Cancer Aged 25–69 Years

Aims: This study aims to develop a prediction tool for the overall survival of cervical cancer patients. Methods: We obtained 4116 female patients diagnosed with cervical cancer aged 25-69 during 2008-2019 from the Surveillance, Epidemiology, and End Results Program. The overall survival between groups was illustrated by the Kaplan-Meier method and compared by a log-rank test adjusted by the Bonferroni-Holm method. We first performed the multivariate Cox regression analysis to evaluate the predictive values of the variables. A prediction model was created using cox regression based on the training set, and the model was presented as a nomogram. The proposed nomogram was designed to predict the 1-year, 3-year, and 5-year overall survival of patients with cervical cancer. Besides the c-index, time-dependent receiver operating curves, and calibration curves were created to evaluate the accuracy of the nomogram at the timepoint of one year, three years, and five years. Results: With a median follow-up of 54 (28, 92) months, 1045 (25.39%) patients were deceased. Compared with alive individuals, the deceased were significantly older and the primary site was more likely to be the cervix uteri site, large tumor size, higher grade, and higher combined summary stage (all p values < 0.001). In the multivariate Cox regression, age at diagnosis, race, tumor size, grade, combined summary stage, pathology, and surgery treatment were significantly associated with the all-cause mortality for patients with cervical cancer. The proposed nomogram showed good performance with a C-index of 0.82 in the training set. The 1-year, 3-year, and 5-year areas under the curves (with 95% confidence interval) of the receiver operating curves were 0.88 (0.84, 0.91), 0.84 (0.81, 0.87), and 0.83 (0.80, 0.86), respectively. Conclusions: This study develops a prediction nomogram model for the overall survival of cervical cancer patients with a good performance. Further studies are required to validate the prediction model further.

Establishment and validation of a nomogram to predict cancer-specific survival in pediatric neuroblastoma patients.

The term "neuroblastoma (NB)" refers to a type of solid pediatric tumor that develops from undivided neuronal cells. According to the American Cancer Society report, between 700 and 800 children under the age of 14 are diagnosed with NB every year in the United States (U.S.). About 6% of all cases of pediatric cancer in the U.S. are caused by NB. NB is the most frequent malignancy in children younger than 1 year; however, it is rarely found in those over the age of 10 and above. To accurately predict cancer-specific survival (CSS) in children with NB, this research developed and validated an all-encompassing prediction model. The present retrospective study used the Surveillance, Epidemiology, and End Results (SEER) database to collect information on 1,448 individuals diagnosed with NB between 1998 and 2019. The pool of potentially eligible patients was randomly split into two groups, a training cohort (N = 1,013) and a validation cohort (N = 435). Using multivariate Cox stepwise regression, we were able to identify the components that independently predicted outcomes. The accuracy of this nomogram was measured employing the consistency index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis (DCA). In this study, we found that age, primary location, tumor size, summary stage, chemotherapy, and surgery were all significant predictors of CSS outcomes and integrated them into our model accordingly. The C-index for the validation cohort was 0.812 (95% CI: 0.773-0.851), while for the training cohort it was 0.795 (95% CI: 0.767-0.823). The C-indexes and AUC values show that the nomogram is able to discriminate well enough. The calibration curves suggest that the nomogram is quite accurate. Also, the DCA curves demonstrated the prediction model's value. A novel nomogram was developed and validated in this work to assess personalized CSS in NB patients, and it has been indicated that this model could be a useful tool for calculating NB patients' survival on an individual basis and enhancing therapeutic decision-making.

Abstract P4-03-12: Cumulative Environmental Quality is Associated with Differential Breast Cancer Incidence by Summary Stage and Urbanicity

Abstract Background: Individual environmental contaminants have been associated with breast cancer; however, evaluations of multiple exposures simultaneously are limited. The USPA has constructed an environmental quality index (EQI), which contains county-level environmental exposure data across five overarching environmental domains (air, water, land, sociodemographic, and built environment). Unfortunately, the links made between breast cancer and the EQI are lacking because these analyses used total breast cancer incidence quantities, masking potential associations between EQI domains and specific stages of disease. In this study, we investigated if multiple exposures in broad EQI domains was associated with incidence of breast cancer, stratified by stage. Methods: The EQI data was linked to county-level age-standardized incidence rates (SIRs) obtained from the North Carolina Central Cancer Registry/NC CCR (2010-2014), a reporting system for all cancer cases diagnosed in residents of the state. Incidence rates and SIRs of total, in situ, localized, regional, and distant breast cancers were evaluated stratified by rural-urban status. Associations between county-level age-adjusted cancer incidence rates for each summary stage were assessed using general linear models (SAS 9.3), linear models with a continuous outcome with a p-value cutoff for statistical significance set at p&amp;lt; 0.05. We also evaluated incidence rates by summary stage comparing across rural versus urban counties using Mann-Whitney rank tests, since not all stages were normally distributed, confirmed by the D’Agostino-Pearson normality test. Results: In counties with poor environmental quality compared to those with good environmental quality, total breast cancer incidence was higher by 10.82 cases per 100,000 persons (95%CI: 2.04, 19.60, p=0.016). This association was most pronounced for localized breast cancer (β=5.59, 95%CI: 0.59, 10.58, p=0.029). Higher incidence of early-stage disease (carcinoma in situ β=5.25, 95%CI: 2.34, 8.16, p=0.001 and localized breast cancer β=6.98, 95%CI: 2.24, 11.73, p=0.004) and total breast cancer (β=11.44, 95%CI: 3.01, 19.87, p=0.008) occurred in counties with poor land quality, especially urban counties. Overall, NC counties averaged 21.6% percent African American (AA) which is higher than the US national average of 12.6% at the time of the U.S. 2010 Census. The percentage of AA in each county in bivariate analyses was associated with increased incidence of regional (incident cases 0.12 cases per % increase in AA population, 95% CI 0.01, 0.22, p=0.022) and distant breast cancers (incident cases 0.06 per % increase in AA population, 95% CI 0.02, 0.10, p=0.003) (Table 1). In addition, in stratified models, associations persisted and were strengthened in urban county models for regional breast cancer and in rural county models for distant breast cancer. Conclusions: Environmental quality is variable across NC akin to variability across the US, as the interquartile range (25th-75th percentile) of total EQI in NC is -0.187 to 0.734 while the interquartile range is –0.606 to 0.706 for the US (USEPA), making NC EQI analyses generalizable to a number of states and counties across the U.S., although what drives poor environmental quality varied by region and by county. Our analyses indicate significant associations between EQI and breast cancer incidence, which differ by breast cancer stage and urbanicity, identifying a critical need to assess cumulative environmental exposures in the context of cancer stage. Funding: Duke Cancer Institute seed grant (GRD, KH) as part of the P30 Cancer Ctr grant; Duke Environmental Health Scholars Award and NIEHS T32-ESO21432-05 (LMG). Table 1. Generalized linear model estimates and associated p-values for county characteristics, per 1% increase. Results are stratified by breast cancer stage and urbanicity. Bolded text indicates statistically significant estimates (p&amp;lt;0.05) Citation Format: Larisa M Gearhart-Serna, Hillary Hsu, Oluwadamilola (Lola) Fayanju, Brittany Mills, Kate Hoffman, Gayathri R Devi. Cumulative Environmental Quality is Associated with Differential Breast Cancer Incidence by Summary Stage and Urbanicity [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-03-12.

A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database.

This study aimed to develop and validate a nomogram for predicting cancer-specific survival (CSS) in patients with non-keratinized large cell squamous cell carcinoma (NKLCSCC) at 3, 5, and 8 years after diagnosis. Data on SCC patients were collected from the Surveillance, Epidemiology, and End Results database. Training (70%) and validation (30%) cohorts were generated using random selection of patients. Independent prognostic factors were selected using the backward stepwise Cox regression model. To predict the CSS rates in patients with NKLCSCC at 3, 5, and 8 years after diagnosis, all of the factors were incorporated into the nomogram. Indicators such as the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision-curve analysis (DCA) were then used to validate the performance of the nomogram. This study included 9,811 patients with NKLCSCC. Twelve prognostic factors were identified by Cox regression analysis in the training cohort, which were age, number of regional nodes examined, number of positive regional nodes, sex, race, marital status, American Joint Committee on Cancer (AJCC) stage, surgery status, chemotherapy status, radiotherapy status, summary stage, and income. The constructed nomogram was validated both internally and externally. The nomogram had good discrimination ability, as indicated by the comparatively high C-indices and AUC values. The nomogram was properly calibrated, as indicated by the calibration curves. Our nomogram was superior to the AJCC model, as illustrated by its superior NRI and IDI values. DCA curves indicated the clinical usability of the nomogram. The first nomogram for prognosis predictions of patients with NKLCSCC has been developed and verified. Its performance and usability demonstrated that the nomogram could be utilized in clinical settings. However, additional external verification is still required.

Lack of hierarchical survival prognosis in AJCC staging for colon cancer: Implications for future summary stage classification.

256 Background: Current staging for colon cancer utilizing the American Joint Committee on Cancer TNM framework stratifies disease into groups according to how advanced the disease is and provides important standardized, individual prognostic information. Ideally, staging systems should exhibit hierarchical logic, with increasing stage reflecting worse prognosis. We sought to evaluate hierarchy within the 8th edition AJCC staging system for colon cancer. Methods: All patients with primary colon (including rectosigmoid) cancer diagnosed 2010-2017 were identified from the National Cancer Database (NCDB). Patients were only included if they carried a histologic diagnosis of invasive primary (non-recurrent) cancer, underwent radical surgery, and had complete pathologic staging data (T, N, M category data). Patients with missing follow-up data were excluded. Kaplan-Meier curves were used to assess overall survival by AJCC 8th edition pathologic summary stage group to determine whether staging was hierarchical for colon cancer. Multivariable modeling and log likelihood ratio test was then used to identify relative contributions of T and N variables to overall survival. Results: Among 270,584 colon cancer patients, pathologic staging was noted to be hierarchical by T category alone and by N category alone. However, summary staging was non-hierarchical in both instances. Multivariable modeling confirmed the AJCC non-hierarchy issue (colon HRs: 1, 1.54, 2.62, 2.60, 1.02, 2.15, and 4.38 for stage 1 as reference, 2a, 2b, 2c, 3a, 3b, 3c respectively). Multivariable modeling demonstrated that high T category (T4a, T4b) conferred the greatest risk of mortality based on hazard ratio (T4a HR 2.76, T4b HR 3.04), while high T category as well as high N category (N2a, N2b) contributed substantially to the survival model based on z-score. Further modeling assessing the predictive power of T and N category with respect to overall survival demonstrated that T category more strongly predicted overall survival than N category in colon cancer (likelihood chi-square test statistic pT:7009 vs pN:3586). Conclusions: Hierarchy in survival based on AJCC summary stage was not observed for patients with stage II-III colon cancer resulting in a paradoxical better observed survival with some stage III cancer subgroups compared to stage II cancer subgroups. High T category appears to more significantly impact survival than N category for patients with N0-N1 disease, while high N category appears more important for patients with T1-3 disease. Future revisions of the AJCC staging system should account for the differential impact of T and N category identified herein to more accurately confer prognostic information.

SEER-Based Survival Nomogram (1998-2015) Based on 'Stage, Lymph Node Dissection, Tumor Size and Degree of Differentiation, and Therapies' for Prognosis of Primary Pulmonary Sarcoma.

Objective: Primary pulmonary sarcoma (PPS) is very rare in terms of incidence, henceforth, the clinical evidence pertinent to the prognosis of PPS is limited. The aim of this study was to construct a nomogram for evaluating the overall survival (OS) of patients diagnosed with PPS based on the stage, lymph node dissection, tumor size and degree of differentiation, and therapies. Methods: A total of 515 patients diagnosed with PPS during the period of 1998 to 2015 were obtained from the surveillance, epidemiology, and end results database and randomly segregated into 'training group' and 'validation group' with a ratio of 7:3. Regression analysis was executed for the training group to obtain the independent factors influencing prognosis of PPS patients. A nomogram was constructed as per the results obtained through multivariate Cox regression analysis subsequently validated using C index, receiver operating characteristic (ROC) curve, and calibration curves. Results: Age, tumor size, histology type, lymph node surgery, summary stage and differentiation grade were independent factors affecting the prognosis. C index was 0.775 and 0.737 for both training group, and validation group, respectively. Areas under the ROC curve of 1-year, 3-year, and 5-year OS were 87.6 (95% CI: 83.8-91.3), 90.1 (95% CI: 86.2-94.0) and 90.6 (95% CI: 85.8-95.4), respectively, in training group. Area under the curve values of 1-year, 3-year, and 5-year OS in the validation group were 83.1 (95% CI: 75.8-90.5), 82.9 (95% CI: 73.2-92.7) and 87.0 (95% CI: 75.9-98.1), respectively. Based on the nomogram, patients were segregated into low-risk group and high-risk group (degree of risk: cutoff score 193). OS of low-risk group was significantly higher when compared to high-risk group (P < .001) in the training group and validation group. Radiotherapy was a risk factor for the low-risk group and adjuvant chemotherapy has not exhibited influence on OS pertinent to low-risk group. However, adjuvant radiotherapy or chemotherapy both significantly improved the prognosis of PPS patients (P < .001) in the high-risk group. Conclusion: Constructed nomogram could have a strong predictive ability with higher accuracy for the prognosis of patients with PPS. Patients at low risk could not benefit from adjuvant radiotherapy or chemotherapy, while the prognosis clearly improved in the high-risk populations treated with either radiotherapy or chemotherapy.

Incidence and survival rates of primary uterine carcinosarcoma in Korea: a National Cancer Registry study.

To investigate the incidence and survival rates of primary uterine carcinosarcoma (UCS) in Korea. From the Korea Central Cancer Registry, we identified patients diagnosed with primary UCS between 1999 and 2018 and collected their information, including age at diagnosis, Surveillance, Epidemiology, and End Results (SEER) summary stage, and treatment. Age-standardized incidence rates (ASRs) and annual percent changes (APCs) were calculated. Baseline characteristics and overall survival (OS) were compared by study periods, ages, and stages at initial diagnosis. Overall, the incidence rate of primary UCS increased markedly during the time period: ASRs, 0.02 per 100,000 in 1999 and 0.25 per 100,000 in 2018 (APC, 13.9%; p<0.001). No difference in OS was observed between patients diagnosed in 1999-2008 and those diagnosed in 2009-2018 (5-year survival rate, 46.0% vs. 48.6%; p=0.871). Considering the mean patient age at diagnosis of UCS, we divided the study population into 2 groups. Patients aged ≥60 years had a more frequent prior radiation history, received less multi-modality treatment, and showed worse OS than those aged <60 years (5-year survival rate, 42.7% vs. 53.6%; p=0.001). In multivariate analysis, both old age at diagnosis (≥60 years) and the SEER summary stage were identified as independent poor prognostic factors for OS, whereas radiation history before the diagnosis of UCS was not. The incidence rate of UCS in Korea increased significantly from 1999 to 2018. Advanced stage and old age (≥60 years) at diagnosis might be poor prognostic factors for survival, but not prior radiation history.

Development and Validation of Coding Algorithms to Identify Patients with Incident Non-Small Cell Lung Cancer in United States Healthcare Claims Data.

We sought to develop and validate an incident non-small cell lung cancer (NSCLC) algorithm for United States (US) healthcare claims data. Diagnoses and procedures, but not medications, were incorporated to support longer-term relevance and reliability. Patients with newly diagnosed NSCLC per Surveillance, Epidemiology, and End Results (SEER) served as cases. Controls included newly diagnosed small-cell lung cancer and other lung cancers, and two 5% random samples for other cancer and without cancer. Algorithms derived from logistic regression and machine learning methods used the entire sample (Approach A) or started with a previous algorithm for those with lung cancer (Approach B). Sensitivity, specificity, positive predictive values (PPV), negative predictive values, and F-scores (compared for 1000 bootstrap samples) were calculated. Misclassification was evaluated by calculating the odds of selection by the algorithm among true positives and true negatives. The best performing algorithm utilized neural networks (Approach B). A 10-variable point-score algorithm was derived from logistic regression (Approach B); sensitivity was 77.69% and PPV = 67.61% (F-score = 72.30%). This algorithm was less sensitive for patients ≥80 years old, with Medicare follow-up time <3 months, or missing SEER data on stage, laterality, or site and less specific for patients with SEER primary site of main bronchus, SEER summary stage 2000 regional by direct extension only, or pre-index chronic pulmonary disease. Our study developed and validated a practical, 10-variable, point-based algorithm for identifying incident NSCLC cases in a US claims database based on a previously validated incident lung cancer algorithm.

Obesity is associated with early recurrence on breast cancer patients that achieved pathological complete response to neoadjuvant chemotherapy

Pathological complete response (pCR) after neoadjuvant chemotherapy (NCT) is associated with good long-term prognosis in breast cancer (BC) patients. However, some patients still recur and eventually die from this disease. For years, clinical stage at diagnosis has been consistently linked to recurrence and survival in the pCR setting. Herein, we aimed to identify other potential predictors of recurrence and survival in patients that achieved pCR. We performed a retrospective analysis of patients diagnosed between 2011 and 2020 in our center. We calculated overall survival (OS), invasive disease-free survival (IDFS), distant disease-free survival (DDFS), and BC-specific survival (BCSS). Among the 241 patients included into our study 36% were obese (Body Mass Index (BMI) > 29.9 kg/m2) and 47% were stage III. Multivariate analysis confirmed that obesity was a significant risk factor associated with early recurrence and poorer survival in these patients. In summary, obesity and clinical stage predict early recurrence and poorer survival in patients that achieved pCR after NCT. Pending further investigation and based on our findings we speculate that weight management could be beneficial for this subset of patients. To our knowledge, this is the first Latin American report linking obesity and recurrence within this setting.

Effects of different types of radiation therapy on cardiac-specific death in patients with thyroid malignancy.

Radiation therapy (RT) is one of the common and widely used treatment method for thyroid tumors. Considering that the thyroid is located close to the heart, the radiation generated during the treatment of thyroid tumors may have an adverse greater impact on the heart. This study is to explore the influencing factors, especially additional effects of RT, on cardiac-specific death among patients with malignant thyroid tumors. Collecting information from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database using SEER*Stat. Patients with malignant thyroid tumors were searched, whether receiving RT or not. Ultimately, 201, 346 eligible patients were included. Propensity Score Matching (PSM) was used to minimize bias of baseline characteristics by adjusting for confounding factors. COX (proportional hazards) and fine-gray (competing risk) model regression analysis were used to explore the effects of various influencing factors on cardiac-specific death. The present analysis showed that, compared with non-RT, RT based upon radioactive implants and beam radiation were associated with lower risk of cardiac-specific death in patients with thyroid malignancy, beam radiation therapy may had a similar effect. Besides, the remaining RT methods did not significantly increase the risk of cardiac-specific death. In addition, Asian or Pacific Islander ethnicity, female sex, marital status, combined summary stage (localized, regional, and distant), high-income, and later year of diagnosis were associated with lower risk of cardiac-specific death. While older age of diagnosis, African ethnicity, non-Hispanic ancestry, and derived AJCC stage (IV) were risk factors for cardiac-specific death. These results help to identify the factors influencing cardiac-specific death among patients with thyroid malignancies. Furthermore, it may helps to improve the clinical application of RT without too much concern about adverse cardiac effects.

Examining racial differences in treatment and survival among patients with Paget’s disease of the breast

BackgroundThe objective of this study was to evaluate racial differences in treatment (ie, surgery, chemotherapy, and radiation) and survival among patients with Paget’s disease of the breast in the Surveillance, Epidemiology and End Result program. MethodsWomen >18 years old diagnosed with localized or regional Paget’s disease between January 1, 2010 to December 31, 2016 in the Surveillance, Epidemiology and End Result program were included. The cohort was divided into Black and White patients. Univariable analysis compared the groups. Using propensity score matching, Black and White patients were nearest matched (1:2) on age at diagnosis; Surveillance, Epidemiology and End Result summary stage; surgery; chemotherapy; and year of diagnosis. The log-rank test evaluated the matched sample's overall survival and disease-specific survival. ResultsOf the 1,181 patients, the racial distribution was 1,049 (88.8%) White and 132 (11.2%) Black. A higher percentage of Black women were Medicaid insured (Black 25.8% vs White 11.1%), lived in neighborhoods with low socioeconomic status (Black 53.0% vs White 25.4%), and had regional disease than White women (Black 41.7% vs White 29%). There were no racial differences in receipt of radiation therapy (P = .90), breast surgery (P = .23), or axillary surgery (P = .25). Black patients were more likely to receive chemotherapy (Black 34.8% vs White 26.3% P = .038). In the propensity matched cohort, Black patients had a worse overall survival (P < .005) and disease-specific survival (P = .05) than White patients. ConclusionIn this cohort of patients with Paget’s disease, despite differences in sociodemographic factors, there were no disparities in locoregional treatment. However, on matched analysis, Black patients had a worse overall survival and disease-specific survival than their White counterparts.

Measuring population-level cure patterns for cancer patients in the United States.

While the life expectancy of cancer survivors has substantially improved over time in the United States, the extent to which cancer patients are cured is not known. Population-level cure patterns are important indicators to quantify cancer survivorships. This population-based cohort study included 8978,721 cancer patients registered in the Surveillance, Epidemiology and End Results (SEER) databases between 1975 and 2018. The primary outcome was cure fractions. Five-year cure probability, time to cure and median survival time of uncured cases were also assessed. All four measures were calculated using flexible parametric models, according to 46 cancer sites, three summary stages, individual age and calendar year at diagnosis. In 2018, cure fractions ranged from 2.7% for distant liver cancer to 100.0% for localized/regional prostate cancer. Localized cancer had the highest cure fraction, followed by regional cancer and distant cancer. Except for localized breast cancer, older patients generally had lower cure fractions. There were 38 cancer site and stage combinations (31.2%) that achieved 95% of cure within 5 years. Median survival time of the uncured cases ranged from 0.3 years for distant liver cancer to 10.9 years for localized urinary bladder cancer. A total of 117 cancer site and stage combinations (93.6%) had increased cure fraction over time. A considerable proportion of cancer patients were cured at the population-level, and the cure patterns varied substantially across cancer site, stage and age at diagnosis. Increases in cure fractions over time likely reflected advances in cancer treatment and early detection.