Clinical and prognostic factors for renal parenchymal, pelvis, and ureter cancers in SEER registries: collaborative stage data collection system, version 2

Racial and ethnic disparities in surgery for kidney cancer: a SEER analysis, 2007-2014

BACKGROUND AND OBJECTIVES

Compared with White patients, Black and American Indian/Alaskan Native (AI/AN) patients experience higher rates of kidney cancer incidence, and Black, AI/AN, and Hispanic patients face later stages of disease at diagnosis, poorer survival rates, and greater risk of mortality. Despite the importance that appropriate treatment has in ensuring positive outcomes, little is known about the association between race and ethnicity and receipt of treatment for kidney cancer. Accordingly, the aim of this study was to explore differences in receipt of treatment and patterns of refusal of recommended treatment by race and ethnicity.

DESIGN

96,745 patients ages 45-84 with kidney cancer were identified in the Surveillance, Epidemiology, and End Results (SEER) program between 2007 and 2014. Logistic regression models were used to examine the association of race and ethnicity with treatment and with patient refusal of recommended treatment. Outcomes of interest were (1) receiving any surgical procedure, and (2) refusing recommended surgery.

RESULTS

Relative to White patients, Black and AI/AN patients had lower odds of undergoing any surgical procedure (OR = 0.76; 95% CI: 0.72-0.81; p < 0.001, and OR = 0.92; 95% CI: 0.76-1.10; p = 0.36, respectively) after adjusting for gender, age, insurance status, stage at diagnosis, unemployment status, education status, and income as additive effects. Black and AI/AN patients also had higher odds of refusing recommended surgery (OR = 1.93; 95% CI: 1.56-2.39; p < 0.001, and OR = 1.99; 95% CI: 1.05-3.76; p = 0.035, respectively). Hispanic patients had slightly higher odds of undergoing any surgical procedure (OR = 1.10; 95% CI: 1.04-1.17; p = 0.001) and lower odds of refusal (OR = 0.67; 95% CI: 0.50-0.90; p = 0.007, respectively).

CONCLUSIONS

Compared with White patients, Black patients were less likely to receive potentially life-saving surgery, and both Black and AI/AN patients were more likely to refuse recommended surgery.

Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection

BACKGROUND

Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery.

METHODS

Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC.

RESULTS

A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy.

CONCLUSIONS

Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.

Cause of death during upper tract urothelial carcinoma survivorship: A contemporary, population-based analysis

Background

Very few studies have been published on the causes of death of upper tract urothelial carcinoma (UTUC). We sought to explore the mortality patterns of contemporary UTUC survivors.

Methods

We performed a retrospective cohort study involving patients with upper urinary tract carcinoma from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database (2000 and 2015). We used standardized mortality ratios (SMRs) to compare death rates among patients with UTUC in the general population and excess absolute risks (EARs) to quantify the disease-specific death burden.

Results

A total of 10,179 patients with UTUC, including 7,133 who died, were included in our study. In total, 302 (17.17%) patients with the localized disease died of UTUC; however, patients who died from other causes were 4.8 times more likely to die from UTUC (n = 1,457 [82.83%]). Cardiovascular disease was the most common non-cancer cause of death (n = 393 [22.34% of all deaths]); SMR, 1.22; 95% confidence intervals [CI], 1.1–1.35; EAR, 35.96). A total of 4,046 (69.99%) patients with regional stage died within their follow-up, 1,413 (34.92%) of whom died from UTUC and 1,082 (26.74%) of whom died from non-cancer causes. UTUC was the main cause of death (SMR, 242.48; 95% CI, 230–255.47; EAR, 542.47), followed by non-tumor causes (SMR, 1.18; 95% CI, 1.11–1.25; EAR, 63.74). Most patients (94.94%) with distant stage died within 3 years of initial diagnosis. Although UTUC was the leading cause of death (n = 721 [54.29%]), these patients also had a higher risk of death from non-cancer than the general population (SMR, 2.08; 95% CI, 1.67–2.56; EAR, 288.26).

Conclusions

Non-UTUC deaths accounted for 82.48% of UTUC survivors among those with localized disease. Patients with regional/distant stages were most likely to die of UTUC; however, there is an increased risk of dying from non-cancer causes that cannot be ignored. These data provide the latest and most comprehensive assessment of the causes of death in patients with UTUC.

Integrative Analysis of Peripheral Blood Indices for the Renal Sinus Invasion Prediction of T1 Renal Cell Carcinoma: An Ensemble Study Using Machine Learning-Assisted Decision-Support Models

Purpose

Renal sinus invasion is an attributive factor affecting the prognosis of renal cell carcinoma (RCC). This study aimed to construct a risk prediction model that could stratify patients with RCC and predict renal sinus invasion with the help of a machine learning (ML) algorithm.

Patients and Methods

We retrospectively recruited 1229 patients diagnosed with T1 stage RCC at the Baotou Cancer Hospital between November 2013 and August 2021. Iterative analysis was used to screen out predictors related to renal sinus invasion, after which ML-based models were developed to predict renal sinus invasion in patients with T1 stage RCC. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.

Results

A total of 21 candidate variables were shortlisted for model building. Iterative analysis screened that neutrophil to albumin ratio (NAR), hemoglobin level * albumin level * lymphocyte count/platelet count ratio (HALP), prognostic nutrition index (PNI), body mass index*serum albumin/neutrophil-lymphocyte ratio (AKI), NAR, and fibrinogen (FIB) concentration (NARFIB), platelet to lymphocyte ratio (PLR), and R.E.N.A.L score was related to renal sinus invasion and contributed significantly to ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.797 to 0.924. The optimal risk probability of renal sinus invasion predicted was RFC (AUC = 0.924, 95% confidence interval [CI]: 0.414–1.434), which showed robust discrimination for identifying high-risk patients.

Conclusion

We successfully develop practical models for renal sinus invasion prediction, particularly the RFC, which could contribute to early detection via integrating systemic inflammatory factors and nutritional parameters.

Reassessment of American Joint Committee on Cancer Staging for Stage III Renal Cell Carcinoma With Nodal Involvement: Propensity Score Matched Analyses of a Large Population-Based Study

Background: To assess the role of nodal involvement in stage III renal cell carcinoma (RCC) according to the American Joint Committee on Cancer (AJCC) 8th staging system. We compared the survival outcomes of RCC patients with pT1-3N1M0 disease and those with pT3N0M0 or stage IV (stratified as pT4NanyM0 and pTanyNanyM1) disease in a large population-based cohort. Methods: A cohort of 3,112 eligible patients with RCC was identified from the Surveillance, Epidemiology, and End Results (SEER) database, registered between January 2004 and December 2015. Kaplan-Meier and Cox proportional hazards models were used to evaluate the overall survival (OS), and cancer-specific survival (CSS). The prognostic value of the modified stage for pT1-3N1M0 disease was assessed by nomogram-based analyses. Propensity score matching (PSM) was used to adjust for potential baseline confounding. Results: Patients with pT1-3N1M0 disease showed similar survival outcomes (median OS 41.0 vs. 38.0 months, P = 0.77; CSS 45.0 vs. 39.0 months, P = 0.59) to pT4NanyM0 patients, whereas the significantly better survival outcome was found for pT3N0M0 patients. After PSM, comparable survival rates were observed between pT1-3N1M0 group and pT4NanyM0 group, which were still significantly worse than the survival of pT3N0M0 patients. The modified stage IIIA (pT3N0M0), IIIB (pT1-3N1M0, pT4NanyM0), and IV (pTanyNanyM1) showed higher predictive accuracy than AJCC stage system in the nomogram-based analyses (concordance index: 0.70 vs. 0.68, P < 0.001 for OS; 0.71 vs. 0.69, P < 0.001 for CSS). Conclusions: The pT1-3N1M0 RCC might be reclassified as stage IIIB together with pT4NanyM0 disease for better prediction of prognosis, further examination and validation are warranted.

Variant morphology in upper urinary tract urothelial carcinoma: a 14-year case series of biopsy and resection specimens

Upper urinary tract urothelial carcinoma exhibiting variant morphology, especially in higher-grade tumors, is a recognized phenomenon but has not been comparatively studied in biopsy versus resection material. We studied the morphologic patterns and clinicopathological features, and provide a comparison between biopsy and resection specimens. Consultation cases were evaluated separately to investigate for possible consultation bias. A total of 383 in-house cases from 352 patients including 314 resection specimens and 69 biopsies from 2001 to 2014 were reviewed from a single institution. Histologic type, tumor grade, invasion, pathologic stage, nodal status, metastasis, and the presence and type of variant morphology for each case were evaluated. Variant morphology was identified in 5 biopsy specimens (7.2%) and 42 resection specimens (13.4%). The most common variant morphologic pattern was squamous differentiation (16 cases, 4.5%) followed by an inverted growth pattern (8 cases, 2.2%). The presence of variant morphology in resection specimens had a significant association with higher tumor grade, higher pT stage, and nonpapillary configuration. Of 69 patients with biopsies, 31 had a subsequent resection. In comparison, 181 consultation cases from 168 patients showed variant morphology in 6 biopsies (7.1%) and 27 resections (28.1%). In conclusion, the frequency of recognizing variant morphology in biopsies is about one-half of that in resections. The inclusion of consultation cases can inflate the incidence of variant morphology. As a result, the frequency of variant morphology in our in-house cases is lower than the percentage reported in the literature, most likely secondary to a consultation bias.

Cancer Incidence and Survival Trends by Subtype Using Data from the Surveillance Epidemiology and End Results Program, 1992-2013

Background: Cancers are heterogeneous, comprising distinct tumor subtypes. Therefore, presenting the burden of cancer in the population and trends over time by these tumor subtypes is important to identify patterns and differences in the occurrence of these subtypes, especially to generalize findings to the U.S. general population.Methods: Using SEER Cancer Registry Data, we present incidence rates according to subtypes for diagnosis years (1992-2013) among men and women for five major cancer sites: breast (female only), esophagus, kidney and renal pelvis, lung and bronchus, and thyroid. We also describe estimates of 5-year relative survival according to subtypes and diagnosis year (1992-2008). We used Joinpoint models to identify years when incidence rate trends changed slope. Finally, recent 5-year age-adjusted incidence rates (2009-2013) are presented for each subtype by race and age.Results: Hormone receptor-positive and HER2-negative was the most common subtype (about 74%) of breast cancers. Adenocarcinoma made up about 69% of esophagus cases among men. Adenocarcinoma also is the most common lung subtype (43% in men and 52% in women). Ninety percent of thyroid subtypes were papillary. Distinct incidence and survival patterns emerged by these subtypes over time among men and women.Conclusions: Histologic or molecular subtype revealed different incidence and/or survival trends that are masked when cancer is considered as a single disease on the basis of anatomic site.Impact: Presenting incidence and survival trends by subtype, whenever possible, is critical to provide more detailed and meaningful data to patients, providers, and the public. Cancer Epidemiol Biomarkers Prev; 26(4); 632-41. ©2016 AACR.