Prognostic factors of overall survival and cancer-specific survival in patients with resected early-stage rectal adenocarcinoma: a SEER-based study

Racial comparisons in treatment of rectal adenocarcinoma and survival in the military health system

BACKGROUND

Racial disparities in treatment and outcomes of rectal cancer have been attributed to patients' differential access to care. We aimed to study treatment and outcomes of rectal cancer in the equal access Military Health System (MHS) to better understand potential racial disparities.

METHODS

We accessed the MilCanEpi database to study a cohort of patients aged 18 and older who were diagnosed with rectal adenocarcinoma between 1998 and 2014. Receipt of guideline recommended treatment per tumor stage, cancer recurrence, and all-cause death were compared between non-Hispanic White and Black patients using multivariable regression models with associations expressed as odds (AORs) or hazard ratios (AHRs) and their 95% confidence intervals (CIs).

RESULTS

The study included 171 Black and 845 White patients with rectal adenocarcinoma. Overall, there were no differences in receipt of guideline concordant treatment (AOR = 0.76, 95% CI = 0.45 to 1.29), recurrence (AHR = 1.34, 95% CI = 0.85 to 2.12), or survival (AHR = 1.08, 95% CI = 0.77 to 1.54) for Black patients compared with White patients. However, Black patients younger than 50 years of age at diagnosis (AOR = 0.34, 95% CI = 0.13 to 0.90) or with stage III or IV tumors (AOR = 0.28, 95% CI = 0.12 to 0.64) were less likely to receive guideline recommended treatment than White patients in stratified analysis.

CONCLUSIONS

In the equal access MHS, although there were no overall racial disparities in rectal cancer treatment or clinical outcomes between Black and White patients, disparities among those with early-onset or late-stage rectal cancers were noted. This suggests that factors other than access to care may play a role in the observed disparities and warrants further research.

Clinical and Pathologic Predictors of Tumor Deposits in Colorectal Cancer

BACKGROUND

Tumor deposits (TDs) are a special metastatic pattern of colorectal cancer (CRC). This study aims to explore the pathological characteristics of TD and find out the risk factors of TD in CRC.

METHODS

TDs cases of CRC were selected and validated by HE staining. The correlation between TDs and T stages, N stages, and microsatellite instability was calculated by the chi-squared (χ2) test.

RESULTS

A total of 2553 patients with colorectal cancer undergoing intestinal resection were included in this study. Two hundred fifty-nine cases of TDs patients were included. The positive rate of TDs was 1.9% (2/105) in T1, 3.8% (10/266) in T2, 11% (231/2305) in T3, and 22.8% (16/77) in T4. T3 and T4 were more prone to TDs than T1 and T2, but there was no difference between T3 and T4. The positive rate of TDs was 7.2% (107/1491) in N0, 14.3% (152/1062) in N + , and N + was more prone to TDs than N0. The positive rate of TDs was 10.5% (256/2432) in MSS, 2.5% (3/121) in MSI, and MSS was more prone to TDs than MSI. Multivariate analysis showed lymph node invasion, T stage, and MSS were independent risk factors for TDs.

CONCLUSION

Lymph node invasion, T stage, and MSS were independent risk factors for TDs.

Assessing the effect of data integration on predictive ability of cancer survival models

Cancer is the second leading cause of death in the United States. To improve cancer prognosis and survival rates, a better understanding of multi-level contributory factors associated with cancer survival is needed. However, prior research on cancer survival has primarily focused on factors from the individual level due to limited availability of integrated datasets. In this study, we sought to examine how data integration impacts the performance of cancer survival prediction models. We linked data from four different sources and evaluated the performance of Cox proportional hazard models for breast, lung, and colorectal cancers under three common data integration scenarios. We showed that adding additional contextual-level predictors to survival models through linking multiple datasets improved model fit and performance. We also showed that different representations of the same variable or concept have differential impacts on model performance. When building statistical models for cancer outcomes, it is important to consider cross-level predictor interactions.