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

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.