Construction of Novel Prognostic Nomogram for Mucinous and Signet Ring Cell Colorectal Cancer Patients with a Survival Longer Than 5 Years.

Abstract

PurposeMucinous adenocarcinoma (MA) and signet ring cell carcinoma (SRCC) are aggressive colorectal cancer histological subtypes with dismal prognosis. This study investigated prognostic factors and constructed novel nomograms for MA and SRCC patients who survived for over 5 years to optimize the follow-up regime, especially for early-onset patients.Patients and MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database registered between 2004 and 2018 were extracted. MA and SRCC patients were divided into two groups with survival time of 5 years as a cut-off point. Prognostic factors for overall survival (OS) and cancer-specific survival (CSS) were determined by Cox regression models, and survival curves were plotted by the Kaplan–Meier method.ResultsWe identified 8286 MA patients (45.73%) and 551 SRCC patients (20.32%) who survived for over 5 years. Multivariable Cox analyses identified age, tumor location, N stage, metastasis, CEA level, surgery, and lymph nodes dissection as independent risk factors for MACSS. SRCC was more aggressive and only N2 stage (P = 0.011) and metastasis (P = 0.043) were inversely associated with SRCCSS. Furthermore, we observed that small tumor size, well differentiation, and chemotherapy no longer provided survival benefit to ≥5-year survivors. Therefore, we constructed novel nomograms appropriate for MA patients who survived for over 5 years. The consistency indexes for predicting 10-year OS and CSS were respectively 0.717, 0.712 in the training cohort and 0.727, 0.735 in the validation cohort.ConclusionOur well-calibrated nomograms represent the first clinical prognostic models developed especially for MA patients with a survival longer than 5 years. For both MA and SRCC patients, TNM stage was a stable prognostic factor, while the prognostic values of tumor size, differentiation grade, and chemotherapy changed over time. We are hopeful that our prognostic models will help define personalized follow-up managements to further prolong patient survival.