AUTHOR=Ren Jiazi , Xu Linfeng , Zhou Siyu , Ouyang Jian , You Weiqiang , Sheng Nengquan , Yan Li , Peng Du , Xie Lu , Wang Zhigang 

TITLE=Clinicopathological Features Combined With Immune Infiltration Could Well Distinguish Outcomes in Stage II and Stage III Colorectal Cancer: A Retrospective Study

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.776997

DOI=10.3389/fonc.2021.776997

ISSN=2234-943X

ABSTRACT=Background: Immunoscore predicts prognosis in patients with colorectal cancer (CRC). However, few studies have incorporated the Immunoscore into the construction of comprehensive prognostic models in CRC, especially stage II CRC. We aimed to construct and validate multi-dimensional models integrating clinicopathological characteristics and Immunoscore to predict the prognosis of patients with stage II-III CRC.
Methods: Patients (n=254) diagnosed with stage II-III CRC from 2009 to 2016 was used to generate Cox models for predicting the disease-free survival (DFS) and overall survival (OS). Variables included basic clinical indicators, blood inflammatory markers, preoperative tumor biomarkers, mismatch repair status and Immunoscore (CD3+ and CD8+ T cell densities). Univariate and multivariate Cox proportional regressions were used to construct the prognostic models for DFS and OS. We validated the predictive accuracy and ability of the prognostic models in our cohort of 254 patients. 
Results: We constructed two predictive prognostic models with C-index of 0.6941 for DFS and 0.7138 for OS in patients with stage II-III CRC. Immunoscore was the most informative predictor of DFS (11.92%) followed by pN stage, CEA, and vascular infiltration. For OS, Immunoscore was the most informative predictor (8.59%) followed by pN stage, age, CA125 and CEA. Based on the prognostic models, nomograms were developed to predict the 3-year and 5-year DFS and OS rates. Patients were divided into three risk groups (low, intermediate, high) according to the risk score obtained from the nomogram, and significant differences were observed in the recurrence and survival of different risk groups (p < 0.0001). Calibration curve and time-dependent ROC analysis showed good accuracy of our models. Furthermore, decision curve analysis indicated that our nomograms had better net benefit than pTNM stage within a wide threshold probability. Especially, we developed a website based on our prognostic models to predict the recurrence and death risk of patients with stage II-III CRC.
Conclusions: Multi-dimensional models including clinicopathological characteristics and Immunoscore were constructed and validated, with good accuracy and convenience, to evaluate the recurrence and death risk of stage II-III CRC patients.