Development and validation of a scoring system incorporating tumor growth pattern and perineural invasion for risk stratification in colorectal cancer

Author:

Liu Yulin123,Wang Yiting4,Yao Su5,Liang Changhong12,Li Qian3,Liu Zaiyi12,Zhu Yaxi4,Cui Yanfen1267,Zhao Ke127

Affiliation:

1. Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

2. Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China

3. School of Medicine, South China University of Technology, Guangzhou, China

4. Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

5. Department of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

6. Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, China

7. Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

Abstract

Tumor growth pattern (TGP) and perineural invasion (PNI) at the invasive margin have been recognized as indicators of tumor invasiveness and prognostic events in colorectal cancer (CRC). This study aims to develop a scoring system incorporating TGP and PNI, and further investigate its prognostic significance for CRC risk stratification. A scoring system, termed tumor-invasion score, was established by summing TGP and PNI scores. The discovery cohort (N = 444) and the validation cohort (N = 339) were used to explore the prognostic significance of the tumor-invasion score. The endpoints of the event were disease-free survival (DFS) and overall survival (OS) which were analyzed by the Cox proportional hazard model. In the discovery cohort, Cox regression analysis showed that DFS and OS were inferior for score 4 group compared with score 1 group (DFS, hazard ratio (HR) 4.44, 95% confidence interval (CI) 2.49–7.92, p < 0.001; OS, 4.41, 2.37–8.19,p < 0.001). The validation cohort showed similar results (DFS, 4.73, 2.39–9.37, p < 0.001; OS, 5.52, 2.55–12.0, p < 0.001). The model combining tumor-invasion score and clinicopathologic information showed good discrimination performance than single predictors. TGP and PNI were associated with tumor invasiveness and survival in CRC. The tumor-invasion score generated by TGP and PNI scores served as an independent prognostic parameter of DFS and OS for CRC patients.

Funder

Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Regional Innovation and Development Joint Fund of National Natural Science Foundation of China

Project Funded by China Postdoctoral Science Foundation

High-level Hospital Construction Project

National Natural Science Foundation of China

national key research and development program of china

the National Science Foundation for Young Scientists of China

Publisher

SAGE Publications

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

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