J Cancer 2021; 12(8):2351-2358. doi:10.7150/jca.49569

Research Paper

Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases

Junjie Hang1#, Kequn Xu1#, Ruohan Yin2#, Yueting Shao1, Muhan Liu1, Haifeng Shi2✉, Xiaoyong Wang3✉, Lixia Wu4✉

1. Department of Oncology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China.
2. Department of Medical Imaging, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China.
3. Department of Gastroenterology, Changzhou No.2 People's Hospital, Nanjing Medical University, Xinglong Road 19, Changzhou 213000, China.
4. Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Zhonghuaxin Road 619, Shanghai 200040, China.
#These authors contributed equally to this work.

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Citation:
Hang J, Xu K, Yin R, Shao Y, Liu M, Shi H, Wang X, Wu L. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases. J Cancer 2021; 12(8):2351-2358. doi:10.7150/jca.49569. Available from https://www.jcancer.org/v12p2351.htm

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Abstract

Objective: The purpose of this study was to evaluate the prognostic value of computed tomography (CT) texture features of pancreatic cancer with liver metastases.

Methods: We included 39 patients with metastatic pancreatic cancer (MPC) with liver metastases and performed texture analysis on primary tumors and metastases. The correlations between texture parameters were assessed using Pearson's correlation. Univariate Cox proportional hazards model was used to assess the correlations between clinicopathological characteristics, texture features and overall survival (OS). The univariate Cox regression model revealed four texture features potentially correlated with OS (P<0.1). A radiomics score (RS) was determined using a sequential combination of four texture features with potential prognostic value that were weighted according to their β-coefficients. Furthermore, all variables with P<0.1 were included in the multivariate analysis. A nomogram,which was developed to predict OS according to independent prognostic factors, was internally validated using the C-index and calibration plots. Kaplan-Meier analysis and the log-rank test were performed to stratify OS according to the RS and nomogram total points (NTP).

Results: Few significant correlations were found between texture features of primary tumors and those of liver metastases. However, texture features within primary tumors or liver metastases were significantly associated. Multivariate analysis showed that Eastern Cooperative Oncology Group performance status (ECOG PS), chemotherapy, Carbohydrate antigen 19-9 (CA19-9), and the RS were independent prognostic factors (P<0.05). The nomogram incorporating these factors showed good discriminative ability (C-index = 0.754). RS and NTP stratified patients into two potential risk groups (P<0.01).

Conclusion: The RS derived from significant texture features of primary tumors and metastases shows promise as a prognostic biomarker of OS of patients with MPC. A nomogram based on the RS and other independent prognostic clinicopathological factors accurately predicts OS.

Keywords: CT texture features, liver metastases, pancreatic cancer, prognostic nomogram, radiomics score