Affiliations: [a] School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China | [b] Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China | [c] Changzhou No. 2 People’s Hospital, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China | [d] Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Shanghai, China
Correspondence:
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Corresponding authors: Junjie Hang, Changzhou No. 2 People’s Hospital, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu 213000, China. E-mail: hjj199141@alumni. sjtu.edu.cn. Lixia Wu, Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Shanghai 200070, China. E-mail: [email protected].
Note: [1] These authors contributed equally.
Abstract: BACKGROUND: Patients with advanced pancreatic cancer (APC) and liver metastases have much poorer prognoses than patients with other metastatic patterns. OBJECTIVE: This study aimed to develop and validate a radiomics model to discriminate patients with pancreatic cancer and liver metastases from those with other metastatic patterns. METHODS: We evaluated 77 patients who had APC and performed texture analysis on the region of interest. 58 patients and 19 patients were allocated randomly into the training and validation cohorts with almost the same proportion of liver metastases. An independentsamples t-test was used for feature selection in the training cohort. Random forest classifier was used to construct models based on these features and a radiomics signature (RS) was derived. A nomogram was constructed based on RS and CA19-9, and was validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. RESULTS: The constructed nomogram demonstrated good discrimination in the training (AUC = 0.93) and validation (AUC = 0.81) cohorts. In both cohorts, patients with RS > 0.61 had much poorer overall survival than patients with RS < 0.61. CONCLUSIONS: This study presents a radiomics nomogram incorporating RS and CA19-9 to discriminate patients who have APC with liver metastases from patients with other metastatic patterns.
