The predictive value of radiomics for postoperative prognosis of pancreatic ductal adenocarcinoma patients
10.12025/j.issn.1008-6358.2025.20250359
- VernacularTitle:影像组学对胰腺导管腺癌术后预后的预测价值
- Author:
Siyang HAN
1
;
Yaolin XU
2
;
Tianwei XU
1
;
Yefei RONG
1
;
Wenhui LOU
2
;
Dansong WANG
1
Author Information
1. Department of Emergency Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
2. Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
- Publication Type:Originalarticle
- Keywords:
pancreatic ductal adenocarcinoma;
radiomics;
postoperative complications;
prediction
- From:
Chinese Journal of Clinical Medicine
2025;32(6):973-981
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the predictive value of radiomics for postoperative pancreatic fistula, infection, and long-term survival in patients with pancreatic ductal adenocarcinoma (PDAC). Methods 206 patients who received radical resection of pancreatic cancer in Zhongshan Hospital, Fudan University from January 2014 to December 2020 and were pathologically confirmed as PDAC after surgery were retrospectively selected, all of whom had complete surgical data and long-term follow-up data. Pyradiomics was used to analyze the enhanced CT images of all patients and extract radiomics features. LASSO dimensionality reduction combined with logistic regression analysis was used to construct a predictive model for pancreatic fistula and abdominal infection after PDAC surgery, and evaluating the model’s effectiveness using ROC curves. A long-term survival prediction model for PDAC patients was constructed using LASSO dimensionality reduction combined with Cox regression analysis, and patient risk scores were calculated. The patients were divided into high-risk and low-risk groups based on the median, and the survival curves were compared to evaluate the effectiveness of the model. The imaging omics features with the highest weight were divided into high expression group and low expression group according to the median, and the prognostic differences and clinical features were compared. Radiomics and clinical features were combined to analyze the influencing factors of long-term prognosis and construct a clinical imaging comprehensive model. Results A total of 1 595 radiomics features were extracted. A predictive model for pancreatic fistula and infection after PDAC surgery was constructed, with AUC values of 0.81 and 0.79, respectively. The PDAC long-term survival prediction model was successfully constructed, and the prognosis of the high-risk group was worse than that of the low-risk group (P<0.001). The weight of the radiomics feature “log-sigma-5-mm-3D_glszm_ZonePercentage” was 59.557. The CA19-9 level in the high expression group is higher than that in the low expression group (P=0.017), and there is a statistically significant difference in survival curves between the two groups (P=0.021). The comprehensive clinical imaging model suggested that age, AJCC stage, lymph infiltration, CA19-9 level and imaging characteristics were risk factors for long-term prognosis of PDAC patients (HR=1.028, 4.084, 2.566, 1.232 and 2.536). Conclusions The predictive model based on radiomics has good predictive performance for pancreatic fistula, infection, and long-term prognosis after PDAC surgery. Patients with high expression of the radiomics feature “log-sigma-5-mm-3D_glszm_ZonePercentage” face poorer prognosis.
