Insights into imaging · 2025
Preoperative prediction of microvascular invasion in pancreatic neuroendocrine tumors through analysis of portal venous phase CT images.
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Chen Hai-Yan, Pan Yao, Li Yu-Wei, Shi Li-Ting, Chen Jie-Yu, Liu Yun-Ying, Yu Ri-Sheng, Shi Lei
Аннотация
OBJECTIVES: To evaluate clinical and CT imaging features on portal venous-phase for predicting microvascular invasion (MVI) in patients with pancreatic neuroendocrine tumors (PNETs) and compare survival outcomes.
MATERIALS AND METHODS: In this retrospective study, 160 patients (training group) and 28 (validation group) who underwent surgical resection for PNETs were included. Demographic data and CT features were collected. The independent predictive factors for predicting MVI were confirmed through univariate and multivariate logistic regression analyses. The predictive performance was assessed by employing the receiver operating characteristic curve for predicting MVI. An R/shiny app based on logistic regression was developed. A Kaplan-Meier survival analysis with a log-rank test was conducted.
RESULTS: In the training group, invasion of surrounding tissues (odds ratio [OR]: 4.12), absolute enhancement (OR: 0.84), and relative enhancement ratio (OR: 16.1) were identified as independent predictors for predicting MVI in PNET patients, with an area under the curve of 0.819 and 0.891 in the training and validation groups, respectively. We have successfully developed a user-friendly web-based R/shiny app for real-time prediction of MVI in patients with PNETs. The median overall survival for patients with MVI was 12 months, compared to 37.5 months for those without MVI (log-rank p = 0.034).
CONCLUSIONS: Imaging features from portal venous-phase CT images can be used to accurately predict the presence of MVI in patients with PNETs. Patients with MVI are associated with worse survival compared to those without MVI. The web-based R/shiny app for predicting MVI provides real-time data-driven estimates of predictive value to facilitate informed decision-making.
CRITICAL RELEVANCE STATEMENT: Imaging features can accurately predict MVI in patients with PNETs, and the web-based R/shiny app provides real-time, data-driven estimates to enhance decision-making, thereby streamlining clinical practice.
KEY POINTS: The presence of microvascular invasion (MVI) in patients was associated with worse survival. Surrounding tissue invasion and absolute/relative enhancement ratio were identified as independent predictors for MVI. This web-based app predicts MVI and provides real-time data-driven estimates of predictive value.
MATERIALS AND METHODS: In this retrospective study, 160 patients (training group) and 28 (validation group) who underwent surgical resection for PNETs were included. Demographic data and CT features were collected. The independent predictive factors for predicting MVI were confirmed through univariate and multivariate logistic regression analyses. The predictive performance was assessed by employing the receiver operating characteristic curve for predicting MVI. An R/shiny app based on logistic regression was developed. A Kaplan-Meier survival analysis with a log-rank test was conducted.
RESULTS: In the training group, invasion of surrounding tissues (odds ratio [OR]: 4.12), absolute enhancement (OR: 0.84), and relative enhancement ratio (OR: 16.1) were identified as independent predictors for predicting MVI in PNET patients, with an area under the curve of 0.819 and 0.891 in the training and validation groups, respectively. We have successfully developed a user-friendly web-based R/shiny app for real-time prediction of MVI in patients with PNETs. The median overall survival for patients with MVI was 12 months, compared to 37.5 months for those without MVI (log-rank p = 0.034).
CONCLUSIONS: Imaging features from portal venous-phase CT images can be used to accurately predict the presence of MVI in patients with PNETs. Patients with MVI are associated with worse survival compared to those without MVI. The web-based R/shiny app for predicting MVI provides real-time data-driven estimates of predictive value to facilitate informed decision-making.
CRITICAL RELEVANCE STATEMENT: Imaging features can accurately predict MVI in patients with PNETs, and the web-based R/shiny app provides real-time, data-driven estimates to enhance decision-making, thereby streamlining clinical practice.
KEY POINTS: The presence of microvascular invasion (MVI) in patients was associated with worse survival. Surrounding tissue invasion and absolute/relative enhancement ratio were identified as independent predictors for MVI. This web-based app predicts MVI and provides real-time data-driven estimates of predictive value.