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Multicenter Comparative Validation of an nnU-Net-Based Automated CT Radiomics Workflow for Acute Pancreatitis
Boyao Hou1, Wen Feng1, Wenxue Li1
1School of Medical Imaging, North Sichuan Medical University, Nanchong, China (B.H., W.F., W.L., W.H., J.G., and H.D.).
Rationale And Objectives:
Computed tomography radiomics has shown potential for predicting acute pancreatitis (AP) severity, but conventional workflows depend on labor-intensive expert pancreatic segmentation. This study evaluated a no-new-Net (nnU-Net)-based automated segmentation-radiomics workflow against an expert manual-segmentation workflow, using AP severity classification as the downstream task, and assessed the automated workflow externally.
Materials And Methods:
This retrospective multicenter study included patients with AP from Center A (n = 214), Center B (n = 56), and Center C (n = 42). Expert manual pancreatic masks served as reference annotations for nnU-Net training and as the comparator for the radiomics workflow. After selecting the optimal nnU-Net configuration, radiomics features were extracted separately from manual and automated regions of interest, and AP severity classifiers were developed using the same analytical framework. Discrimination between the two workflows was compared in the independent internal test cohort using the area under the curve (AUC) and the paired DeLong test, with Benjamini-Hochberg false discovery rate (FDR) and Bonferroni corrections applied to P values.
Results:
In the internal test cohort, the Light Gradient Boosting Machine classifier achieved AUCs of 0.799 (95% confidence interval (CI), 0.683-0.915) and 0.733 (95% CI, 0.593-0.873) with manual and automated segmentations, respectively. The absolute AUC difference was 0.066, and the paired DeLong comparison was not statistically significant after FDR correction (FDR-adjusted p = 0.360; Z = 1.1755, unadjusted p = 0.2398). The automated workflow achieved AUCs of 0.784 (95% CI, 0.653-0.915) in Center B and 0.715 (95% CI, 0.559-0.871) in Center C. The selected 2D nnU-Net achieved a mean Dice coefficient of 0.798 during five-fold cross-validation and 0.726 in the independent internal test cohort.
Conclusion:
The nnU-Net-based automated segmentation-radiomics workflow showed no significant difference in discrimination compared with the expert manual workflow in the internal test cohort. It also maintained moderate discrimination across two external cohorts. These findings support the feasibility of reducing dependence on manual pancreatic segmentation, although the present analysis does not establish formal equivalence or interchangeability.