Postoperative Pancreatic Fistula After Pancreatoduodenectomy: Can Radiomics Improve Clinical Risk Scores?

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Abstract

Assess the potential added benefit of radiomics to clinical models for predicting postoperative pancreatic fistula (POPF) after pancreatoduodenectomy (PD). Radiomics extracts quantitative data from medical imaging based on enhancement patterns. Clinical applications of radiomics have been investigated in pancreas, lung, breast, and prostate cancers. This single center retrospective study included all patients with available preoperative CT scan before PD from 2009-2021. Radiomic features that reflect heterogeneity in enhancement patterns were extracted from manually segmented future remnant pancreas. Clinical variables and radiomic features were used with random forest classifier to design five predictive models for grade B/C clinically relevant POPF (CR-POPF): preoperative (PreClin), intraoperative (IntraClin), radiomics alone (Rad), PreClin with radiomics (PreClin-Rad), and IntraClin with radiomics (IntraClin-Rad). Training data was randomly selected and comprised 70% (n=339) of the cohort, and the remaining 30% (n=145) was the test set. A prospective validation cohort (n=60) was also created. From 1855 eligible PD, all 234 with POPF were included, random sampling was used on the remainder to select 250 without POPF. In the test set, PreClin (AUC=0.74), IntraClin (AUC=0.78), and Rad (AUC=0.75) performed similarly. Best results were noted with combined models, AUC=0.82 for PreClin-Rad and AUC=0.84 for IntraClin-Rad. The same patterns were noted in the prospective validation cohort with PreClin-Rad performing best (AUC=0.78). Radiomics compared favorably with clinical risk scores for CR-POPF after PD, and combined models with radiomics and clinical data offer the strongest prediction. The PreClin-Rad model demonstrates the greatest clinical utility with excellent predictive outcomes relying entirely on preoperative data in the test and prospective validation cohorts.

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