Arterial CT imaging was successfully used to predict POPF using radiomics and was compared with a preoperative multicenter validated scoring system. These results demonstrate the potential of radiomics for advancing the perioperative management of patients with pancreatic head carcinoma. PPM is useful for predicting the development of clinically relevant fistulas. Using APM, we were able to demonstrate that the amylase activity in the drainage on the third postoperative day is a suitable biomarker for predicting fistula development. This approach allowed us to provide a transfer possibility of radiological knowledge gain to clinical-surgical relevance.
Overall, 39.7% of 68 patients developed POPF. Following the current literature, this corresponded to a rather enriched collective, with comparative values ranging from 3–30% 2,3,8,9. The high incidence of POPF in our cohort may diminish the transferability of our results to other cohorts. This may represent a selection bias because many patients with grade BL cross over into B because they are ambulatory during clinical follow-up; therefore, the drain cannot be pulled until 21 days.
Most of the significantly correlated features in our models were second-order features, which primarily reflected the relationships among voxels within the ROI using matrices. A possible explanation for this result is the complexity of the pancreatic structure and its large cell variance in a comparatively small organ volume23. Thus, the complexity of pancreatic parenchyma is reflected in the matrices of this class. This is a clue for radiomics as a relevant tool in pancreas-related research because the depth of second-order matrices exceeds human image interpretation. This assumption is supported by the fact that in previous pancreas-related radiomics studies, the most prevalent significant radiomics features observed were second-order as well15–17,23.
Isolated radiological analysis of pancreatic parenchyma using radiomics (AUC = 0.779) was inferior to prediction using Roberts’ score (AUC = 0.831), whose comparatively high value was consistent with the results of recent studies Therefore, we investigated whether the combination of radiomics and clinical parameters used for the clinical score would alter the value of POPF prediction.
Although BMI did not have an expected effect on predictive strength, the implementation of duct width significantly improved the predictive performance of our models. We observed an increase in the AUC of PPM (+ 0,113) after combining it with the ductal size determined on preoperative CT scans. On the one hand, this could be explained by the influence of duct width on the possibility of surgical re-anastomosis. On the other hand, it could indicate a limitation of pure radiomics analysis to adequately distinguish the parenchyma and ductal system within the ROI. This finding supports our approach of combining radiomics with clinical parameters.
In contrast, the lack of a positive effect of BMI on the power of our prediction model may be explained by the distribution of BMI in our cohort, in which BMI was relatively low overall. The median BMI of 24.7 kg/m2 (IQR = 4.4) was within the normal range. Nevertheless, we decided to include BMI to ensure the best possible comparability with Roberts score 17,21,24.
The maximum AUC of 0.897 (CI = 82.3–97.1%) for PPM can be considered good to very good according to comparable studies20,25,26. The potential of combining radiomics with clinical parameters is reflected in the increase in AUC due to the correlation with duct width and in the outcome of both models.
The predictive power of PPM compared to Roberts’ score is critical for realistic consideration of the results. The lack of significance of our results can be explained by the strong clinical score and small cohort size17,23,27. In the future, our model should be applied to larger cohorts to further verify the significance level and transferability to other cohorts.
Constructing the APM, we succeeded in detecting a total of 11 radiomics features that significantly correlated with an increase in enzyme activity in the drainage above 1000U/l. Our result, with an AUC of 0.936 (CI = 88.0-99.1%) suggests a strong possibility to predict the healing process after PD perioperatively using radiomics13,20,23.
Thus, to the best of our knowledge, this approach represents an innovation because there have been no comparable approaches in perioperative drainage management after PD. Postoperative management is controversial in the current literature regarding prophylactic drainage, timing of cutoff determination, cutoff level and timing of removal12,13.
This shows a gap between the theoretical retrospective classification according to ISGPF and its value for the clinical perioperative management of POPF. This fact is reflected in all prediction systems developed so far, which cannot distinguish between BL and clinically relevant grades B and C in the interpretation of results16,20,25,26. This may explain the clinically practical importance of a cutoff level as a maxim for action.
The potential limitations of our study were its retrospective design and small patient population size. The results are based exclusively on data from a single center and require further external validation. Similarly, the small cohort size could equally influence the results. The large reduction in the number of patients could be explained by strict inclusion criteria. Furthermore, we decided not to use so-called "preprocessing". This was done to obtain as unbiased findings as possible20,24,27,28. These considerations refer to a conflict in research with AI: On the one hand, the implemented data should be made comparable. However, any change in the datasets entails a change in the algorithm processes, and thus, the results. Furthermore, most radiomics studies to date have used manual segmentation, which could be a potential methodological limitation in terms of time and measurement error20,23,24. Moreover, a cornerstone of radiomics research is the evaluation of one's model using the clinical gold standard, which limits the drain-amylase prediction model20,24. We were not aware of any standards against which validation would have been possible. The same applies to the upper limit of amylase in the drainage that was established.