Analysis of Intraoperative Frozen Pancreatic Resection Margin and Prediction of Postoperative Pancreatic Fistula Risk During Pancreatoduodenectomy

Acinar content at pancreatic resection margin is significantly associated with clinically relevant pancreatic fistula after partial pancreatoduodenectomy

BACKGROUND

This study aimed to evaluate the clinical significance of acinar content at the pancreatic resection margin after partial pancreatoduodenectomy (PD).

METHODS

A total of 228 consecutive patients undergoing PD were included for analysis. Resection margins were assessed for acinar, fibrosis, and fat contents by 2 pathologists blinded to the patients' clinical data. Univariate and multivariable analyses of possible predictors for clinically relevant postoperative pancreatic fistula (cr-POPF) were performed.

RESULTS

The median acinar, fibrosis, and fat contents were 70% (IQR, 25%-82%), 13% (IQR, 5%-40%), and 15% (IQR, 9.25%-25%), respectively. The rates of cr-POPF were significantly higher in patients with an acinar content of >70% than in patients with an acinar content of ≤70% (26.4% vs 5.5%, respectively; P < .001). In addition, the rates of postoperative hyperamylasemia (POH) were significantly higher in patients with an acinar content of ≥70% than in patients with an acinar content of ≤70% (55.2% vs 13.8%, respectively; P < .001). The median fat content did not differ between patients with and without cr-POPF (13.0% [IQR, 7.5%-20.0%] vs 15.0% [IQR, 10.0%-30.0%], respectively; P = .06). An acinar content of >70% at the pancreatic resection margin (odds ratio [OR], 4.85; 95% CI, 1.61-14.58; P = .005) and a soft pancreatic texture (OR, 2.82; 95% CI, 1.02-7.76; P = .046) were independent predictive factors of cr-POPF in the multivariable analysis.

CONCLUSION

An acinar content of ≥70% at the pancreatic resection margin was a significant predictive factor for cr-POPF after PD and was also significantly associated with POH, a precursor of cr-POPF after PD in many cases. Fatty infiltration of the pancreatic resection margin was not associated with cr-POPF.

Tomoelastography and Pancreatic Extracellular Volume Fraction Derived From MRI for Predicting Clinically Relevant Postoperative Pancreatic Fistula

BACKGROUND

Pancreatic stiffness and extracellular volume fraction (ECV) are potential imaging biomarkers for pancreatic fibrosis. Clinically relevant postoperative fistula (CR-POPF) is one of the most severe complications after pancreaticoduodenectomy. Which imaging biomarker performs better for predicting the risk of CR-POPF remains unknown.

PURPOSE

To evaluate the diagnostic performance of ECV and tomoelastography-derived pancreatic stiffness for predicting the risk of CR-POPF in patients undergoing pancreaticoduodenectomy.

STUDY TYPE

Prospective.

POPULATION

Eighty patients who underwent multiparametric pancreatic MRI before pancreaticoduodenectomy, among whom 16 developed CR-POPF and 64 did not.

FIELD STRENGTH/SEQUENCE

3 T/tomoelastography and precontrast and postcontrast T1 mapping of the pancreas.

ASSESSMENT

Pancreatic stiffness was measured on the tomographic c-map, and pancreatic ECV was calculated from precontrast and postcontrast T1 maps. Pancreatic stiffness and ECV were compared with histological fibrosis grading (F0-F3). The optimal cutoff values for predicting CR-POPF were determined, and the correlation between CR-POPF and imaging parameters was evaluated.

STATISTICAL TESTS

The Spearman's rank correlation and multivariate linear regression analysis was conducted. The receiver operating characteristic curve analysis and logistic regression analysis was performed. A double-sided P < 0.05 indicated a statistically significant difference.

RESULTS

Pancreatic stiffness and ECV both showed a significantly positive correlation with histological pancreatic fibrosis (r = 0.73 and 0.56, respectively). Patients with advanced pancreatic fibrosis had significantly higher pancreatic stiffness and ECV compared to those with no/mild fibrosis. Pancreatic stiffness and ECV were also correlated with each other (r = 0.58). Lower pancreatic stiffness (<1.38 m/sec), lower ECV (<0.28), nondilated main pancreatic duct (<3 mm) and pathological diagnosis other than pancreatic ductal adenocarcinoma were associated with higher risk of CR-POPF at univariate analysis, and pancreatic stiffness was independently associated with CR-POPF at multivariate analysis (odds ratio: 18.59, 95% confidence interval: 4.45, 77.69).

DATA CONCLUSION

Pancreatic stiffness and ECV were associated with histological fibrosis grading, and pancreatic stiffness was an independent predictor for CR-POPF.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 5.

Either High or Low Risk: The Acinar Score at the Resection Margin Dichotomizes the Risk Spectrum of Pancreas-specific Complications After Pancreatoduodenectomy

BACKGROUND

Pancreatic acinar content (Ac) has been associated with pancreas-specific complications after pancreatoduodenectomy. The aim of this study was to improve the prediction ability of intraoperative risk stratification by integrating the pancreatic acinar score.

METHODS

A training and validation cohort underwent pancreatoduodenectomy with a subsequent histologic assessment of pancreatic section margins for Ac, fibrosis (Fc), and fat. Intraoperative risk stratification (pancreatic texture, duct diameter) and pancreas-specific complications (postoperative hyperamylasemia [POH], postpancreatectomy acute pancreatitis [PPAP], pancreatic fistula [POPF]) were classified according to ISGPS definitions.

RESULTS

In the validation cohort (n= 373), the association of pancreas-specific complications with higher Ac and lower Fc was replicated (all P <0.001). In the entire cohort (n= 761), the ISGPS classification allocated 275 (36%) patients into intermediate-risk classes B (POH 32%/PPAP 3%/POPF 17%) and C (POH 36%/PPAP 9%/POPF 33%). Using the acinar score (Ac ≥60% and/or Fc ≤10%), intermediate-risk patients could be dichotomized into a low-risk (POH 5%/PPAP 1%/POPF 6%) and a high-risk (POH 51%/PPAP 9%/POPF 38%) group (all P <0.001). The acinar score AUC for POPF prediction was 0.70 in the ISGPS intermediate-risk classes. Overall, 239 (31%) patients were relocated into the high-risk group from lower ISGPS risk classes using the acinar score.

CONCLUSIONS

The risk of pancreas-specific complications appears to be dichotomous-either high or low-according to the acinar score, a tool to better target the application of mitigation strategies in cases of intermediate macroscopic features.

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data

BACKGROUND

Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.

METHODS

We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features' clinical relevance and technical feasibility.

RESULTS

In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was "surgical skill and quality of performance" for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was "Instrument" (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were "intraoperative adverse events", "action performed with instruments", "vital sign monitoring", and "difficulty of surgery".

CONCLUSION

Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.