Preoperative Evaluation of Pancreatic Fibrosis and Lipomatosis: Correlation of Magnetic Resonance Findings With Histology Using Magnetization Transfer Imaging and Multigradient Echo Magnetic Resonance Imaging. Invest Radiol. 2018;53:720-727. [PMID: 30247172 DOI: 10.1097/rli.0000000000000496]

Utility of dual-energy CT and advanced multiparametric MRI based imaging biomarkers of pancreatic fibrosis in grading the severity of chronic pancreatitis

PURPOSE

To non-invasively quantify pancreatic fibrosis and grade severity of chronic pancreatitis (CP) on dual-energy CT (DECT) and multiparametric MRI (mpMRI).

METHODS

We included 72 patients (mean age:30years; 59 men) with suspected or confirmed CP from December 2019 to December 2021 graded as equivocal(n = 20), mild(n = 18), and moderate-marked(n = 34) using composite imaging and endoscopic ultrasound criteria. Study patients underwent multiphasic DECT and mpMRI of the abdomen. Normalized iodine concentration(NIC) and fat fraction(FF) on 6-minute delayed DECT, and T1 relaxation time(T1Rt), extracellular volume fraction(ECVf), intravoxel incoherent motion-based perfusion fraction(PF), and magnetization transfer ratio(MTR) on mpMRI of pancreas were compared. 20 renal donors(for DECT) and 20 patients with renal mass(for mpMRI) served as controls.

RESULTS

NIC of pancreas in controls and progressive grades of CP were 0.24 ± 0.05, 0.80 ± 0.18, 1.06 ± 0.23, 1.40 ± 0.36, FF were 9.28 ± 5.89, 14.19 ± 5.29, 17.31 ± 5.99, 29.32 ± 12.22, T1Rt were 590.11 ± 61.13, 801.93 ± 211.01, 1006.79 ± 352.18, 1388.01 ± 312.23ms, ECVf were 0.07 ± 0.03, 0.30 ± 0.12, 0.41 ± 0.12, 0.53 ± 0.13, PF were 0.38 ± 0.04, 0.28 ± 0.07, 0.25 ± 0.09, 0.21 ± 0.05 and MTR were 0.12 ± 0.03, 0.15 ± 0.06, 0.21 ± 0.07, 0.26 ± 0.06, respectively. There were significant differences for all quantitative parameters between controls and mild CP; for NIC, PF, and ECVf between controls and progressive CP grades (p < 0.05). Area under curve for NIC, FF, T1Rt, ECVf, PF, and MTR in differentiating controls and mild CP were 1.00, 0.86, 0.95, 1.00, 0.90 and 0.84 respectively and for NIC, FF, ECVf and PF in differentiating controls and equivocal CP were 1.00, 0.76, 0.95 and 0.92 respectively.

CONCLUSION

DECT and mpMRI were useful in quantifying pancreatic fibrosis and grading the severity of CP. NIC was the most accurate marker.

Quantitative Magnetic Resonance Imaging for the Pancreas: Current Status

ABSTRACT

Magnetic resonance imaging (MRI) is important for evaluating pancreatic disorders, and anatomical landmarks play a major role in the interpretation of results. Quantitative MRI is an effective diagnostic modality for various pathologic conditions, as it allows the investigation of various physical parameters. Recent advancements in quantitative MRI techniques have significantly improved the accuracy of pancreatic MRI. Consequently, this method has become an essential tool for the diagnosis, treatment, and monitoring of pancreatic diseases. This comprehensive review article presents the currently available evidence on the clinical utility of quantitative MRI of the pancreas.

Prediction of pancreatic fibrosis by dual-energy CT-derived extracellular volume fraction: Comparison with MRI

OBJECTIVES

To investigate the correlation between dual-energy CT (DECT) and MRI measurements of the extracellular volume fraction (ECV) and to assess the accuracy of both methods in predicting pancreatic fibrosis (PF).

METHODS

We retrospectively analyzed 43 patients who underwent pancreatectomy and preoperative pancreatic DECT and MRI between November 2018 and May 2022. The ECV was calculated using the T1 relaxation time (for MR-ECV) or absolute enhancement (for DECT-ECV) at equilibrium phase (180 s after contrast injection in our study). Pearson coefficient and Bland-Altman analysis were used to compare the correlation between the two ECVs, Spearman correlations were used to investigate the association between imaging parameters and PF, Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the ECVs for advanced fibrosis (F2-F3), and multivariate logistic regression analysis was used to examine the relationship between PF and imaging parameters.

RESULTS

There was a strong correlation between DECT- and MR-derived ECVs (r = 0.948; p < 0.001). The two ECVs were positively correlated with PF (DECT: r = 0.647, p < 0.001; MR: r = 0.614, p < 0.001), and the mean values were 0.34 ± 0.08 (range: 0.22-0.62) and 0.35 ± 0.09 (range: 0.24-0.66), respectively. The area under the operating characteristic curve (AUC) for subjects with advanced fibrosis diagnosed by ECV was 0.86 for DECT-ECV and 0.87 for MR-ECV. Multivariate logistic regression analysis showed that the DECT-ECV was an independent predictor of PF.

CONCLUSIONS

The ECV could be an effective predictor of histological fibrosis, and DECT is equivalent to MRI for characterizing pancreatic ECV changes.

Quantitative imaging predicts pancreatic fatty infiltration on routine CT examination

PURPOSE

The purpose of this study was to assess the performance of quantitative computed tomography (CT) imaging for detecting pancreatic fatty infiltration, using the results of histopathological analysis as reference.

MATERIALS AND METHODS

Sixty patients who underwent pancreatic surgery for a pancreatic tumor between 2016 and 2019 were retrospectively included. There were 33 women and 27 men with a mean age of 56 ± 12 (SD) years (age range: 18-79 years). Patients with dilatation of the main pancreatic duct, chronic pancreatitis, or preoperative treatment were excluded to prevent any bias in the radiological-pathological correlation. Pancreatic fatty infiltration was recorded at pathology. Pancreatic surface lobularity, pancreatic attenuation, visceral fat area, and subcutaneous fat area were derived from preoperative CT images. The performance for the prediction of fatty infiltration was assessed using area under receiver operating characteristic curve (AUC) and backward binary logistic regression analysis. Results were validated in a separate cohort of 34 patients (17 women; mean age, 50 ± 14 [SD] years; age range: 18-73).

RESULTS

A total of 28/60 (47%) and 17/34 (50%) patients had pancreatic fatty infiltration in the derivation and validation cohorts, respectively. In the derivation cohort, patients with pancreatic fatty infiltration had a significantly higher PSL (P < 0.001) and a lower pancreatic attenuation on both precontrast and portal venous phase images (P = 0.011 and 0.003, respectively), and higher subcutaneous fat area and visceral fat area (P = 0.010 and 0.007, respectively). Multivariable analysis identified pancreatic surface lobularity > 7.6 and pancreatic attenuation on portal venous phase images < 83.5 Hounsfield units as independently associated with fatty infiltration. The combination of these variables resulted in an AUC of 0.85 (95% CI: 0.74-0.95) and 0.83 (95% CI: 0.67-0.99) in the derivation and validation cohorts, respectively.

CONCLUSION

CT-based quantitative imaging accurately predicts pancreatic fatty infiltration.

Dual-Energy CT Iodine Concentration to Evaluate Postoperative Pancreatic Fistula after Pancreatoduodenectomy

Background Pancreatic fibrosis and fatty infiltration are associated with postoperative pancreatic fistula (POPF), but accurate preoperative assessment remains a challenge. Iodine concentration (IC) and fat fraction derived from dual-energy CT (DECT) may reflect the amount of fibrosis and steatosis, potentially enabling the preoperative prediction of POPF. Purpose To identify multiphasic DECT-derived IC and fat fraction that improve the prediction of POPF risks compared with contrast-enhanced CT attenuation values and to evaluate the underlying histopathologic changes. Materials and Methods This retrospective study included patients who underwent pancreatoduodenectomy and DECT (including pancreatic parenchymal, portal venous, and delayed phase scanning) between January 2020 and December 2020. The relationships of the quantitative DECT-derived IC and fat fraction, along with CT attenuation values from enhanced images with POPF risk, were analyzed with logistic regression analysis. The predictive performance of the IC was compared with that of the CT values. The histopathologic underpinnings of IC were evaluated with multivariable linear regression analysis. Results A total of 107 patients (median age, 65 years; interquartile range, 57-70 years; 56 men) were included. Of these, 23 (21%) had POPF. The pancreatic parenchymal-to-portal venous phase IC ratio (adjusted odds ratio [OR], 13; 95% CI: 2, 162; P < .001) was an independent predictor of POPF occurrence. The accuracy of the pancreatic parenchymal-to-portal venous phase IC ratio in predicting POPF was higher than that of the CT value ratio in the same phases (78% vs 65%, P < .001). The pancreatic parenchymal-to-portal venous phase IC ratio was independently associated with pancreatic fibrosis (β = -1.04; 95% CI: -0.44, -1.64; P = .001). Conclusion A higher pancreatic parenchymal-to-portal venous phase IC ratio was associated with less histologic fibrosis and greater risk of POPF. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Yoon in this issue.

Development of a standardized MRI protocol for pancreas assessment in humans

Magnetic resonance imaging (MRI) has detected changes in pancreas volume and other characteristics in type 1 and type 2 diabetes. However, differences in MRI technology and approaches across locations currently limit the incorporation of pancreas imaging into multisite trials. The purpose of this study was to develop a standardized MRI protocol for pancreas imaging and to define the reproducibility of these measurements. Calibrated phantoms with known MRI properties were imaged at five sites with differing MRI hardware and software to develop a harmonized MRI imaging protocol. Subsequently, five healthy volunteers underwent MRI at four sites using the harmonized protocol to assess pancreas size, shape, apparent diffusion coefficient (ADC), longitudinal relaxation time (T1), magnetization transfer ratio (MTR), and pancreas and hepatic fat fraction. Following harmonization, pancreas size, surface area to volume ratio, diffusion, and longitudinal relaxation time were reproducible, with coefficients of variation less than 10%. In contrast, non-standardized image processing led to greater variation in MRI measurements. By using a standardized MRI image acquisition and processing protocol, quantitative MRI of the pancreas performed at multiple locations can be incorporated into clinical trials comparing pancreas imaging measures and metabolic state in individuals with type 1 or type 2 diabetes.

Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

OBJECTIVES

To evaluate whether a magnetic resonance imaging (MRI) radiomics-based machine learning classifier can predict postoperative pancreatic fistula (POPF) after pancreaticoduodenectomy (PD) and to compare its performance to T1 signal intensity ratio (T1 SIratio).

METHODS

Sixty-two patients who underwent 3 T MRI before PD between 2008 and 2018 were retrospectively analyzed. POPF was graded and split into clinically relevant POPF (CR-POPF) vs. biochemical leak or no POPF. On T1- and T2-weighted images, 2 regions of interest were placed in the pancreatic corpus and cauda. 173 radiomics features were extracted using pyRadiomics. Additionally, the pancreas-to-muscle T1 SIratio was measured. The dataset was augmented and split into training (70 %) and test sets (30 %). A Boruta algorithm was used for feature reduction. For prediction of CR-POPF models were built using a gradient-boosted tree (GBT) and logistic regression from the radiomics features, T1 SIratio and a combination of the two. Diagnostic accuracy of the models was compared using areas under the receiver operating characteristics curve (AUCs).

RESULTS

Five most important radiomics features were identified for prediction of CR-POPF. A GBT using these features achieved an AUC of 0.82 (95 % Confidence Interval [CI]: 0.74 - 0.89) when applied on the original (non-augmented) dataset. Using T1 SIratio, a GBT model resulted in an AUC of 0.75 (CI: 0.63 - 0.84) and a logistic regression model delivered an AUC of 0.75 (CI: 0.63 - 0.84). A GBT model combining radiomics features and T1 SIratio resulted in an AUC of 0.90 (CI 0.84 - 0.95).

CONCLUSION

MRI-radiomics with routine sequences provides promising prediction of CR-POPF.

Evaluation of Pancreatic Fibrosis Grading by Multi Parametric Quantitative Magnetic Resonance Imaging

BACKGROUND

Early detection and grading of pancreatic fibrosis (PF) are important and challenging clinical goals.

PURPOSE

To determine main pancreatic duct (MPD) diameter, pancreatic thickness, and grades of PF via magnetic resonance elastography (MRE), T1 mapping, and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), assessing respective diagnostic performances.

STUDY TYPE

Prospective.

SUBJECTS

Histopathologic and imaging records (MRE, T1 mapping, and IVIM-DWI) generated by 144 patients between December 2018 and May 2020 were collected for analysis. Grades of PF were distributed as follows: F0, 82; F1, 22; F2, 22; and F3, 18.

FIELD STRENGTH/SEQUENCE

3 T pancreatic MRI, encompassing MRE, T1 mapping, and IVIM-DWI.

ASSESSMENT

In all patients, T1 relaxation times, pancreatic stiffness values, IVIM-DWI parameters, MPD diameter, and pancreatic thickness were measured.

STATISTICAL TESTS

Receiver operating characteristic (ROC) analysis served to assess imaging parameters useful in diagnosing PF. To identify relations between specific parameters and grades of PF, logistic regression analysis was invoked.

RESULTS

Both pancreatic stiffness (r = 0.754; P < 0.001) and T1 relaxation time (r = 0.433; P < 0.001) correlated significantly with PF (%). To determine PF grades ≥F1, a combined model (area under the curve [AUC] = 0.906) performed significantly better than pancreatic stiffness (AUC = 0.855; P < 0.001) or T1 relaxation time (AUC = 0.754; P < 0.001) alone. For PF grades ≥F2 or grade F3, both the combined model (≥F2: AUC = 0.910; F3: AUC = 0.939) and pancreatic stiffness (≥F2: AUC = 0.906; F3: AUC = 0.929) outperformed T1 relaxation time (≥F2: AUC = 0.768 [P = 0.005 and P = 0.004, respectively]; F3: AUC = 0.816 [both P < 0.005]). All IVIM-DWI parameters generated AUC values <0.700.

DATA CONCLUSION

A combination of MRE and T1 mapping seems promising in diagnosing various grades of PF, particularly at an early stage.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Pancreatic fistulas following distal pancreatectomy are unrelated to the texture quality of the pancreas

PURPOSE

The relevance of pancreatic texture for pancreatic fistula (POPF) formation after distal pancreatectomy (DP) remains ill defined. Recent POPF definition adjustments and common subjective pancreatic texture assessment are further drawbacks in the investigation of pancreatic texture as a factor for POPF development after DP.

METHODS

The predictive value of pancreatic texture by histologic assessment was investigated for POPF formation after DP, respecting the updated 2016 fistula definition. Histologic evaluation at the resection margin included amount of steatosis, degree of fibrosis, and pancreatic duct size.

RESULTS

A total of 102 patients who underwent DP were included. Thirty-six patients developed POPF. There was no difference in histologic variables in patients with and without POPF. In the univariate analysis, none of the three histologic features showed significant correlation with POPF formation. The ROC (receiver operating characteristic) curve demonstrated poor utility for the grade of steatosis 0.481 ± 0.058 (p = 0.75) and grade of fibrosis 0.466 ± 0.058 (p = 0.57) as predictive factors for POPF formation.

CONCLUSION

Results indicate that pancreatic texture does not predict POPF formation following DP. This is particularly relevant in the context of the increasing use of robotic and laparoscopic approaches for DPs with limited clinical pancreatic texture assessment by palpation.

Partial pancreatic tail preserving subtotal pancreatectomy for pancreatic cancer: Improving glycemic control and quality of life without compromising oncological outcomes

BACKGROUND

Total pancreatectomy (TP) is usually considered a therapeutic option for pancreatic cancer in which Whipple surgery and distal pancreatectomy are undesirable, but brittle diabetes and poor quality of life (QoL) remain major concerns. A subset of patients who underwent TP even died due to severe hypoglycemia. For pancreatic cancer involving the pancreatic head and proximal body but without invasion to the pancreatic tail, we performed partial pancreatic tail preserving subtotal pancreatectomy (PPTP-SP) in selected patients, in order to improve postoperative glycemic control and QoL without compromising oncological outcomes.

AIM

To evaluate the efficacy of PPTP-SP for patients with pancreatic cancer.

METHODS

We retrospectively reviewed 56 patients with pancreatic ductal adenocarcinoma who underwent PPTP-SP (n = 18) or TP (n = 38) at our institution from May 2014 to January 2019. Clinical outcomes were compared between the two groups, with an emphasis on oncological outcomes, postoperative glycemic control, and QoL. QoL was evaluated using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30 and EORTC PAN26). All patients were followed until May 2019 or until death.

RESULTS

A total of 56 consecutive patients were enrolled in this study. Perioperative outcomes, recurrence-free survival, and overall survival were comparable between the two groups. No patients in the PPTP-SP group developed cancer recurrence in the pancreatic tail stump or splenic hilum, or a clinical pancreatic fistula. Patients who underwent PPTP-SP had significantly better glycemic control, based on their higher rate of insulin-independence (P = 0.014), lower hemoglobin A1c (HbA1c) level (P = 0.046), lower daily insulin dosage (P < 0.001), and less frequent hypoglycemic episodes (P < 0.001). Global health was similar in the two groups, but patients who underwent PPTP-SP had better functional status (P = 0.036), milder symptoms (P = 0.013), less severe diet restriction (P = 0.011), and higher confidence regarding future life (P = 0.035).

CONCLUSION

For pancreatic cancer involving the pancreatic head and proximal body, PPTP-SP achieves perioperative and oncological outcomes comparable to TP in selected patients while significantly improving long-term glycemic control and QoL.

The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study

BACKGROUND

Postoperative pancreatic fistula remains an unsolved challenge after pancreatoduodenectomy. Important in this regard is the presence of a soft pancreatic texture which is a major risk factor. Advances in machine learning and texture analysis of medical images allow identification of features of parenchyma that are invisible to the human eye. The aim of this study was to investigate the potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced computed tomography.

METHODS

We screened a prospectively assessed database including all patients undergoing pancreatoduodenectomy at a tertiary center from 2008 until 2018 for patients based on the occurrence of postoperative pancreatic fistula. In total, 110 patients were included, consisting of 55 patients who developed a postoperative pancreatic fistula and 55 without postoperative pancreatic fistula. For machine learning-based texture analysis preoperative, non-contrast-enhanced computed tomography axial images were used. Machine learning results were tested using 10-fold cross validation. Previously validated clinical fistula risk scores (original and alternative fistula risk scores) served as reference tests.

RESULTS

Both the original and the alternative fistula risk scores showed good discrimination between patients without and with postoperative pancreatic fistula (area under the curve 0.76 and 0.72, respectively). Machine learning-based texture analysis showed potential to detect histologic fibrosis (area under the curve 0.84, sensitivity 75%; specificity 92%), histologic lipomatosis (area under the curve 0.82, sensitivity 78%; specificity 89%), and intraoperative pancreatic hardness (area under the curve 0.70, sensitivity 78%; specificity 74%). The features of the machine learning-based texture analysis were most accurate in predicting the occurrence of postoperative pancreatic fistula (area under the curve 0.95, sensitivity of 96%; specificity 98%) after pancreatoduodenectomy.

CONCLUSION

This proof-of-principle study suggests the ability of machine learning in recognizing important features of pancreatic texture associated with an increased risk of postoperative pancreatic fistula based on preoperative computed tomography.