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Objective: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. Methods: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker—DeepCT-PDAC—by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. Results: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50–2.75; HR: 2.47, CI: 1.35–4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89–3.28; HR: 2.15, CI: 1.14–4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19–0.64), but did not affect OS in the subgroup with high risk. Conclusions: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Background: The feasibility and reproducibility of multifrequency MR elastography (MRE) for diagnosing pancreatic ductal adenocarcinoma (PDAC) have not been reported. Purpose: To determine the feasibility and reproducibility of multifrequency MRE for assessing pancreatic stiffness in healthy and diseased pancreases. Study type: Prospective. Subjects: A total of 40 healthy volunteers and 10 patients with PDAC were prospectively recruited between March 2018 and October 2021. Field strength/sequence: A 3.0-T pancreatic MRE at frequencies in the order of 30, 40, 60, 80, and 100 Hz. Assessment: Body mass index (BMI) and wave distance of the healthy pancreas and PDAC were measured. Image quality was assessed using the image quality score (IQS: 1-4, ≥3 were considered diagnostic quality). Three readers independently performed the pancreatic stiffness and IQS assessments to evaluate reproducibility. Statistical tests: Logistic regression analyses were performed to determine variables that influenced IQS. Statistical significance was set at P <0.05. Levels of inter-and intrarater agreement were assessed using intraclass correlation coefficients (ICC) and Cohen's kappa coefficient (κ). Good reproducibility was set at ICC and κ ≥ 0.8. Results: In logistic regression analysis, a diagnostic IQS in healthy volunteers was independently associated with a lower BMI (odds ratio [OR] = 0.89 kg/m À2 ), shorter wave distance (OR = 0.70 cm À1 ), and lower frequency (30 and 40 Hz: OR = 170.01 and 96.02). In PDAC, frequency was the only independent factor for diagnostic IQS 46.18, and 17.20, respectively) with 100 Hz as a reference. In healthy volunteers, good reproducibility was observed at 30 and 40 Hz. In PDAC, good reproducibility was observed at 30-60 Hz. Data conclusion: MRE at 30 and 40 Hz provides diagnostic wave images and reliable measurements of pancreatic stiffness in healthy volunteers. MRE at 30-60 Hz is acceptable for PDACs (IQS ≥ 3, ICC and κ ≥ 0.80).

Objectives Three-dimensional magnetic resonance elastography (3D-MRE) allows for multiparametric modeling of both elastic and viscous tissue characteristics. Our aim was to compare 3D-MRE with conventional liver shear stiffness assessment in gauging obstructive jaundice (OJ), predicting the adequacy of biliary decompression after drainage, and discriminating OJ from liver fibrosis. Methods Patients with no histories of liver disease (n = 201) were studied in retrospect, grouped by bilirubin levels as no jaundice (NJ ≤ 2 mg/dL; n = 75), mild OJ (>2 mg/dL and ≤ 4 mg/dL; n = 56), and severe OJ (> 4 mg/dL; n = 70). For comparison, another 75 patients with chronic hepatitis B and C infections and histologically proven liver fibrosis were similarly analyzed. Each patient underwent spin-echo echo-planar-imaging MRE at 60 Hz with 3D wave postprocessing. Logistic regression and ordinary regression models were used to compare the 3D-MRE model with liver shear stiffness. Results Liver shear stiffness, loss modulus, and damping ratio were incorporated into a 3D-MRE model, which significantly outperformed shear stiffness in predicting OJ severity (accuracy: 0.801 vs 0.672; p < 0.001). Both the 3D-MRE model and liver shear stiffness performed equally well in predicting the outcome of biliary drainage procedure (C-statistics: 0.852 vs 0.847; p = 0.48). The 3D-MRE model also demonstrated significantly better C-statistics than that of liver shear stiffness in discriminating mild OJ from F1-F2 liver fibrosis (0.765 vs 0.641; p = 0.005) and severe OJ from F3-F4 liver fibrosis (0.750 vs 0.635; p = 0.031). Conclusions 3D-MRE is an innovative imaging method for gauging OJ severity, predicting the outcome of biliary drainage procedure, and discriminating OJ from liver fibrosis. Key Points • 3D-MR elastography achieved promising results for predicting the severity of obstructive jaundice.• Advanced parameters of 3D-MR elastography demonstrated significantly better performance than that of shear stiffness of 2D-MR elastography in discriminating obstructive jaundice from liver fibrosis caused by chronic hepatitis B/C. • Both 3D-MR elastography and 2D-MR elastography were equivalent in predicting the outcome of biliary drainage procedure.

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