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High-acceleration pancreatobiliary MRI with deep learning-based super-resolution reconstruction for evaluating presumed pancreatic intraductal papillary mucinous neoplasm.

October 24, 2025pubmed logopapers

Authors

Jeon SK,Lee JM,Park J,Hwang S,Ryu RR

Affiliations (7)

  • Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak‑ro, Jongno‑gu, Seoul, 03080, Korea.
  • Department of Radiology, Seoul National University College of Medicine, 103 Daehak‑ro, Jongno‑gu, Seoul, 03080, Korea.
  • Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, 101 Daehak‑ro, Jongno‑gu, Seoul, 03080, Korea. [email protected].
  • Department of Radiology, Seoul National University College of Medicine, 103 Daehak‑ro, Jongno‑gu, Seoul, 03080, Korea. [email protected].
  • Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak‑ro, Jongno‑gu, Seoul, 03080, Korea. [email protected].
  • Department of Radiology, Inje University Ilsan Paik Hospital, 170 Juhwa-ro, Ilsanseo-gu, Goyang-si, 10380, Gyeonggi-do, Korea.
  • Department of Radiology, Chung-Ang University Hospital, 102, Heukseok-ro, Dongjak- gu, Seoul, 06973, Korea.

Abstract

To evaluate the feasibility and diagnostic utility of a deep learning (DL)-based super-resolution (SR) reconstruction algorithm applied to pancreatobiliary MRI for assessing pancreatic intraductal papillary mucinous neoplasms (IPMNs). This retrospective study included 162 patients with presumed pancreatic IPMN (≥ 1 cm) who underwent pancreatobiliary MRI between May 2019 and May 2022. Two portal venous phase (PVP) images of dynamic T1-wegithed imaging were sequentially acquired: early PVP image obtained using standard compressed sensing (CS)-volumetric interpolated breath-hold examination (VIBE) (standard CS-VIBE) and late PVP image obtained using CS-VIBE with DL-based SR reconstruction algorithm to generate 1 mm-thickness images (DL-SR CS-VIBE). Arterial phase and 3-min delayed phase were also acquired using DL-SR CS-VIBE. The image quality of standard and DL-SR CS-VIBE PVP sequences was compared using Wilcoxon signed-rank test. The diagnostic performance of full-sequence pancreatobiliaryMRI including DL-SR CS-VIBE for predicting malignant IPMN was assessed using multi-reader multi-case analysis. Diagnostic accuracy was assessed using receiver operating characteristic analysis, while sensitivity and specificity were estimated with corresponding 95% confidence intervals. Among 162 patients, 15 had malignant IPMN, while 147 had benign IPMN. DL-SR CS-VIBE demonstrated significantly better overall image quality (3.73 ± 0.33 vs. 3.22 ± 0.43) and cystic lesion conspicuity (3.37 ± 0.50 vs. 2.71 ± 0.52) than standard CS-VIBE (all Ps < 0.001). The area under the ROC curve (AUC) for predicting malignant IPMN was 0.858 (95% CI: 0.807, 0.909). Using the presence of high-risk stigmata as an indicator of test-positive, pooled sensitivity and pooled specificity of pancreatobiliary MRI including DL-SR CS-VIBE for malignant IPMN were 71.1% (95% confidence interval [CI]: 55.7, 83.6) and 82.8% (95% CI: 78.9, 86.2), respectively. Among MRI features, diagnostic accuracy was highest for mural nodules ≥ 5 mm (AUC, 0.736) and main pancreatic duct size ≥ 10 mm (AUC, 0.720). Pancreatobiliary MRI with DL-SR CS-VIBE enhances image quality and lesion conspicuity, offering promising diagnostic accuracy for malignant IPMN, though further studies with larger cohorts are needed to refine these findings and evaluate clinical impact.

Topics

Deep LearningMagnetic Resonance ImagingPancreatic NeoplasmsPancreatic Intraductal NeoplasmsCarcinoma, Pancreatic DuctalAdenocarcinoma, MucinousJournal Article

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