Caterina Tartari , Fabio Porões , Sabine Schmidt , Daniel Abler , Thomas Vetterli , Adrien Depeursinge , Clarisse Dromain , Naïk Vietti Violi , Mario Jreige
{"title":"MRI and CT radiomics for the diagnosis of acute pancreatitis","authors":"Caterina Tartari , Fabio Porões , Sabine Schmidt , Daniel Abler , Thomas Vetterli , Adrien Depeursinge , Clarisse Dromain , Naïk Vietti Violi , Mario Jreige","doi":"10.1016/j.ejro.2025.100636","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP).</div></div><div><h3>Materials and methods</h3><div>We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48–72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed.</div></div><div><h3>Results</h3><div>The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5–74.7), 80.1 % (78.2–83.2), and 0.834 (0.819–0.844) for T2WI (p = 0.001), 74.4 % (71.5–76.4), 58.7 % (56.3–61.1), and 0.654 (0.630–0.677) for non-enhanced T1WI, 62.1 % (60.1–64.2), 78.7 % (77.1–81), and 0.787 (0.771–0.810) for venous T1WI, and 66.4 % (64.8–50.9), 48.4 % (46–50.9), and 0.610 (0.586–0.626) for CECT, respectively.</div><div>The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80–80.3), 78.1 % (75.9–80.2), and 0.911 (0.902–0.920) (p = 0.001).</div></div><div><h3>Conclusion</h3><div>The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.</div></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"14 ","pages":"Article 100636"},"PeriodicalIF":1.8000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Radiology Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352047725000036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP).
Materials and methods
We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48–72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed.
Results
The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5–74.7), 80.1 % (78.2–83.2), and 0.834 (0.819–0.844) for T2WI (p = 0.001), 74.4 % (71.5–76.4), 58.7 % (56.3–61.1), and 0.654 (0.630–0.677) for non-enhanced T1WI, 62.1 % (60.1–64.2), 78.7 % (77.1–81), and 0.787 (0.771–0.810) for venous T1WI, and 66.4 % (64.8–50.9), 48.4 % (46–50.9), and 0.610 (0.586–0.626) for CECT, respectively.
The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80–80.3), 78.1 % (75.9–80.2), and 0.911 (0.902–0.920) (p = 0.001).
Conclusion
The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.