• Boxhoorn L, Voermans RP, Bouwense SA et al (2020) Acute pancreatitis. Lancet 396(10252):726–734

    PubMed 
    Article 

    Google Scholar
     

  • Banks PA, Bollen TL, Dervenis C et al (2013) Classification of acute pancreatitis—2012: revision of the Atlanta classification and definitions by international consensus. Gut 62(1):102–111

    PubMed 
    Article 

    Google Scholar
     

  • Knaus WA, Draper EA, Wagner DP, Zimmerman JE (1985) APACHE II: a severity of disease classification system. Crit Care Med 13(10):818–829

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Wu BU, Johannes RS, Sun X et al (2008) The early prediction of mortality in acute pancreatitis: a large population-based study. Gut 57(12):1698–1703

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Beyer G, Habtezion A, Werner J, Lerch MM, Mayerle J (2020) Chronic pancreatitis. Lancet 396(10249):499–512

    PubMed 
    Article 

    Google Scholar
     

  • Wolske KM, Ponnatapura J, Kolokythas O, Burke LMB, Tappouni R, Lalwani N (2019) Chronic pancreatitis or pancreatic tumor? A problem-solving approach. Radiographics 39(7):1965–1982

    PubMed 
    Article 

    Google Scholar
     

  • Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    PubMed 
    Article 

    Google Scholar
     

  • O’Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762

    PubMed 
    Article 

    Google Scholar
     

  • Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338

    PubMed 
    Article 

    Google Scholar
     

  • Gu D, Hu Y, Ding H et al (2019) CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study. Eur Radiol 29(12):6880–6890

    PubMed 
    Article 

    Google Scholar
     

  • Rigiroli F, Hoye J, Lerebours R et al (2021) CT radiomic features of superior mesenteric artery involvement in pancreatic ductal adenocarcinoma: a pilot study. Radiology 301(3):610–622

    PubMed 
    Article 

    Google Scholar
     

  • Mapelli P, Bezzi C, Palumbo D et al (2022) 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-022-05677-0

    Article 
    PubMed 

    Google Scholar
     

  • Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK (2020) Quality reporting of radiomics analysis in mild cognitive impairment and Alzheimer’s disease: a roadmap for moving forward. Korean J Radiol 21(12):1345–1354

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Kao YS, Lin KT (2021) A meta-analysis of computerized tomography-based radiomics for the diagnosis of COVID-19 and viral pneumonia. Diagnostics 11(6):991

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ponsiglione A, Stanzione A, Cuocolo R et al (2021) Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol. https://doi.org/10.1007/s00330-021-08375-x

    Article 
    PubMed 

    Google Scholar
     

  • Abunahel BM, Pontre B, Kumar H, Petrov MS (2021) Pancreas image mining: a systematic review of radiomics. Eur Radiol 31(5):3447–3467

    PubMed 
    Article 

    Google Scholar
     

  • Virarkar M, Wong VK, Morani AC, Tamm EP, Bhosale P (2021) Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY). https://doi.org/10.1007/s00261-021-03216-3

    Article 

    Google Scholar
     

  • Bartoli M, Barat M, Dohan A et al (2020) CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 38(12):1111–1124

    PubMed 
    Article 

    Google Scholar
     

  • Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK (2020) Radiomics in stratification of pancreatic cystic lesions: machine learning in action. Cancer Lett 469:228–237

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Bezzi C, Mapelli P, Presotto L et al (2021) Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 48(12):4002–4015

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zhong J, Hu Y, Si L et al (2021) A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 31(3):1526–1535

    PubMed 
    Article 

    Google Scholar
     

  • Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 162(1):55–63

    PubMed 
    Article 

    Google Scholar
     

  • Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536

    PubMed 
    Article 

    Google Scholar
     

  • Park JE, Kim HS, Kim D et al (2020) A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features. BMC Cancer 20(1):29

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30(1):523–536

    PubMed 
    Article 

    Google Scholar
     

  • Park CJ, Park YW, Ahn SS et al (2022) Quality of radiomics research on brain metastasis: a roadmap to promote clinical translation. Korean J Radiol 23(1):77–88

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46(13):2656–2672

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Dang Y, Hou Y (2021) The prognostic value of late gadolinium enhancement in heart diseases: an umbrella review of meta-analyses of observational studies. Eur Radiol 31(7):4528–4537

    PubMed 
    Article 

    Google Scholar
     

  • Kalliala I, Markozannes G, Gunter MJ et al (2017) Obesity and gynaecological and obstetric conditions: umbrella review of the literature. BMJ j4511

  • Chen Y, Chen TW, Wu CQ et al (2019) Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis. Eur Radiol 29(8):4408–4417

    PubMed 
    Article 

    Google Scholar
     

  • Cheng MF, Guo YL, Yen RF et al (2018) Clinical Utility of FDG PET/CT in patients with autoimmune pancreatitis: a case-control study. Sci Rep 8(1):3651

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Cui W, Zhang W, Zhou L, Jin X, Xiao D (2021) Predictive value of CT texture analysis for recurrence in children with acute pancreatitis. Chin J Dig Surg 20(4):459–465. https://doi.org/10.3760/cma.j.cn115610-20210331-00156 (in Chinese)

    Article 

    Google Scholar
     

  • Das A, Nguyen CC, Li F, Li B (2008) Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest Endosc 67(6):861–867

    PubMed 
    Article 

    Google Scholar
     

  • Deng Y, Ming B, Zhou T et al (2021) Radiomics Model Based on MR images to discriminate pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis lesions. Front Oncol 11:620981

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • E L, Xu Y, Wu Z, et al (2020) Differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma using radiomics based on multiphasic computed tomography. J Comput Assist Tomogr 44(4):511–518

    Article 

    Google Scholar
     

  • Frøkjær JB, Lisitskaya MV, Jørgensen AS et al (2020) Pancreatic magnetic resonance imaging texture analysis in chronic pancreatitis: a feasibility and validation study. Abdom Radiol (NY) 45(5):1497–1506

    Article 

    Google Scholar
     

  • Hu Y, Huang X, Liu N, Tang L (2021) The value of T2WI sequence-based radiomics in predicting recurrence of acute pancreatitis. Chin J Magn Reson Imaging 12(10):12–15. https://doi.org/10.12015/issn.1674-8034.2021.10.003 (in Chinese)

    Article 

    Google Scholar
     

  • Iranmahboob AK, Kierans AS, Huang C, Ream JM, Rosenkrantz AB (2017) Preliminary investigation of whole-pancreas 3D histogram ADC metrics for predicting progression of acute pancreatitis. Clin Imaging 42:172–177

    PubMed 
    Article 

    Google Scholar
     

  • Li J, Liu F, Fang X et al (2021) CT radiomics features in differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma: a propensity score analysis. Acad Radiol 29(3):358–366

    PubMed 
    Article 

    Google Scholar
     

  • Li SS, Wu ZF, Lin N (2021) CT signs combined with texture features in the differential diagnosis between focal autoimmune pancreatitis and pancreatic cancer. J Chin Clin Med Imaging 32(5):347–350. https://doi.org/10.12117/jccmi.2021.05.010 (in Chinese)

    Article 

    Google Scholar
     

  • Lin Y, Shen Y, Zou X, Li Z, Hu D, Feng C (2019) The value of CT texture analysis in differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma. J Pract Radiol 35(11):1174–1178. https://doi.org/10.3969/ji.sn.1002G1671.2019.11.015 (in Chinese)

    Article 

    Google Scholar
     

  • Lin Q, Ji YF, Chen Y et al (2020) Radiomics model of contrast-enhanced MRI for early prediction of acute pancreatitis severity. J Magn Reson Imaging 51(2):397–406

    PubMed 
    Article 

    Google Scholar
     

  • Liu Z, Li M, Zuo C et al (2021) Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis. Eur Radiol 31(9):6983–6991

    PubMed 
    Article 

    Google Scholar
     

  • Liu J, Hu L, Zhou B, Wu C, Cheng Y (2022) Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis. Transl Oncol 18:101357

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ma X, Wang YR, Zhuo LY et al (2022) Retrospective Analysis of the Value of Enhanced CT radiomics analysis in the differential diagnosis between pancreatic cancer and chronic pancreatitis. Int J Gen Med 15:233–241

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Mashayekhi R, Parekh VS, Faghih M, Singh VK, Jacobs MA, Zaheer A (2020) Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol 123:108778

    PubMed 
    Article 

    Google Scholar
     

  • Park S, Chu LC, Hruban RH et al (2020) Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging 101(9):555–564

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Peng L, Zha Y, Zeng F, Liu B, Yan Y (2020) The value-based T2 histogram analysis for differential diagnosis in solid pancreatic lesions. Chin J Magn Reson Imaging 11(3):201–206. https://doi.org/10.12015/issn.1674-8034.2020.03.008 (in Chinese)

    Article 

    Google Scholar
     

  • Ren S, Zhang J, Chen J et al (2019) Evaluation of texture analysis for the differential diagnosis of mass-forming pancreatitis from pancreatic ductal adenocarcinoma on contrast-enhanced CT images. Front Oncol 9:1171

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ren S, Zhao R, Zhang J et al (2020) Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 45(5):1524–1533

    Article 

    Google Scholar
     

  • Ren H, Mori N, Hamada S et al (2021) Effective apparent diffusion coefficient parameters for differentiation between mass-forming autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 46(4):1640–1647

    Article 

    Google Scholar
     

  • Zhang MM, Yang H, Jin ZD, Yu JG, Cai ZY, Li ZS (2010) Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images. Gastrointest Endosc 72(5):978–985

    PubMed 
    Article 

    Google Scholar
     

  • Zhang Y, Cheng C, Liu Z et al (2019) Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT. Med Phys 46(10):4520–4530

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Zhang Y, Cheng C, Liu Z et al (2019) Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in 18F-FDG PET/CT. J Biomedical Eng 36(5):755–762. https://doi.org/10.7507/1001-5515.201807012 (in Chinese)

    Article 

    Google Scholar
     

  • Zhang J, Li Q, Wang J et al (2019) Contrast-enhanced CT and texture analysis of mass-forming pancreatitis and cancer in the pancreatic head. Natl Med J China 99(33):2575–2580. https://doi.org/10.3760/cma.j.issn.0376-2491.2019.33.004 (in Chinese)

  • Zhou T, Xie CL, Chen Y et al (2021) Magnetic resonance imaging-based radiomics models to predict early extrapancreatic necrosis in acute pancreatitis. Pancreas 50(10):1368–1375

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Zhu M, Xu C, Yu J et al (2013) Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test. PLoS One 8(5):e63820

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zhu J, Wang L, Chu Y et al (2015) A new descriptor for computer-aided diagnosis of EUS imaging to distinguish autoimmune pancreatitis from chronic pancreatitis. Gastrointest Endosc 82(5):831–836

    PubMed 
    Article 

    Google Scholar
     

  • Ziegelmayer S, Kaissis G, Harder F et al (2020) Deep convolutional neural network-assisted feature extraction for diagnostic discrimination and feature visualization in pancreatic ductal adenocarcinoma (PDAC) versus autoimmune pancreatitis (AIP). J Clin Med 9(12):4013

    PubMed Central 
    Article 

    Google Scholar
     

  • Collins GS, Dhiman P, Andaur Navarro CL et al (2021) Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 11(7):e048008

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Sounderajah V, Ashrafian H, Rose S et al (2021) A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med 27(10):1663–1665

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Vasey B, Nagendran M, Campbell B,: DECIDE-AI expert group, et al (2022) Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 28(5):924–933

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Cruz Rivera S, Liu X, Chan AW, et al. (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26(9):1351–1363

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Working Group (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26(9):1364–1374

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Sounderajah V, Ashrafian H, Aggarwal R et al (2020) Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med 26(6):807–808

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Mongan J, Moy L, Kahn CE Jr (2020) Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2(2):e200029

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Guiot J, Vaidyanathan A, Deprez L et al (2022) A review in radiomics: making personalized medicine a reality via routine imaging. Med Res Rev 42(1):426–440

    PubMed 
    Article 

    Google Scholar
     

  • Si L, Zhong J, Huo J et al (2022) Deep learning in knee imaging: a systematic review utilizing a Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Eur Radiol 32(2):1353–1361

    PubMed 
    Article 

    Google Scholar
     

  • Ursprung S, Beer L, Bruining A et al (2020) Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol 30(6):3558–3566

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

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