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Open Access 01-12-2023 | Magnetic Resonance Imaging | Original Article

Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative

Authors: Roberto Cannella, Federica Vernuccio, Michail E. Klontzas, Andrea Ponsiglione, Ekaterina Petrash, Lorenzo Ugga, Daniel Pinto dos Santos, Renato Cuocolo

Published in: Insights into Imaging | Issue 1/2023

Abstract

Objectives

To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies.

Methods

A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients.

Results

A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1–13) for reader 1, 8 (22.2%, IQR 3–12) for reader 2, and 10 (27.8%; IQR 5–14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62–0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1).

Conclusions

Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.

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Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169CrossRef

Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483–1503. https://doi.org/10.1148/rg.2017170056CrossRef

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 30:3558–3566. https://doi.org/10.1007/s00330-020-06666-3CrossRef

Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S (2021) Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY) 46:3674–3685. https://doi.org/10.1007/s00261-021-03085-wCrossRef

Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141CrossRef

Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C et al (2019) Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int 13:546–559. https://doi.org/10.1007/s12072-019-09973-0CrossRef

Harding-Theobald E, Louissaint J, Maraj B et al (2021) Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 54:890–901. https://doi.org/10.1111/apt.16563CrossRef

Wang Q, Li C, Zhang J et al (2021) Radiomics models for predicting microvascular invasion in hepatocellular carcinoma: a systematic review and radiomics quality score assessment. Cancers (Basel) 13:5864. https://doi.org/10.3390/cancers13225864CrossRef

Zhong X, Long H, Su L et al (2022) Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 47:2071–2088. https://doi.org/10.1007/s00261-022-03496-3CrossRef

10.

Fiz F, Viganò L, Gennaro N et al (2020) Radiomics of liver metastases: a systematic review. Cancers (Basel) 12:2881. https://doi.org/10.3390/cancers12102881CrossRef

11.

Joo I, Lee JM, Yoon JH (2018) Imaging diagnosis of intrahepatic and perihilar cholangiocarcinoma: recent advances and challenges. Radiology 288:7–13. https://doi.org/10.1148/radiol.2018171187CrossRef

12.

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:b2535. https://doi.org/10.1136/bmj.b2535CrossRef

13.

Benchoufi M, Matzner-Lober E, Molinari N, Jannot AS, Soyer P (2020) Interobserver agreement issues in radiology. Diagn Interv Imaging 101:639–641. https://doi.org/10.1016/j.diii.2020.09.001CrossRef

14.

Chu H, Liu Z, Liang W et al (2021) Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma. Eur Radiol 31:2368–2376. https://doi.org/10.1007/s00330-020-07250-5CrossRef

15.

Duda D, Krętowski M, Bézy-Wendling J (2013) Computer-aided diagnosis of liver tumors based on multi-image texture analysis of contrast-enhanced CT. Selection of the most appropriate texture features. Stud Log Gramm Rhetor 35:49–70. https://doi.org/10.2478/slgr-2013-0039CrossRef

16.

Hamm CA, Wang CJ, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 29:3338–3347. https://doi.org/10.1007/s00330-019-06205-9CrossRef

17.

Huang X, Shu J, Yan Y et al (2021) Feasibility of magnetic resonance imaging-based radiomics features for preoperative prediction of extrahepatic cholangiocarcinoma stage. Eur J Cancer 155:227–235. https://doi.org/10.1016/j.ejca.2021.06.053CrossRef

18.

Ji GW, Zhu FP, Zhang YD et al (2019) A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma. Eur Radiol 29:3725–3735. https://doi.org/10.1007/s00330-019-06142-7CrossRef

19.

Ji GW, Zhang YD, Zhang H et al (2019) Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology 290:90–98. https://doi.org/10.1148/radiol.2018181408CrossRef

20.

King MJ, Hectors S, Lee KM et al (2020) Outcomes assessment in intrahepatic cholangiocarcinoma using qualitative and quantitative imaging features. Cancer Imaging 20:43. https://doi.org/10.1186/s40644-020-00323-0CrossRef

21.

Liang W, Xu L, Yang P et al (2018) Novel nomogram for preoperative prediction of early recurrence in intrahepatic cholangiocarcinoma. Front Oncol 8:360. https://doi.org/10.3389/fonc.2018.00360CrossRef

22.

Liu X, Khalvati F, Namdar K et al (2021) Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol 31:244–255. https://doi.org/10.1007/s00330-020-07119-7CrossRef

23.

Mosconi C, Cucchetti A, Bruno A et al (2020) Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation. Eur Radiol 30:4534–4544. https://doi.org/10.1007/s00330-020-06795-9CrossRef

24.

Nakai H, Fujimoto K, Yamashita R et al (2021) Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study. Jpn J Radiol 39:690–702. https://doi.org/10.1007/s11604-021-01106-8CrossRef

25.

Park HJ, Park B, Park SY et al (2021) Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features. Eur Radiol 31:8638–8648. https://doi.org/10.1007/s00330-021-07926-6CrossRef

26.

Park S, Lee JM, Park J et al (2021) Volumetric CT texture analysis of intrahepatic mass-forming cholangiocarcinoma for the prediction of postoperative outcomes: fully automatic tumor segmentation versus semi-automatic segmentation. Korean J Radiol 22:1797–1808. https://doi.org/10.3348/kjr.2021.0055CrossRef

27.

Ponnoprat D, Inkeaw P, Chaijaruwanich J et al (2020) Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans. Med Biol Eng Comput 58:2497–2515. https://doi.org/10.1007/s11517-020-02229-2CrossRef

28.

Qin H, Hu X, Zhang J et al (2021) Machine-learning radiomics to predict early recurrence in perihilar cholangiocarcinoma after curative resection. Liver Int 41:837–850. https://doi.org/10.1111/liv.14763CrossRef

29.

Sadot E, Simpson AL, Do RK et al (2015) Cholangiocarcinoma: correlation between molecular profiling and imaging phenotypes. PLoS One 10:e0132953. https://doi.org/10.1371/journal.pone.0132953CrossRef

30.

Silva M, Maddalo M, Leoni E et al (2021) Integrated prognostication of intrahepatic cholangiocarcinoma by contrast-enhanced computed tomography: the adjunct yield of radiomics. Abdom Radiol (NY) 46:4689–4700. https://doi.org/10.1007/s00261-021-03183-9CrossRef

31.

Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J (2021) Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer 21:1268. https://doi.org/10.1186/s12885-021-08947-6CrossRef

32.

Tang Y, Zhang T, Zhou X et al (2021) The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Oncol 19:45. https://doi.org/10.1186/s12957-021-02162-0CrossRef

33.

Wang X, Wang S, Yin X, Zheng Y (2022) MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med 141:105058. https://doi.org/10.1016/j.compbiomed.2021.105058CrossRef

34.

Wang Y, Shao J, Wang P et al (2021) Deep learning radiomics to predict regional lymph node staging for hilar cholangiocarcinoma. Front Oncol 11:721460. https://doi.org/10.3389/fonc.2021.721460CrossRef

35.

Xu H, Zou X, Zhao Y et al (2021) Differentiation of intrahepatic cholangiocarcinoma and hepatic lymphoma based on radiomics and machine learning in contrast-enhanced computer tomography. Technol Cancer Res Treat 20:15330338211039124. https://doi.org/10.1177/15330338211039125CrossRef

36.

Xu L, Wan Y, Luo C et al (2021) Integrating intratumoral and peritumoral features to predict tumor recurrence in intrahepatic cholangiocarcinoma. Phys Med Biol. https://doi.org/10.1088/1361-6560/ac01f3CrossRef

37.

Xu L, Yang P, Liang W et al (2019) A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics 9:5374–5385. https://doi.org/10.7150/thno.34149CrossRef

38.

Xue B, Wu S, Zheng M et al (2021) Development and validation of a Radiomic-based model for prediction of intrahepatic Cholangiocarcinoma in patients with intrahepatic Lithiasis complicated by imagologically diagnosed mass. Front Oncol 10:598253. https://doi.org/10.3389/fonc.2020.598253CrossRef

39.

Xue B, Wu S, Zhang M et al (2021) A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis. Abdom Radiol (NY) 46:3835–3844. https://doi.org/10.1007/s00261-021-03027-6CrossRef

40.

Yang C, Huang M, Li S et al (2020) Radiomics model of magnetic resonance imaging for predicting pathological grading and lymph node metastases of extrahepatic cholangiocarcinoma. Cancer Lett 470:1–7. https://doi.org/10.1016/j.canlet.2019.11.036CrossRef

41.

Yao X, Huang X, Yang C et al (2020) A novel approach to assessing differentiation degree and lymph node metastasis of extrahepatic cholangiocarcinoma: prediction using a radiomics-based particle swarm optimization and support vector machine model. JMIR Med Inform 8:e23578. https://doi.org/10.2196/23578CrossRef

42.

Zhang J, Wu Z, Zhang X et al (2020) Machine learning: an approach to preoperatively predict PD-1/PD-L1 expression and outcome in intrahepatic cholangiocarcinoma using MRI biomarkers. ESMO Open 5:e000910. https://doi.org/10.1136/esmoopen-2020-000910CrossRef

43.

Zhang J, Wang X, Zhang L et al (2020) Radiomics predict postoperative survival of patients with primary liver cancer with different pathological types. Ann Transl Med 8:820. https://doi.org/10.21037/atm-19-4668CrossRef

44.

Zhang J, Huang Z, Cao L et al (2020) Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning. Ann Transl Med 8:119. https://doi.org/10.21037/atm.2020.01.126CrossRef

45.

Zhang J, Wu Z, Zhao J et al (2021) Intrahepatic cholangiocarcinoma: MRI texture signature as predictive biomarkers of immunophenotyping and survival. Eur Radiol 31:3661–3672. https://doi.org/10.1007/s00330-020-07524-yCrossRef

46.

Zhao J, Zhang W, Fan CL et al (2021) Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: a pilot study. Eur J Radiol 138:109631. https://doi.org/10.1016/j.ejrad.2021.109631CrossRef

47.

Zhao J, Zhang W, Zhu YY et al (2022) Development and validation of noninvasive MRI-based signature for preoperative prediction of early recurrence in perihilar cholangiocarcinoma. J Magn Reson Imaging 55:787–802. https://doi.org/10.1002/jmri.27846CrossRef

48.

Zhao L, Ma X, Liang M et al (2019) Prediction for early recurrence of intrahepatic mass-forming cholangiocarcinoma: quantitative magnetic resonance imaging combined with prognostic immunohistochemical markers. Cancer Imaging 19:49. https://doi.org/10.1186/s40644-019-0234-4CrossRef

49.

Zhou Y, Zhou G, Zhang J, Xu C, Wang X, Xu P (2021) Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol 31:6846–6855. https://doi.org/10.1007/s00330-021-07793-1CrossRef

50.

Zhu Y, Mao Y, Chen J et al (2021) Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma. Sci Rep 11:6933. https://doi.org/10.1038/s41598-021-86497-4CrossRef

51.

Zhu Y, Mao Y, Chen J et al (2021) Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep 11:18347. https://doi.org/10.1038/s41598-021-97796-1CrossRef

52.

Pinto Dos Santos D, Dietzel M, Baessler B (2021) A decade of radiomics research: Are images really data or just patterns in the noise? Eur Radiol 31:1–4CrossRef

53.

van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 11:91. https://doi.org/10.1186/s13244-020-00887-2CrossRef

54.

Shur JD, Doran SJ, Kumar S et al (2021) Radiomics in oncology: a practical guide. Radiographics 41:1717–1732. https://doi.org/10.1148/rg.2021210037CrossRef

55.

Spadarella G, Calareso G, Garanzini E, Ugga L, Cuocolo A, Cuocolo R (2021) MRI based radiomics in nasopharyngeal cancer: systematic review and perspectives using radiomic quality score (RQS) assessment. Eur J Radiol 140:109744. https://doi.org/10.1016/j.ejrad.2021.109744CrossRef

56.

Ponsiglione A, Stanzione A, Cuocolo R et al (2022) Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment. Eur Radiol 32:2629–2638. https://doi.org/10.1007/s00330-021-08375-xCrossRef

57.

Chang S, Han K, Suh YJ, Choi BW (2022) Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review. Eur Radiol. https://doi.org/10.1007/s00330-022-08587-9CrossRef

58.

Pinto Dos Santos D (2022) Radiomics in endometrial cancer and beyond-a perspective from the editors of the EJR. Eur J Radiol 150:110266CrossRef

59.

Carbonell G, Kennedy P, Bane O et al (2022) Precision of MRI radiomics features in the liver and hepatocellular carcinoma. Eur Radiol 32:2030–2040. https://doi.org/10.1007/s00330-021-08282-1CrossRef

60.

Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338. https://doi.org/10.1148/radiol.2020191145CrossRef

61.

Stanzione A, Gambardella M, Cuocolo R, Ponsiglione A, Romeo V, Imbriaco M (2020) Prostate MRI radiomics: a systematic review and radiomic quality score assessment. Eur J Radiol 129:109095. https://doi.org/10.1016/j.ejrad.2020.109095CrossRef

62.

Abdurixiti M, Nijiati M, Shen R, Ya Q, Abuduxiku N, Nijiati M (2021) Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review. Br J Radiol 94:20201272CrossRef

63.

Ugga L, Perillo T, Cuocolo R et al (2021) Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. Neuroradiology 63:1293–1304. https://doi.org/10.1007/s00234-021-02668-0CrossRef

Title: Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative
Authors: Roberto Cannella
Federica Vernuccio
Michail E. Klontzas
Andrea Ponsiglione
Ekaterina Petrash
Lorenzo Ugga
Daniel Pinto dos Santos
Renato Cuocolo
Publication date: 01-12-2023
Publisher: Springer Vienna
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Computed Tomography
Computed Tomography
Cholangiocarcinoma
Cholangiocarcinoma
Published in: Insights into Imaging / Issue 1/2023
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-023-01365-1

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative

Abstract

Objectives

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

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