Медицинская литература. Новинки

 

 

 

 

 

 

Радиомика при заболеваниях печени и поджелудочной железы. Обзор литературы

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Проведено изучение зарубежной литературы, посвященной применению текстурного анализа, а также сравнение литературных данных с результатами изучения радиомики специалистами НМИЦ хирургии им. А.В. Вишневского. Публикации отбирали по ключевым словам “radiomics”, “radiology”, “texture analysis”, “perspectives”, “clinical implementation”. Поиск ограничивали только работами на английском языке за последние 5 лет, преимущественно посвященными заболеваниям печени и поджелудочной железы. Отмечено, что новые данные появляются регулярно, а тема не теряет актуальности. По мнению большинства авторов, радиомика действительно может быть эффективна в диагностике, наблюдении за пациентами и планировании лечения, что подтверждают результаты, полученные специалистами НМИЦ хирургии им. А.В. Вишневского. Однако консенсус по применению радиомики не достигнут, что задерживает ее внедрение в клиническую практику.

Ключевые слова:
радиомика, текстурный анализ, перспективы, возможности, проблемы внедрения, поджелудочная железа, печень, radiomics, texture analysis, prospects, opportunities, implementation issues, pancreas, liver

Литература:
1.Chetan M.R., Gleeson F.V. Radiomics in predicting treatment response in nonsmall-cell lung cancer: current status, challenges and future perspectives. Eur. Radiol. 2021; 31 (2): 1049-1058. https://doi.org/10.1007/s00330-020-07141-9
2.Ibrahim A., Primakov S., Beuque M., Woodruff H.C., Halilaj I., Wu G., Refaee T., Granzier R., Widaatalla Y., Hustinx R., Mottaghy F.M., Lambin P. Radiomics for precision medicine: current challenges, future prospects, and the proposal of a new framework. Methods. 2021; 188: 20-29. https://doi.org/10.1016/j.ymeth.2020.05.022
3.Gruzdev I.S., Zamyatina K.A., Tikhonova V.S., Kondratyev E.V., Glotov A.V., Karmazanovsky G.G., Revishvili A.S. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur. J. Radiol. 2020; 133: 109371. https://doi.org/10.1016/j.ejrad.2020.109371
4.Smits M. MRI biomarkers in neuro-oncology. Nat. Rev. Neurol. 2021; 17 (8): 486-500. https://doi.org/10.1038/s41582-021-00510-y
5.Laudicella R., Comelli A., Stefano A., Szostek M., Croce L., Vento A., Spataro A., Comis A.D., La Torre F., Gaeta M., Baldari S., Alongi P. Artificial neural networks in cardiovascular diseases and its potential for clinical application in molecular imaging. Curr. Radiopharm. 2021; 14 (3): 209-219. https://doi.org/10.2174/1874471013666200621191259
6.Chen M.D., Zhang J., Yang G.X., Lin J.M., Feng Y.Q. Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging. Nan Fang Yi Ke DaXueXueBao. 2018; 38 (4): 428-433. Chinese. https://doi.org/10.3969/jissn.1673-4254.2018.04.10
7.Yang L., Gu D., Wei J., Yang C., Rao S., Wang W., Chen C., Ding Y., Tian J., Zeng M.A. Radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer. 2019; 8 (5): 373-386. https://doi.org/10.1159/000494099
8.Fornacon-Wood I., Mistry H., Ackermann C.J., Blackhall F., McPartlin A., Faivre-Finn C., Price G.J., O''Connor J.P.B. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur. Radiol. 2020; 30 (11): 6241-6250. https://doi.org/10.1007/s00330-020-06957-9
9.Kim S., Shin J., Kim D.Y., Choi G.H., Kim M.J., Choi J.Y. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma. Clin. Cancer Res. 2019; 25 (13): 3847-3855. https://doi.org/10.1158/1078-0432.CCR-18-2861
10.Kim K., Kim S., Han K., Bae H., Shin J., Lim J.S. Diagnostic performance of deep learning-based lesion detection algorithm in CT for detecting hepatic metastasis from colorectal cancer. Korean J. Radiol. 2021; 22 (6): 912-921. https://doi.org/10.3348/kjr.2020.0447
11.Limkin E.J., Sun R., Dercle L., Zacharaki E.I., Robert C., Reuze S., Schernberg A., Paragios N., Deutsch E., Ferte C. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann. Oncol. 2017; 28 (6): 1191-1206. https://doi.org/10.1093/annonc/mdx034.
12.Zhovannik I., Bussink J., Traverso A., Shi Z., Kalendralis P., Wee L., Dekker A., Fijten R., Monshouwer R. Learning from scanners: bias reduction and feature correction in radiomics. Clin. Transl. Radiat. Oncol. 2019; 19: 33-38. https://doi.Org/10.1016/j.ctro.2019.07.003.
13.Van Timmeren J.E., Leijenaar R.T.H., van Elmpt W., Wang J., Zhang Z., Dekker A., Lambin P. Test-retest data for radiomics feature stability analysis: generalizable or study-specific? Tomography. 2016; 2 (4): 361-365. https://doi.org/10.18383Xj.tom.2016.00208
14.Szczypinski P.M., Strzelecki M., Materka A., Klepaczko A. MaZda - a software package for image texture analysis.Comput. Methods Programs Biomed. 2009; 94 (1): 66-76. https://doi.org/10.1016/j.cmpb.2008.08.005
15.Fiset S., Welch M.L., Weiss J., Pintilie M., Conway J.L., Milosevic M., Fyles A., Traverso A., Jaffray D., Metser U., Xie J., Han K. Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother. Oncol. 2019; 135: 107-114. https://doi.org/10.1016/j.radonc.2019.03.001
16.Sugano D., Sanford D., Abreu A., Duddalwar V., Gill I., Cacciamani G.E. Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr. Opin. Urol. 2020; 30 (6): 754-781. ttps://doi.org/10.1097/MOU.0000000000000822
17.Peerlings J., Woodruff H.C., Winfield J.M., Ibrahim A., Van Beers B.E., Heerschap A., Jackson A., Wildberger J.E., Mottaghy F.M., DeSouza N.M., Lambin P. Stability of radiomics features in apparent diffusion coefficient maps from a multicentre test-retest trial. Sci. Rep. 2019; 9 (1): 4800. https://doi.org/10.1038/s41598-019-41344-5
18.Calderaro J., Ziol M., Paradis V., Zucman-Rossi J. Molecular and histological correlations in liver cancer. J. Hepatol. 2019; 71 (3): 616-630. https://doi.org/10.1016/j.jhep.2019.06.001
19.Badic B., Tixier F.T., Cheze Le Rest C., Hatt M., Visvikis D. Radiogenomics in сolorectal сancer. Cancers (Basel). 2021; 13 (5): 973. https://doi.org/10.3390/cancers13050973
20.Image Biomarker Standartisation Initiative; 2019 [обновлено 21 сентября 2021; процитировано 30 октября 2016]. Доступно: https://theibsi.github.io/news/
21.Stanzione A., Verde F., Romeo V., Boccadifuoco F., Mainenti P.P., Maurea S. Radiomics and machine learning applications in rectal cancer: сurrent update and future perspectives. World J. Gastroenterol. 2021; 27 (32): 5306-5321. https://doi.org/10.3748/wjg.v27.i32.5306
22.European Commission. Joint Research Centre Robustness and Explainability of Artificial Intelligence: From Technical to Policy Solutions. (accessed on 2 March 2021); Available online: https://data.europa.eu/doi/10.2760/57493. [Reflist]
23.Mackin D., Fave X., Zhang L., Fried D., Yang J., Taylor B., Rodriguez-Rivera E., Dodge C., Jones A.K., Court L. Measuring computed tomography scanner variability of radiomics features. Invest. Radiol. 2015; 50 (11): 757-765. https://doi.org/10.1097/RLI.0000000000000180
24.Chetan M.R., Gleeson F.V. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur. Radiol. 2021; 31 (2): 1049-1058. https://doi.org/10.1007/s00330-020-07141-9
25.Frix A.N., Cousin F., Refaee T., Bottari F., Vaidyanathan A., Desir C., Vos W., Walsh S., Occhipinti M., Lovinfosse P., Leijenaar R.T.H., Hustinx R., Meunier P., Louis R., Lambin P., Guiot J. Radiomics in lung diseases imaging: state-of-the-art for clinicians. J. Pers. Med. 2021; 11 (7): 602. https://doi.org/10.3390/jpm11070602
26.Webb A., Kagadis G.C. Introduction to biomedical imaging. Med. Phys. 2003; 30 (8): 2267. https://doi.org/10.1118/1.1589017
27.Lu L., Ehmke R.C., Schwartz L.H., Zhao B. Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS One. 2016; 11 (12): e0166550. https://doi.org/10.1371/journal.pone.0166550
28.Berenguer R., Pastor-Juan M.D.R., Canales-Vazquez R.J., Castro-Garcia M., Villas M.V., Mansilla Legorburo F., Sabater S. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology. 2018; 288 (2): 407-415. https://doi.org/10.1148/radiol.2018172361
29.Bae K.T. Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology. 2010; 256 (1): 32-61. https://doi.org/10.1148/radiol.10090908
30.Murgia A., Balestrieri A., Crivelli P., Suri J.S., Conti M., Cademartiri F., Saba L. Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis. Cardiovasc. Diagn. Ther. 2020; 10 (6): 2005-2017. https://doi.org/10.21037/cdt-20-156
31.Van Griethuysen J.J.M., Fedorov A., Parmar C., Hosny A., Aucoin N., Narayan V., Beets-Tan R.G.H., Fillion-Robin J.C., Pieper S., Aerts H.J.W.L. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017; 77 (21): e104-e107. https://doi.org/10.1158/0008-5472.CAN-17-0339
32.Zhang L., Fried D.V., Fave X.J., Hunter L.A., Yang J., Court L.E. IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics. Med. Phys. 2015; 42 (3): 1341-1353. https://doi.org/10.1118/1.4908210
33.Nioche C., Orlhac F., Boughdad S., Reuze S., Goya-Outi J., Robert C., Pellot-Barakat C., Soussan M., Frouin F., Buvat I. LIFEx: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018; 78 (16): 4786-4789. https://doi.org/10.1158/0008-5472.CAN-18-0125
34.Apte A.P., Iyer A., Crispin-Ortuzar M., Pandya R., van Dijk L.V., Spezi E., Thor M., Um H., Veeraraghavan H., Oh J.H., Shukla-Dave A., Deasy J.O. Technical note: extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research. Med. Phys. 2018; 10.1002/mp.13046. https://doi.org/10.1002/mp.13046
35.Sugano D., Sanford D., Abreu A., Duddalwar V., Gill I., Cacciamani G.E. Impact of radiomics on prostate cancer detection: a systematic review of clinical applications. Curr. Opin. Urol. 2020; 30 (6): 754-781. https://doi.org/10.1097/MOU.0000000000000822
36.Bezzi C., Mapelli P., Presotto L., Neri I., Scifo P., Savi A., Bettinardi V., Partelli S., Gianolli L., Falconi M., Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur. J. Nucl. Med. Mol. Imaging. 2021; 48 (12): 4002-4015. https://doi.org/10.1007/s00259-021-05338-8

Radiomics in liver and pancreatic disorders: a review

A study of the international literature on texture analysis was performed, and the reported data was compared to the findings of radiomics studies performed by the specialists of our institute. The relevant papers were searched using a combination of the following search terms: “radiomics”, “radiology”, “texture analysis”, “perspectives”, and “clinical implementation”. The search was limited to papers published in English within the last 5 years, which essentially focused on liver and pancreas disorders. Due to the publication of new data on a fairly daily basis, the topic has not lost its relevance. The vast majority of authors confirm that radiomics can be efficiently used during diagnosis, treatment planning, and patient monitoring. However, consensus on the implementation of radiomics has not been reached yet, thereby delaying its introduction into clinical practice. The data collected in our institution reports that the clinical application of texture analysis methods may be very promising.

Keywords:
радиомика, текстурный анализ, перспективы, возможности, проблемы внедрения, поджелудочная железа, печень, radiomics, texture analysis, prospects, opportunities, implementation issues, pancreas, liver