SCIENTIFIC AND INFORMATION-ANALYTICAL MAGAZINE

ISSN 1024-6177 (Print), ISSN 2618-9615 (Online)

JOURNAL DESCRIPTION

The Medical Radiology and Radiation Safety journal ISSN 1024-6177 was founded in January 1956 (before December 30, 1993 it was entitled Medical Radiology, ISSN 0025-8334). In 2018, the journal received Online ISSN: 2618-9615 and was registered as an electronic online publication in Roskomnadzor on March 29, 2018. It publishes original research articles which cover questions of radiobiology, radiation medicine, radiation safety, radiation therapy, nuclear medicine and scientific reviews. In general the journal has more than 30 headings and it is of interest for specialists working in thefields of medicine¸ radiation biology, epidemiology, medical physics and technology. Since July 01, 2008 the journal has been published by State Research Center - Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency. The founder from 1956 to the present time is the Ministry of Health of the Russian Federation, and from 2008 to the present time is the Federal Medical Biological Agency.

Members of the editorial board are scientists specializing in the field of radiation biology and medicine, radiation protection, radiation epidemiology, radiation oncology, radiation diagnostics and therapy, nuclear medicine and medical physics. The editorial board consists of academicians (members of the Russian Academy of Science (RAS)), the full member of Academy of Medical Sciences of the Republic of Armenia, corresponding members of the RAS, Doctors of Medicine, professor, candidates and doctors of biological, physical mathematics and engineering sciences. The editorial board is constantly replenished by experts who work in the CIS and foreign countries.

Six issues of the journal are published per year, the volume is 13.5 conventional printed sheets, 88 printer’s sheets, 1.000 copies. The journal has an identical full-text electronic version, which, simultaneously with the printed version and color drawings, is posted on the sites of the Scientific Electronic Library (SEL) and the journal's website. The journal is distributed through the Rospechat Agency under the contract № 7407 of June 16, 2006, through individual buyers and commercial structures. The publication of articles is free.

The journal is included in the List of Russian Reviewed Scientific Journals of the Higher Attestation Commission. Since 2008 the journal has been available on the Internet and indexed in the RISC database which is placed on Web of Science. Since February 2nd, 2018, the journal "Medical Radiology and Radiation Safety" has been indexed in the SCOPUS abstract and citation database.

Brief electronic versions of the Journal have been publicly available since 2005 on the website of the Medical Radiology and Radiation Safety Journal: http://www.medradiol.ru. Since 2011, all issues of the journal as a whole are publicly available, and since 2016 - full-text versions of scientific articles. Since 2005, subscribers can purchase full versions of other articles of any issue only through the National Electronic Library. The editor of the Medical Radiology and Radiation Safety Journal in accordance with the National Electronic Library agreement has been providing the Library with all its production since 2005 until now.

The main working language of the journal is Russian, an additional language is English, which is used to write titles of articles, information about authors, annotations, key words, a list of literature.

Since 2017 the journal Medical Radiology and Radiation Safety has switched to digital identification of publications, assigning to each article the identifier of the digital object (DOI), which greatly accelerated the search for the location of the article on the Internet. In future it is planned to publish the English-language version of the journal Medical Radiology and Radiation Safety for its development. In order to obtain information about the publication activity of the journal in March 2015, a counter of readers' references to the materials posted on the site from 2005 to the present which is placed on the journal's website. During 2015 - 2016 years on average there were no more than 100-170 handlings per day. Publication of a number of articles, as well as electronic versions of profile monographs and collections in the public domain, dramatically increased the number of handlings to the journal's website to 500 - 800 per day, and the total number of visits to the site at the end of 2017 was more than 230.000.

The two-year impact factor of RISC, according to data for 2017, was 0.439, taking into account citation from all sources - 0.570, and the five-year impact factor of RISC - 0.352.

Medical Radiology and Radiation Safety. 2025. Vol. 70. № 5

DOI:10.33266/1024-6177-2025-70-5-93-97

V.P. Neustroev1, Yu.D. Udalov2, M.I. Muslimov3, E.N. Mingazova3,4,5

Use of Radiomics in Mri Studies of Metastatic Liver Lesions

1 N.N. Blokhin National Medical Research Center of Oncology, Moscow, Russia

2 A.I. Burnazyan Federal Medical Biophysical Center, Moscow, Russia

3 Russian Medical Academy of Continuous Professional Education, Moscow, Russia

4 N.A. Semashko National Research Institute of Public Health, Moscow, Russia

5 Kazan State Medical University, Kazan, Russia

Contact person: E.N. Mingazova, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Abstract

Currently, radiomics, as a rapidly developing technology, is increasingly used to solve diagnostic, prognostic and predictive problems in studies on liver metastases based on MRI images. In addition to its effectiveness in diagnosing and classifying tumors, radiomics demonstrates particularly impressive prognostic capabilities for malignant liver tumors (MLT) of the liver as a high-risk organ. Before the advent of radiomics, molecular genetic, biochemical and histological studies were used for these purposes. Radiomics of liver MLT is in its early stages of development, which suggests the presence of certain difficulties and obstacles, the elimination of which is the primary focus, in particular, in the field of developing standards necessary for use in clinical practice.

Keywords: radiomics, magnetic resonance imaging, MRI, medical imaging, texture analysis, metastasis, liver, precision medicine

For citation: Neustroev VP, Udalov YuD, Muslimov MI, Mingazova EN. Use of Radiomics in Mri Studies of Metastatic Liver Lesions. Medical Radiology and Radiation Safety. 2025;70(5):93–97. (In Russian). DOI:10.33266/1024-6177-2025-70-5-93-97

 

References

  1. Shur JD, Doran SJ, Kumar S, et al. Radiomics in Oncology: A Practical Guide. Radiographics. 2021;41(6):1717-1732. doi:10.1148/rg.2021210037.
  2. Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30(9):1234-1248. doi:10.1016/j.mri.2012.06.010.
  3. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762. doi:10.1038/nrclinonc.2017.141.
  4. Parekh V, Jacobs MA. Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev. 2016;1(2):207-226. doi:10.1080/23808993.2016.1164013.
  5. Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36. Published 2018 Nov 14. doi:10.1186/s41747-018-0068-z.
  6. Alderson PO, Summers RM. The Evolving Status of Radiomics. J Natl Cancer Inst. 2020;112(9):869-870. doi:10.1093/jnci/djaa018.
  7. Ding H, Wu C, Liao N, et al. Radiomics in Oncology: A 10-Year Bibliometric Analysis. Front Oncol. 2021;11:689802. Published 2021 Sep 20. doi:10.3389/fonc.2021.689802.
  8. McCague C, Ramlee S, Reinius M, et al. Introduction to radiomics for a clinical audience. Clin Radiol. 2023;78(2):83-98. doi:10.1016/j.crad.2022.08.149.
  9. Maniaci A, Lavalle S, Gagliano C, et al. The Integration of Radiomics and Artificial Intelligence in Modern Medicine. Life (Basel). 2024;14(10):1248. Published 2024 Oct 1. doi:10.3390/life14101248.
  10. Maino C, Vernuccio F, Cannella R, et al. Radiomics and liver: Where we are and where we are headed?. Eur J Radiol. 2024;171:111297. doi:10.1016/j.ejrad.2024.111297.
  11. Hacking C, Southi J, Fahrenhorst-Jones T, Silverstone L., et al. Hepatic metastases. Reference article. Radiopaedia.org. 2025; doi:10.53347/rID-6931.
  12. Cervantes A, Adam R, Roselló S, et al. Metastatic colorectal cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34(1):10-32. doi:10.1016/j.annonc.2022.10.003.
  13. Tsilimigras DI, Brodt P, Clavien PA, et al. Liver metastases. Nat Rev Dis Primers. 2021;7(1):27. Published 2021 Apr 15. doi:10.1038/s41572-021-00261-6.
  14. Stoletov K, Beatty PH, Lewis JD. Novel therapeutic targets for cancer metastasis. Expert Rev Anticancer Ther. 2020;20(2):97-109. doi:10.1080/14737140.2020.1718496.
  15. Chang HH, Leeper WR, Chan G, Quan D, Driman DK. Infarct-like necrosis: a distinct form of necrosis seen in colorectal carcinoma liver metastases treated with perioperative chemotherapy. Am J Surg Pathol. 2012;36(4):570-576. doi:10.1097/PAS.0b013e31824057e7.
  16. Zimmermann A. Metastatic Liver Disease: Secondary Alterations of Hepatic Metastases. Tumors and Tumor-Like Lesions of the Hepatobiliary Tract. General and surgical pathology. Cham: Springer, 2017. Р. 1947–1964. doi:10.1007/978-3-319-26956-6_109.
  17. Çakır M, Tüzün S, Savaş A, Tosyalı Y. Two pseudotumor cases mimicking liver malignancy. Turk J Surg. 2015;33(3):212-216. Published 2015 Jul 2. doi:10.5152/UCD.2015.2912.
  18. Pohnan R, Ryska M, Hytych V, Matej R, Hrabal P, Pudil J. Echinococcosis mimicking liver malignancy: A case report. Int J Surg Case Rep. 2017;36:55-58. doi:10.1016/j.ijscr.2017.04.032.
  19. Khalil A, Taha A. Hepatic Sarcoid-Like Reaction Mimicking Liver Metastases in a 36-Year-Old Female With Rheumatoid Arthritis. Cureus. 2023;15(8):e43974. Published 2023 Aug 23. doi:10.7759/cureus.43974.
  20. Oyama A, Hiraoka Y, Obayashi I, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep. 2019;9(1):8764. Published 2019 Jun 19. doi:10.1038/s41598-019-45283-z.
  21. Fiz F, Viganò L, Gennaro N, et al. Radiomics of Liver Metastases: A Systematic Review. Cancers (Basel). 2020;12(10):2881. Published 2020 Oct 7. doi:10.3390/cancers12102881.
  22. Li S, Li Z, Huang X, et al. CT, MRI, and radiomics studies of liver metastasis histopathological growth patterns: an up-to-date review. Abdom Radiol (NY). 2022;47(10):3494-3506. doi:10.1007/s00261-022-03616-z.
  23. Granata V, Fusco R, Setola SV, et al. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer. 2023;18(1):18. Published 2023 Mar 16. doi:10.1186/s13027-023-00495-x.
  24. Baishya NK, Baishya K, Baishya K, Sarma R, Ray S. MRI Radiomics in Imaging of Focal Hepatic Lesions: A Narrative Review. Cureus. 2024;16(6):e62570. Published 2024 Jun 17. doi:10.7759/cureus.62570.
  25. Haghshomar M, Rodrigues D, Kalyan A, Velichko Y, Borhani A. Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies. Front Oncol. 2024;14:1362737. Published 2024 May 8. doi:10.3389/fonc.2024.1362737.
  26. Shu Z, Fang S, Ding Z, et al. MRI-based Radiomics nomogram to detect primary rectal cancer with synchronous liver metastases. Sci Rep. 2019;9(1):3374. Published 2019 Mar 4. doi:10.1038/s41598-019-39651-y.
  27. Hu SX, Yang K, Wang XR, et al. Sichuan Da Xue Xue Bao Yi Xue Ban. 2021;52(2):311-318. doi:10.12182/20210360202.
  28. Granata V, Fusco R, De Muzio F, et al. Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study. Cancers (Basel). 2022;14(5):1110. Published 2022 Feb 22. doi:10.3390/cancers14051110.
  29. Granata V, Fusco R, De Muzio F, et al. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023;128(11):1310-1332. doi:10.1007/s11547-023-01710-w.
  30. Huang Y, Zhou S, Luo Y, et al. Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients. Radiat Oncol. 2023;18(1):79. Published 2023 May 10. doi:10.1186/s13014-023-02273-w.
  31. Li ZF, Kang LQ, Liu FH, et al. Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis. Abdom Radiol (NY). 2023;48(3):833-843. doi:10.1007/s00261-022-03773-1.
  32. Chen Y, Lu T, Zhang Y, Li H, Xu J, Li M. Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases. Abdom Radiol (NY). 2024;49(8):2585-2594. doi:10.1007/s00261-024-04492-5.
  33. Ma J, Nie X, Kong X, et al. MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer. BMC Med Imaging. 2024;24(1):262. Published 2024 Oct 4. doi:10.1186/s12880-024-01439-6.
  34. Wang X, Liu Z, Yin X, Yang C, Zhang J. A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis. BMC Gastroenterol. 2023;23(1):308. Published 2023 Sep 12. doi:10.1186/s12876-023-02922-0.
  35. Jin WH, Simpson GN, Dogan N, et al. MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy. Sci Rep. 2022;12(1):18631. Published 2022 Nov 3. doi:10.1038/s41598-022-22826-5.
  36. Della Corte A, Mori M, Calabrese F, et al. Preoperative MRI radiomic analysis for predicting local tumor progression in colorectal liver metastases before microwave ablation. Int J Hyperthermia. 2024;41(1):2349059. doi:10.1080/02656736.2024.2349059.
  37. Yoon S, Kim YJ, Jeon JS, Ahn SJ, Choi SJ. Radiomics and machine learning analysis of liver magnetic resonance imaging for prediction and early detection of tumor response in colorectal liver metastases. Korean J Clin Oncol. 2024;20(1):27-35. doi:10.14216/kjco.24005.
  38. Song C, Li W, Cui J, et al. Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models. Abdom Radiol (NY). 2024;49(12):4239-4248. doi:10.1007/s00261-024-04290-z.
  39. Lu W, Wu G, Miao X, et al. The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy. Cancer Immunol Immunother. 2024;73(5):87. Published 2024 Mar 30. doi:10.1007/s00262-024-03644-2
  40. Bodalal Z, Bogveradze N, Ter Beek LC, et al. Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases. Insights Imaging. 2023;14(1):133. Published 2023 Jul 21. doi:10.1186/s13244-023-01474-x.
  41. Yuan Z, Shu Z, Peng J, et al. Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning. Abdom Radiol (NY). 2024;49(1):117-130. doi:10.1007/s00261-023-04047-0.
  42. van der Reijd DJ, Chupetlovska K, van Dijk E, et al. Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?. Eur J Radiol. 2024;172:111346. doi:10.1016/j.ejrad.2024.111346.
  43. Park JH, Cho ES, Yoon J, et al. MRI radiomics model differentiates small hepatic metastases and abscesses in periampullary cancer patients. Sci Rep. 2024;14(1):23541. Published 2024 Oct 9. doi:10.1038/s41598-024-74311-w.
  44. Zwanenburg A, Vallières M, Abdalah MA, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295(2):328-338. doi:10.1148/radiol.2020191145.
  45. Kocak B, Baessler B, Bakas S, et al. CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging. 2023;14(1):75. Published 2023 May 4. doi:10.1186/s13244-023-01415-8.
  46. Kocak B, Yuzkan S, Mutlu S, Bulut E, Kavukoglu I. Publications poorly report the essential RadiOmics ParametERs (PROPER): A meta-research on quality of reporting. Eur J Radiol. 2023;167:111088. doi:10.1016/j.ejrad.2023.111088.
  47. Kocak B, Akinci D’Antonoli T, Mercaldo N, et al. METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII. Insights Imaging. 2024;15(1):8. Published 2024 Jan 17. doi:10.1186/s13244-023-01572-w.
  48. Long ZD, Yu X, Xing ZX, Wang R. Multiparameter magnetic resonance imaging-based radiomics model for the prediction of rectal cancer metachronous liver metastasis. World J Gastrointest Oncol. 2025;17(1):96598. doi:10.4251/wjgo.v17.i1.96598
  49. Shahveranova A, Balli HT, Aikimbaev K, Piskin FC, Sozutok S, Yucel SP. Prediction of Local Tumor Progression After Microwave Ablation in Colorectal Carcinoma Liver Metastases Patients by MRI Radiomics and Clinical Characteristics-Based Combined Model: Preliminary Results. Cardiovasc Intervent Radiol. 2023;46(6):713-725. doi:10.1007/s00270-023-03454-6. 

 

 

 PDF (RUS) Full-text article (in Russian)

 

Conflict of interest. The authors declare no conflict of interest.

Financing. The study had no sponsorship.

Contribution. Article was prepared with equal participation of the authors.

Article received: 20.05.2025. Accepted for publication: 25.06.2025.

 

Contact Information

 

46, Zhivopisnaya st., 123098, Moscow, Russia Phone: +7 (499) 190-95-51. E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Journal location

Attendance

4004919
Today
Yesterday
This week
Last week
This month
Last month
For all time
5556
3887
18552
30856
134625
124261
4004919
Forecast today
5424

Your IP:216.73.217.31
Visitor Counter

© Журнал "Медицинская радиология и радиационная безопасность" 2020г.

Яндекс.Метрика

Наверх