Theoretical basics of abdominal СT radiomics: a review
https://doi.org/10.22328/2079-5343-2025-16-1-33-46
Abstract
INTRODUCTION: Radiomics is the analysis of medical images to assess the structure of a tumor by extracting a large number of quantitative features. The review describes the process of conducting texture analysis and creating radiomics models.
OBJECTIVE: Тo analyze literature data on the technology, prospects and problems of radiomics of CT images of the abdomen.
MATERIALS AND METHODS: 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. A search was conducted for scientific publications in the information and analytical system PubMed for 2018–2024, which essentially focused on the abdominal CT radiomics by keywords: «radiomics», «CT», «texture analysis», «pancreas», «liver», «metastases» and «GIST».
RESULTS: The literature review describes the methods, directions and prospects of radiomics, the problems of using texture analysis in clinical practice and ways to solve the presented problems.
CONCLUSION: Radiomics is a promising method that can be used in medicine for diagnostics. However, there are currently limitations to the application of radiomics in clinical practice. In the presented literature review, we described the process of radiomics, using mainly studies on radiomics of CT images of the abdomen.
About the Authors
E. V. KondratyevRussian Federation
Evgeny V. Kondratyev — Cand. of Sci. (Med.), Senior Researcher of Radiology Department
Moscow
S. A. Shmeleva
Russian Federation
Sofiia A. Shmeleva — second year resident physician in the specialty «Radiology»
Moscow
A. A. Ustalov
Russian Federation
Andrey A. Ustalov — Junior Researcher of Radiology Department
Moscow
V. I. Gurina
Russian Federation
Vera I. Gurina — Cand. of Sci. (Med.), Researcher of Radiology Department
Moscow
G. G. Karmazanovsky
Russian Federation
Grigory G. Karmazanovsky — Dr. of Sci. (Med.), Professor, Academician of RAS, Head of the Radiology Department; Professor of the Department of Radiation Diagnostics and Therapy of the Faculty of Medicine and Biology
Moscow
References
1. Smirnova A.D., Karmazanovsky G.G., Kondratyev E.V., Karelskaya N.A., Galkin V.N., Popov A.Yu., Gurmikov B.N., Kalinin D.V. Radiomics and radiogenomics in intrahepatic cholangiocarcinoma. Research and Practical Medicine Journal, 2024, Vol. 11, No. 1, рр. 54–69 (In Russ.). doi: 10.17709/2410-1893-2024-11-1-5. EDN: TLBFTQ.
2. Karmazanovsky G., Gruzdev I., Tikhonova V. et al. Computed tomography-based radiomics approach in pancreatic tumors characterization // Radiol. Med. 2021. Vol. 126. P. 1388–1395. doi: 10.1007/s11547-021-01405-0.
3. Mayerhoefer M.E., Materka A., Langs G., Häggström I., Szczypiński P., Gibbs P., Cook G. Introduction to Radiomics // J. Nucl. Med. 2020. Vol. 61, No. 4. P. 488– 495. doi: 10.2967/jnumed.118.222893.
4. Zwanenburg A., Vallières M., Abdalah et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping // Radiology. 2020. Vol. 295, Nо. 2. P. 328–338. doi: 10.1148/radiol.2020191145.
5. Jha A.K., Mithun S., Jaiswar V., Sherkhane U.B., Purandare N.C., Prabhash K., Rangarajan V., Dekker A., Wee L., Traverso A. Repeatability and reproducibility study of radiomic features on a phantom and human cohort // Sci Rep. 2021. Vol. 11. doi: 10.1038/s41598-021-81526-8.
6. Clark K., Vendt B., Smith K., Freymann J., Kirby J., Koppel P., Moore S., Phillips S., Maffitt D., Pringle M., Tarbox L., Prior F. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository // J. Digit Imaging. 2013. Vol. 26, No. 6. P. 1045–1057. doi: 10.1007/s10278-013-9622-7.
7. Avanzo M., Wei L., Stancanello J., Vallières M., Rao A., Morin O., Mattonen S.A., El Naqa I. Machine and deep learning methods for radiomics // Med. Phys. 2020. Vol. 47, No. 5. P. 185–202. doi: 10.1002/mp.13678.
8. Wang Y., Wang Y., Ren J., Jia L., Ma L., Yin X., Yang F., Gao B.L. Malignancy risk of gastrointestinal stromal tumors evaluated with noninvasive radiomics: A multicenter study // Front Oncol. 2022. Vol. 12. doi: 10.3389/fonc.2022.966743.
9. Kаrmаzаnovsky G.G., Shantarevich M.Y., Stashkiv V.I., Revishvili A.Sh. Reproducibility of CT and MRI texture features of hepatocellular carcinoma. Medical Visualization, 2023, Vol. 27, No. 3, рр. 84–93 (In Russ.). doi: 10.24835/1607-0763-1372.
10. Zamyatina K.A., Godzenko M.V., Kаrmаzаnovsky G.G., Revishvili A.Sh. Radiomics in liver and pancreatic disorders: a review. Annals of Surgical Hepatology, 2022, Vol. 27, No. 1, рр. 40–47 (In Russ.). doi: 10.16931/1995-5464.2022-1-40-47.
11. Zarei M., Sotoudeh-Paima S., McCabe C., Abadi E., Samei E. Harmonizing CT Images via Physics-based Deep Neural Networks // Proc. SPIE Int. Soc. Opt. Eng. 2023; doi: 10.1117/12.2654215.
12. Singh A., Horng H., Chitalia R., Roshkovan L., Katz S.I., Noël P., Shinohara R.T., Kontos D. Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans // Sci Rep. 2022. Vol. 12, No. 1. doi: 10.1038/s41598-022-26083-4.
13. Mali S.A., Ibrahim A., Woodruff H.C., Andrearczyk V., Müller H., Primakov S., Salahuddin Z., Chatterjee A., Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods // J. Pers. Med. 2021. Vol. 11, No. 9. doi: 10.3390/jpm11090842.
14. Refaee T., Salahuddin Z., Widaatalla Y., Primakov S., Woodruff H.C., Hustinx R., Mottaghy F.M., Ibrahim A., Lambin P. CT Reconstruction Kernels and the Effect of Pre- and Post-Processing on the Reproducibility of Handcrafted Radiomic Features // J. Pers. Med. 2022. Vol. 12, No. 4. doi: 10.3390/jpm12040553.
15. Ramli Z., Farizan A., Tamchek N., Haron Z., Abdul Karim M.K. Impact of Image Enhancement on the Radiomics Stability of Diffusion-Weighted MRI Images of Cervical Cancer // Cureus. 2024. Vol. 16, No. 1. doi: 10.7759/cureus.52132.
16. Deng H., Deng W., Sun X., Liu M., Ye C., Zhou X. Mammogram Enhancement Using Intuitionistic Fuzzy Sets // IEEE Transactions on Biomedical Engineering. Vol. 64, No. 8. P. 1803–1814. 2017. doi: 10.1109/TBME.2016.2624306.
17. Andrearczyk V., Depeursinge A., Müller H. Neural network training for cross-protocol radiomic feature standardization in computed tomography // J. Med. Imaging. (Bellingham). 2019 Vol. 6, No. 2. Р. 024008. doi: 10.1117/1.JMI.6.2.024008.
18. Ligero M., Jordi-Ollero O., Bernatowicz K., Garcia-Ruiz A., Delgado-Muñoz E., Leiva D., Mast R., Suarez C., Sala-Llonch R., Calvo N., Escobar M., NavarroMartin A., Villacampa G., Dienstmann R., Perez-Lopez R. Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis // Eur. Radiol. 2021. Vol. 31, No. 3. Р. 1460–1470. doi: 10.1007/s00330-020-07174-0.
19. Deng Y., Yang D., Tan X., Xu H., Xu L., Ren A., Liu P., Yang Z. Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study // BMC Med Imaging. 2024. Vol. 24, No. 1. Р. 29. doi: 10.1186/s12880-024-01206-7.
20. Zhao H., Feng Z., Li H., Yao S., Zheng W., Rong P. Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma // Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2022. Vol. 47, No. 8. P. 1049–1057. English, Chinese. doi: 10.11817/j.issn.1672-7347.2022.220027.
21. Van Timmeren J.E., Cester D., Tanadini-Lang S., Alkadhi H., Baessler B. Radiomics in medical imaging-»how-to» guide and critical reflection // Insights Imaging. 2020. Vol. 11, No. 1. doi: 10.1186/s13244-020-00887-2.
22. Tharmaseelan H., Vellala A.K., Hertel A., Tollens F., Rotkopf L.T., Rink J., Woźnicki P., Ayx I., Bartling S., Nörenberg D., Schoenberg S.O., Froelich M.F. Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning // Cancer Imaging. 2023. Vol. 23, No. 1. doi: 10.1186/s40644-023-00612-4.
23. Stüber A.T., Coors S., Schachtner B., Weber T., Rügamer D., Bender A., Mittermeier A., Öcal O., Seidensticker M., Ricke J., Bischl B., Ingrisch M. A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC // Invest Radiol. 2023. Vol. 58, No. 12. P. 874–881. doi: 10.1097/RLI.0000000000001009.
24. Zhu H., Wu M., Wei P., Tian M., Zhang T., Hu C., Han Z. A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study // Front. Oncol. 2023. Vol. 13. doi: 10.3389/fonc.2023.1086039.
25. Fiz F., Rossi N., Langella S., Ruzzenente A., Serenari M., Ardito F., Cucchetti A., Gallo T., Zamboni G., Mosconi C., Boldrini L., Mirarchi M., Cirillo S., De Bellis M., Pecorella I., Russolillo N., Borzi M., Vara G., Mele C., Ercolani G., Giuliante F., Ravaioli M., Guglielmi A., Ferrero A., Sollini M., Chiti A., Torzilli G., Ieva F., Viganò L. Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model // Cancers (Basel). 2023. Vol. 15 No. 17. doi: 10.3390/cancers15174204.
26. Chu H., Liu Z., Liang W., Zhou Q., Zhang Y., Lei K. et al. Radiomics using CT images for preoperative prediction of futile resection in intrahepatic cholangiocarcinoma // Eur. Radiol. 2021. Vol. 31, No. 4. P. 2368–2376. doi: 10.1007/s00330-020-07250-5.
27. Gao Y., Wang X., Zhao X., Zhu C., Li C., Li J., Wu X. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (<4 cm) clear cell renal cell carcinoma // BMC Cancer. 2023. Vol. 23, No. 1. Р. 953. doi: 10.1186/s12885-023-11454-5.
28. Negreros-Osuna A.A., Ramírez-Mendoza D.A., Casas-Murillo C., Guerra-Cepeda A., Hernández-Barajas D., Elizondo-Riojas G. Clinical-radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors // Oncol Lett. 2022. Vol. 24, No. 6. doi: 10.3892/ol.2022.13566.
29. Li Y., Li J., Meng M., Duan S., Shi H., Hang J. Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer // Diagnostics (Basel). 2023. Vol. 13, No. 18. doi: 10.3390/diagnostics13182937.
30. Huang L., Feng W., Lin W., Chen J., Peng S., Du X., Li X., Liu T., Ye Y. Enhanced and unenhanced: Radiomics models for discriminating between benign and malignant cystic renal masses on CT images: A multi-center study // PLoS One. 2023. Vol. 18, No. 9. PMID: 37768941; PMCID: PMC10538730. doi: 10.1371/journal.pone.0292110.
31. Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.C., Pujol S., Bauer C., Jennings D., Fennessy F., Sonka M., Buatti J., Aylward S., Miller J.V., Pieper S., Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network // Magn. Reson. Imaging. 2012. Vol. 30, No. 9. doi: 10.1016/j.mri.2012.05.001.
32. Xue G., Liu H., Cai X., Zhang Z., Zhang S., Liu L., Hu B., Wang G. Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors // Front Oncol. 2023. Vol. 13. doi: 10.3389/fonc.2023.1167745.
33. Сappello G., Giannini V., Cannella R., Tabone E., Ambrosini I., Molea F., Damiani N., Landolfi I., Serra G., Porrello G., Gozzo C., Incorvaia L., Badalamenti G., Grignani G., Merlini A., D’Ambrosio L., Bartolotta T.V., Regge D. A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST) // Eur. J. Radiol. Open. 2023. Vol. 11. doi: 10.1016/j.ejro.2023.100505.
34. Larue R.T.H.M., van Timmeren J.E., de Jong E.E.C., Feliciani G., Leijenaar R.T.H., Schreurs W.M.J.,Lambin P. Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study // Acta Oncologica. 2017. Vol. 56, No. 11. P. 1544– 1553. doi: 10.1080/0284186X.2017.1351624.
35. Van Griethuysen J.J.M., Fedorov A., Parmar C., Hosny A., Aucoin N., Narayan V., Beets-Tan R.G.H., Fillon-Robin J.C., Pieper S., Aerts H.J.W.L. Computational Radiomics System to Decode the Radiographic Phenotype // Cancer Research. 2017. Vol. 77, No. 21. P. 104–107. doi: 10.1158/0008-5472.CAN-17-0339.
36. Rizzo S., Botta F., Raimondi S., Origgi D., Fanciullo C., Morganti A.G., Bellomi M. Radiomics: the facts and the challenges of image analysis // Eur. Radiol. Exp. 2018. Vol. 2, No. 1. doi: 10.1186/s41747-018-0068-z.
37. Bettinelli A., Marturano F. ImSURE Phantoms. figshare // Collection. 2022. doi: 10.6084/m9.figshare.c.5625439.v2.
38. Nioche C., Orlhac F., Boughdad S., Reuzé S., Goya-Outi J., Robert C., Pellot-Barakat C., Soussan M., Frouin F., Buvat I. LIFEx: A freeware for radiomic feature calculation in multi- modality imaging to accelerate advances in the characterization of tumor heterogeneity // Cancer Res. 2018; Vol. 78, No. 16. P. 4786–4789. 10.1158/0008-5472.CAN-18-0125.
39. Deasy J.O., Blanco A.I., Clark V.H. CERR: a computational environment for radiotherapy research // Med. Phys. 2003. Vol. 30, No. 5. P. 979–985. doi: 10.1118/1.1568978.
40. Fornacon-Wood I., Mistry H., Ackermann C.J. et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform // Eur. Radiol. 2020. Vol. 30. P. 6241–6250. doi: 10.1007/s00330-020-06957-9.
41. Fahmy D., Alksas A., Elnakib A., Mahmoud A., Kandil H., Khalil A., Ghazal M., van Bogaert E., Contractor S., El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation., Detection., and Management of Hepatocellular Carcinoma // Cancers (Basel). 2022. Vol. 14 No. 24. doi: 10.3390/cancers14246123.
42. Kim D., Jensen L.J., Elgeti T., Steffen I.G., Hamm B., Nagel S.N. Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features // Tomography. 2021. Vol. 7, No. 3. P. 477–487. doi: 10.3390/tomography7030041.
43. Stanzione., Arnaldo et al. Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges // Cancers. 2022. Vol. 14, No. 19. doi: 10.3390/cancers14194871.
44. Huang L., Song M., Shen H., Hong H., Gong P., Deng H.W., Zhang C. Deep Learning Methods for Omics Data Imputation // Biology (Basel). 2023. Vol. 12 No 10. doi: 10.3390/biology12101313.
45. Chung Y.E., Kim M.J., Park Y.N., Choi J.Y., Pyo J.Y., Kim Y.C. et al. Varying appearances of cholangiocarcinoma: radiologic-pathologic correlation // Radiographics. 2009. Vol. 29 No. 3. P. 683–700. doi: 10.1148/rg.293085729.
46. Zhang Y., Lobo-Mueller E.M., Karanicolas P., Gallinger S., Haider M.A., Khalvati F. CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging // BMC Med. Imaging. 2020. Vol. 20 No. 1. doi: 10.1186/s12880-020-0418-1.
47. Li M., Zhu Y.Z., Zhang Y.C., Yue Y.F., Yu H.P., Song B. Radiomics of rectal cancer for predicting distant metastasis and overall survival // World J. Gastroenterol. 2020. Vol. 26 No. 33. doi: 10.3748/wjg.v26.i33.5008.
48. Nardone V., Reginelli A., Grassi R., Boldrini L., Vacca G., D’Ippolito E., Annunziata S., Farchione A., Belfiore M.P., Desideri I., Cappabianca S. Delta radiomics: a systematic review // Radiol Med. 2021. Vol. 126 No. 12. P. 1571–1583. doi: 10.1007/s11547-021-01436-7.
49. Prior O., Macarro C., Navarro V., Monreal C., Ligero M., Garcia-Ruiz A., Serna G., Simonetti S., Braña I., Vieito M., Escobar M., Capdevila J., Byrne A.T., Dienstmann R., Toledo R., Nuciforo P., Garralda E., Grussu F., Bernatowicz K., Perez-Lopez R. Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer // Radiol Artif Intell. 2024. Vol. 6, No. 2. doi: 10.1148/ryai.230118.
50. Wei L., Niraula D., Gates E.D.H., Fu J., Luo Y., Nyflot M.J., Bowen S.R., El Naqa I.M., Cui S. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration // Br. J. Radiol. 2023. Vol. 96, No. 1150. doi: 10.1259/bjr.20230211.
Review
For citations:
Kondratyev E.V., Shmeleva S.A., Ustalov A.A., Gurina V.I., Karmazanovsky G.G. Theoretical basics of abdominal СT radiomics: a review. Diagnostic radiology and radiotherapy. 2025;16(1):33-46. (In Russ.) https://doi.org/10.22328/2079-5343-2025-16-1-33-46