Смирнова А.Д., Кармазановский Г.Г., Кондратьев Е.В., Карельская Н.А., Галкин В.Н., Попов А.Ю., Гурмиков Б.Н., Калинин Д.В. Радиомика и радиогеномика при внутрипеченочной холангиокарциноме // Research’n and Practical Medicine Journal. 2024. Т. 11 № 1. С. 54–69. doi: 10.17709/2410-1893-2024-11-1-5. EDN: TLBFTQ..
DOI: 10.17709/2410-1893-2024-11-1-5. EDN: TLBFTQ
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..
DOI: 10.1007/s11547-021-01405-0
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..
DOI: 10.2967/jnumed.118.222893
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..
DOI: 10.1148/radiol.2020191145
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..
DOI: 10.1038/s41598-021-81526-8
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..
DOI: 10.1007/s10278-013-9622-7
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..
DOI: 10.1002/mp.13678
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..
DOI: 10.3389/fonc.2022.966743
Кармазановский Г.Г., Шантаревич М.Ю., Сташкив В.И., Ревишвили А.Ш. Воспроизводимость текстурных показателей КТ- и МРТ-изображений гепатоцеллюлярного рака // Медицинская визуализация. 2023. Т. 27, № 3. С. 84–93. doi: 10.24835/1607-0763-1372..
DOI: 10.24835/1607-0763-1372
Замятина К.А., Годзенко М.В., Кармазановский Г.Г., Ревишвили А.Ш. Радиомика при заболеваниях печени и поджелудочной железы. Обзор литературы // Анналы хирургической гепатологии. 2022. Т. 27, № 1. С. 40–47. doi: 10.16931/1995-5464.2022-1-40-47..
DOI: 10.16931/1995-5464.2022-1-40-47
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..
DOI: 10.1117/12.2654215
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..
DOI: 10.1038/s41598-022-26083-4
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..
DOI: 10.3390/jpm11090842
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..
DOI: 10.3390/jpm12040553
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..
DOI: 10.7759/cureus.52132
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..
DOI: 10.1109/TBME.2016.2624306
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..
DOI: 10.1117/1.JMI.6.2.024008
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..
DOI: 10.1007/s00330-020-07174-0
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..
DOI: 10.1186/s12880-024-01206-7
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..
DOI: 10.11817/j.issn.1672-7347.2022.220027
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..
DOI: 10.1186/s13244-020-00887-2
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..
DOI: 10.1186/s40644-023-00612-4
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..
DOI: 10.1097/RLI.0000000000001009
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..
DOI: 10.3389/fonc.2023.1086039
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..
DOI: 10.3390/cancers15174204
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..
DOI: 10.1007/s00330-020-07250-5
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..
DOI: 10.1186/s12885-023-11454-5
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..
DOI: 10.3892/ol.2022.13566
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..
DOI: 10.3390/diagnostics13182937
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..
DOI: 10.1371/journal.pone.0292110
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..
DOI: 10.1016/j.mri.2012.05.001
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..
DOI: 10.3389/fonc.2023.1167745
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..
DOI: 10.1016/j.ejro.2023.100505
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..
DOI: 10.1080/0284186X.2017.1351624
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..
DOI: 10.1158/0008-5472.CAN-17-0339
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..
DOI: 10.1186/s41747-018-0068-z
Bettinelli A., Marturano F. ImSURE Phantoms. figshare // Collection. 2022. doi: 10.6084/m9.figshare.c.5625439.v2..
DOI: 10.6084/m9.figshare.c.5625439.v2
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.
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..
DOI: 10.1118/1.1568978
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..
DOI: 10.1007/s00330-020-06957-9
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..
DOI: 10.3390/cancers14246123
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..
DOI: 10.3390/tomography7030041
Stanzione., Arnaldo et al. Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges // Cancers. 2022. Vol. 14, No. 19. doi: 10.3390/cancers14194871..
DOI: 10.3390/cancers14194871
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..
DOI: 10.3390/biology12101313
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..
DOI: 10.1148/rg.293085729
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..
DOI: 10.1186/s12880-020-0418-1
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..
DOI: 10.3748/wjg.v26.i33.5008
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..
DOI: 10.1007/s11547-021-01436-7
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..
DOI: 10.1148/ryai.230118
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..
DOI: 10.1259/bjr.20230211