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    Academic Journal

    Source: Diagnostic radiology and radiotherapy; Том 11, № 1 (2020); 78-92 ; Лучевая диагностика и терапия; Том 11, № 1 (2020); 78-92 ; 2079-5343 ; 10.22328/2079-5343-2020-1

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    Relation: https://radiag.bmoc-spb.ru/jour/article/view/480/384; Cheson B.D., Fisher R.I., Barrington S.F., Cavalli F., Schwartz L.H., Zucca E., Lister T.A. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification // J. Clin. Oncol. 2014. Vol. 32. P. 3059–3068. https://doi.org/10.1200/JCO.2013.54.8800.; Хоружик С.А., Жаврид Э.А., Сачивко Н.В. Сравнение диагностической эффективности рентгеновской компьютерной томографии, магнитно-резонансной томографии и диффузионно-взвешенной магнитно-резонансной томографии при дифференциации остаточных опухолей и образований посттерапевтического характера у пациентов с лимфомами после завершения лечения // Онкогематология. 2016. № 3. С. 40–48. https://doi.org/10.17650/1818-8346-2016-11-3-40-48.; Padhani A.R., Lecouvet F.E., Tunariu N., Koh D.M., De Keyzer F., Collins D.J., Sala E., Schlemmer H.P., Petralia G., Vargas H.A., Fanti S., Tombal H.B., de Bono J. METastasis Reporting and Data System for Prostate Cancer: Practical Guidelines for Acquisition, Interpretation, and Reporting of Whole-body Magnetic Resonance Imaging-based Evaluations of Multiorgan Involvement in Advanced Prostate Cancer // Eur. Urol. 2017. Vol. 71. P. 81–92. https://doi.org/10.1016/j.eururo.2016.05.033.; Lecouvet F.E, Talbot J.N., Messiou C., Bourguet P., Liu Y., de Souza N.M. Monitoring the response of bone metastases to treatment with Magnetic Resonance Imaging and nuclear medicine techniques: a review and position statement by the European Organisation for Research and Treatment of Cancer imaging group // Eur. J. Cancer. 2014. Vol. 50. P. 2519–2531. https://doi.org/10.1016/j.ejca.2014.07.002.; Dimopoulos M.A., Hillengass J., Usmani S., Zamagni E., Lentzsch S., Davies F.E., Raje N., Sezer O., Zweegman S., Shah J., Badros A., Shimizu K., Moreau P., Chim C.S., Lahuerta J.J., Hou J., Jurczyszyn A., Goldschmidt H., Sonneveld P., Palumbo A., Ludwig H., Cavo M., Barlogie B., Anderson K., Roodman G.D., Rajkumar S.V., Durie B.G., Terpos E. Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement // J. Clin. Oncol. 2015. Vol. 33. P. 657–664. https://doi.org/10.1200/JCO.2014.57.9961.; Barnes A., Alonzi R., Blackledge M., Charles-Edwards G., Collins DJ., Cook G., Coutts G., Goh V., Graves M., Kelly C., Koh DM., McCallum H., Miquel M.E., O’Connor J., Padhani A., Pearson R., Priest A., Rockall A., Stirling J., Taylor S., Tunariu N. van der Meulen J., Walls D., Winfield J., Punwani S. UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer // Br. J. Radiol. 2018. Vol. 91. P. 20170577. https://doi.org/10.1259/bjr.20170577.; De Souza N.M., Winfield J.M., Waterton J.C., Weller A., Papoutsaki M.V., Doran S.J., Collins D.J., Fournier L., Sullivan D., Chenevert T., Jackson A., Boss M., Trattnig S., Liu Y. Implementing diffusion-weighted MRI for body imaging in prospective multicenter trials: current considerations and future perspectives // Eur. Radiol. 2018. Vol. 28. P. 1118–1131. https://doi.org/10.1007/s00330-017-4972-z.; Taouli B., Beer A.J., Chenevert T., Collins D., Lehman C., Matos C., Padhani A.R., Rosenkrantz A.B., Shukla-Dave A., Sigmund E., Tanenbaum L., Thoeny H., Thomassin-Naggara I., Barbieri S., CorcueraSolano I., Orton M., Partridge S.C., Koh D.M. Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop // J. Magn. Reson. Imaging. 2016. Vol. 44. P. 521–540. https://doi.org/10.1002/jmri.25196.; Albano D., Patti C., La Grutta L., Agnello F., Grassedonio E., Mulè A., et al. Comparison between whole-body MRI with diffusion-weighted imaging and PET/CT in staging newly diagnosed FDG-avid lymphomas // Eur. J. Radiol. 2016. Vol. 85. P. 313–318. https://doi.org/10.1016/j.ejrad.2015.; Maggialetti N., Ferrari C., Minoia C., Asabella A.N., Ficco M., Loseto G., De Tullio G., de Fazio V., Calabrese A., Guarini A., Rubini G., Brunese L. Role of WB-MR/DWIBS compared to 18 F-FDG PET/CT in the therapy response assessment of lymphoma // Radiol. Med. 2016. Vol. 121. P. 132–143. https://doi.org/10.1007/s11547-015-0581-6.; Herrmann K., Queiroz M., Huellner M.W., de Galiza Barbosa F., Buck A., Schaefer N., Stolzman P., Veit-Haibach P. Diagnostic performance of FDGPET/MRI and WB-DW-MRI in the evaluation of lymphoma: a prospective comparison to standard FDG-PET/CT // BMC Cancer. 2015. Vol. 15. P. 1002. https://doi.org/10.1186/s12885-015-2009-z.; Mayerhoefer M.E., Karanikas G., Kletter K., Prosch H., Kiesewetter B., Skrabs C., Porpaczy E., Weber M., Knogler T., Sillaber C., Jaeger U., Simonitsch-Klupp I., Ubl P., Müllauer L., Dolak W., Lukas J., Raderer M. Evaluation of Diffusion-Weighted Magnetic Resonance Imaging for Follow-up and Treatment Response Assessment of Lymphoma: Results of an 18 F-FDGPET/CT-Controlled Prospective Study in 64 Patients // Clin. Cancer Res. 2015. Vol. 21. P. 2506–2513. https://doi.org/10.1158/1078-0432.CCR-14-2454.; Littooij A.S., Kwee T.C., de Keizer B., Bruin M.C., Coma A., Beek F.J., Fijnheer R., Nievelstein R.A. Whole-body MRI-DWI for assessment of residual disease after completion of therapy in lymphoma: A prospective multicenter study // J. Magn. Reson. Imaging. 2015. Vol. 42. P. 1646–1655. https://doi.org/10.1002/jmri.24938.; Meignan M., Gallamini A., Meignan M., Gallamini A., Haioun C. Report on the First International Workshop on Interim-PET-Scan in Lymphoma // Leuk. Lymphoma. 2009. Vol. 50. P. 1257–1260. https://doi.org/10.1080/10428190903040048.; Meignan M., Itti E., Gallamini A., Younes A. FDG PET/CT imaging as a biomarker in lymphoma // Eur. J. Nucl. Med. Mol. Imaging. 2015. Vol. 42. P. 623–633. https://doi.org/10.1007/s00259-014-2973-6.; Barrington S.F., Mikhaeel N.G., Kostakoglu L., Meignan M., Hutchings M., Müeller S.P., Schwartz L.H., Zucca E., Fisher R.I., Trotman J., Hoekstra O.S., Hicks R.J., O’Doherty M.J., Hustinx R., Biggi A., Cheson B.D. Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group // J. Clin. Oncol. 2014. Vol. 32. P. 3048–3058.; Tsuji K., Kishi S., Tsuchida T., Yamauchi T., Ikegaya S., Urasaki Y., Fujiwara Y., Ueda T., Okazawa H., Kimura H. Evaluation of staging and early response to chemotherapy with whole-body diffusion-weighted MRI in malignant lymphoma patients: A comparison with FDG-PET/CT // J. Magn. Reson. Imaging. 2015. Vol. 41. P. 1601–1607. https://doi.org/10.1002/jmri.24714.; Алгоритмы диагностики и лечения злокачественных новообразований: клинический протокол / Министерство здравоохранения Республики Беларусь. Минск: Профессиональные издания, 2019. 616 с.; Хоружик С.А., Жаврид Э.А., Сачивко Н.В. Диагностическая эффективность диффузионно-взвешенной магнитно-резонансной томографии всего тела при очаговом и диффузном поражении костного мозга у пациентов с лимфомой // Медицинская визуализация. 2017. № 5. C. 66–81. https://doi.org/10.24835/1607-0763-2017-5-66-81.; Hoane B.R., Shields A.F., Porter B.A., Shulman H.M. Detection of lymphomatous bone marrow involvement with magnetic resonance imaging // Blood. 1991. Vol. 78. P. 728–738.; Adams H.J., Kwee T.C., Vermoolen M.A., de Keizer B., de Klerk J.M., Adam J.A., Fijnheer R., Kersten M.J., Stoker J., Nievelstein R.A. Whole-body MRI for the detection of bone marrow involvement in lymphoma: prospective study in 116 patients and comparison with FDG-PET // Eur. Radiol. 2013. Vol. 23. P. 2271–228. https://doi.org/10.1007/s00330-013-2835-9.; El Khouli R.H., Macura K.J., Barker P.B., Habba M.R., Jacobs M.A., Bluemke D.A. Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast // J. Magn. Reson. Imaging. 2009. Vol. 30. P. 999–1004. https://doi.org/10.1002/jmri.21947.; Crewson P.E. Reader agreement studies // Am. J. Roentgenol. 2005. Vol. 184. P. 1391–1397.; Padhani A.R., Koh D.M., Collins D.J. Whole-body diffusion-weighted MR imaging in cancer: current status and research directions // Radiology. 2011. Vol. 261. P. 700–718. https://doi.org/10.1148/radiol.11110474.; Сакович Р.А., Хоружик С.А., Дзюбан А.В., Барановский О.А., Поддубный К.В., Готто С.И., Жаврид Э.А. Интерпретация исследований ФДГ-ПЭТ/КТ всего тела у пациентов с лимфомой после химиотерапии с использованием шкалы Довиль и полуколичественного анализа // Онкологический журнал. 2017. Т. 11, № 3. С. 5–16.; Adams H.J.A, Kwee T.C. Proportion of false-positive follow-up FDG-PET scans in lymphoma: Systematic review and meta-analysis // Crit. Rev. Oncol. Hematol. 2019. Vol. 141. P. 73–81. https://doi.org/10.1016/j.critrevonc.2019.05.010.; Adams H.J., Nievelstein R.A., Kwee T.C. Prognostic value of complete remission status at end-oftreatment FDG-PET in R-CHOP-treated diffuse large B-cell lymphoma: systematic review and meta-analysis // Br. J. Haematol. 2015. Vol. 170. P. 185–191. https://doi.org/10.1111/bjh.13420.; Хоружик С.А., Жаврид Э.А., Сачивко Н.В. Диффузионно-взвешенная магнитно-резонансная томография с расчетом измеряемого коэффициента диффузии при мониторинге и раннем прогнозировании регрессии опухолевых очагов в процессе химиотерапии лимфом // Медицинская визуализация. 2015. № 5. С. 83–99.; Toledano-Massiah S., Luciani A., Itti E., Zerbib P., Vignaud A., Belhadj K., Baranes L., Haioun C., Lin C., Rahmouni A. Whole-body diffusion-weighted imaging in Hodgkin lymphoma and diffuse large B-cell lymphoma // Radiographics. 2015. Vol. 35. P. 747–764. https://doi.org/10.1148/rg.2015140145.; Younes A., Hilden P., Coiffier B., Hagenbeek A., Salles G., Wilson W. International Working Group consensus response evaluation criteria in lymphoma (RECIL 2017) // Ann. Oncol. 2017. Vol. 28. P. 1436–1447. https://doi.org/10.1093/annonc/mdx097.; Bernstine H., Domachevsky L., Nidam M., Goldberg N., Abadi-Korek I., Shpilberg O., Groshar D. 18 F-FDG PET/MR imaging of lymphoma nodal target lesions: Comparison of PET standardized uptake value (SUV) with MR apparent diffusion coefficient (ADC) // Medicine (Baltimore). 2018. Vol. 97. e0490. https://doi.org/10.1097/MD.0000000000010490.

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    Academic Journal

    Source: Neurology, Neuropsychiatry, Psychosomatics; Vol 11, No 3 (2019); 26-34 ; Неврология, нейропсихиатрия, психосоматика; Vol 11, No 3 (2019); 26-34 ; 2310-1342 ; 2074-2711 ; 10.14412/2074-2711-2019-3

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    Relation: https://nnp.ima-press.net/nnp/article/view/1169/924; https://nnp.ima-press.net/nnp/article/view/1169/925; Puig J, Blasco G, Schlaug G, et al. Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology. 2017 Apr;59(4):343-351. doi:10.1007/s00234-017-1816-0. Epub 2017 Mar 14.; Stebbins GT, Murphy CM. Diffusion tensor imaging in Alzheimer's disease and mild cognitive impairment. Behav Neurol. 2009;21(1): 39-49. doi:10.3233/BEN-2009-0234.; Lindenberg R, Renga V, Zhu LL, et al. Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology. 2010 Jan 26;74(4):280-7. doi:10.1212/WNL.0b013e3181ccc6d9.; Park CH, Kou N, Boudrias MH, et al. Assessing a standardised approach to measuring corticospinal integrity after stroke with DTI. Neuroimage Clin. 2013 Apr 11;2:521-33. doi:10.1016/j.nicl.2013.04.002. eCollection 2013.; Song J, Nair VA, Young BM, et al. DTI measures track and predict motor function outcomes in stroke rehabilitation utilizing BCI technology. Front Hum Neurosci. 2015 Apr 27; 9:195. doi:10.3389/fnhum.2015.00195. eCollection 2015.; Puig J, Pedraza S, Blasco G, et al. Wallerian degeneration in the corticospinal tract evaluated by diffusion tensor imaging correlates with motor deficit 30 days after middle cerebral artery ischemic stroke. AJNR Am J Neuroradiol. 2010 Aug;31(7):1324-30. doi:10.3174/ajnr.A2038. Epub 2010 Mar 18.; Thomalla G, Glauche V, Koch MA, et al. Diffusion tensor imaging detects early Wallerian degeneration of the pyramidal tract after ischemic stroke. Neuroimage. 2004 Aug;22(4): 1767-74.; Stinear CM, Barber PA, Petoe M, et al. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012 Aug;135(Pt 8):2527-35. doi:10.1093/brain/aws146. Epub 2012 Jun 10.; Stinear CM, Byblow WD, Ackerley SJ, et al. PREP2: A biomarker-based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol. 2017 Oct 24;4(11):811-820. doi:10.1002/acn3.488. eCollection 2017 Nov.; Kim SH, Jang SH. Prediction of aphasia outcome using diffusion tensor tractography for arcuate fasciculus in stroke. AJNR Am J Neuroradiol. 2013 Apr;34(4):785-90. doi:10.3174/ajnr.A3259. Epub 2012 Oct 4.; Kulesh A, Drobakha V, Kuklina E, et al. Tract-Specific Fractional Anisotropy, and Brain Morphometry in Post-Stroke Cognitive Impairment. J Stroke Cerebrovasc Dis. 2018 Jul;27(7):1752-1759. doi:10.1016/j.jstrokecerebrovasdis.2018.02.004. Epub 2018 Mar 30.; Кулеш АА, Дробаха ВЕ, Куклина ЕМ и др. Когнитивная траектория пациентов в восстановительном периоде ишемического инсульта: роль нейровоспаления и структурных церебральных факторов. Казанский медицинский журнал. 2017;(4):513-8. doi:10.17750/KMJ2017-513.; Baron RM, Kenny DA. The moderator– mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986 Dec;51(6):1173-82.; Mandonnet E, Sarubbo S, Petit L. The Nomenclature of Human White Matter Association Pathways: Proposal for a Systematic Taxonomic Anatomical Classification. Front Neuroanat. 2018 Nov 6;12:94. doi:10.3389/ fnana.2018.00094. eCollection 2018.; Schiemanck SK, Kwakkel G, Post MW, et al. Predictive value of ischemic lesion volume assessed with magnetic resonance imaging for neurological deficits and functional outcome poststroke: A critical review of the literature. Neurorehabil Neural Repair. 2006 Dec;20(4): 492-502. doi:10.1177/1545968306289298.; Schmahmann JD, Pandya DN, Wang R, et al. Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain. 2007 Mar; 130(Pt 3):630-53. Epub 2007 Feb 9. doi:10.1093/brain/awl359.; Jang SH, Jang WH. Ideomotor Apraxia Due to Injury of the Superior Longitudinal Fasciculus. Am J Phys Med Rehabil. 2016 Aug; 95(8):e117-20. doi:10.1097/PHM.0000000000000483.; Jang SH, Chang CH, Jung YJ, et al. Bilateral injury of the superior longitudinal fasciculus in a patient with Balint syndrome. Neurology. 2016 Oct 4;87(14):1519-1520.; Koyama T, Domen K. Diffusion Tensor Fractional Anisotropy in the Superior Longitudinal Fasciculus Correlates with Functional Independence Measure Cognition Scores in Patients with Cerebral Infarction. J Stroke Cerebrovasc Dis. 2017 Aug;26(8): 1704-1711. doi:10.1016/j.jstrokecerebrovasdis.2017.03.034. Epub 2017 May 3.; Ramsey LE, Siegel JS, Lang CE, et al. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1. pii: 0038. doi:10.1038/s41562-016-0038. Epub 2017 Feb 17.; Dong G, Li H, Potenza MN. Short-Term Internet-Search Training Is Associated with Increased Fractional Anisotropy in the Superior Longitudinal Fasciculus in the Parietal Lobe. Front Neurosci. 2017 Jun 29;11:372. doi:10.3389/fnins.2017.00372. eCollection 2017.; Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018 Sep; 92:104-127. doi:10.1016/j.neubiorev.2018. 05.008. Epub 2018 May 16.; Lebel C, Gee M, Camicioli R, et al. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage. 2012 Mar;60(1):340-52. doi:10.1016/j.neuroimage.2011.11.094. Epub 2011 Dec 8.; Santiago C, Herrmann N, Swardfager W, et al. White Matter Microstructural Integrity Is Associated with Executive Function and Processing Speed in Older Adults with Coronary Artery Disease. Am J Geriatr Psychiatry. 2015 Jul;23(7):754-63. doi:10.1016/j.jagp.2014.09.008. Epub 2014 Sep 28.; Cox SR, Ritchie SJ, Tucker-Drob EM, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016 Dec 15;7:13629. doi:10.1038/ncomms13629.; Кайлева НА, Кулеш АА, Горст НХ и др. Роль интактного полушария в определении реабилитационного потенциала в остром периоде ишемического инсульта: диффузионно-перфузионная модель. Неврология, нейропсихиатрия, психосоматика. 2019; 11(1):28-35. doi:10.14412/2074-2711-2019-1-28-35.; Nanda P, Banks GP, Pathak YJ, et al. Connectivity-based parcellation of the anterior limb of the internal capsule. Hum Brain Mapp. 2017 Dec;38(12):6107-6117. doi:10.1002/hbm.23815. Epub 2017 Sep 14.; Кулеш АА, Кайлева НА, Горст НХ и др. Связь между интегральной оценкой магнитно-резонансных маркеров церебральной болезни мелких сосудов, клиническим и функциональным статусом в остром периоде ишемического инсульта. Неврология, нейропсихиатрия, психосоматика. 2018; 10(1):24-31. doi:10.14412/ 2074-2711-2018-1-24-31; Кулеш АА, Дробаха ВЕ, Шестаков ВВ. Церебральная спорадическая неамилоидная микроангиопатия: патогенез, диагностика и особенности лечебной тактики. Неврология, нейропсихиатрия, психосоматика. 2018; 10(4):13-22. doi:10.14412/2074-2711-2018-4-13-22.; Парфенов ВА, Остроумова ТМ, Остроумова ОД и др. Диффузионно-тензорная магнитно-резонансная томография в диагностике поражения белого вещества головного мозга у пациентов среднего возраста с неосложненной эссенциальной артериальной гипертензией. Неврология, нейропсихиатрия, психосоматика. 2018;10(2):20-6. doi:10.14412/2074-2711-2018-2-20-26; Granziera C, Ay H, Koniak SP, et al. Diffusion tensor imaging shows structural remodeling of stroke mirror region: results from a pilot study. Eur Neurol. 2012;67(6):370-6. doi:10.1159/000336062. Epub 2012 May 17.; Murase N, Duque J, Mazzocchio R, et al. Influence of interhemispheric interactions on motor function in chronic stroke. Ann Neurol. 2004 Mar;55(3):400-9. doi:10.1002/ana.10848.; Tuch DS, Salat DH, Wisco JJ, et al. 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  4. 4
    Academic Journal

    Source: Almanac of Clinical Medicine; Vol 46, No 3 (2018); 289-295 ; Альманах клинической медицины; Vol 46, No 3 (2018); 289-295 ; 2587-9294 ; 2072-0505 ; 10.18786/2072-0505-2018-46-3

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  5. 5
    Academic Journal

    Source: Medical Visualization; № 5 (2017); 66-81 ; Медицинская визуализация; № 5 (2017); 66-81 ; 2408-9516 ; 1607-0763

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