medvisМедицинская визуализацияMedical Visualization1607-07632408-9516RDS-Media Ltd.10.24835/1607-0763-1359medvis-1359Research ArticleМАЛЫЙ ТАЗSMALL PELVISРадиомика фетальной магнитно-резонансной томографии при врожденной диафрагмальной грыжеRadiomics of fetal magnetic resonance imaging in congenital diaphragmatic herniahttps://orcid.org/0000-0003-4043-907XСыркашевЕ. М.SyrkashevE. M.

Сыркашев Егор Михайлович – канд. мед. наук, научный сотрудник

117997 Москва, ул. Академика Опарина, д. 4; 109029 Москва, Средняя Калитниковская ул., д. 28, стр. 1

тел.: +7-915-107-52-28

Egor M. Syrkashev – Cand. of Sci. (Med.), Researcher

4, Akademika Oparina street, Moscow 117997; 24, Petrovka str., Moscow 127051

Phone: +7-915-107-52-28

egorsrkshv@mail.ruhttps://orcid.org/0000-0001-7374-9803БуровА. А.BurovA. A.

Буров Артем Александрович – канд. мед. наук, заведующий по клинической работе

117997 Москва, ул. Академика Опарина, д. 4

Artem A. Burov – Cand. of Sci. (Med.), Chief Clinician

4, Akademika Oparina street, Moscow 117997

https://orcid.org/0000-0002-9928-6234ПодуровскаяЮ. Л.PodurovskayaYu. L.

Подуровская Юлия Леонидовна – канд. мед. наук, заведующий отделением хирургии новорожденных

117997 Москва, ул. Академика Опарина, д. 4

Yulia L. Podurovskaya – Cand. of Sci. (Med.), Head of the neonatal surgery department

4, Akademika Oparina street, Moscow 117997

https://orcid.org/0009-0004-7787-3413КадырбердиеваФ. З.KadyrberdiyevaF. Z.

Кадырбердиева Фаина Залимхановна – канд. мед. наук, научный сотрудник

117997 Москва, ул. Академика Опарина, д. 4

Faina Z. Kadyrberdiyeva – Cand. of Sci. (Med.), Researcher

4, Akademika Oparina street, Moscow 117997

https://orcid.org/0000-0002-1780-6903ИкрянниковЕ. О.IkryannikovE. O.

Икрянников Егор Олегович – техник

109029 Москва, Средняя Калитниковская ул., д. 28, стр. 1

Egor O. Ikryannikov – technician

24, Petrovka str., Moscow 127051

https://orcid.org/0000-0002-4293-2514СеменовД. С.SemenovD. S.

Семенов Дмитрий Сергеевич – начальник сектора

109029 Москва, Средняя Калитниковская ул., д. 28, стр. 1

Dmitriy S. Semenov – Head of sector

24, Petrovka str., Moscow 127051

https://orcid.org/0000-0001-5792-3912ШароваД. Е.SharovaD. E.

Шарова Дарья Евгеньевна – руководитель отдела инновационных технологий

109029 Москва, Средняя Калитниковская ул., д. 28, стр. 1

Daria E. Sharova – Head of innovative technologies department

24, Petrovka str., Moscow 127051

https://orcid.org/0000-0002-0208-5218ВасильевЮ. А.VasilevYu. A.

Васильев Юрий Александрович – канд. мед. наук, директор

109029 Москва, Средняя Калитниковская ул., д. 28, стр. 1

Yuriy A. Vasilev – Cand. of Sci. (Med.), Director

24, Petrovka str., Moscow 127051

https://orcid.org/0000-0003-1377-3128ГусА. И.GusA. I.

Гус Александр Иосифович – доктор мед. наук, главный научный сотрудник

117997 Москва, ул. Академика Опарина, д. 4

Aleksandr I. Gus – Doct. of Sci. (Med.), Chief Researcher

4, Akademika Oparina street, Moscow 117997

ФГБУ “Национальный медицинский исследовательский центр акушерства, гинекологии и перинатологии имени академика В.И. Кулакова” Минздрава России; ГБУЗ “Научно-практический клинический центр диагностики и телемедицинских технологий ДЗ города Москвы”РоссияResearch Center for Obstetrics, Gynecology and Perinatology named by V.I. Kulakov of the Ministry of Healthcare of the Russian Federation; Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Healthcare DepartmentRussian FederationФГБУ “Национальный медицинский исследовательский центр акушерства, гинекологии и перинатологии имени академика В.И. Кулакова” Минздрава РоссииРоссияResearch Center for Obstetrics, Gynecology and Perinatology named by V.I. Kulakov of the Ministry of Healthcare of the Russian FederationRussian FederationГБУЗ “Научно-практический клинический центр диагностики и телемедицинских технологий ДЗ города Москвы”РоссияResearch and Practical Clinical Center for Diagnostics and Telemedicine Technologies of Moscow Healthcare DepartmentRussian Federation202401122023281157167Copyright © Сыркашев Е.М., Буров А.А., Подуровская Ю.Л., Кадырбердиева Ф.З., Икрянников Е.О., Семенов Д.С., Шарова Д.Е., Васильев Ю.А., Гус А.И., 20242024Сыркашев Е.М., Буров А.А., Подуровская Ю.Л., Кадырбердиева Ф.З., Икрянников Е.О., Семенов Д.С., Шарова Д.Е., Васильев Ю.А., Гус А.И.Syrkashev E.M., Burov A.A., Podurovskaya Y.L., Kadyrberdiyeva F.Z., Ikryannikov E.O., Semenov D.S., Sharova D.E., Vasilev Y.A., Gus A.I.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://medvis.rdsmedia.ru/jour/article/view/1359Цель исследования

Цель исследования: анализ возможностей радиомики в качестве источника дополнительной диагностической информации о структурной зрелости легких.

Материал и методы

Материал и методы. В ретроспективное исследование включили 72 беременных: 35 с изолированной врожденной диафрагмальной грыжей плода (1-я группа, основная) и 37 без патологии легких плода (2-я группа, контрольная). Были получены фронтальные или кософронтальные Т2ВИ (T2 FSE). Сегментация зон интереса на уровне легких плода проводилась вручную с использованием ITK-Snap. С использованием pyradiomics было извлечено 107 радиомических признаков. Статистический анализ проводился с помощью пакета статистического анализа Statistica 10 (США) для выявления корреляции между значениями признаков и целевой переменной (наличие патологии легких при врожденной диафрагмальной грыже), а также для отображения различий в группах сравнения в зависимости от выявленных показателей.

Результаты

Результаты. Были определены статистически значимые признаки для 2D- и 3D-сегментаций (p < 0,05). Для 2D- и 3D-сегментаций количество значимых признаков оказалось равно 14 и 73 соответственно. После исключения признаков, имеющих взаимные корреляции, их количество сократилось до 6 и 8 для одиночных срезов и 3D-изображений соответственно. Также были подсчитаны коэффициенты корреляции между признаками и наличием патологии легких. В случае использования 3D-изображений количество признаков, имеющих значимые коэффициенты корреляции (r > 0,4, p < 0,05), оказалось равно 20, в то время как для одиночных срезов данный показатель равен 3.

Заключение

Заключение. Полученные данные позволяют сделать вывод о целесообразности применения текстурного анализа 3D-МРТ-изображений в качестве источника дополнительной диагностической информации о структурной зрелости легких.

Objectives

Objectives. Analysis of possibilities of radiomics as a source of additional diagnostic information about the structural maturity of the lungs

Materials and methods

Materials and methods. A retrospective study included 72 pregnant women: 35 with congenital fetal diaphragmatic hernia (group 1) and 37 without fetal lung pathology (group 2). Frontal or co-frontal T2 images (T2 FSE) were obtained. Segmentation of regions of interest at the fetal lung level was performed manually with ITK-Snap. A total of 107 radiomic features were extracted using pyradiomics. The statistical analysis was performed using the STATISTICA 10 statistical analysis package (USA) to detect correlation between trait values and the target variable (presence of lung pathology in CDH), and to show differences in the comparison groups according to the detected parameters.

Results

Results. Statistically significant features were identified for 2D and 3D segmentations (p < 0.05). For 2D and 3D segmentations, the number of significant features was 14 and 73, respectively. After exclusion of features with cross-correlations, their number decreased to 6 and 8 for single slices and 3D images, respectively. Correlation coefficients between the features and the presence of lung pathology were also calculated. In the case of 3D images, the number of features with significant correlation coefficients (r > 0.4, p < 0.05) equaled 20, while for single-slice images this number was 3.

Conclusion

Conclusion. The data obtained allow to conclude that it is reasonable to use texture analysis of the 3D MRI images as a source of additional diagnostic information concerning the structural maturity of the lungs.

фетальная МРТрадиомикаакушерство и гинекологиятекстурный анализврожденная диафрагмальная грыжаfetal MRIradiomicsobstetrics and gynecologytexture analysiscongenital diaphragmatic herniaReferencesLambin P., Rios-Velazquez E., Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012; 48 (4): 441–446. http://doi.org/10.1016/j.ejca.2011.11.036Lambin P., Rios-Velazquez E., Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 2012; 48 (4): 441–446. http://doi.org/10.1016/j.ejca.2011.11.036Kumar V., Gu Y., Basu S. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012; 30 (9): 1234–1248. http://doi.org/10.1016/j.mri.2012.06.010Kumar V., Gu Y., Basu S. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging. 2012; 30 (9): 1234–1248. http://doi.org/10.1016/j.mri.2012.06.010Mirestean C.C., Pagute O., Buzea C. et al. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019; 14 (2): 126–130. http://doi.org/10.26574/maedica.2019.14.2.126Mirestean C.C., Pagute O., Buzea C. et al. Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology. Maedica (Bucur). 2019; 14 (2): 126–130. http://doi.org/10.26574/maedica.2019.14.2.126Говорухина В.Г., Семенов С.С., Гележе П.Б., Диденко В.В., Морозов С.П., Андрейченко А.Е. Роль маммографии в радиомике рака молочной железы. Digital Diagnostics. 2021; 2 (2): 185–199. http://doi.org/10.17816/DD70479Govorukhina V.G., Semenov S.S., Gelezhe P.B. et al. The role of mammography in breast cancer radiomics. Digital Diagnostics. 2021; 2 (2): 185–199. http://doi.org/10.17816/DD70479 (In Russian)Bae S., Choi Y.S., Ahn S.S. et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology. 2018; 289 (3): 797–806. http://doi.org/10.1148/radiol.2018180200Bae S., Choi Y.S., Ahn S.S. et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology. 2018; 289 (3): 797–806. http://doi.org/10.1148/radiol.2018180200Coroller T.P., Agrawal V., Huynh E. et al. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J. Thorac. Oncol. 2017; 12 (3): 467–476. http://doi.org/10.1016/j.jtho.2016.11.2226Coroller T.P., Agrawal V., Huynh E. et al. Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J. Thorac. Oncol. 2017; 12 (3): 467–476. http://doi.org/10.1016/j.jtho.2016.11.2226Du Y., Fang Z., Jiao J. et al. Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and/or pre-eclampsia. Ultrasound Obstet Gynecol. 2021; 57 (5): 804–812. http://doi.org/10.1002/uog.22037Du Y., Fang Z., Jiao J. et al. Application of ultrasound-based radiomics technology in fetal-lung-texture analysis in pregnancies complicated by gestational diabetes and/or pre-eclampsia. Ultrasound Obstet Gynecol. 2021; 57 (5): 804–812. http://doi.org/10.1002/uog.22037Watzenboeck M.L., Heidinger B.H., Rainer J. et al. Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study. Insights. Imaging. 2023; 14 (1): 31. http://doi.org/10.1186/s13244-023-01376-yWatzenboeck M.L., Heidinger B.H., Rainer J. et al. Reproducibility of 2D versus 3D radiomics for quantitative assessment of fetal lung development: a retrospective fetal MRI study. Insights. Imaging. 2023; 14 (1): 31. http://doi.org/10.1186/s13244-023-01376-yHeinzerling N., Wagner A. Fetal Diagnosis and Therapy: A Reference Handbook for Pediatric Surgeons. Fetal Diagn. Ther. 2013.Heinzerling N., Wagner A. Fetal Diagnosis and Therapy: A Reference Handbook for Pediatric Surgeons. Fetal Diagn. Ther. 2013.Jokhi R.P., Whitby E.H. Magnetic resonance imaging of the fetus. Dev. Med. Child Neurol. 53 (1): 18–28. http://doi.org/10.1111/j.1469-8749.2010.03813.xJokhi R.P., Whitby E.H. Magnetic resonance imaging of the fetus. Dev. Med. Child Neurol. 53 (1): 18–28. http://doi.org/10.1111/j.1469-8749.2010.03813.xPrayer D., Malinger G., De Catte L. et al.; ISUOG Clinical Standards Committee. ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging. Ultrasound Obstet Gynecol. 2023; 61 (2): 278–287. http://doi.org/10.1002/uog.26129Prayer D., Malinger G., De Catte L. et al.; ISUOG Clinical Standards Committee. ISUOG Practice Guidelines (updated): performance of fetal magnetic resonance imaging. Ultrasound Obstet Gynecol. 2023; 61 (2): 278–287. http://doi.org/10.1002/uog.26129Prayer D., Malinger G., Brugger P.C. et al. ISUOG Practice Guidelines: performance of fetal magnetic resonance imaging. Ultrasound Obstet. Gynecol. 2017; 49 (5): 671–680. http://doi.org/10.1002/uog.17412Prayer D., Malinger G., Brugger P.C. et al. ISUOG Practice Guidelines: performance of fetal magnetic resonance imaging. Ultrasound Obstet. Gynecol. 2017; 49 (5): 671–680. http://doi.org/10.1002/uog.17412Dütemeyer V., Cordier A.G., Cannie M.M. et al. Prenatal prediction of postnatal survival in fetuses with congenital diaphragmatic hernia using MRI: lung volume measurement, signal intensity ratio, and effect of experience. J. Matern. Fetal Neonatal Med. 2022; 35 (6): 1036–1044. http://doi.org/10.1080/14767058.2020.1740982Dütemeyer V., Cordier A.G., Cannie M.M. et al. Prenatal prediction of postnatal survival in fetuses with congenital diaphragmatic hernia using MRI: lung volume measurement, signal intensity ratio, and effect of experience. J. Matern. Fetal Neonatal Med. 2022; 35 (6): 1036–1044. http://doi.org/10.1080/14767058.2020.1740982Сыркашев Е.М., Солопова А.Е., Быченко В.Г., Буров А.А., Подуровская Ю.Л., Гус А.И. Антенатальная биометрия легких при врожденной диафрагмальной грыже по данным МРТ. REJR. 2020; 10 (4): 169–178. http://doi.org/10.21569/2222-7415-2020-10-4-169-178Syrkashev E.M., Solopova A.E., Bychenko V.G. et al. Fetal pulmonary biometry in congenital diaphragmal hernia using MRI. REJR. 2020; 10 (4): 169–178. http://doi.org/10.21569/2222-7415-2020-10-4-169-178 (In Russian)Farrugia M.K., Raza S.A., Gould S., Lakhoo K. Congenital lung lesions: classification and concordance of radiological appearance and surgical pathology. Pediatr. Surg. Int. 2008; 24 (9): 987–991. http://doi.org/10.1007/s00383-008-2201-1Farrugia M.K., Raza S.A., Gould S., Lakhoo K. Congenital lung lesions: classification and concordance of radiological appearance and surgical pathology. Pediatr. Surg. Int. 2008; 24 (9): 987–991. http://doi.org/10.1007/s00383-008-2201-1Zani A., Chung W.K., Deprest J. et al. Congenital diaphragmatic hernia. Nat. Rev. Dis. Primers. 2022; 8 (1): 37. http://doi.org/10.1038/s41572-022-00362-wZani A., Chung W.K., Deprest J. et al. Congenital diaphragmatic hernia. Nat. Rev. Dis. Primers. 2022; 8 (1): 37. http://doi.org/10.1038/s41572-022-00362-wYushkevich P.A., Gao Y., Gerig G. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA. 2016; 3342–3345. http://doi.org/10.1109/EMBC.2016.7591443Yushkevich P.A., Gao Y., Gerig G. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA. 2016; 3342–3345. http://doi.org/10.1109/EMBC.2016.7591443Shchegolev A.I., Tumanova U.N. Pulmonary hypoplasia: causes and pathological finding. Int. J. Appl. Fundam. Res. 2017; 4: 101–153.Shchegolev A.I., Tumanova U.N. Pulmonary hypoplasia: causes and pathological finding. Int. J. Appl. Fundam. Res. 2017; 4: 101–153.Ogawa R., Kido T., Nakamura M. et al. Magnetic resonance assessment of fetal lung maturity: comparison between signal intensity and volume measurement. Jpn. J. Radiol. 2018; 36 (7): 444–449. http://doi.org/10.1007/s11604-018-0745-0Ogawa R., Kido T., Nakamura M. et al. Magnetic resonance assessment of fetal lung maturity: comparison between signal intensity and volume measurement. Jpn. J. Radiol. 2018; 36 (7): 444–449. http://doi.org/10.1007/s11604-018-0745-0Keller T.M., Rake A., Michel S.C. et al. MR assessment of fetal lung development using lung volumes and signal intensities. Eur. Radiol. 2004; 14 (6): 984–989. http://doi.org/10.1007/s00330-004-2256-xKeller T.M., Rake A., Michel S.C. et al. MR assessment of fetal lung development using lung volumes and signal intensities. Eur. Radiol. 2004; 14 (6): 984–989. http://doi.org/10.1007/s00330-004-2256-xOka Y., Rahman M., Sasakura C. et al. Prenatal diagnosis of fetal respiratory function: evaluation of fetal lung maturity using lung-to-liver signal intensity ratio at magnetic resonance imaging. Prenat. Diagn. 2014; 34 (13): 1289–1294. http://doi.org/10.1002/pd.4469Oka Y., Rahman M., Sasakura C. et al. Prenatal diagnosis of fetal respiratory function: evaluation of fetal lung maturity using lung-to-liver signal intensity ratio at magnetic resonance imaging. Prenat. Diagn. 2014; 34 (13): 1289–1294. http://doi.org/10.1002/pd.4469Moshiri M., Mannelli L., Richardson M.L. et al. Fetal lung maturity assessment with MRI fetal lung-to-liver signal-intensity ratio. Am. J. Roentgenol. 2013; 201 (6): 1386–1390. http://doi.org/10.2214/AJR.12.9679Moshiri M., Mannelli L., Richardson M.L. et al. Fetal lung maturity assessment with MRI fetal lung-to-liver signal-intensity ratio. Am. J. Roentgenol. 2013; 201 (6): 1386–1390. http://doi.org/10.2214/AJR.12.9679Mills M., Winter T.C., Kennedy A.M., Woodward P.J. Determination of fetal lung maturity using magnetic resonance imaging signal intensity measurements. Ultrasound Q. 2014; 30 (1): 61–67. http://doi.org/10.1097/RUQ.0000000000000054Mills M., Winter T.C., Kennedy A.M., Woodward P.J. Determination of fetal lung maturity using magnetic resonance imaging signal intensity measurements. Ultrasound Q. 2014; 30 (1): 61–67. http://doi.org/10.1097/RUQ.0000000000000054Yamoto M., Iwazaki T., Takeuchi K. et al. The fetal lung-to-liver signal intensity ratio on magnetic resonance imaging as a predictor of outcomes from isolated congenital diaphragmatic hernia. Pediatr. Surg. Int. 2018; 34 (2): 161–168. http://doi.org/10.1007/s00383-017-4184-2Yamoto M., Iwazaki T., Takeuchi K. et al. The fetal lung-to-liver signal intensity ratio on magnetic resonance imaging as a predictor of outcomes from isolated congenital diaphragmatic hernia. Pediatr. Surg. Int. 2018; 34 (2): 161–168. http://doi.org/10.1007/s00383-017-4184-2Cordier A.G., Russo F.M., Deprest J., Benachi A. Prenatal diagnosis, imaging, and prognosis in Congenital Diaphragmatic Hernia. Semin. Perinatol. 2020; 44 (1): 51163. http://doi.org/10.1053/j.semperi.2019.07.002Cordier A.G., Russo F.M., Deprest J., Benachi A. Prenatal diagnosis, imaging, and prognosis in Congenital Diaphragmatic Hernia. Semin. Perinatol. 2020; 44 (1): 51163. http://doi.org/10.1053/j.semperi.2019.07.002Madenci A.L., Church J.T., Gajarski R.J. et al. Pulmonary Hypertension in Patients with Congenital Diaphragmatic Hernia: Does Lung Size Matter? Eur. J. Pediatr. Surg. 2018; 28 (6): 508–514. http://doi.org/10.1055/s-0037-1607291Madenci A.L., Church J.T., Gajarski R.J. et al. Pulmonary Hypertension in Patients with Congenital Diaphragmatic Hernia: Does Lung Size Matter? Eur. J. Pediatr. Surg. 2018; 28 (6): 508–514. http://doi.org/10.1055/s-0037-1607291Basurto D., Russo F.M., Papastefanou I. et al. Pulmonary hypertension in congenital diaphragmatic hernia: Antenatal prediction and impact on neonatal mortality. Prenat. Diagn. 2022; 42 (10): 1303–1311. http://doi.org/10.1002/pd.6207Basurto D., Russo F.M., Papastefanou I. et al. Pulmonary hypertension in congenital diaphragmatic hernia: Antenatal prediction and impact on neonatal mortality. Prenat. Diagn. 2022; 42 (10): 1303–1311. http://doi.org/10.1002/pd.6207Petroze R.T., Caminsky N.G., Trebichavsky J. et al. Prenatal prediction of survival in congenital diaphragmatic hernia: An audit of postnatal outcomes. J. Pediatr. Surg. 2019; 54 (5): 925–931. http://doi.org/10.1016/j.jpedsurg.2019.01.021Petroze R.T., Caminsky N.G., Trebichavsky J. et al. Prenatal prediction of survival in congenital diaphragmatic hernia: An audit of postnatal outcomes. J. Pediatr. Surg. 2019; 54 (5): 925–931. http://doi.org/10.1016/j.jpedsurg.2019.01.021Moore R.J., Strachan B., Tyler D.J. et al. In vivo diffusion measurements as an indication of fetal lung maturation using echo planar imaging at 0.5T. Magn. Reson. Med. 2001; 45 (2): 247–253. http://doi.org/10.1002/1522-2594(200102)45:2<247::aid-mrm1033>3.0.co;2-mMoore R.J., Strachan B., Tyler D.J. et al. In vivo diffusion measurements as an indication of fetal lung maturation using echo planar imaging at 0.5T. Magn. Reson. Med. 2001; 45 (2): 247–253. http://doi.org/10.1002/1522-2594(200102)45:2<247::aid-mrm1033>3.0.co;2-mBalassy C., Kasprian G., Brugger P.C. et al. Diffusion-weighted MR imaging of the normal fetal lung. Eur. Radiol. 2008; 18 (4): 700–706. http://doi.org/10.1007/s00330-007-0784-xBalassy C., Kasprian G., Brugger P.C. et al. Diffusion-weighted MR imaging of the normal fetal lung. Eur. Radiol. 2008; 18 (4): 700–706. http://doi.org/10.1007/s00330-007-0784-xCannie M., Jani J., De Keyzer F. et al. Diffusion-weighted MRI in lungs of normal fetuses and those with congenital diaphragmatic hernia. Ultrasound Obstet. Gynecol. 2009; 34 (6): 678–686. http://doi.org/10.1002/uog.7326Cannie M., Jani J., De Keyzer F. et al. Diffusion-weighted MRI in lungs of normal fetuses and those with congenital diaphragmatic hernia. Ultrasound Obstet. Gynecol. 2009; 34 (6): 678–686. http://doi.org/10.1002/uog.7326Manganaro L., Perrone A., Sassi S. et al. Diffusion-weighted MR imaging and apparent diffusion coefficient of the normal fetal lung: preliminary experience. Prenat. Diagn. 2008 Aug;28(8):745–748. http://doi.org/10.1002/pd.2041Manganaro L., Perrone A., Sassi S. et al. Diffusion-weighted MR imaging and apparent diffusion coefficient of the normal fetal lung: preliminary experience. Prenat. Diagn. 2008 Aug;28(8):745–748. http://doi.org/10.1002/pd.2041Afacan O., Gholipour A., Mulkern R.V. et al. Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age. J. Magn. Reson. Imaging. 2016; 44 (6): 1650–1655. http://doi.org/10.1002/jmri.25294Afacan O., Gholipour A., Mulkern R.V. et al. Fetal lung apparent diffusion coefficient measurement using diffusion-weighted MRI at 3 Tesla: Correlation with gestational age. J. Magn. Reson. Imaging. 2016; 44 (6): 1650–1655. http://doi.org/10.1002/jmri.25294Ercolani G., Capuani S., Antonelli A. et al. IntraVoxel Incoherent Motion (IVIM) MRI of fetal lung and kidney: Can the perfusion fraction be a marker of normal pulmonary and renal maturation? Eur. J. Radiol. 2021; 139: 109726. http://doi.org/10.1016/j.ejrad.2021.109726Ercolani G., Capuani S., Antonelli A. et al. IntraVoxel Incoherent Motion (IVIM) MRI of fetal lung and kidney: Can the perfusion fraction be a marker of normal pulmonary and renal maturation? Eur. J. Radiol. 2021; 139: 109726. http://doi.org/10.1016/j.ejrad.2021.109726Sethi S., Giza S.A., Goldberg E. et al. Quantification of 1.5 T T1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies. J. Magn. Reson. Imaging. 2021; 54 (1): 113–121. http://doi.org/10.1002/jmri.27547Sethi S., Giza S.A., Goldberg E. et al. Quantification of 1.5 T T1 and T2* Relaxation Times of Fetal Tissues in Uncomplicated Pregnancies. J. Magn. Reson. Imaging. 2021; 54 (1): 113–121. http://doi.org/10.1002/jmri.27547Prayer F., Watzenböck M.L., Heidinger B.H. et al. Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity. Eur. Radiol. 2023; 33 (6): 4205–4213. http://doi.org/10.1007/s00330-022-09367-1Prayer F., Watzenböck M.L., Heidinger B.H. et al. Fetal MRI radiomics: non-invasive and reproducible quantification of human lung maturity. Eur. Radiol. 2023; 33 (6): 4205–4213. http://doi.org/10.1007/s00330-022-09367-1

The authors declare that there are no conflicts of interest present.