{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:11:12Z","timestamp":1780355472156,"version":"3.54.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The AUC of the CNNs pertaining to the single and combined image sets were 0.88\u20130.95 and 0.87\u20130.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88\u20130.95.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00808-3","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T15:03:53Z","timestamp":1651503833000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists"],"prefix":"10.1186","volume":"22","author":[{"given":"Aiko","family":"Urushibara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsukasa","family":"Saida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshitaka","family":"Ishiguro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kei","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomohiko","family":"Masumoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toyomi","family":"Satoh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahito","family":"Nakajima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"issue":"3","key":"808_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209\u201349.","journal-title":"CA Cancer J Clin"},{"issue":"2","key":"808_CR2","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1089\/jwh.2018.6956","volume":"28","author":"GD Constantine","year":"2019","unstructured":"Constantine GD, Kessler G, Graham S, Goldstein SR. Increased incidence of endometrial cancer following the women\u2019s health initiative: an assessment of risk factors. J Womens Health (Larchmt). 2019;28(2):237\u201343.","journal-title":"J Womens Health (Larchmt)"},{"issue":"6","key":"808_CR3","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.2214\/AJR.06.1196","volume":"188","author":"E Sala","year":"2007","unstructured":"Sala E, Wakely S, Senior E, Lomas D. MRI of malignant neoplasms of the uterine corpus and cervix. AJR Am J Roentgenol. 2007;188(6):1577\u201387.","journal-title":"AJR Am J Roentgenol"},{"issue":"2","key":"808_CR4","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1148\/radiol.11110984","volume":"262","author":"P Beddy","year":"2012","unstructured":"Beddy P, Moyle P, Kataoka M, Yamamoto AK, Joubert I, Lomas D, et al. Evaluation of depth of myometrial invasion and overall staging in endometrial cancer: comparison of diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology. 2012;262(2):530\u20137.","journal-title":"Radiology"},{"issue":"2","key":"808_CR5","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1007\/s00330-018-5515-y","volume":"29","author":"S Nougaret","year":"2019","unstructured":"Nougaret S, Horta M, Sala E, Lakhman Y, Thomassin-Naggara I, Kido A, et al. Endometrial cancer MRI staging: updated guidelines of the European society of urogenital radiology. Eur Radiol. 2019;29(2):792\u2013805.","journal-title":"Eur Radiol"},{"issue":"2","key":"808_CR6","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102\u201327.","journal-title":"Z Med Phys"},{"key":"808_CR7","doi-asserted-by":"crossref","unstructured":"Fujioka T, Mori M, Kubota K, Oyama J, Yamaga E, Yashima Y, et al. The utility of deep learning in breast ultrasonic imaging: a review. Diagnostics (Basel). 2020;10(12).","DOI":"10.3390\/diagnostics10121055"},{"key":"808_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103438","volume":"114","author":"Y Kurata","year":"2019","unstructured":"Kurata Y, Nishio M, Kido A, Fujimoto K, Yakami M, Isoda H, et al. Automatic segmentation of the uterus on MRI using a convolutional neural network. Comput Biol Med. 2019;114: 103438.","journal-title":"Comput Biol Med"},{"issue":"3","key":"808_CR9","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1148\/radiol.2018180547","volume":"290","author":"S Soffer","year":"2019","unstructured":"Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional neural networks for radiologic images: a radiologist\u2019s guide. Radiology. 2019;290(3):590\u2013606.","journal-title":"Radiology"},{"issue":"1","key":"808_CR10","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1038\/s41598-020-80068-9","volume":"11","author":"E Hodneland","year":"2021","unstructured":"Hodneland E, Dybvik JA, Wagner-Larsen KS, Solteszova V, Munthe-Kaas AZ, Fasmer KE, et al. Automated segmentation of endometrial cancer on MR images using deep learning. Sci Rep. 2021;11(1):179.","journal-title":"Sci Rep"},{"key":"808_CR11","doi-asserted-by":"crossref","unstructured":"Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, et al. Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics (Basel). 2020;10(5).","DOI":"10.3390\/diagnostics10050330"},{"issue":"4","key":"808_CR12","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021200184","volume":"3","author":"R Gauriau","year":"2021","unstructured":"Gauriau R, Bizzo BC, Kitamura FC, Landi Junior O, Ferraciolli SF, Macruz FBC, et al. A deep learning-based model for detecting abnormalities on brain mr images for triaging: preliminary results from a multisite experience. Radiol Artif Intell. 2021;3(4): e200184.","journal-title":"Radiol Artif Intell"},{"key":"808_CR13","doi-asserted-by":"crossref","unstructured":"Fujioka T, Katsuta L, Kubota K, Mori M, Kikuchi Y, Kato A, et al. Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks. Ultrason Imaging. 2020:161734620932609.","DOI":"10.1177\/0161734620932609"},{"issue":"3","key":"808_CR14","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2019190938","volume":"293","author":"P Schelb","year":"2019","unstructured":"Schelb P, Kohl S, Radtke JP, Wiesenfarth M, Kickingereder P, Bickelhaupt S, et al. Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology. 2019;293(3):607\u201317.","journal-title":"Radiology"},{"key":"808_CR15","unstructured":"The ImageMagick Development Team. ImageMagick. https:\/\/imagemagick.org\/. 2021."},{"key":"808_CR16","first-page":"1800","volume":"2017","author":"F Chollet","year":"2017","unstructured":"Chollet F. Xception: Deep learning with depthwise separa-ble convolutions. IEEE Conf Comput Vis Pattern Recognit (CVPR). 2017;2017:1800\u20137.","journal-title":"IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"issue":"3","key":"808_CR17","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet large scale visual recognition challenge. Int J Comput Vision. 2015;115(3):211\u201352.","journal-title":"Int J Comput Vision"},{"issue":"2","key":"808_CR18","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1111\/j.1365-2753.2005.00598.x","volume":"12","author":"A Linden","year":"2006","unstructured":"Linden A. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J Eval Clin Pract. 2006;12(2):132\u20139.","journal-title":"J Eval Clin Pract"},{"key":"808_CR19","doi-asserted-by":"crossref","unstructured":"Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1).","DOI":"10.2307\/2529310"},{"key":"808_CR20","doi-asserted-by":"publisher","first-page":"12823","DOI":"10.2147\/CMAR.S279990","volume":"12","author":"J Zhou","year":"2020","unstructured":"Zhou J, Zeng ZY, Li L. Progress of artificial intelligence in gynecological malignant tumors. Cancer Manag Res. 2020;12:12823\u201340.","journal-title":"Cancer Manag Res"},{"issue":"7","key":"808_CR21","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.11625","volume":"3","author":"Q Wu","year":"2020","unstructured":"Wu Q, Wang S, Zhang S, Wang M, Ding Y, Fang J, et al. Development of a deep learning model to identify lymph node metastasis on magnetic resonance imaging in patients with cervical cancer. JAMA Netw Open. 2020;3(7): e2011625.","journal-title":"JAMA Netw Open"},{"key":"808_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109471","volume":"135","author":"A Urushibara","year":"2020","unstructured":"Urushibara A, Saida T, Mori K, Ishiguro T, Sakai M, Masuoka S, et al. Diagnosing uterine cervical cancer on a single T2-weighted image: comparison between deep learning versus radiologists. Eur J Radiol. 2020;135: 109471.","journal-title":"Eur J Radiol"},{"issue":"9","key":"808_CR23","doi-asserted-by":"publisher","first-page":"4985","DOI":"10.1007\/s00330-020-06870-1","volume":"30","author":"X Chen","year":"2020","unstructured":"Chen X, Wang Y, Shen M, Yang B, Zhou Q, Yi Y, et al. Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol. 2020;30(9):4985\u201394.","journal-title":"Eur Radiol"},{"key":"808_CR24","doi-asserted-by":"crossref","unstructured":"Dong HC, Dong HK, Yu MH, Lin YH, Chang CC. Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using MR images: a pilot study. Int J Environ Res Public Health. 2020;17(16).","DOI":"10.3390\/ijerph17165993"},{"issue":"3","key":"808_CR25","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1148\/rg.293085130","volume":"29","author":"CS Whittaker","year":"2009","unstructured":"Whittaker CS, Coady A, Culver L, Rustin G, Padwick M, Padhani AR. Diffusion-weighted MR imaging of female pelvic tumors: a pictorial review. Radiographics. 2009;29(3):759\u201374.","journal-title":"Radiographics."},{"issue":"4","key":"808_CR26","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1097\/00002142-200308000-00005","volume":"14","author":"SA Funt","year":"2003","unstructured":"Funt SA, Hricak H. Ovarian malignancies. Top Magn Reson Imaging. 2003;14(4):329\u201337.","journal-title":"Top Magn Reson Imaging"},{"issue":"2","key":"808_CR27","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/s00330-007-0769-9","volume":"18","author":"S Fujii","year":"2008","unstructured":"Fujii S, Matsusue E, Kigawa J, Sato S, Kanasaki Y, Nakanishi J, et al. Diagnostic accuracy of the apparent diffusion coefficient in differentiating benign from malignant uterine endometrial cavity lesions: initial results. Eur Radiol. 2008;18(2):384\u20139.","journal-title":"Eur Radiol"},{"issue":"3","key":"808_CR28","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1002\/jmri.20997","volume":"26","author":"K Tamai","year":"2007","unstructured":"Tamai K, Koyama T, Saga T, Umeoka S, Mikami Y, Fujii S, et al. Diffusion-weighted MR imaging of uterine endometrial cancer. J Magn Reson Imaging. 2007;26(3):682\u20137.","journal-title":"J Magn Reson Imaging"},{"issue":"2","key":"808_CR29","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1007\/s00330-019-06417-z","volume":"30","author":"N Aldoj","year":"2020","unstructured":"Aldoj N, Lukas S, Dewey M, Penzkofer T. Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network. Eur Radiol. 2020;30(2):1243\u201353.","journal-title":"Eur Radiol"},{"issue":"1","key":"808_CR30","doi-asserted-by":"publisher","first-page":"20331","DOI":"10.1038\/s41598-020-77389-0","volume":"10","author":"J Lee","year":"2020","unstructured":"Lee J, Wang N, Turk S, Mohammed S, Lobo R, Kim J, et al. Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning. Sci Rep. 2020;10(1):20331.","journal-title":"Sci Rep"},{"key":"808_CR31","doi-asserted-by":"crossref","unstructured":"Mehrtash A, Sedghi A, Ghafoorian M, Taghipour M, Tempany CM, Wells WM, 3rd, et al. Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 2017;10134.","DOI":"10.1117\/12.2277123"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00808-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00808-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00808-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T15:05:30Z","timestamp":1651503930000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00808-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,30]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["808"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00808-3","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,30]]},"assertion":[{"value":"3 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of the University of Tsukuba Hospital (approval number: R02-054), and the requirement for written informed consent was waived. All methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"80"}}