{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:09:22Z","timestamp":1768925362096,"version":"3.49.0"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T00:00:00Z","timestamp":1577664000000},"content-version":"vor","delay-in-days":29,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>There are often multiple lesions in breast magnetic resonance imaging (MRI) reports and radiologists usually focus on describing the index lesion that is most crucial to clinicians in determining the management and prognosis of patients. Natural language processing (NLP) has been used for information extraction from mammography reports. However, few studies have investigated NLP in breast MRI data based on free-form text. The objective of the current study was to assess the validity of our NLP program to accurately extract index lesions and their corresponding imaging features from free-form text of breast MRI reports.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>This cross-sectional study examined 1633 free-form text reports of breast MRIs from 2014 to 2017. First, the NLP system was used to extract 9 features from all the lesions in the reports according to the Breast Imaging Reporting and Data System (BI-RADS) descriptors. Second, the index lesion was defined as the lesion with the largest number of imaging features. Third, we extracted the values of each imaging feature and the BI-RADS category from each index lesion. To evaluate the accuracy of our system, 478 reports were manually reviewed by two individuals. The time taken to extract data by NLP was compared with that by reviewers.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>The NLP system extracted 889 lesions from 478 reports. The mean number of imaging features per lesion was 6.5\u00a0\u00b1\u00a02.1 (range: 3\u20139; 95% CI: 6.362\u20136.638). The mean number of imaging features per index lesion was 8.0\u00a0\u00b1\u00a01.1 (range: 5\u20139; 95% CI: 7.901\u20138.099). The NLP system demonstrated a recall of 100.0% and a precision of 99.6% for correct identification of the index lesion. The recall and precision of NLP to correctly extract the value of imaging features from the index lesions were 91.0 and 92.6%, respectively. The recall and precision for the correct identification of the BI-RADS categories were 96.6 and 94.8%, respectively. NLP generated the total results in less than 1\u2009s, whereas the manual reviewers averaged 4.47\u2009min and 4.56\u2009min per report.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Our NLP method successfully extracted the index lesion and its corresponding information from free-form text.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12911-019-0997-3","type":"journal-article","created":{"date-parts":[[2019,12,30]],"date-time":"2019-12-30T16:02:51Z","timestamp":1577721771000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports"],"prefix":"10.1186","volume":"19","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Han","sequence":"additional","affiliation":[]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-0895","authenticated-orcid":false,"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,30]]},"reference":[{"key":"997_CR1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3322\/caac.20107","volume":"61","author":"A Jemal","year":"2011","unstructured":"Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69\u201390. https:\/\/doi.org\/10.3322\/caac.20107.","journal-title":"CA Cancer J Clin"},{"issue":"16","key":"997_CR2","doi-asserted-by":"publisher","first-page":"3413","DOI":"10.1200\/JCO.2002.08.600","volume":"20","author":"GF Tillman","year":"2002","unstructured":"Tillman GF, Orel SG, Schnall MD, Schultz DJ, Tan JE, Solin LJ. Effect of breast magnetic resonance imaging on the clinical management of women with early-stage breast carcinoma. J Clin Oncol. 2002;20(16):3413\u201323. https:\/\/doi.org\/10.1200\/JCO.2002.08.600.","journal-title":"J Clin Oncol"},{"issue":"3","key":"997_CR3","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1002\/cncr.11490","volume":"98","author":"I Bedrosian","year":"2003","unstructured":"Bedrosian I, Mick R, Orel SG, Schnall M, Reynolds C, Spitz FR, et al. Changes in the surgical management of patients with breast carcinoma based on preoperative magnetic resonance imaging. Cancer. 2003;98(3):468\u201373. https:\/\/doi.org\/10.1002\/cncr.11490.","journal-title":"Cancer."},{"issue":"8","key":"997_CR4","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1200\/JCO.2005.12.002","volume":"23","author":"N Hylton","year":"2005","unstructured":"Hylton N. Magnetic resonance imaging of the breast: opportunities to improve breast cancer management. J Clin Oncol. 2005;23(8):1678\u201384. https:\/\/doi.org\/10.1200\/JCO.2005.12.002.","journal-title":"J Clin Oncol"},{"issue":"1","key":"997_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10549-007-9767-5","volume":"111","author":"M Braun","year":"2008","unstructured":"Braun M, P\u00f6lcher M, Schrading S, Zivanovic O, Kowalski T, Flucke U, et al. Influence of preoperative MRI on the surgical management of patients with operable breast cancer. Breast Cancer Res Treat. 2008;111(1):179\u201387. https:\/\/doi.org\/10.1007\/s10549-007-9767-5.","journal-title":"Breast Cancer Res Treat"},{"issue":"5","key":"997_CR6","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.amjsurg.2007.01.015","volume":"193","author":"JD Beatty","year":"2007","unstructured":"Beatty JD, Porter BA. Contrast-enhanced breast magnetic resonance imaging: the surgical perspective. Am J Surg. 2007;193(5):600\u20135. https:\/\/doi.org\/10.1016\/j.amjsurg.2007.01.015.","journal-title":"Am J Surg"},{"key":"997_CR7","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.ejso.2004.02.003","volume":"30","author":"K Schelfout","year":"2004","unstructured":"Schelfout K, Van Goethem M, Kersschot E, Colpaert C, Schelfhout AM, Leyman P, et al. Contrast-enhanced MR imaging of breast lesions and effect on treatment. Eur J Surg Oncol. 2004;30:501\u20137. https:\/\/doi.org\/10.1016\/j.ejso.2004.02.003.","journal-title":"Eur J Surg Oncol"},{"issue":"3","key":"997_CR8","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/BF02967595","volume":"9","author":"Y Zhang","year":"2002","unstructured":"Zhang Y, Fukatsu H, Naganawa S, Satake H, Sato Y, Ohiwa M, et al. The role of contrast-enhanced MR mammography for determining candidates for breast conservation surgery. Breast Cancer. 2002;9(3):231\u20139.","journal-title":"Breast Cancer"},{"issue":"1","key":"997_CR9","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1200\/JCO.1999.17.1.110","volume":"17","author":"L Esserman","year":"1999","unstructured":"Esserman L, Hylton N, Yassa L, Barclay J, Frankel S, Sickles E. Utility of magnetic resonance imaging in the management of breast cancer: evidence for improved preoperative staging. J Clin Oncol. 1999;17(1):110\u20139. https:\/\/doi.org\/10.1200\/JCO.1999.17.1.110.","journal-title":"J Clin Oncol"},{"issue":"12","key":"997_CR10","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1016\/j.jacr.2009.07.023","volume":"6","author":"ES Burnside","year":"2009","unstructured":"Burnside ES, Sickles EA, Bassett LW, Rubin DL, Lee CH, Ikeda DM, et al. The ACR BI-RADS experience: learning from history. J Am Coll Radiol. 2009;6(12):851\u201360. https:\/\/doi.org\/10.1016\/j.jacr.2009.07.023.","journal-title":"J Am Coll Radiol"},{"key":"997_CR11","first-page":"128","volume":"1","author":"SM Meystre","year":"2008","unstructured":"Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform. 2008;1:128\u201344.","journal-title":"Yearb Med Inform"},{"key":"997_CR12","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.jbi.2009.08.007","volume":"42","author":"D Demner-Fushman","year":"2009","unstructured":"Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? J Biomed Inform. 2009;42:760\u201372. https:\/\/doi.org\/10.1016\/j.jbi.2009.08.007.","journal-title":"J Biomed Inform"},{"key":"997_CR13","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1136\/amiajnl-2014-003009","volume":"22","author":"S Bozkurt","year":"2015","unstructured":"Bozkurt S, Lipson JA, Senol U, Rubin DL. Automatic abstraction of imaging features with their characteristics from mammography reports. J Am Med Inform Assoc. 2015;22:81\u201392. https:\/\/doi.org\/10.1136\/amiajnl-2014-003009.","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"997_CR14","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1148\/rg.2016150080","volume":"36","author":"T Cai","year":"2016","unstructured":"Cai T, Giannopoulos AA, Yu S, Kelil T, Ripley B, Kumamaru KK, et al. Natural language processing technologies in radiology research and clinical applications. Radiographics. 2016;36(1):76\u201391. https:\/\/doi.org\/10.1148\/rg.2016150080.","journal-title":"Radiographics"},{"key":"997_CR15","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1136\/amiajnl-2011-000464","volume":"18","author":"PM Nadkarni","year":"2011","unstructured":"Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc. 2011;18:544\u201351. https:\/\/doi.org\/10.1136\/amiajnl-2011-000464.","journal-title":"J Am Med Inform Assoc"},{"key":"997_CR16","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.jbi.2015.01.010","volume":"54","author":"H Gao","year":"2015","unstructured":"Gao H, Aiello Bowles EJ, Carrel D, Buist DS. Using natural language processing to extract mammographic findings. J Biomed Inform. 2015;54:77\u201384.","journal-title":"J Biomed Inform"},{"key":"997_CR17","unstructured":"Jain NL, Friedman C. Identification of findings suspicious for breast cancer based on natural language processing of mammogram reports. Proc AMIA Annu Fall Symp. 1997:829\u201333."},{"key":"997_CR18","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/s10278-011-9411-0","volume":"25","author":"M Sevenster","year":"2012","unstructured":"Sevenster M, van Ommering R, Qian Y. Automatically correlating clinical findings and body locations in radiology reports using MedLEE. J Digit Imaging. 2012;25:240\u20139. https:\/\/doi.org\/10.1007\/s10278-011-9411-0.","journal-title":"J Digit Imaging"},{"issue":"1","key":"997_CR19","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1089\/neu.2014.3801","volume":"33","author":"Emily Smitherman","year":"2016","unstructured":"Smitherman E, Hernandez A, Stavinoha PL, Huang R, Kernie SG, Diaz-Arrastia R, Miles DK. Predicting outcomes after pediatric traumatic brain injury by early magnetic resonance imaging lesion location and volume. J Neurotrauma. 2016 1;33(1):35\u201348.","journal-title":"Journal of Neurotrauma"},{"issue":"5\u20136","key":"997_CR20","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1159\/000441153","volume":"40","author":"D Liu","year":"2015","unstructured":"Liu D, Scalzo F, Starkman S, Rao NM, Hinman JD, Kim D, et al. DWI lesion patterns predict outcome in stroke patients with thrombolysis. Cerebrovasc Dis. 2015;40(5\u20136):279\u201385.","journal-title":"Cerebrovasc Dis"},{"issue":"10","key":"997_CR21","doi-asserted-by":"publisher","first-page":"2404","DOI":"10.1002\/ijc.27895","volume":"132","author":"C Allemani","year":"2013","unstructured":"Allemani C, Minicozzi P, Berrino F, Bastiaannet E, Gavin A, Galceran J, et al. Predictions of survival up to 10 years after diagnosis for European women with breast cancer in 2000-2002. Int J Cancer. 2013 May 15;132(10):2404\u201312.","journal-title":"Int J Cancer"},{"issue":"1","key":"997_CR22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1148\/radiol.2241011118","volume":"224","author":"G Hripcsak","year":"2002","unstructured":"Hripcsak G, Austin JH, Alderson PO, Friedman C. Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology. 2002;224(1):157\u201363. https:\/\/doi.org\/10.1148\/radiol.2241011118.","journal-title":"Radiology."},{"issue":"2","key":"997_CR23","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1148\/radiol.2341040049","volume":"234","author":"KJ Dreyer","year":"2005","unstructured":"Dreyer KJ, Kalra MK, Maher MM, Hurier AM, Asfaw BA, Schultz T, et al. Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study. Radiology. 2005;234(2):323\u20139. https:\/\/doi.org\/10.1148\/radiol.2341040049.","journal-title":"Radiology."},{"issue":"10","key":"997_CR24","doi-asserted-by":"publisher","first-page":"742","DOI":"10.7326\/0003-4819-144-10-200605160-00125","volume":"144","author":"B Chaudhry","year":"2006","unstructured":"Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742\u201352.","journal-title":"Ann Intern Med"},{"issue":"3","key":"997_CR25","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1148\/radiol.13122633","volume":"269","author":"SD O\u2019Connor","year":"2013","unstructured":"O\u2019Connor SD, Silverman SG, Ip IK, Maehara CK, Khorasani R. Simple cyst-appearing renal masses at unenhanced CT: can they be presumed to be benign? Radiology. 2013;269(3):793\u2013800. https:\/\/doi.org\/10.1148\/radiol.13122633.","journal-title":"Radiology."},{"issue":"1","key":"997_CR26","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10278-011-9425-7","volume":"25","author":"JJ Zopf","year":"2012","unstructured":"Zopf JJ, Langer JM, Boonn WW, Kim W, Zafar HM. Development of automated detection of radiology reports citing adrenal finding. J Digit Imaging. 2012;25(1):43\u20139. https:\/\/doi.org\/10.1007\/s10278-011-9425-7.","journal-title":"J Digit Imaging"},{"issue":"5","key":"997_CR27","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1136\/amiajnl-2011-000607","volume":"19","author":"B Percha","year":"2012","unstructured":"Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc. 2012;19(5):913\u20136. https:\/\/doi.org\/10.1136\/amiajnl-2011-000607.","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"997_CR28","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/s10278-017-9945-x","volume":"30","author":"D Forsberg","year":"2017","unstructured":"Forsberg D, Sjoblom E, Sunshine JL. Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J Digit Imaging. 2017;30(4):406\u201312. https:\/\/doi.org\/10.1007\/s10278-017-9945-x.","journal-title":"J Digit Imaging"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0997-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-019-0997-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0997-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:31:12Z","timestamp":1609201872000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-019-0997-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["997"],"URL":"https:\/\/doi.org\/10.1186\/s12911-019-0997-3","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12]]},"assertion":[{"value":"23 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This was a retrospective study that received approval from the responsible institutional review board of Peking University First Hospital with waiver of informed consent (No.:2016[1178]).","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"288"}}