{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:48:42Z","timestamp":1742928522384,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322502"},{"type":"electronic","value":"9783030322519"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32251-9_67","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"611-619","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI"],"prefix":"10.1007","author":[{"given":"Jacob","family":"Antunes","sequence":"first","affiliation":[]},{"given":"Zhouping","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Charlems","family":"Alvarez-Jimenez","sequence":"additional","affiliation":[]},{"given":"Eduardo","family":"Romero","sequence":"additional","affiliation":[]},{"given":"Marwa","family":"Ismail","sequence":"additional","affiliation":[]},{"given":"Anant","family":"Madabhushi","sequence":"additional","affiliation":[]},{"given":"Pallavi","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Satish E.","family":"Viswanath","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"13","key":"67_CR1","doi-asserted-by":"publisher","first-page":"R150","DOI":"10.1088\/0031-9155\/61\/13\/R150","volume":"61","author":"S Yip","year":"2016","unstructured":"Yip, S., Aerts, H.: Applications and limitations of radiomics. Phys. Med. Biol. 61(13), R150 (2016)","journal-title":"Phys. Med. Biol."},{"key":"67_CR2","doi-asserted-by":"publisher","first-page":"e23421","DOI":"10.7554\/eLife.23421","volume":"6","author":"P Grossman","year":"2017","unstructured":"Grossman, P., et al.: Defining the biological basis of radiomic phenotypes in lung cancer. Elife 6, e23421 (2017)","journal-title":"Elife"},{"key":"67_CR3","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1002\/jmri.25969","volume":"48","author":"Y Sun","year":"2018","unstructured":"Sun, Y., et al.: Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: preliminary findings. J. Magn. Reson. Imaging 48, 615\u2013621 (2018)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"67_CR4","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1148\/radiol.2018172300","volume":"287","author":"N Horvat","year":"2018","unstructured":"Horvat, N., et al.: MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287(3), 833\u2013843 (2018)","journal-title":"Radiology"},{"key":"67_CR5","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"issue":"2","key":"67_CR6","doi-asserted-by":"publisher","first-page":"021219","DOI":"10.1117\/1.JMI.5.2.021219","volume":"5","author":"S Rathore","year":"2018","unstructured":"Rathore, S., et al.: Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J. Med. Imaging 5(2), 021219 (2018)","journal-title":"J. Med. Imaging"},{"issue":"8","key":"67_CR7","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/42.938251","volume":"20","author":"C Davatzikos","year":"2001","unstructured":"Davatzikos, C., et al.: A framework for predictive modeling of anatomical deformations. IEEE Trans. Med. Imaging 20(8), 836\u2013843 (2001)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"67_CR8","doi-asserted-by":"publisher","first-page":"15829","DOI":"10.1038\/s41598-017-13443-8","volume":"7","author":"S Ghose","year":"2017","unstructured":"Ghose, S., et al.: Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: preliminary findings. Nat. Sci. Rep. 7, 15829 (2017)","journal-title":"Nat. Sci. Rep."},{"key":"67_CR9","doi-asserted-by":"publisher","first-page":"072301","DOI":"10.1118\/1.4881515","volume":"41","author":"M Rusu","year":"2014","unstructured":"Rusu, M., et al.: Prostatome: a combined anatomical and disease based MRI atlas of the prostate. Med. Phys. 41, 072301 (2014)","journal-title":"Med. Phys."},{"issue":"1","key":"67_CR10","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1038\/s41598-018-37615-2","volume":"9","author":"P Prasanna","year":"2017","unstructured":"Prasanna, P., et al.: Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: a feasibility study. Nat. Sci. Rep. 9(1), 1145 (2017)","journal-title":"Nat. Sci. Rep."},{"issue":"1","key":"67_CR11","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2010","unstructured":"Klein, S., et al.: elastix: a toolbox for intensity based medical image registration. IEEE Trans. Med. Imaging 29(1), 196\u2013205 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"67_CR12","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., et al.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226\u20131238 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"67_CR13","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/j.ijrobp.2007.04.047","volume":"69","author":"S Yoon","year":"2007","unstructured":"Yoon, S., et al.: Clinical parameters predicting pathologic tumor response after preoperative chemoradiotherapy for rectal cancer. Int. J. Radiat. Oncol. Biol. Phys. 69(4), 1167\u20131172 (2007)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"67_CR14","unstructured":"Jessup, J., et al.: Colon and rectum. AJCC Cancer Staging Manual (2018)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32251-9_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:19:49Z","timestamp":1728519589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_67","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"539","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.07","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6.31","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}