{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:18:03Z","timestamp":1743128283543,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031450860"},{"type":"electronic","value":"9783031450877"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-45087-7_4","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T19:01:37Z","timestamp":1696705297000},"page":"31-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The Value of Ensemble Learning Model Based on Conventional Non-Contrast MRI in the Pathological Grading of Cervical Cancer"],"prefix":"10.1007","author":[{"given":"Zhimin","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fajin","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhibo","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"issue":"3","key":"4_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209\u2013249 (2021)","journal-title":"CA Cancer J. Clin."},{"issue":"08","key":"4_CR2","first-page":"835","volume":"34","author":"YL Xie","year":"2019","unstructured":"Xie, Y.L., et al.: The value of texture analysis based on dynamic contrast-enhanced MRI for differentiating cervical adenocarcinoma from squamous cell carcinoma and its prediction of stages. Radiol. Pract. 34(08), 835\u2013840 (2019). (in Chinese)","journal-title":"Radiol. Pract."},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.cca.2018.05.025","volume":"483","author":"M Zhu","year":"2018","unstructured":"Zhu, M., et al.: Pretreatment neutrophil-lymphocyte and platelet-lymphocyte ratio predict clinical outcome and prognosis for cervical Cancer. Clin. Chim. Acta 483, 296\u2013302 (2018)","journal-title":"Clin. Chim. Acta"},{"issue":"2","key":"4_CR4","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s00432-018-2793-3","volume":"145","author":"LC Horn","year":"2019","unstructured":"Horn, L.C., et al.: Prognostic relevance of low-grade versus high-grade FIGO IB1 squamous cell uterine cervical carcinomas. J. Cancer Res. Clin. Oncol. 145(2), 457\u2013462 (2019)","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijsu.2017.05.074","volume":"44","author":"J Zhou","year":"2017","unstructured":"Zhou, J., et al.: The prognostic value of histologic subtype in node-positive early-stage cervical cancer after hysterectomy and adjuvant radiotherapy. Int. J. Surg. 44, 1\u20136 (2017)","journal-title":"Int. J. Surg."},{"issue":"8","key":"4_CR6","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.1002\/cncr.23817","volume":"113","author":"E Vincens","year":"2008","unstructured":"Vincens, E., et al.: Accuracy of magnetic resonance imaging in predicting residual disease in patients treated for stage IB2\/II cervical carcinoma with chemoradiation therapy: correlation of radiologic findings with surgicopathologic results. Cancer 113(8), 2158\u20132165 (2008)","journal-title":"Cancer"},{"issue":"8","key":"4_CR7","doi-asserted-by":"publisher","first-page":"5576","DOI":"10.1007\/s00330-020-07612-z","volume":"31","author":"Q Zhang","year":"2021","unstructured":"Zhang, Q., et al.: Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study. Eur. Radiol. 31(8), 5576\u20135585 (2021)","journal-title":"Eur. Radiol."},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1186\/s12885-022-09826-4","volume":"22","author":"Z He","year":"2022","unstructured":"He, Z., et al.: The value of HPV genotypes combined with clinical indicators in the classification of cervical squamous cell carcinoma and adenocarcinoma. BMC Cancer 22(1), 776 (2022)","journal-title":"BMC Cancer"},{"key":"4_CR9","unstructured":"Wang, C., et al.: Application of DCE-MRI combined with DWI in the evaluation of clinical staging of patients with cervical squamous cell carcinoma. Pract. J. Cancer. 37(03), 492\u2013494+500 (2022) (in Chinese)"},{"issue":"12","key":"4_CR10","first-page":"29","volume":"12","author":"JR Liu","year":"2021","unstructured":"Liu, J.R., et al.: Multiparametric magnetic resonance imaging to characterize pathological grading and stage of cervical squamous cell carcinoma. Chin. J. Magn. Reson. Imaging 12(12), 29\u201333 (2021). (in Chinese)","journal-title":"Chin. J. Magn. Reson. Imaging"},{"issue":"3","key":"4_CR11","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s10534-016-9931-7","volume":"29","author":"M Rogosnitzky","year":"2016","unstructured":"Rogosnitzky, M., Branch, S.: Gadolinium-based contrast agent toxicity: a review of known and proposed mechanisms. Biometals 29(3), 365\u2013376 (2016)","journal-title":"Biometals"},{"issue":"2","key":"4_CR12","doi-asserted-by":"publisher","first-page":"W138","DOI":"10.2214\/AJR.10.4885","volume":"196","author":"MR Prince","year":"2011","unstructured":"Prince, M.R., et al.: Incidence of immediate gadolinium contrast media reactions. AJR Am. J. Roentgenol. 196(2), W138\u2013W143 (2011)","journal-title":"AJR Am. J. Roentgenol."},{"issue":"4","key":"4_CR13","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin, P., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441\u2013446 (2012)","journal-title":"Eur. J. Cancer"},{"issue":"2","key":"4_CR14","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures. Data. Radiol. 278(2), 563\u2013577 (2016)","journal-title":"Data. Radiol."},{"issue":"12","key":"4_CR15","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin, P., et al.: Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14(12), 749\u2013762 (2017)","journal-title":"Nat. Rev. Clin. Oncol."},{"issue":"6","key":"4_CR16","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1177\/02841851211014188","volume":"63","author":"W Wang","year":"2022","unstructured":"Wang, W., et al.: Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage. Acta Radiol. 63(6), 847\u2013856 (2022)","journal-title":"Acta Radiol."},{"key":"4_CR17","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.ejrad.2019.01.003","volume":"114","author":"T Wang","year":"2019","unstructured":"Wang, T., et al.: Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging. Eur. J. Radiol. 114, 128\u2013135 (2019)","journal-title":"Eur. J. Radiol."},{"issue":"3","key":"4_CR18","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/jmri.27101","volume":"52","author":"M Xiao","year":"2020","unstructured":"Xiao, M., et al.: Multiparametric MRI-based radiomics nomogram for predicting lymph node metastasis in early-stage cervical cancer. J. Magn. Reson. Imaging 52(3), 885\u2013896 (2020)","journal-title":"J. Magn. Reson. Imaging"},{"issue":"6","key":"4_CR19","first-page":"1082","volume":"40","author":"JW Xu","year":"2018","unstructured":"Xu, J.W., Yang, Y.: Ensemble learning methods: a research review. J. Yunnan Univ. 40(6), 1082\u20131092 (2018). (in Chinese)","journal-title":"J. Yunnan Univ."},{"issue":"2","key":"4_CR20","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","volume":"5","author":"DH Wolpert","year":"1992","unstructured":"Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241\u2013259 (1992)","journal-title":"Neural Netw."},{"issue":"2","key":"4_CR21","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155\u2013163 (2016)","journal-title":"J. Chiropr. Med."},{"issue":"6","key":"4_CR22","doi-asserted-by":"publisher","DOI":"10.3802\/jgo.2018.29.e91","volume":"29","author":"K Matsuo","year":"2018","unstructured":"Matsuo, K., et al.: Association of tumor differentiation grade and survival of women with squamous cell carcinoma of the uterine cervix. J. Gynecol. Oncol. 29(6), e91 (2018)","journal-title":"J. Gynecol. Oncol."},{"issue":"06","key":"4_CR23","first-page":"477","volume":"11","author":"YQ Cui","year":"2020","unstructured":"Cui, Y.Q., et al.: Advances in radiomics of cervical cancer. Chin. J. Magn. Reson. Imaging 11(06), 477\u2013480 (2020). (in Chinese)","journal-title":"Chin. J. Magn. Reson. Imaging"},{"issue":"10","key":"4_CR24","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1007\/s11547-019-01055-3","volume":"124","author":"M Ciolina","year":"2019","unstructured":"Ciolina, M., et al.: Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix. Radiol. Med. 124(10), 955\u2013964 (2019)","journal-title":"Radiol. Med."},{"issue":"12","key":"4_CR25","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.crad.2010.07.008","volume":"65","author":"M Costantini","year":"2010","unstructured":"Costantini, M., et al.: Diffusion-weighted imaging in breast cancer: relationship between apparent diffusion coefficient and tumour aggressiveness. Clin. Radiol. 65(12), 1005\u20131012 (2010)","journal-title":"Clin. Radiol."},{"issue":"5","key":"4_CR26","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.2214\/AJR.14.13350","volume":"204","author":"Y Lin","year":"2015","unstructured":"Lin, Y., et al.: Correlation of histogram analysis of apparent diffusion coefficient with uterine cervical pathologic finding. AJR Am. J. Roentgenol. 204(5), 1125\u20131131 (2015)","journal-title":"AJR Am. J. Roentgenol."},{"issue":"2","key":"4_CR27","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ygyno.2009.09.044","volume":"116","author":"GS Payne","year":"2010","unstructured":"Payne, G.S., et al.: Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer. Gynecol. Oncol. 116(2), 246\u2013252 (2010)","journal-title":"Gynecol. Oncol."},{"issue":"4","key":"4_CR28","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1007\/s00330-012-2681-1","volume":"23","author":"F Kuang","year":"2013","unstructured":"Kuang, F., et al.: The value of apparent diffusion coefficient in the assessment of cervical cancer. Eur. Radiol. 23(4), 1050\u20131058 (2013)","journal-title":"Eur. Radiol."},{"issue":"04","key":"4_CR29","first-page":"148","volume":"37","author":"G Zhou","year":"2018","unstructured":"Zhou, G., Guo, F.L.: Research on ensemble learning. Comput. Technol. Autom. 37(04), 148\u2013153 (2018). (in Chinese)","journal-title":"Comput. Technol. Autom."},{"issue":"07","key":"4_CR30","first-page":"869","volume":"18","author":"Y Liu","year":"2021","unstructured":"Liu, Y., Sun, J.F., Ding, S.: Diagnostic value of routine inflammatory markers combined with squamous cell carcinoma associated antigen and carbohydrate antigen 199 in cervical adenocarcinoma. Lab. Med. Clin. 18(07), 869\u2013873 (2021). (in Chinese)","journal-title":"Lab. Med. Clin."}],"container-title":["Lecture Notes in Computer Science","Computational Mathematics Modeling in Cancer Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45087-7_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T14:01:51Z","timestamp":1702216911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45087-7_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031450860","9783031450877"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45087-7_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CMMCA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Mathematics Modeling in Cancer Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cmmca2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cmmca.github.io\/cmmca2023\/","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":"25","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":"17","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":"68% - 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":"2","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":"3","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)"}}]}}