{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:35:44Z","timestamp":1743086144634,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031138287"},{"type":"electronic","value":"9783031138294"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13829-4_68","type":"book-chapter","created":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T09:03:13Z","timestamp":1660467793000},"page":"757-766","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Ensemble Framework Integrating Whole Slide Pathological Images and miRNA Data to Predict Radiosensitivity of Breast Cancer Patients"],"prefix":"10.1007","author":[{"given":"Chao","family":"Dong","sequence":"first","affiliation":[]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wenhui","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Mengmeng","family":"Han","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Junfeng","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Yannan","family":"Bin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"issue":"3","key":"68_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, R.L., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-a Cancer J. Clin. 71(3), 209\u2013249 (2021)","journal-title":"Ca-a Cancer J. Clin."},{"doi-asserted-by":"crossref","unstructured":"Chen, X., Zheng, J., Zhuo, M.L., et al.: A six-gene-based signature for breast cancer radiotherapy sensitivity estimation. Biosci. Rep. 40(12) (2020). BSR20202376","key":"68_CR2","DOI":"10.1042\/BSR20202376"},{"key":"68_CR3","doi-asserted-by":"publisher","first-page":"102887","DOI":"10.1016\/j.critrevonc.2020.102887","volume":"147","author":"A Montero","year":"2020","unstructured":"Montero, A., Ciervide, R., Garcia-Aranda, M., et al.: Postmastectomy radiation therapy in early breast cancer: utility or futility? Crit. Rev. Oncol. Hematol. 147, 102887 (2020)","journal-title":"Crit. Rev. Oncol. Hematol."},{"issue":"1","key":"68_CR4","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1002\/ijc.31034","volume":"142","author":"M Lagendijk","year":"2018","unstructured":"Lagendijk, M., van Maaren, M.C., Saadatmand, S., et al.: Breast conserving therapy and mastectomy revisited: breast cancer-specific survival and the influence of prognostic factors in 129,692 patients. Int. J. Cancer 142(1), 165\u2013175 (2018)","journal-title":"Int. J. Cancer"},{"issue":"4","key":"68_CR5","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.ijrobp.2018.11.017","volume":"103","author":"H Quon","year":"2019","unstructured":"Quon, H., McNutt, T., Lee, J., et al.: Needs and challenges for radiation oncology in the era of precision medicine. Int. J. Radiat. Oncol. Biol. Phys. 103(4), 809\u2013817 (2019)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"4","key":"68_CR6","doi-asserted-by":"publisher","first-page":"957","DOI":"10.3390\/cancers12040957","volume":"12","author":"P Wen","year":"2020","unstructured":"Wen, P., Gao, Y., Chen, B., et al.: Pan-cancer analysis of radiotherapy benefits and immune infiltration in multiple human cancers. Cancers 12(4), 957 (2020)","journal-title":"Cancers"},{"key":"68_CR7","doi-asserted-by":"publisher","first-page":"628","DOI":"10.3389\/fonc.2020.00628","volume":"10","author":"J Meehan","year":"2020","unstructured":"Meehan, J., Gray, M., Martinez-Perez, C., et al.: Precision medicine and the role of biomarkers of radiotherapy response in breast cancer. Front. Oncol. 10, 628 (2020)","journal-title":"Front. Oncol."},{"issue":"18","key":"68_CR8","doi-asserted-by":"publisher","first-page":"5134","DOI":"10.1158\/1078-0432.CCR-12-0891","volume":"18","author":"SA Eschrich","year":"2012","unstructured":"Eschrich, S.A., Fulp, W.J., Pawitan, Y., et al.: Validation of a radiosensitivity molecular signature in breast cancer. Clin. Cancer Res. 18(18), 5134\u20135143 (2012)","journal-title":"Clin. Cancer Res."},{"issue":"16","key":"68_CR9","doi-asserted-by":"publisher","first-page":"3667","DOI":"10.1158\/1078-0432.CCR-14-2898","volume":"21","author":"C Speers","year":"2015","unstructured":"Speers, C., Zhao, S., Liu, M., et al.: Development and validation of a novel radiosensitivity signature in human breast cancer. Clin. Cancer Res. 21(16), 3667\u20133677 (2015)","journal-title":"Clin. Cancer Res."},{"key":"68_CR10","doi-asserted-by":"publisher","first-page":"960","DOI":"10.3389\/fgene.2020.00960","volume":"11","author":"J Liu","year":"2020","unstructured":"Liu, J., Han, M., Yue, Z., et al.: Prediction of radiosensitivity in head and neck squamous cell carcinoma based on multiple omics data. Front. Genet. 11, 960 (2020)","journal-title":"Front. Genet."},{"issue":"33","key":"68_CR11","doi-asserted-by":"publisher","first-page":"34649","DOI":"10.18632\/oncotarget.5299","volume":"6","author":"N Liu","year":"2015","unstructured":"Liu, N., Boohaker, R.J., Jiang, C., et al.: A radiosensitivity miRNA signature validated by the tcga database for head and neck squamous cell carcinomas. Oncotarget 6(33), 34649\u201334657 (2015)","journal-title":"Oncotarget"},{"issue":"3","key":"68_CR12","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1089\/cbr.2019.3220","volume":"35","author":"B Yang","year":"2020","unstructured":"Yang, B., Kuai, F., Chen, Z., et al.: Mir-634 decreases the radioresistance of human breast cancer cells by targeting stat3. Cancer Biother. Radiopharm. 35(3), 241\u2013248 (2020)","journal-title":"Cancer Biother. Radiopharm."},{"issue":"13","key":"68_CR13","doi-asserted-by":"publisher","first-page":"7504","DOI":"10.1111\/jcmm.15377","volume":"24","author":"J-H Zhang","year":"2020","unstructured":"Zhang, J.-H., Hou, R., Pan, Y., et al.: A five-microRNA signature for individualized prognosis evaluation and radiotherapy guidance in patients with diffuse lower-grade glioma. J. Cell Mol. Med. 24(13), 7504\u20137514 (2020)","journal-title":"J. Cell Mol. Med."},{"issue":"1","key":"68_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/s13578-021-00675-5","volume":"11","author":"Y Kang","year":"2021","unstructured":"Kang, Y., Wan, L., Wang, Q., et al.: Long noncoding RNA snhg1 promotes tert expression by sponging mir-18b-5p in breast cancer. Cell Biosci. 11(1), 169 (2021)","journal-title":"Cell Biosci."},{"issue":"1","key":"68_CR15","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.yexcr.2019.03.025","volume":"380","author":"D Li","year":"2019","unstructured":"Li, D., Wang, X., Yang, M., et al.: Mir3609 sensitizes breast cancer cells to adriamycin by blocking the programmed death-ligand 1 immune checkpoint. Exp. Cell Res. 380(1), 20\u201328 (2019)","journal-title":"Exp. Cell Res."},{"issue":"3","key":"68_CR16","doi-asserted-by":"publisher","first-page":"491","DOI":"10.4314\/tjpr.v20i3.7","volume":"20","author":"L Ma","year":"2021","unstructured":"Ma, L., Zheng, L., Zhang, D., et al.: Effect of cbx4\/mir-137\/notch1 signaling axis on the proliferation and migration of breast cancer cells. Trop. J. Pharm. Res. 20(3), 491\u2013496 (2021)","journal-title":"Trop. J. Pharm. Res."},{"issue":"1","key":"68_CR17","doi-asserted-by":"publisher","first-page":"9054","DOI":"10.1038\/s41598-020-65680-z","volume":"10","author":"N Masoudi-Khoram","year":"2020","unstructured":"Masoudi-Khoram, N., Abdolmaleki, P., Hosseinkhan, N., et al.: Differential miRNAs expression pattern of irradiated breast cancer cell lines is correlated with radiation sensitivity. Sci. Rep. 10(1), 9054 (2020)","journal-title":"Sci. Rep."},{"issue":"2","key":"68_CR18","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1158\/0008-5472.CAN-16-3105","volume":"78","author":"M Pajic","year":"2018","unstructured":"Pajic, M., Froio, D., Daly, S., et al.: Mir-139-5p modulates radiotherapy resistance in breast cancer by repressing multiple gene networks of DNA repair and ros defense. Can. Res. 78(2), 501\u2013515 (2018)","journal-title":"Can. Res."},{"issue":"3","key":"68_CR19","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1002\/1878-0261.12635","volume":"14","author":"C Grinan-Lison","year":"2020","unstructured":"Grinan-Lison, C., Olivares-Urbano, M.A., Jimenez, G., et al.: miRNAs as radio-response biomarkers for breast cancer stem cells. Mol. Oncol. 14(3), 556\u2013570 (2020)","journal-title":"Mol. Oncol."},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Zhu, F., et al.: WSISA: making survival prediction from whole slide histopathological images. In: 2017 IEEE Conferenceon Computer Visionand Pattern Recognition, pp. 6855\u20136863 (2017)","key":"68_CR20","DOI":"10.1109\/CVPR.2017.725"},{"key":"68_CR21","doi-asserted-by":"publisher","first-page":"e8668","DOI":"10.7717\/peerj.8668","volume":"8","author":"L Lu","year":"2020","unstructured":"Lu, L., Daigle, B.J., Jr.: Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma. PeerJ 8, e8668 (2020)","journal-title":"PeerJ"},{"key":"68_CR22","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cmpb.2018.04.008","volume":"161","author":"D Sun","year":"2018","unstructured":"Sun, D., Li, A., Tang, B., et al.: Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Comput. Methods Programs Biomed. 161, 45\u201353 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"68_CR23","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., Kooi, T., Bejnordi, B.E., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., Yao, J., Huang, J.: Deep convolutional neural network for survival analysis with pathological images. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine, pp. 544\u2013547 (2016)","key":"68_CR24","DOI":"10.1109\/BIBM.2016.7822579"},{"issue":"1A","key":"68_CR25","doi-asserted-by":"publisher","first-page":"A68","DOI":"10.5114\/wo.2014.47136","volume":"19","author":"K Tomczak","year":"2015","unstructured":"Tomczak, K., Czerwinska, P., Wiznerowicz, M.: The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. Wsp\u00f3lczesna Onkologia 19(1A), A68-77 (2015)","journal-title":"Contemp. Oncol. Wsp\u00f3lczesna Onkologia"},{"issue":"8","key":"68_CR26","doi-asserted-by":"publisher","first-page":"e71","DOI":"10.1093\/nar\/gkv1507","volume":"44","author":"A Colaprico","year":"2016","unstructured":"Colaprico, A., Silva, T.C., Olsen, C., et al.: Tcgabiolinks: an R\/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44(8), e71 (2016)","journal-title":"Nucleic Acids Res."},{"issue":"3","key":"68_CR27","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1002\/cam4.1369","volume":"7","author":"L Chen","year":"2018","unstructured":"Chen, L., Wen, Y., Zhang, J., et al.: Prediction of radiotherapy response with a 5-microRNA signature-based nomogram in head and neck squamous cell carcinoma. Cancer Med. 7(3), 726\u2013735 (2018)","journal-title":"Cancer Med."},{"key":"68_CR28","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/2153-3539.119005","volume":"4","author":"A Goode","year":"2013","unstructured":"Goode, A., Gilbert, B., Harkes, J., et al.: Openslide: a vendor-neutral software foundation for digital pathology. J. Pathol. Inform. 4, 27 (2013)","journal-title":"J. Pathol. Inform."},{"key":"68_CR29","doi-asserted-by":"publisher","first-page":"19","DOI":"10.4103\/jpi.jpi_10_20","volume":"11","author":"D Anand","year":"2020","unstructured":"Anand, D., Kurian, N.C., Dhage, S., et al.: Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images. J. Pathol. Inform. 11, 19 (2020)","journal-title":"J. Pathol. Inform."},{"issue":"1","key":"68_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/009053604000001048","volume":"33","author":"A Gelman","year":"2005","unstructured":"Gelman, A.: Analysis of variance-why it is more important than ever. Ann. Stat. 33(1), 1\u201353 (2005)","journal-title":"Ann. Stat."},{"issue":"12","key":"68_CR31","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love, M.I., Huber, W., Anders, S.: Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biol. 15(12), 550 (2014)","journal-title":"Genome Biol."},{"key":"68_CR32","first-page":"16","volume":"2011","author":"X Jiang","year":"2011","unstructured":"Jiang, X., Osl, M., Kim, J., et al.: Smooth isotonic regression: a new method to calibrate predictive models. AMIA Summits Transl. Sci. Proc. 2011, 16\u201320 (2011)","journal-title":"AMIA Summits Transl. Sci. Proc."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13829-4_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:11:59Z","timestamp":1710360719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13829-4_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031138287","9783031138294"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13829-4_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2022\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"IC-ICC-CN","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"449","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":"209","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":"47% - 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","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":"2.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}