{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:40:59Z","timestamp":1774939259228,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030898465","type":"print"},{"value":"9783030898472","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-89847-2_6","type":"book-chapter","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T10:03:32Z","timestamp":1634637812000},"page":"59-68","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fully Automatic Head and Neck Cancer Prognosis Prediction in PET\/CT"],"prefix":"10.1007","author":[{"given":"Pierre","family":"Fontaine","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent","family":"Andrearczyk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentin","family":"Oreiller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00ebl","family":"Castelli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario","family":"Jreige","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John O.","family":"Prior","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrien","family":"Depeursinge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,20]]},"reference":[{"issue":"2","key":"6_CR1","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, they are data. Radiology 278(2), 563\u2013577 (2016)","journal-title":"Radiology"},{"issue":"2","key":"6_CR2","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg, A., et al.: The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2), 328\u2013338 (2020)","journal-title":"Radiology"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Vallieres, M., et al.: Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci. Rep. 7(1), 1\u201314 (2017)","DOI":"10.1038\/s41598-017-10371-5"},{"key":"6_CR4","unstructured":"Andrearczyk, V., et al.: Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans. In International Conference on Medical Imaging with Deep Learning (MIDL) (2020)"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Apostolova, I., et al.: Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer. Eur. Radiol. 24(9), 2077\u20132087 (2014)","DOI":"10.1007\/s00330-014-3269-8"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Andrearczyk, V., et al.: Overview of the HECKTOR challenge at MICCAI 2020: automatic head and neck tumor segmentation in PET\/CT. In: Lecture Notes in Computer Science (LNCS) Challenges (2021)","DOI":"10.1007\/978-3-030-67194-5"},{"key":"6_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"10","key":"6_CR8","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR9","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Baid, U., et al.: Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 369\u2013379. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11726-9_33","DOI":"10.1007\/978-3-030-11726-9_33"},{"key":"6_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/978-3-319-75238-9_25","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"F Isensee","year":"2018","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287\u2013297. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_25"},{"key":"6_CR12","unstructured":"Andrearczyk, V., Oreiller, V., Depeursinge, A.: Oropharynx detection in PET-CT for tumor segmentation. In: Irish Machine Vision and Image Processing (2020)"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Harrell Jr, F.E., Lee, K.L., Mark, D.B.: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361\u2013387 (1996)","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Lambin, P., et al.: Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14(12), 749\u2013762 (2017)","DOI":"10.1038\/nrclinonc.2017.141"},{"issue":"1","key":"6_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40644-020-00329-8","volume":"20","author":"Y Suter","year":"2020","unstructured":"Suter, Y., et al.: Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer Imaging 20(1), 1\u201313 (2020)","journal-title":"Cancer Imaging"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"David, C.R., et al.: Regression models and life tables (with discussion). J. R. Stat. Soc. 34(2), 187\u2013220 (1972)","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Ishwaran, H., et al.: Random survival forests. Ann. Appl. Stat. 2(3), 841\u2013860 (2008)","DOI":"10.1214\/08-AOAS169"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Lowekamp, B.C., Chen, D.T., Ib\u00e1\u00f1ez, L., Blezek, D.: The design of simpleitk. Front. Neuroinf. 7, 45 (2013)","DOI":"10.3389\/fninf.2013.00045"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017)","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"6_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"212","key":"6_CR21","first-page":"1","volume":"21","author":"S P\u00f6lsterl","year":"2020","unstructured":"P\u00f6lsterl, S.: scikit-survival: a library for time-to-event analysis built on top of scikit-learn. J. Mach. Learn. Res. 21(212), 1\u20136 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR22","doi-asserted-by":"publisher","unstructured":"Andrearczyk, V., et al.: Multi-task deep segmentation and radiomics for automatic prognosis in head and neck cancer. In: Rekik, I., Adeli, E., Park, S.H., Schnabel, J. (eds.) Predictive Intelligence in Medicine. PRIME 2021. Lecture Notes in Computer Science, vol. 12928, Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87602-9_14","DOI":"10.1007\/978-3-030-87602-9_14"},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-540-24775-3_3","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"RR Bouckaert","year":"2004","unstructured":"Bouckaert, R.R., Frank, E.: Evaluating the replicability of significance tests for comparing learning algorithms. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 3\u201312. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24775-3_3"},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Vorwerk, H., et al.: The delineation of target volumes for radiotherapy of lung cancer patients. Radiother. Oncol. 91(3), 455\u2013460 (2009)","DOI":"10.1016\/j.radonc.2009.03.014"},{"issue":"1","key":"6_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-76310-z","volume":"10","author":"P Fontaine","year":"2020","unstructured":"Fontaine, P., Acosta, O., Castelli, J., De Crevoisier, R., M\u00fcller, H., Depeursinge, A.: The importance of feature aggregation in radiomics: a head and neck cancer study. Sci. Rep. 10(1), 1\u201311 (2020)","journal-title":"Sci. Rep."},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Zhai, T.T., et al.: Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters. Radiother. Oncol. 124(2), 256\u2013262 (2017)","DOI":"10.1016\/j.radonc.2017.07.013"}],"container-title":["Lecture Notes in Computer Science","Multimodal Learning for Clinical Decision Support"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-89847-2_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T10:45:19Z","timestamp":1634640319000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-89847-2_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030898465","9783030898472"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-89847-2_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML-CDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Multimodal Learning for Clinical Decision Support","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml-cds2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/mcbr-cds.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":"16","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":"10","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":"63% - 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":"2.19","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":"1.46","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":"The workshop was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}