{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:16:09Z","timestamp":1767057369928,"version":"3.48.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030668426"},{"type":"electronic","value":"9783030668433"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-66843-3_21","type":"book-chapter","created":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T11:03:20Z","timestamp":1609326200000},"page":"212-228","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0984-3998","authenticated-orcid":false,"given":"Thomas C.","family":"Booth","sequence":"first","affiliation":[]},{"given":"Bernice","family":"Akpinar","sequence":"additional","affiliation":[]},{"given":"Andrei","family":"Roman","sequence":"additional","affiliation":[]},{"given":"Haris","family":"Shuaib","sequence":"additional","affiliation":[]},{"given":"Aysha","family":"Luis","sequence":"additional","affiliation":[]},{"given":"Alysha","family":"Chelliah","sequence":"additional","affiliation":[]},{"given":"Ayisha","family":"Al Busaidi","sequence":"additional","affiliation":[]},{"given":"Ayesha","family":"Mirchandani","sequence":"additional","affiliation":[]},{"given":"Burcu","family":"Alparslan","sequence":"additional","affiliation":[]},{"given":"Nina","family":"Mansoor","sequence":"additional","affiliation":[]},{"given":"Keyoumars","family":"Ashkan","sequence":"additional","affiliation":[]},{"given":"Sebastien","family":"Ourselin","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Modat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1056\/NEJMoa043330","volume":"352","author":"R Stupp","year":"2005","unstructured":"Stupp, R., et al.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987\u2013996 (2005). https:\/\/doi.org\/10.1056\/NEJMoa043330","journal-title":"N. Engl. J. Med."},{"key":"21_CR2","unstructured":"FDA-NIH Biomarker Working Group: BEST (Biomarkers, EndpointS, and other Tools) Resource. Food and Drug Administration (US), Silver Spring. Co-published by National Institutes of Health (US), Bethesda (2016)"},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1200\/jco.1990.8.7.1277","volume":"8","author":"D MacDonald","year":"2010","unstructured":"MacDonald, D., Cascino, T.L., Schold, S.C., Cairncross, J.G.: Response criteria for phase II studies of supratentorial malignant glioma. J. Clin. Oncol. 8, 1277\u20131280 (2010). https:\/\/doi.org\/10.1200\/jco.1990.8.7.1277","journal-title":"J. Clin. Oncol."},{"key":"21_CR4","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1200\/JCO.2009.26.3541","volume":"28","author":"PY Wen","year":"2010","unstructured":"Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., Galanis, E.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro- oncology working group. J. Clin. Oncol. 28, 1963\u20131972 (2010). https:\/\/doi.org\/10.1200\/JCO.2009.26.3541","journal-title":"J. Clin. Oncol."},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3174\/ng.3110014","volume":"01","author":"TC Booth","year":"2011","unstructured":"Booth, T.C., et al.: Neuro-oncology single-photon emission CT: a current overview. Neurographics 01, 108\u2013120 (2011)","journal-title":"Neurographics"},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s11060-006-9241-y","volume":"82","author":"MC Chamberlain","year":"2007","unstructured":"Chamberlain, M.C., Glantz, M.J., Chalmers, L., Van Horn, A., Sloan, A.E.: Early necrosis following concurrent Temodar and radiotherapy in patients with glioblastoma. J. Neurooncol. 82, 81\u201383 (2007). https:\/\/doi.org\/10.1007\/s11060-006-9241-y","journal-title":"J. Neurooncol."},{"key":"21_CR7","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/S1474-4422(10)70181-2","volume":"9","author":"FG Dhermain","year":"2010","unstructured":"Dhermain, F.G., et al.: Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet Neurol. 9, 906\u2013920 (2010). https:\/\/doi.org\/10.1016\/S1474-4422(10)70181-2","journal-title":"Lancet Neurol."},{"key":"21_CR8","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/S1470-2045(08)70125-6","volume":"9","author":"D Brandsma","year":"2008","unstructured":"Brandsma, D., et al.: Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 9, 453\u2013461 (2008)","journal-title":"Lancet Oncol."},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1093\/neuonc\/nou129","volume":"17","author":"A Radbruch","year":"2015","unstructured":"Radbruch, A., et al.: Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro Oncol. 17, 151\u2013159 (2015)","journal-title":"Neuro Oncol."},{"key":"21_CR10","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1093\/neuonc\/nos307","volume":"15","author":"N Verma","year":"2013","unstructured":"Verma, N., et al.: Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol. 15, 515\u2013534 (2013)","journal-title":"Neuro Oncol."},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.crad.2019.07.001","volume":"75","author":"TC Booth","year":"2020","unstructured":"Booth, T.C., Williams, M., Luis, A., Cardoso, J., Ashkan, K., Shuaib, H.: Machine learning and glioma imaging biomarkers. Clin. Radiol. 75, 20\u201332 (2020). https:\/\/doi.org\/10.1016\/j.crad.2019.07.001","journal-title":"Clin. Radiol."},{"key":"21_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-030-11723-8_4","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"TC Booth","year":"2019","unstructured":"Booth, T.C.: An update on machine learning in neuro-oncology diagnostics. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 37\u201344. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_4"},{"key":"21_CR13","doi-asserted-by":"publisher","unstructured":"McInnes, M.D.F., Moher, D., Thombs, B.D., McGrath, T.A., Bossuyt, P.M., The PRISMA-DTA Group: Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA 319, 388\u2013396 (2018). https:\/\/doi.org\/10.1001\/jama.2017.19163","DOI":"10.1001\/jama.2017.19163"},{"key":"21_CR14","unstructured":"Bossuyt, P.M., Leeflang, M.M.: Chapter 6: Developing criteria for including studies. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4. The Cochrane Collaboration (2008)"},{"key":"21_CR15","unstructured":"de Vet, H.C.W., Eisinga, A., Riphagen, I.I., Aertgeerts, B., Pewsner, D.: Chapter 7: Searching for studies. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4. The Cochrane Collaboration (2008)"},{"key":"21_CR16","unstructured":"Reitsma, J.B., Rutjes, A.W.S., Whiting, P., Vlassov, V.V., Leeflang, M.M.G., Deeks, J.J.: Chapter 9: Assessing methodological quality. In: Deeks, J.J., Bossuyt, P.M., Gatsonis, C. (eds.) Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 1.0.0. The Cochrane Collaboration (2009). http:\/\/srdta.cochrane\/org\/"},{"key":"21_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting, P.F., et al.: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 155, 529\u2013536 (2011)","journal-title":"Ann. Intern. Med."},{"key":"21_CR18","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/j.crad.2016.01.014","volume":"5","author":"TC Booth","year":"2016","unstructured":"Booth, T.C., et al.: Re: \u201cTumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma\u201d. Clin. Radiol. 5, 495\u2013496 (2016)","journal-title":"Clin. Radiol."},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Booth, T.C., et al.: Comment on \u201cThe role of imaging in the management of progressive glioblastoma. A systematic review and evidence-based clinical practice guideline\u201d. J. Neurooncol. 121, 423\u2013424 (2015)","DOI":"10.1007\/s11060-014-1649-1"},{"issue":"2","key":"21_CR20","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s13311-016-0507-6","volume":"14","author":"BM Ellingson","year":"2017","unstructured":"Ellingson, B.M., Wen, P.Y., Cloughesy, T.F.: Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurotherapeutics 14(2), 307\u2013320 (2017). https:\/\/doi.org\/10.1007\/s13311-016-0507-6","journal-title":"Neurotherapeutics"},{"key":"21_CR21","doi-asserted-by":"publisher","first-page":"0176528","DOI":"10.1371\/journal.pone.0176528","volume":"12","author":"TC Booth","year":"2017","unstructured":"Booth, T.C., et al.: Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma. PLoS ONE 12, 0176528 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0176528","journal-title":"PLoS ONE"},{"key":"21_CR22","doi-asserted-by":"publisher","first-page":"842","DOI":"10.1148\/radiol.12111472","volume":"266","author":"S Gahramanov","year":"2013","unstructured":"Gahramanov, S., et al.: Pseudoprogression of glioblastoma after chemo- and radiation therapy: diagnosis by using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging with ferumoxytol versus gadoteridol and correlation with survival. Radiology 266, 842\u2013852 (2013)","journal-title":"Radiology"},{"key":"21_CR23","unstructured":"Howick, J., et al.: The Oxford 2011 levels of evidence. Oxford Centre for Evidence-Based Medicine, Oxford (2016). http:\/\/www.cebm.net\/index.aspx?o=5653"},{"key":"21_CR24","unstructured":"Buwanabala, J., et al.: The (mis)use of imaging criteria in the assessment of glioblastoma treatment response. American Society of Neuroradiology, Boston. Scientific Poster 2616 (2019)"},{"issue":"2","key":"21_CR25","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s11060-018-03037-3","volume":"141","author":"M Holdhoff","year":"2018","unstructured":"Holdhoff, M., et al.: The consistency of neuropathological diagnoses in patients undergoing surgery for suspected recurrence of glioblastoma. J. Neurooncol. 141(2), 347\u2013354 (2018). https:\/\/doi.org\/10.1007\/s11060-018-03037-3. pmid:30414096","journal-title":"J. Neurooncol."},{"key":"21_CR26","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1093\/neuonc\/noaa045","volume":"22","author":"C Davatzikos","year":"2020","unstructured":"Davatzikos, C., et al.: AI-based prognostic imaging biomarkers for precision neurooncology: the ReSPOND consortium. Neuro Oncol. 22, 886\u2013888 (2020). https:\/\/doi.org\/10.1093\/neuonc\/noaa045","journal-title":"Neuro Oncol."},{"key":"21_CR27","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1093\/neuonc\/noy133","volume":"21","author":"JY Kim","year":"2019","unstructured":"Kim, J.Y., et al.: Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro-oncology 21, 404\u2013414 (2019)","journal-title":"Neuro-oncology"},{"issue":"11","key":"21_CR28","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1007\/s00234-019-02255-4","volume":"61","author":"JY Kim","year":"2019","unstructured":"Kim, J.Y., Yoon, M.J., Park, J.E., Choi, E.J., Lee, J., Kim, H.S.: Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma. Neuroradiology 61(11), 1261\u20131272 (2019). https:\/\/doi.org\/10.1007\/s00234-019-02255-4","journal-title":"Neuroradiology"},{"key":"21_CR29","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.jocn.2019.10.003","volume":"70","author":"S Bacchi","year":"2019","unstructured":"Bacchi, S., et al.: Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: a pilot study. J. Clin. Neurosci. 70, 11\u201313 (2019)","journal-title":"J. Clin. Neurosci."},{"key":"21_CR30","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1038\/s41467-019-11007-0","volume":"10","author":"N Elshafeey","year":"2019","unstructured":"Elshafeey, N., et al.: Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma. Nat. Commun. 10, 3170 (2019)","journal-title":"Nat. Commun."},{"key":"21_CR31","doi-asserted-by":"publisher","first-page":"4042","DOI":"10.1002\/nbm.4042","volume":"32","author":"G Verma","year":"2019","unstructured":"Verma, G., et al.: Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma. NMR Biomed. 32, 4042 (2019)","journal-title":"NMR Biomed."},{"key":"21_CR32","doi-asserted-by":"publisher","first-page":"2187","DOI":"10.3174\/ajnr.A5858","volume":"39","author":"M Ismail","year":"2018","unstructured":"Ismail, M., et al.: Shape features of the lesion habitat to differentiate brain tumor progression from pseudoprogression on routine multiparametric MRI: a multisite study. AJNR Am. J. Neuroradiol. 39, 2187\u20132193 (2018)","journal-title":"AJNR Am. J. Neuroradiol."},{"key":"21_CR33","first-page":"1","volume":"1","author":"A Bani-Sadr","year":"2019","unstructured":"Bani-Sadr, A., et al.: Conventional MRI radiomics in patients with suspected early- or pseudo-progression. Neurooncol. Adv. 1, 1\u20139 (2019)","journal-title":"Neurooncol. Adv."},{"key":"21_CR34","doi-asserted-by":"publisher","first-page":"3191","DOI":"10.2147\/CMAR.S244262","volume":"12","author":"XY Gao","year":"2020","unstructured":"Gao, X.Y., et al.: Differentiation of treatment-related effects from glioma recurrence using machine learning classifiers based upon pre-and post-contrast T1WI and T2 FLAIR subtraction features: a two-center study. Cancer Manag. Res. 12, 3191\u20133201 (2020)","journal-title":"Cancer Manag. Res."},{"key":"21_CR35","doi-asserted-by":"publisher","first-page":"12516","DOI":"10.1038\/s41598-018-31007-2","volume":"8","author":"BS Jang","year":"2018","unstructured":"Jang, B.S., Jeon, S.H., Kim, I.H., Kim, I.A.: Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci. Rep. 8, 12516 (2018)","journal-title":"Sci. Rep."},{"key":"21_CR36","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1002\/mp.14003","volume":"47","author":"M Li","year":"2020","unstructured":"Li, M., Tang, H., Chan, M.D., Zhou, X., Qian, X.: DC-AL GAN: pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet. Med. Phys. 47, 1139\u20131150 (2020)","journal-title":"Med. Phys."},{"key":"21_CR37","doi-asserted-by":"publisher","unstructured":"Akbari, H., et al.: Histopathology- validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma. Cancer 0, 1\u201312 (2020). https:\/\/doi.org\/10.1002\/cncr.32790","DOI":"10.1002\/cncr.32790"},{"key":"21_CR38","doi-asserted-by":"publisher","unstructured":"Li, X., Xu, G., Cao, Q., Zou, W., Xu, Y., Cong, P.: Identification of glioma pseudoprogression based on gabor dictionary and sparse representation model. NeuroQuantology 16, 43\u201351 (2018). https:\/\/doi.org\/10.14704\/nq.2018.16.1.1178","DOI":"10.14704\/nq.2018.16.1.1178"},{"key":"21_CR39","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1038\/s41416-018-0342-0","volume":"120","author":"S Wang","year":"2019","unstructured":"Wang, S., et al.: Multiparametric magnetic resonance imaging in the assessment of anti-EGFRvIII chimeric antigen receptor T cell therapy in patients with recurrent glioblastoma. Br. J. Cancer 120, 54\u201356 (2019)","journal-title":"Br. J. Cancer"},{"key":"21_CR40","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.radonc.2018.11.025","volume":"131","author":"K Yang","year":"2019","unstructured":"Yang, K., et al.: Cancer genetic markers according to radiotherapeutic response in patients with primary glioblastoma - radiogenomic approach for precision medicine. Radiother. Oncol. 131, 66\u201374 (2019)","journal-title":"Radiother. Oncol."},{"issue":"3","key":"21_CR41","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s00259-018-4180-3","volume":"46","author":"M Lundemann","year":"2018","unstructured":"Lundemann, M., et al.: Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma. Eur. J. Nucl. Med. Mol. Imaging 46(3), 603\u2013613 (2018). https:\/\/doi.org\/10.1007\/s00259-018-4180-3","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66843-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:04:16Z","timestamp":1767056656000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66843-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030668426","9783030668433"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66843-3_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RNO-AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Radiomics and Radiogenomics in Neuro-oncology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","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":"rno-ai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/rno-ai-2020\/home","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":"None","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","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":"8","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":"100% - 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":"No","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 due to the COVID-19 pandemic. Three keynote talks are also included.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}