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In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.<\/jats:p>","DOI":"10.1098\/rsfs.2016.0163","type":"journal-article","created":{"date-parts":[[2017,12,15]],"date-time":"2017-12-15T10:10:19Z","timestamp":1513332619000},"page":"20160163","update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator"],"prefix":"10.1098","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-3248","authenticated-orcid":false,"given":"Eleftherios","family":"Ouzounoglou","sequence":"first","affiliation":[{"name":"In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eleni","family":"Kolokotroni","sequence":"additional","affiliation":[{"name":"In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Stanulla","sequence":"additional","affiliation":[{"name":"Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2054-477X","authenticated-orcid":false,"given":"Georgios S.","family":"Stamatakos","sequence":"additional","affiliation":[{"name":"In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"175","published-online":{"date-parts":[[2017,12,15]]},"reference":[{"key":"e_1_3_7_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2013.2284276"},{"key":"e_1_3_7_3_2","doi-asserted-by":"publisher","DOI":"10.4172\/2155-9538.1000e114"},{"key":"e_1_3_7_4_2","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2008.0089"},{"key":"e_1_3_7_5_2","unstructured":"Kolokotroni E Ouzounoglou E Stanulla M Dionysiou D Stamatakos GS. 2014 In silico oncology: developing and clinically adapting the acute lymphoblastic leukemia (ALL) Oncosimulator by exploiting pathway based gene expression analysis in the context of the ALL-BFM 2000 clinical study. 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