000169807 001__ 169807
000169807 005__ 20240229133656.0
000169807 0247_ $$2doi$$a10.1016/j.compbiomed.2021.104624
000169807 0247_ $$2pmid$$apmid:34247131
000169807 0247_ $$2ISSN$$a0010-4825
000169807 0247_ $$2ISSN$$a1879-0534
000169807 0247_ $$2altmetric$$aaltmetric:108779899
000169807 037__ $$aDKFZ-2021-01561
000169807 041__ $$aEnglish
000169807 082__ $$a570
000169807 1001_ $$aAldraimli, Mahmoud$$b0
000169807 245__ $$aA data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy.
000169807 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2021
000169807 3367_ $$2DRIVER$$aarticle
000169807 3367_ $$2DataCite$$aOutput Types/Journal article
000169807 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1626094424_13917
000169807 3367_ $$2BibTeX$$aARTICLE
000169807 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000169807 3367_ $$00$$2EndNote$$aJournal Article
000169807 520__ $$aThe prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity 'REQUITE'. We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.
000169807 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
000169807 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
000169807 650_7 $$2Other$$aClassification
000169807 650_7 $$2Other$$aDesquamation
000169807 650_7 $$2Other$$aEarly toxicities
000169807 650_7 $$2Other$$aImbalanced learning
000169807 650_7 $$2Other$$aMachine learning
000169807 650_7 $$2Other$$aMeta-learning
000169807 650_7 $$2Other$$aREQUITE
000169807 650_7 $$2Other$$aRadiotherapy
000169807 650_7 $$2Other$$aSMOTE
000169807 7001_ $$aSoria, Daniele$$b1
000169807 7001_ $$aGrishchuck, Diana$$b2
000169807 7001_ $$aIngram, Samuel$$b3
000169807 7001_ $$aLyon, Robert$$b4
000169807 7001_ $$aMistry, Anil$$b5
000169807 7001_ $$aOliveira, Jorge$$b6
000169807 7001_ $$aSamuel, Robert$$b7
000169807 7001_ $$aShelley, Leila E A$$b8
000169807 7001_ $$aOsman, Sarah$$b9
000169807 7001_ $$aDwek, Miriam V$$b10
000169807 7001_ $$aAzria, David$$b11
000169807 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b12$$udkfz
000169807 7001_ $$aGutiérrez-Enríquez, Sara$$b13
000169807 7001_ $$aDe Santis, Maria Carmen$$b14
000169807 7001_ $$aRosenstein, Barry S$$b15
000169807 7001_ $$aDe Ruysscher, Dirk$$b16
000169807 7001_ $$aSperk, Elena$$b17
000169807 7001_ $$aSymonds, R Paul$$b18
000169807 7001_ $$aStobart, Hilary$$b19
000169807 7001_ $$aVega, Ana$$b20
000169807 7001_ $$aVeldeman, Liv$$b21
000169807 7001_ $$aWebb, Adam$$b22
000169807 7001_ $$aTalbot, Christopher J$$b23
000169807 7001_ $$aWest, Catharine M$$b24
000169807 7001_ $$aRattay, Tim$$b25
000169807 7001_ $$aconsortium, REQUITE$$b26$$eCollaboration Author
000169807 7001_ $$aChaussalet, Thierry J$$b27
000169807 773__ $$0PERI:(DE-600)1496984-1$$a10.1016/j.compbiomed.2021.104624$$gVol. 135, p. 104624 -$$p104624$$tComputers in biology and medicine$$v135$$x0010-4825$$y2021
000169807 909CO $$ooai:inrepo02.dkfz.de:169807$$pVDB
000169807 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aDeutsches Krebsforschungszentrum$$b12$$kDKFZ
000169807 9130_ $$0G:(DE-HGF)POF3-313$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vCancer risk factors and prevention$$x0
000169807 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0
000169807 9141_ $$y2021
000169807 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-01-30$$wger
000169807 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bCOMPUT BIOL MED : 2019$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-30
000169807 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2021-01-30
000169807 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lC020 Epidemiologie von Krebs$$x0
000169807 980__ $$ajournal
000169807 980__ $$aVDB
000169807 980__ $$aI:(DE-He78)C020-20160331
000169807 980__ $$aUNRESTRICTED