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| 001 | 169807 | ||
| 005 | 20240229133656.0 | ||
| 024 | 7 | _ | |a 10.1016/j.compbiomed.2021.104624 |2 doi |
| 024 | 7 | _ | |a pmid:34247131 |2 pmid |
| 024 | 7 | _ | |a 0010-4825 |2 ISSN |
| 024 | 7 | _ | |a 1879-0534 |2 ISSN |
| 024 | 7 | _ | |a altmetric:108779899 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2021-01561 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Aldraimli, Mahmoud |b 0 |
| 245 | _ | _ | |a A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2021 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1626094424_13917 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a The 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. |
| 536 | _ | _ | |a 313 - Krebsrisikofaktoren und Prävention (POF4-313) |0 G:(DE-HGF)POF4-313 |c POF4-313 |x 0 |f POF IV |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de |
| 650 | _ | 7 | |a Classification |2 Other |
| 650 | _ | 7 | |a Desquamation |2 Other |
| 650 | _ | 7 | |a Early toxicities |2 Other |
| 650 | _ | 7 | |a Imbalanced learning |2 Other |
| 650 | _ | 7 | |a Machine learning |2 Other |
| 650 | _ | 7 | |a Meta-learning |2 Other |
| 650 | _ | 7 | |a REQUITE |2 Other |
| 650 | _ | 7 | |a Radiotherapy |2 Other |
| 650 | _ | 7 | |a SMOTE |2 Other |
| 700 | 1 | _ | |a Soria, Daniele |b 1 |
| 700 | 1 | _ | |a Grishchuck, Diana |b 2 |
| 700 | 1 | _ | |a Ingram, Samuel |b 3 |
| 700 | 1 | _ | |a Lyon, Robert |b 4 |
| 700 | 1 | _ | |a Mistry, Anil |b 5 |
| 700 | 1 | _ | |a Oliveira, Jorge |b 6 |
| 700 | 1 | _ | |a Samuel, Robert |b 7 |
| 700 | 1 | _ | |a Shelley, Leila E A |b 8 |
| 700 | 1 | _ | |a Osman, Sarah |b 9 |
| 700 | 1 | _ | |a Dwek, Miriam V |b 10 |
| 700 | 1 | _ | |a Azria, David |b 11 |
| 700 | 1 | _ | |a Chang-Claude, Jenny |0 P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253 |b 12 |u dkfz |
| 700 | 1 | _ | |a Gutiérrez-Enríquez, Sara |b 13 |
| 700 | 1 | _ | |a De Santis, Maria Carmen |b 14 |
| 700 | 1 | _ | |a Rosenstein, Barry S |b 15 |
| 700 | 1 | _ | |a De Ruysscher, Dirk |b 16 |
| 700 | 1 | _ | |a Sperk, Elena |b 17 |
| 700 | 1 | _ | |a Symonds, R Paul |b 18 |
| 700 | 1 | _ | |a Stobart, Hilary |b 19 |
| 700 | 1 | _ | |a Vega, Ana |b 20 |
| 700 | 1 | _ | |a Veldeman, Liv |b 21 |
| 700 | 1 | _ | |a Webb, Adam |b 22 |
| 700 | 1 | _ | |a Talbot, Christopher J |b 23 |
| 700 | 1 | _ | |a West, Catharine M |b 24 |
| 700 | 1 | _ | |a Rattay, Tim |b 25 |
| 700 | 1 | _ | |a consortium, REQUITE |b 26 |e Collaboration Author |
| 700 | 1 | _ | |a Chaussalet, Thierry J |b 27 |
| 773 | _ | _ | |a 10.1016/j.compbiomed.2021.104624 |g Vol. 135, p. 104624 - |0 PERI:(DE-600)1496984-1 |p 104624 |t Computers in biology and medicine |v 135 |y 2021 |x 0010-4825 |
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