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000180190 0247_ $$2doi$$a10.1016/j.adro.2021.100890
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000180190 1001_ $$aAldraimli, Mahmoud$$b0
000180190 245__ $$aDevelopment and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.
000180190 260__ $$aAmsterdam$$bElsevier$$c2022
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000180190 520__ $$aSome patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study.Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation.One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the 'hero' model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort.ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.
000180190 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0
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000180190 7001_ $$aOsman, Sarah$$b1
000180190 7001_ $$aGrishchuck, Diana$$b2
000180190 7001_ $$aIngram, Samuel$$b3
000180190 7001_ $$aLyon, Robert$$b4
000180190 7001_ $$aMistry, Anil$$b5
000180190 7001_ $$aOliveira, Jorge$$b6
000180190 7001_ $$aSamuel, Robert$$b7
000180190 7001_ $$aShelley, Leila E A$$b8
000180190 7001_ $$aSoria, Daniele$$b9
000180190 7001_ $$aDwek, Miriam V$$b10
000180190 7001_ $$aAguado-Barrera, Miguel E$$b11
000180190 7001_ $$aAzria, David$$b12
000180190 7001_ $$0P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253$$aChang-Claude, Jenny$$b13$$udkfz
000180190 7001_ $$aDunning, Alison$$b14
000180190 7001_ $$aGiraldo, Alexandra$$b15
000180190 7001_ $$aGreen, Sheryl$$b16
000180190 7001_ $$aGutiérrez-Enríquez, Sara$$b17
000180190 7001_ $$aHerskind, Carsten$$b18
000180190 7001_ $$avan Hulle, Hans$$b19
000180190 7001_ $$aLambrecht, Maarten$$b20
000180190 7001_ $$aLozza, Laura$$b21
000180190 7001_ $$aRancati, Tiziana$$b22
000180190 7001_ $$aReyes, Victoria$$b23
000180190 7001_ $$aRosenstein, Barry S$$b24
000180190 7001_ $$ade Ruysscher, Dirk$$b25
000180190 7001_ $$ade Santis, Maria C$$b26
000180190 7001_ $$0P:(DE-He78)fd17a8dbf8d08ea5bb656dfef7398215$$aSeibold, Petra$$b27$$udkfz
000180190 7001_ $$aSperk, Elena$$b28
000180190 7001_ $$aSymonds, R Paul$$b29
000180190 7001_ $$aStobart, Hilary$$b30
000180190 7001_ $$aTaboada-Valadares, Begoña$$b31
000180190 7001_ $$aTalbot, Christopher J$$b32
000180190 7001_ $$aVakaet, Vincent J L$$b33
000180190 7001_ $$aVega, Ana$$b34
000180190 7001_ $$aVeldeman, Liv$$b35
000180190 7001_ $$aVeldwijk, Marlon R$$b36
000180190 7001_ $$aWebb, Adam$$b37
000180190 7001_ $$aWeltens, Caroline$$b38
000180190 7001_ $$aWest, Catharine M$$b39
000180190 7001_ $$aChaussalet, Thierry J$$b40
000180190 7001_ $$aRattay, Tim$$b41
000180190 7001_ $$aconsortium, REQUITE$$b42$$eCollaboration Author
000180190 773__ $$0PERI:(DE-600)2847724-8$$a10.1016/j.adro.2021.100890$$gVol. 7, no. 3, p. 100890 -$$n3$$p100890$$tAdvances in radiation oncology$$v7$$x2452-1094$$y2022
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