Details

Autor(en) / Beteiligte

• Leger, Stefan
• Zwanenburg, Alex
• Pilz, Karoline
• Lohaus, Fabian
• Linge, Annett
• Zöphel, Klaus
• Kotzerke, Jörg
• Schreiber, Andreas
• Tinhofer, Inge
• Budach, Volker
• Sak, Ali
• Stuschke, Martin
• Balermpas, Panagiotis
• Rödel, Claus
• Ganswindt, Ute
• Belka, Claus
• Pigorsch, Steffi
• Combs, Stephanie E.
• Mönnich, David
• Zips, Daniel
• Krause, Mechthild
• Baumann, Michael
• Troost, Esther G. C.
• Löck, Steffen
• Richter, Christian

Titel

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Ist Teil von

• Scientific reports, 2017-10, Vol.7 (1), p.13206-11, Article 13206

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2017

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e . g ., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.