Objekt-Metadaten

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling
Leger, Stefan ;  Zwanenburg, Alex ;  Pilz, Karoline ;  Lohaus, Fabian ;  Linge, Annett ;  Zoephel, Klaus ;  Kotzerke, Joerg ;  Schreiber, Andreas ;  Tinhofer, Inge ;  Budach, Volker

HaupttitelA comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling
AutorLeger, Stefan
AutorZwanenburg, Alex
AutorPilz, Karoline
AutorLohaus, Fabian
AutorLinge, Annett
AutorZoephel, Klaus
Autor Kotzerke, Joerg
AutorSchreiber, Andreas
AutorTinhofer, Inge
AutorBudach, Volker
Seitenzahl11 S.
Freie SchlagwörterCancer imaging; Prognostic markers
DDC610 Medizin und Gesundheit
Auch erschienen inScientific Reports. - 7 (2017), Artikel Nr. 13206
ZusammenfassungRadiomics 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.
Dokumente
PDF-Datei von FUDOCS_document_000000028489
Falls Ihr Browser eine Datei nicht öffnen kann, die Datei zuerst herunterladen und dann öffnen.
 
Fachbereich/EinrichtungMedizinische Fakultät Charité - Universitätsmedizin Berlin
Erscheinungsjahr2017
Dokumententyp/-SammlungenWissenschaftlicher Artikel
SpracheEnglisch
RechteCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Anmerkungen des AutorsDer Artikel wurde in einer reinen Open-Access-Zeitschrift publiziert.
Erstellt am16.11.2017 - 11:56:07
Letzte Änderung16.11.2017 - 11:56:39
 
Statische URLhttp://edocs.fu-berlin.de/docs/receive/FUDOCS_document_000000028489
DOI10.1038/s41598-017-13448-3
Zugriffsstatistik
 

LOADING...