Titre :

The Technome - A Predictive Internal Calibration Approach for Quantitative Imaging Biomarker Research.

Auteur(s) :

Mühlberg, Alexander [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Katzmann, Alexander [Auteur]
Ilmenau University of Technology [Germany] [TU]
Heinemann, Volker [Auteur]
University-Hospital Munich-Großhadern [München]
Kärgel, Rainer [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Wels, Michael [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Taubmann, Oliver [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Lades, Félix [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Huber, Thomas [Auteur]
University-Hospital Munich-Großhadern [München]
Maurus, Stefan [Auteur]
University-Hospital Munich-Großhadern [München]
Holch, Julian [Auteur]
University-Hospital Munich-Großhadern [München]
Faivre, Jean-Baptiste [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Sühling, Michael [Auteur]
Siemens Healthineers, Digital Services, Digital Technology and Innovation
Nörenberg, Dominik [Auteur]
University-Hospital Munich-Großhadern [München]
Remy, Martine [Auteur]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Titre de la revue :

Scientific Reports

Nom court de la revue :

Sci Rep

Numéro :

10

Pagination :

1103

Date de publication :

2020-01-28

ISSN :

2045-2322

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of ...
Lire la suite >The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (ROI) are calibrated by further ROIs such as air, adipose tissue, liver, etc. that are used as control regions (CR). Our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by status-quo radiomics stability analysis techniques to only collect information from the CRs which is relevant given a respective task. These criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the CRs. Our calibration enhanced the performance for centrilobular emphysema prediction in a COPD study and prediction of patients’ one-year-survival in an oncological study.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CHU Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2023-11-15T09:30:34Z
2023-12-07T08:42:19Z