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Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study

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Item Type:Article
Title:Identification of radiomic biomarkers in a set of four skeletal muscle groups on Dixon MRI of the NAKO MR study
Creators Name:Fischer, M. and Küstner, T. and Pappa, S. and Niendorf, T. and Pischon, T. and Kröncke, T. and Bette, S. and Schramm, S. and Schmidt, B. and Haubold, J. and Nensa, F. and Nonnenmacher, T. and Palm, V. and Bamberg, F. and Kiefer, L. and Schick, F. and Yang, B.
Abstract:In this work, we propose a processing pipeline for the extraction and identification of meaningful radiomics biomarkers in skeletal muscle tissue as displayed using Dixon-weighted MRI. Diverse and robust radiomics features can be identified that may be of aid in the accurate quantification e.g. varying degrees of sarcopenia in respective muscles of large cohorts. As such, the approach comprises the texture feature extraction from raw data based on well established approaches, such as a nnU-Net neural network and the Pyradiomics toolbox, a subsequent selection according to adequate conditions for the muscle tissue of the general population, and an importance-based ranking to further narrow the amount of meaningful features with respect to auxiliary targets. The performance was investigated with respect to the included auxiliary targets, namely age, body mass index (BMI), and fat fraction (FF). Four skeletal muscles with different fiber architecture were included: the mm. glutaei, m. psoas, as well as the extensors and adductors of the thigh. The selection allowed for a reduction from 1015 available texture features to 65 for age, 53 for BMI, and 36 for FF from the available fat/water contrast images considering all muscles jointly. Further, the dependence of the importance rankings calculated for the auxiliary targets on validation sets (in a cross-validation scheme) was investigated by boxplots. In addition, significant differences between subgroups of respective auxiliary targets as well as between both sexes were shown to be present within the ten lowest ranked features by means of Kruskal-Wallis H-tests and Mann-Whitney U-tests. The prediction performance for the selected features and the ranking scheme were verified on validation sets by a random forest based multi-class classification, with strong area under the curve (AUC) values of the receiver operator characteristic (ROC) of 73.03 ± 0.70 % and 73.63 ± 0.70 % for the water and fat images in age, 80.68 ± 0.30 % and 88.03 ± 0.89 % in BMI, as well as 98.36 ± 0.03 % and 98.52 ± 0.09 % in FF.
Keywords:German National Cohort, Radiomics, Texture Feature Analysis, Magnetic Resonance Imaging
Source:BMC Medical Imaging
Publisher:BioMed Central
Page Range:104
Date:8 August 2023
Official Publication:https://doi.org/10.1186/s12880-023-01056-9
PubMed:View item in PubMed

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