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European Radiology

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18.01.2021 | Imaging Informatics and Artificial Intelligence

Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging

verfasst von: Hubert Beaumont, Antoine Iannessi, Anne-Sophie Bertrand, Jean Michel Cucchi, Olivier Lucidarme

Erschienen in: European Radiology | Ausgabe 8/2021

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Abstract

Objectives

Following the craze for radiomic features (RF), their lack of reliability raised the question of the generalizability of classification models. Inter-site harmonization of images therefore becomes a central issue. We compared RF harmonization processing designed to detect liver diseases in CT images.

Methods

We retrospectively analyzed 76 multi-center portal CT series of non-diseased (NDL) and diseased liver (DL) patients. In each series, we positioned volumes of interest in spleen and liver, then extracted 9 RF (histogram and texture). We evaluated two RF harmonization approaches. First, in each series, we computed the Z-score of liver measurements based on those computed in the spleen. Second, we evaluated the ComBat method according to each imaging center; parameters were computed in the spleen and applied to the liver. We compared RF distributions and classification performances before/after harmonization. We classified NDL versus spleen and versus DL tissues.

Results

The RF distributions were all different between liver and spleen (p < 0.05). The Z-score harmonization outperformed for the detection of liver versus spleen: AUC = 93.1% (p < 0.001). For the detection of DL versus NDL, in a case/control setting, we found no differences between the harmonizations: mean AUC = 73.6% (p = 0.49). Using the whole datasets, the performances were improved using ComBat (p = 0.05) AUC = 82.4% and degraded with Z-score AUC = 67.4% (p = 0.008).

Conclusions

Data harmonization requires to first focus on data structuring to not degrade the performances of subsequent classifications. Liver tissue classification after harmonization of spleen-based RF is a promising strategy for improving the detection of DL tissue.

Key Points

• Variability of acquisition parameter makes radiomics of CT features non-reproducible.

• Data harmonization can help circumvent the inter-site variability of acquisition protocols.

• Inter-site harmonization must be carefully implemented and requires designing consistent data sets.

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Titel: Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging
verfasst von: Hubert Beaumont
Antoine Iannessi
Anne-Sophie Bertrand
Jean Michel Cucchi
Olivier Lucidarme
Publikationsdatum: 18.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07641-8

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Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging