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A solution to the realization of multicentric radiomic studies

​A team from SHFJ proposes a predictive model that corrects the "acquisition center" effect thanks to an unprecedented harmonization method in medical imaging.

Published on 11 February 2019

Nonbiological differences related to CT scanner type can be removed from radiomic feature values, allowing radiomics features to be combined in multicenter or multivendor studies.

Radiomics extracts features from medical images more precisely and more accurately than visual assessment. However, radiomics features are affected by CT scanner parameters such as reconstruction kernel or section thickness, thus obscuring underlying biologically important texture features.

To investigate whether a compensation method could correct for the variations of radiomic feature values caused by using different CT protocols.

Materials and Methods
Phantom data involving 10 texture patterns and 74 patients in cohorts 1 (19 men; 42 patients; mean age, 60.4 years; September–October 2013) and 2 (16 men; 32 patients; mean age, 62.1 years; January–September 2007) scanned by using different CT protocols were retrospectively included. For any radiomic feature, the compensation approach identified a protocol-specific transformation to express all data in a common space that were devoid of protocol effects. The differences in statistical distributions between protocols were assessed by using Friedman tests before and after compensation. Principal component analyses were performed on the phantom data to evaluate the ability to distinguish between texture patterns after compensation.

In the phantom data, the statistical distributions of features were different between protocols for all radiomic features and texture patterns (P < .05). After compensation, the protocol effect was no longer detectable (P > .05). Principal component analysis demonstrated that each texture pattern was no longer displayed as different clusters corresponding to different imaging protocols, unlike what was observed before compensation. The correction for scanner effect was confirmed in patient data with 100% (10 of 10 features for cohort 1) and 98% (87 of 89 features for cohort 2) of P values less than .05 before compensation, compared with 30% (three of 10) and 15% (13 of 89) after compensation.

Image compensation successfully realigned feature distributions computed from different CT imaging protocols and should facilitate multicenter radiomic studies.

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