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Abstract #1052

Does Harmonization Impact Radiomics Features in Longitudinal MRI of Glioblastoma? A Comparison between ComBat and longComBat

Marta P. Loureiro1,2, Catarina Passarinho1,2, Ana Matoso1,3, Rita Reis Nunes1, José Maria Moreira3, Pedro Vilela4, Patrícia Figueiredo1, and Rita G. Nunes1
1Institute for Systems and Robotics – Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, 2Hospital da Luz, Luz Saúde, Lisbon, Portugal, 3Hospital da Luz Learning Health, Luz Saúde, Lisbon, Portugal, 4Imaging Department, Hospital da Luz, Lisbon, Portugal

Synopsis

Keywords: Radiomics, Data Processing, Harmonization, Glioblastoma, Longitudinal

Motivation: MRI-derived radiomics features are highly affected by acquisition and reconstruction protocols. This may impact the performance of Machine Learning models developed for managing disease progression, such as in Glioblastoma.

Goal(s): To compare longitudinal ComBat (longComBat) with ComBat for harmonizing radiomics features in a longitudinal Glioblastoma MRI dataset.

Approach: Both methods (ComBat and longComBat) were applied to harmonize radiomics features from multi-scanner longitudinal MRI of Glioblastoma patients. The resulting features were compared using PCA, and the performance of models trained to classify images as pre- or post-operative was assessed.

Results: ComBat was best at removing batch effects, while longComBat yielded the best classification performance.

Impact: Technical variations greatly influence MRI-derived radiomics features. We harmonized radiomics features from longitudinal MRI of Glioblastoma using both ComBat and longComBat. The latter improved Machine Learning classification into pre- and post-operative scans.

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Keywords