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RT Journal Article
SR Electronic
T1 Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1291
OP 1298
DO 10.3174/ajnr.A8280
VO 45
IS 9
A1 Bathla, Girish
A1 Soni, Neetu
A1 Mark, Ian T.
A1 Liu, Yanan
A1 Larson, Nicholas B.
A1 Kassmeyer, Blake A.
A1 Mohan, Suyash
A1 Benson, John C.
A1 Rathore, Saima
A1 Agarwal, Amit K.
YR 2024
UL http://www.ajnr.org/content/45/9/1291.abstract
AB BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed through various preprocessing methods. However, very few studies have evaluated the downstream impact of variable preprocessing on model classification performance in a multiclass setting. We sought to evaluate the impact of Smallest Univalue Segment Assimilating Nucleus (SUSAN) denoising and Combining Batches harmonization on model classification performance.MATERIALS AND METHODS: A total of 493 cases (410 internal and 83 external data sets) of glioblastoma, intracranial metastatic disease, and primary CNS lymphoma underwent semiautomated 3D-segmentation post-baseline image processing (BIP) consisting of resampling, realignment, coregistration, skull-stripping, and image normalization. Post-BIP, 2 sets were generated, one with and another without SUSAN denoising. Radiomics features were extracted from both data sets and batch-corrected to produce 4 data sets: (a) BIP, (b) BIP with SUSAN denoising, (c) BIP with Combining Batches, and (d) BIP with both SUSAN denoising and Combining Batches harmonization. Performance was then summarized for models using a combination of 6 feature-selection techniques and 6 machine learning models across 4 mask-sequence combinations with features derived from 1 to 3 (multiparametric) MRI sequences.RESULTS: Most top-performing models on the external test set used BIP+SUSAN denoising–derived features. Overall, the use of SUSAN denoising and Combining Batches harmonization led to a slight but generally consistent improvement in model performance on the external test set.CONCLUSIONS: The use of image-preprocessing steps such as SUSAN denoising and Combining Batches harmonization may be more useful in a multi-institutional setting to improve model generalizability. Models derived from only T1 contrast-enhanced images showed comparable performance to models derived from multiparametric MRI.BIPbaseline image processingCEcontrast-enhancedComBatCombining BatchesETenhancing tumorGBglioblastomaICCintraclass correlation coefficientIMDintracranial metastatic diseasemAUCmulticlass area under the receiver operating characteristic curveMLmachine learningPCNSLprimary central nervous system lymphomasPTRperitumoral regionSDSUSAN denoisingSUSANSmallest Univalue Segment Assimilating Nucleus