Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-
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COMPUTED TOMOGRAPHY
Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis Marta Ligero 1 & Olivia Jordi-Ollero 2 & Kinga Bernatowicz 1 & Alonso Garcia-Ruiz 1 & Eric Delgado-Muñoz 1 & David Leiva 3 & Richard Mast 4 & Cristina Suarez 5 & Roser Sala-Llonch 6 & Nahum Calvo 3 & Manuel Escobar 4 & Arturo Navarro-Martin 7 & Guillermo Villacampa 8 & Rodrigo Dienstmann 8 & Raquel Perez-Lopez 1,4 Received: 14 February 2020 / Revised: 23 June 2020 / Accepted: 10 August 2020 # The Author(s) 2020
Abstract Objective To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CTimage correction method to reduce variability and improve radiomics classification in both phantom and clinical applications. Methods CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction. Results Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness–kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20). Conclusion CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07174-0) contains supplementary material, which is available to authorized users. * Raquel Perez-Lopez [email protected] 1
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Medical Oncology, Vall d’Hebron Institute of Oncology (VHIO), Hospital Universitari Vall d´Hebron, Vall d’Hebron Barcelona Hospital Campus (Spain), Barcelona, Spain
Radiomics Group, Vall d’Hebron Institute of Oncology (VHIO), Hospit
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