2026-04-14T05:26:20Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1766512025-12-05T11:08:30Zcom_2072_1057col_2072_478780col_2072_478916col_2072_478917

00925njm 22002777a 4500 dc Ligero Hernández, Marta author Jordi Ollero, Olivia author Bernatowicz, Kinga author Garcia Ruiz, Alonso author Delgado Muñoz, Eric author Leiva Pedraza, David author Mast, Richard author Suarez, Cristina author Sala Llonch, Roser author Calvo, Nahum author Escobar, Manuel author Navarro Martín, Arturo author Villacampa, Guillermo author Dienstmann, Rodrigo author Pérez López, Raquel author 2021-04-22T13:20:43Z 2021-04-22T13:20:43Z 2020-09-09 2021-04-22T13:20:43Z 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 Càncer Metàstasi Marcadors bioquímics Tomografia Cancer Metastasis Biochemical markers Tomography Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis