Background and Objective: Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul. Methods: Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics. Results: Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82–0.98)) and CCC (0.82 (0.69–0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84–0.92)) and (0.80 (0.73–0.84)), respectively. Conclusion: Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis.

Repeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration / Pisani, Noemi; Destito, Michela; Ricciardi, Carlo; Pellecchia, Maria Teresa; Cesarelli, Mario; Esposito, Fabrizio; Spadea, Maria Francesca; Amato, Francesco. - In: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. - ISSN 0169-2607. - 265:(2025), pp. 1-7. [10.1016/j.cmpb.2025.108738]

Repeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration

Pisani, Noemi;Ricciardi, Carlo;Amato, Francesco
2025

Abstract

Background and Objective: Radiomics extracts quantitative features from magnetic resonance images (MRI) and is especially useful in the presence of subtle pathological changes within human soft tissues. This scenario, however, may not cover the effects of intrinsic, e.g., aging-related, (physiological) neurodegeneration of normal brain tissue. The aim of the work was to study the repeatability of radiomic features extracted from an apparently normal area in longitudinally acquired T1-weighted MR brain images using three different intensity normalization approaches typically used in radiomics: Z-score, WhiteStripe and Nyul. Methods: Fifty-nine images of hearing impaired, yet cognitively intact, patients were repeatedly acquired in two different time points within six months. Ninety-one radiomic features were obtained from an area within the pons region, considered to be a healthy brain tissue according to previous analyses and reports. The Intraclass Correlation Coefficient (ICC) and the Concordance Correlation Coefficient (CCC) in the repeatability study were used as metrics. Results: Features extracted from the MRI normalized with Z-score showed results comparable in both ICC (0.90 (0.82–0.98)) and CCC (0.82 (0.69–0.95)) distribution values, in terms of median and quartiles, with those extracted from the images normalized with WhiteStripe (0.89 (0.84–0.92)) and (0.80 (0.73–0.84)), respectively. Conclusion: Our findings underline the importance of, providing useful guidelines for, the intensity normalization of brain MRI prior to a longitudinal radiomic analysis.
2025
Repeatability of radiomic features from brain T1-W MRI after image intensity normalization: Implications for longitudinal studies on structural neurodegeneration / Pisani, Noemi; Destito, Michela; Ricciardi, Carlo; Pellecchia, Maria Teresa; Cesarelli, Mario; Esposito, Fabrizio; Spadea, Maria Francesca; Amato, Francesco. - In: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. - ISSN 0169-2607. - 265:(2025), pp. 1-7. [10.1016/j.cmpb.2025.108738]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1000844
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