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J Nucl Med. 2018 Aug;59(8):1321-1328. doi: 10.2967/jnumed.117.199935. Epub 2018 Jan 4.

A Postreconstruction Harmonization Method for Multicenter Radiomic Studies in PET.

Author information

1
Imagerie Moléculaire In Vivo, CEA-SHFJ, INSERM, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France orlhacf@gmail.com.
2
Imagerie Moléculaire In Vivo, CEA-SHFJ, INSERM, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France.
3
Department of Nuclear Medicine, Institut Curie-René Huguenin, Saint-Cloud, France.
4
NeuroSpin/UNATI, CEA, Université Paris-Saclay, Gif-sur-Yvette, France; and.
5
Department of Nuclear Medicine, AP-HP, Hôpital Avicenne, Bobigny, France.

Abstract

Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols. Methods: Pretreatment 18F-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization. Results: In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S (P < 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments (P > 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department. Conclusion: The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.

KEYWORDS:

PET; harmonization; radiomics; texture analysis; tumor heterogeneity

PMID:
29301932
DOI:
10.2967/jnumed.117.199935

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