Authors:
Maria Irene Tenerani
1
;
2
;
Silvia Arezzini
2
;
Antonino Formuso
2
;
Francesca Lizzi
2
;
Enrico Mazzoni
2
;
Stefania Pallotta
3
;
4
;
5
;
Alessandra Retico
2
;
Camilla Scapicchio
2
;
Cinzia Talamonti
3
;
4
;
5
and
Maria Evelina Fantacci
1
;
2
Affiliations:
1
Department of Physics, University of Pisa, Pisa, Italy
;
2
National Institute for Nuclear Physics, Pisa, Italy
;
3
University of Florence, Department of Experimental and Clinical Biomedical Sciences ”Mario Serio”, Florence, Italy
;
4
Medical Physics Unit, AOU Careggi, Florence, Italy
;
5
National Institute for Nuclear Physics, Florence, Italy
Keyword(s):
Lung Cancer Screening, Low-Dose Computed Tomography, Computed Tomography Acquisition Protocol, Phantom, Multi-Centric Study, Radiomics, Medical Data Sharing Platform.
Abstract:
Radiomics is a quantitative biomedical image analysis tool involving the mathematical extraction of image features that can be used, particularly in oncology, to build predictive models based on artificial intelligence for diagnosis and treatment outcome prediction. In Lung cancer screening via Low-Dose Computed Tomography (LDCT), radiomics-based models could increase lung nodules detectability simplifying the implementation of large-scale screening. However, their transposition into clinical practice is slowed by the instability that radiomic feature values show in changes in CT image acquisition and reconstruction parameters. To build more robust models, it is essential to conduct multi-centric radiomic studies leveraging the use of various types of phantoms to overcome the challenges associated with patient data complexity. However, many difficulties may arise related to both the image acquisition and reconstruction process and the extraction and analysis of radiomic features. In
this paper, from the results of a pilot study conducted with two phantoms, guidelines for a multi-centric radiomic study on phantoms LDCTs are proposed, focusing on crucial aspects such as phantom positioning, image acquisition and reconstruction protocol, and radiomic feature extraction pipeline. Finally, a XNAT-based platform for data sharing and management, image quality control implementation and radiomic feature extraction automation is proposed.
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