Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors
Fecha de publicación:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Pastor, AJ
- Sierra, JMC
- Pötschger, U
- Taschner-Mandl, S
- Neri, E
- Ladenstein, R
- Hero, B
- Alberich-Bayarri, A
Grupos
Abstract
Purpose: To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods: A retrospective study included 419 patients (mean age, 29 months +/- 34 [SD]; 220 male, 199 female) with neuroblastic tumors diagnosed between 2002 and 2023, within the scope of the PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences at diagnosis and/or after initial chemotherapy. Images underwent processing steps (denoising, inhomogeneity bias field correction, normalization, and resampling). Tumors were automatically segmented, and 107 shape, first-order, and second-order radiomics features were extracted, considered as the reference standard. Subsequently, the previous image processing settings were modified, and volumetric masks were applied. New radiomics features were extracted and compared with the reference standard. Reproducibility was assessed using the concordance correlation coefficient (CCC); intrasubject repeatability was measured using the coefficient of variation (CoV). Results: When normalization was omitted, only 5% of the radiomics features demonstrated high reproducibility. Statistical analysis revealed significant changes in the normalization and resampling processes (P P < .001). Inhomogeneities removal had the least impact on radiomics (83% of parameters remained stable). Shape features remained stable after mask modifications, with a CCC greater than 0.90. Mask modifications were the most favorable changes for achieving high CCC values, with a radiomics features stability of 70%. Only 7% of second-order radiomics features showed an excellent CoV of less than 0.10. Conclusion: Modifications in the T2-weighted MRI preparation process in patients with neuroblastoma resulted in changes in radiomics features, with normalization identified as the most influential factor for reproducibility. Inhomogeneities removal had the least impact on radiomics features.
Datos de la publicación
- ISSN/ISSNe:
- 2638-6100, 2638-6100
- Tipo:
- Article
- Páginas:
- -
- DOI:
- 10.1148/ryai.230208
- PubMed:
- 38864742
- Factor de Impacto:
- 4,729 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
Radiology-Artificial Intelligence RADIOLOGICAL SOC NORTH AMERICA (RSNA)
Citas Recibidas en Web of Science: 8
Documentos
- No hay documentos
Filiaciones
Keywords
- Pediatrics; MR Imaging; Oncology; Radiomics; Reproducibility; Repeatability; Neuroblastic Tumors
Proyectos y Estudios Clínicos
PRIMAGE. PREDICTIVE IN-SILICO MULTISCALE ANALYTICS TO SUPPORT CANCER PERSONALIZED DIAGNOSIS AND PROGNOSIS, EMPOWERED BY IMAGING BIOMARKERS.
Investigador Principal: LUIS MARTÍ BONMATÍ
826494 . COMISION EUROPEA . 2018
Cita
VEIGA D,FERNÁNDEZ M,ALBERICH LC,Pastor AJ,MAYA AG,Sierra JMC,NEBOT CS,DE LAS HERAS BM,Pötschger U,Taschner S,Neri E,CAÑETE A,Ladenstein R,Hero B,Alberich A,MARTÍ L. Reproducibility Analysis of Radiomic Features on T2-weighted MR Images after Processing and Segmentation Alterations in Neuroblastoma Tumors. Radiology-Artificial Intelligence. 2024. 6. (4):e230208. IF:13,200. (1).
Portal de investigación