The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

MRI radiomics in head and neck cancer from reproducibility to combined approaches / A. Corti, S. Cavalieri, G. Calareso, D. Mattavelli, M. Ravanelli, T. Poli, L. Licitra, V. Corino, L. Mainardi. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - 14:1(2024), pp. 9451.1-9451.13. [10.1038/s41598-024-60009-6]

MRI radiomics in head and neck cancer from reproducibility to combined approaches

S. Cavalieri
Secondo
;
L. Licitra;
2024

Abstract

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.
Cluster analysis; Head and neck squamous cell carcinoma; Magnetic resonance imaging; Overall survival; Prognostic models; Radiomic features
Settore MEDS-09/A - Oncologia medica
Settore MEDS-22/A - Diagnostica per immagini e radioterapia
Settore IBIO-01/A - Bioingegneria
   Big Data and models for personalized Head and Neck Cancer Decision support
   BD2Decide
   European Commission
   Horizon 2020 Framework Programme
   689715
2024
Article (author)
File in questo prodotto:
File Dimensione Formato  
s41598-024-60009-6.pdf

accesso aperto

Descrizione: Article
Tipologia: Publisher's version/PDF
Dimensione 2.58 MB
Formato Adobe PDF
2.58 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1097708
Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact