The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study
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Published:2024-08-03
Issue:9
Volume:129
Page:1369-1381
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ISSN:1826-6983
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Container-title:La radiologia medica
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language:en
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Short-container-title:Radiol med
Author:
Tagliabue Marta, Ruju Francesca, Mossinelli ChiaraORCID, Gaeta Aurora, Raimondi Sara, Volpe Stefania, Zaffaroni Mattia, Isaksson Lars Johannes, Garibaldi Cristina, Cremonesi Marta, Rapino Anna, Chiocca Susanna, Pietrobon Giacomo, Alterio Daniela, Trisolini Giuseppe, Morbini Patrizia, Rampinelli Vittorio, Grammatica Alberto, Petralia Giuseppe, Jereczek-Fossa Barbara Alicja, Preda Lorenzo, Ravanelli Marco, Maroldi Roberto, Piazza Cesare, Benazzo Marco, Ansarin Mohssen
Abstract
Abstract
Purpose
Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).
Materials and methods
Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.
Results
We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.
Conclusion
Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.
Publisher
Springer Science and Business Media LLC
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