{Reference Type}: Journal Article
{Title}: The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.
{Author}: Tagliabue M;Ruju F;Mossinelli C;Gaeta A;Raimondi S;Volpe S;Zaffaroni M;Isaksson LJ;Garibaldi C;Cremonesi M;Rapino A;Chiocca S;Pietrobon G;Alterio D;Trisolini G;Morbini P;Rampinelli V;Grammatica A;Petralia G;Jereczek-Fossa BA;Preda L;Ravanelli M;Maroldi R;Piazza C;Benazzo M;Ansarin M;
{Journal}: Radiol Med
{Volume}: 129
{Issue}: 9
{Year}: 2024 Sep 3
{Factor}: 6.313
{DOI}: 10.1007/s11547-024-01859-y
{Abstract}: <B>OBJECTIVE: </B>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).<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.