Salvatore Alfieri1, Rebecca Romanò1, Marco Bologna2, Giuseppina Calareso3, Valentina Corino2, Aurora Mirabile4, Andrea Ferri5, Luca Bellanti5, Tito Poli6, Alessandra Marcantoni7, Enrica Grosso8, Achille Tarsitano9, Salvatore Battaglia9, Fulvia Blengio10, Iolanda De Martino10, Sara Valerini11, Stefania Vecchio12, Antonella Richetti13, Letizia Deantonio13, Francesco Martucci13, Alberto Grammatica14, Marco Ravanelli15, Toni Ibrahim16, Damiano Caruso17, Laura Deborah Locati1, Ester Orlandi18, Paolo Bossi19, Luca Mainardi2, Lisa F Licitra1,20. 1. Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy. 2. Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy. 3. Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy. 4. Department of Oncology, Division of Experimental Medicine, IRCCS San Raffaele Hospital, Milan, Italy. 5. Department of Surgery, Maxillo-Facial Surgery Division, University Hospital of Parma, Parma, Italy. 6. Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), Unit of Maxillo-Facial Surgery, University of Parma, Parma, Italy. 7. ENT Department, Santa Chiara Hospital, Trento, Italy. 8. Division of Head and Neck Surgery, Istituto Europeo di Oncologia (IEO), Milan, Italy. 9. Department of Biomedical and Neuromotor Sciences, Maxillofacial Surgery Unit, S. Orsola-Malpighi Hospital, Alma Mater Studiorum University of Bologna, Bologna, Italy. 10. Medical Oncology Department, AO SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy. 11. Neuroscience Head and Neck Department, Otolaryngology Unit, Azienda Ospedaliero Universitaria di Modena, Modena, Italy. 12. Medical Oncology 2, IRCCS Ospedale Policlinico San Martino, Genova, Italy. 13. Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland. 14. Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili di Brescia, University of Brescia, Brescia, Italy. 15. Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, Unit of Radiology, University of Brescia, Brescia, Italy. 16. Osteoncology and Rare Tumors Center, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy. 17. Department of Surgical and Medical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy. 18. Radiotherapy Unit 2, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy. 19. Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public, Health University of Brescia, ASST-Spedali Civili, Brescia, Italy. 20. University of Milan, Milan, Italy.
Abstract
OBJECTIVES: To identify and validate baseline magnetic resonance imaging (b-MRI) radiomic features (RFs) as predictors of disease outcomes in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS: Training set (TS) and validation set (VS) were retrieved from preexisting datasets (HETeCo and BD2Decide trials, respectively). Only patients with both pre- and post-contrast enhancement T1 and T2-weighted b-MRI and at least 2 years of follow-up (FUP) were selected. The combination of the best extracted RFs was used to classify low risk (LR) vs. high risk (HR) of disease recurrence. Sensitivity, specificity, and area under the curve (AUC) of the radiomic model were computed on both TS and VS. Overall survival (OS) and 5-year disease-free survival (DFS) Kaplan-Meier (KM) curves were compared for LR vs. HR. The radiomic-based risk class was used in a multivariate Cox model, including well-established clinical prognostic factors (TNM, sub-site, human papillomavirus [HPV]). RESULTS: In total, 57 patients of TS and 137 of VS were included. Three RFs were selected for the signature. Sensitivity of recurrence risk classifier was 0.82 and 0.77, specificity 0.78 and 0.81, AUC 0.83 and 0.78 for TS and VS, respectively. VS KM curves for LR vs. HR groups significantly differed both for 5-year DFS (p<.0001) and OS (p=.0004). A combined model of RFs plus TNM improved prognostic performance as compared to TNM alone, both for VS 5-year DFS (C-index: 0.76 vs. 0.60) and OS (C-index: 0.74 vs. 0.64). CONCLUSIONS: Radiomics of b-MRI can help to predict recurrence and survival outcomes in HNSCC.
OBJECTIVES: To identify and validate baseline magnetic resonance imaging (b-MRI) radiomic features (RFs) as predictors of disease outcomes in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS: Training set (TS) and validation set (VS) were retrieved from preexisting datasets (HETeCo and BD2Decide trials, respectively). Only patients with both pre- and post-contrast enhancement T1 and T2-weighted b-MRI and at least 2 years of follow-up (FUP) were selected. The combination of the best extracted RFs was used to classify low risk (LR) vs. high risk (HR) of disease recurrence. Sensitivity, specificity, and area under the curve (AUC) of the radiomic model were computed on both TS and VS. Overall survival (OS) and 5-year disease-free survival (DFS) Kaplan-Meier (KM) curves were compared for LR vs. HR. The radiomic-based risk class was used in a multivariate Cox model, including well-established clinical prognostic factors (TNM, sub-site, human papillomavirus [HPV]). RESULTS: In total, 57 patients of TS and 137 of VS were included. Three RFs were selected for the signature. Sensitivity of recurrence risk classifier was 0.82 and 0.77, specificity 0.78 and 0.81, AUC 0.83 and 0.78 for TS and VS, respectively. VS KM curves for LR vs. HR groups significantly differed both for 5-year DFS (p<.0001) and OS (p=.0004). A combined model of RFs plus TNM improved prognostic performance as compared to TNM alone, both for VS 5-year DFS (C-index: 0.76 vs. 0.60) and OS (C-index: 0.74 vs. 0.64). CONCLUSIONS: Radiomics of b-MRI can help to predict recurrence and survival outcomes in HNSCC.
Entities:
Keywords:
Radiomic; head and neck squamous cell carcinoma; magnetic resonance imaging (MRI); predictive; pretreatment; prognostic; recurrence
Authors: Letizia Deantonio; Maria Luisa Garo; Gaetano Paone; Maria Carla Valli; Stefano Cappio; Davide La Regina; Marco Cefali; Maria Celeste Palmarocchi; Alberto Vannelli; Sara De Dosso Journal: Front Oncol Date: 2022-03-15 Impact factor: 6.244