A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study
Valentina D. A. Corino,
Marco Bologna,
Giuseppina Calareso,
Lisa Licitra,
Mariagrazia Ghi,
Gaetana Rinaldi,
Francesco Caponigro,
Franco Morelli,
Mario Airoldi,
Giacomo Allegrini,
Alessandra Cassano,
Daris Ferrari,
Aurora Mirabile,
Alicia Tosoni,
Danilo Galizia,
Marco Merlano,
Andrea Sponghini,
Gabriella Moretti,
Luca Mainardi,
Paolo Bossi
Affiliations
Valentina D. A. Corino
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, Italy
Marco Bologna
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, Italy
Giuseppina Calareso
Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy
Lisa Licitra
Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, University of Milan, 20133 Milan, Italy
Mariagrazia Ghi
Oncology 2 Unit, IRCCS Istituto Oncologico Veneto, 35128 Padua, Italy
Gaetana Rinaldi
Medical Oncology Unit, Policlinico P. Giaccone University Hospital, 90127 Palermo, Italy
Francesco Caponigro
Head and Neck Medical and Experimental Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131 Naples, Italy
Franco Morelli
Department of Oncology, IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, Italy
Mario Airoldi
Medical Oncology 2 Unit, University Hospital “Città della Salute e della Scienza”, 10126 Turin, Italy
Giacomo Allegrini
Azienda USL Toscana Nord Ovest, 56121 Tuscany, Italy
Alessandra Cassano
Medical Oncology Unit, Policlinico Gemelli, 00168 Rome, Italy
Daris Ferrari
Medical Oncology Unit, San Paolo Hospital, 20142 Milan, Italy
Aurora Mirabile
Medical Oncology Unit, San Raffaele Hospital, 20132 Segrate, Italy
Alicia Tosoni
Medical Oncology Department, Azienda USL/IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy
Danilo Galizia
Multidisciplinary Outpatient Oncology Clinic, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
Marco Merlano
Multidisciplinary Outpatient Oncology Clinic, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy
Andrea Sponghini
“Maggiore della Carità” University Hospital, 28100 Novara, Italy
Gabriella Moretti
GM Medical Oncology Unit, IRCCS Arcispedale S. Maria Nuova, 42123 Reggio Emilia, Italy
Luca Mainardi
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, Italy
Paolo Bossi
Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health University of Brescia, ASST-Spedali Civili, 25123 Brescia, Italy
Baseline clinical prognostic factors for recurrent and/or metastatic (RM) head and neck squamous cell carcinoma (HNSCC) treated with immunotherapy are lacking. CT-based radiomics may provide additional prognostic information. A total of 85 patients with RM-HNSCC were enrolled for this study. For each tumor, radiomic features were extracted from the segmentation of the largest tumor mass. A pipeline including different feature selection steps was used to train a radiomic signature prognostic for 10-month overall survival (OS). Features were selected based on their stability to geometrical transformation of the segmentation (intraclass correlation coefficient, ICC > 0.75) and their predictive power (area under the curve, AUC > 0.7). The predictive model was developed using the least absolute shrinkage and selection operator (LASSO) in combination with the support vector machine. The model was developed based on the first 68 enrolled patients and tested on the last 17 patients. Classification performance of the radiomic risk was evaluated accuracy and the AUC. The same metrics were computed for some baseline predictors used in clinical practice (volume of largest lesion, total tumor volume, number of tumor lesions, number of affected organs, performance status). The AUC in the test set was 0.67, while accuracy was 0.82. The performance of the radiomic score was higher than the one obtainable with the clinical variables (largest lesion volume: accuracy 0.59, AUC = 0.55; number of tumoral lesions: accuracy 0.71, AUC 0.36; number of affected organs: accuracy 0.47; AUC 0.42; total tumor volume: accuracy 0.59, AUC 0.53; performance status: accuracy 0.41, AUC = 0.47). Radiomics may provide additional baseline prognostic value compared to the variables used in clinical practice.