A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study
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 eac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/6/979 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Valentina D. A. Corino |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:55:52Z |
| format | Article |
| id | doaj.art-a6f54f48f555409380e2526732486452 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T10:55:52Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-a6f54f48f555409380e25267324864522023-11-21T21:52:15ZengMDPI AGDiagnostics2075-44182021-05-0111697910.3390/diagnostics11060979A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory StudyValentina D. A. Corino0Marco Bologna1Giuseppina Calareso2Lisa Licitra3Mariagrazia Ghi4Gaetana Rinaldi5Francesco Caponigro6Franco Morelli7Mario Airoldi8Giacomo Allegrini9Alessandra Cassano10Daris Ferrari11Aurora Mirabile12Alicia Tosoni13Danilo Galizia14Marco Merlano15Andrea Sponghini16Gabriella Moretti17Luca Mainardi18Paolo Bossi19Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, ItalyRadiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, ItalyHead and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, University of Milan, 20133 Milan, ItalyOncology 2 Unit, IRCCS Istituto Oncologico Veneto, 35128 Padua, ItalyMedical Oncology Unit, Policlinico P. Giaccone University Hospital, 90127 Palermo, ItalyHead and Neck Medical and Experimental Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131 Naples, ItalyDepartment of Oncology, IRCCS Casa Sollievo della Sofferenza, 71013 San Giovanni Rotondo, ItalyMedical Oncology 2 Unit, University Hospital “Città della Salute e della Scienza”, 10126 Turin, ItalyAzienda USL Toscana Nord Ovest, 56121 Tuscany, ItalyMedical Oncology Unit, Policlinico Gemelli, 00168 Rome, ItalyMedical Oncology Unit, San Paolo Hospital, 20142 Milan, ItalyMedical Oncology Unit, San Raffaele Hospital, 20132 Segrate, ItalyMedical Oncology Department, Azienda USL/IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, ItalyMultidisciplinary Outpatient Oncology Clinic, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, ItalyMultidisciplinary Outpatient Oncology Clinic, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy“Maggiore della Carità” University Hospital, 28100 Novara, ItalyGM Medical Oncology Unit, IRCCS Arcispedale S. Maria Nuova, 42123 Reggio Emilia, ItalyDepartment of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, ItalyMedical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health University of Brescia, ASST-Spedali Civili, 25123 Brescia, ItalyBaseline 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.https://www.mdpi.com/2075-4418/11/6/979head and neck squamous cell carcinomaradiomicsCToverall survival |
| spellingShingle | 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 A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study Diagnostics head and neck squamous cell carcinoma radiomics CT overall survival |
| title | A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study |
| title_full | A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study |
| title_fullStr | A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study |
| title_full_unstemmed | A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study |
| title_short | A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study |
| title_sort | ct based radiomic signature can be prognostic for 10 months overall survival in metastatic tumors treated with nivolumab an exploratory study |
| topic | head and neck squamous cell carcinoma radiomics CT overall survival |
| url | https://www.mdpi.com/2075-4418/11/6/979 |
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