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Article

Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients

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Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, I-80131 Naples, Italy
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Medical Oncology Division, Igea SpA, I-80013 Naples, Italy
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R & D Lab. of Tecnologie Avanzate TA Srl, Science and Technology Park, I-10153 Udine, Italy
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Division of Radiodiagnostic, Azienda Ospedaliero-Universitaria Careggi, I-50134 Firenze, Italy
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Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, I-20122 Milan, Italy
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Division of Radiodiagnostic, Università degli Studi della Campania Luigi Vanvitelli, I-80128 Naples, Italy
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Thoracic Medical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, I-80131 Naples, Italy
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Division of Oncology, Azienda Ospedaliero-Universitaria Careggi, I-50134 Firenze, Italy
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Dipartimento Diagnosi e Terapia per Immagini, Radiologia Diagnostica, IRCCS Istituto Tumori G, Paolo II, I-70124 Bari, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Paolo Graziano and Giulio Rossi
Cancers 2021, 13(16), 3992; https://doi.org/10.3390/cancers13163992
Received: 28 June 2021 / Revised: 3 August 2021 / Accepted: 4 August 2021 / Published: 7 August 2021
The objective of the study was to assess the radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. We demonstrated that specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy by predicting OS or PFS time.
Purpose: To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. Methods: Seventy-four patients (median age 63 years, range 42–86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36–86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. Results: A total of 38 patients (median age 61; range 41–78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). Conclusions: Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group. View Full-Text
Keywords: lung adenocarcinoma; radiomics; computed tomography; texture analysis; morphological analysis lung adenocarcinoma; radiomics; computed tomography; texture analysis; morphological analysis
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MDPI and ACS Style

Granata, V.; Fusco, R.; Costa, M.; Picone, C.; Cozzi, D.; Moroni, C.; La Casella, G.V.; Montanino, A.; Monti, R.; Mazzoni, F.; Grassi, R.; Malagnino, V.G.; Cappabianca, S.; Grassi, R.; Miele, V.; Petrillo, A. Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers 2021, 13, 3992. https://doi.org/10.3390/cancers13163992

AMA Style

Granata V, Fusco R, Costa M, Picone C, Cozzi D, Moroni C, La Casella GV, Montanino A, Monti R, Mazzoni F, Grassi R, Malagnino VG, Cappabianca S, Grassi R, Miele V, Petrillo A. Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers. 2021; 13(16):3992. https://doi.org/10.3390/cancers13163992

Chicago/Turabian Style

Granata, Vincenza, Roberta Fusco, Matilde Costa, Carmine Picone, Diletta Cozzi, Chiara Moroni, Giorgia V. La Casella, Agnese Montanino, Riccardo Monti, Francesca Mazzoni, Roberta Grassi, Valeria G. Malagnino, Salvatore Cappabianca, Roberto Grassi, Vittorio Miele, and Antonella Petrillo. 2021. "Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients" Cancers 13, no. 16: 3992. https://doi.org/10.3390/cancers13163992

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