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Article

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

1
Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Napoli, Italy
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Medical Oncology Division, Igea SpA, 41012 Carpi, Italy
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Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
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Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Fisciano, Italy
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Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 81100 Caserta, Italy
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Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Napoli, Italy
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Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Firenze, Italy
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Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
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Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Napoli, Italy
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Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Napoli, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Éric Chastre
Cancers 2022, 14(5), 1239; https://doi.org/10.3390/cancers14051239
Received: 18 January 2022 / Revised: 24 February 2022 / Accepted: 25 February 2022 / Published: 27 February 2022
(This article belongs to the Special Issue Colorectal Cancer Metastasis)
The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.
The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. View Full-Text
Keywords: Liver metastasis; Magnetic Resonance Imaging; artificial intelligence; radiomics Liver metastasis; Magnetic Resonance Imaging; artificial intelligence; radiomics
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MDPI and ACS Style

Granata, V.; Fusco, R.; De Muzio, F.; Cutolo, C.; Setola, S.V.; Dell’Aversana, F.; Ottaiano, A.; Nasti, G.; Grassi, R.; Pilone, V.; Miele, V.; Brunese, M.C.; Tatangelo, F.; Izzo, F.; Petrillo, A. EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases. Cancers 2022, 14, 1239. https://doi.org/10.3390/cancers14051239

AMA Style

Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell’Aversana F, Ottaiano A, Nasti G, Grassi R, Pilone V, Miele V, Brunese MC, Tatangelo F, Izzo F, Petrillo A. EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases. Cancers. 2022; 14(5):1239. https://doi.org/10.3390/cancers14051239

Chicago/Turabian Style

Granata, Vincenza, Roberta Fusco, Federica De Muzio, Carmen Cutolo, Sergio Venanzio Setola, Federica Dell’Aversana, Alessandro Ottaiano, Guglielmo Nasti, Roberta Grassi, Vincenzo Pilone, Vittorio Miele, Maria Chiara Brunese, Fabiana Tatangelo, Francesco Izzo, and Antonella Petrillo. 2022. "EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases" Cancers 14, no. 5: 1239. https://doi.org/10.3390/cancers14051239

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