Giuditta Chiti1, Giulia Grazzini2,3, Federica Flammia1, Benedetta Matteuzzi1, Paolo Tortoli4, Silvia Bettarini4, Elisa Pasqualini5, Vincenza Granata6, Simone Busoni4, Luca Messserini7, Silvia Pradella1,8, Daniela Massi5, Vittorio Miele1. 1. Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134, Florence, Italy. 2. Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134, Florence, Italy. grazzini.giulia@gmail.com. 3. Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy. grazzini.giulia@gmail.com. 4. Medical Physics Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy. 5. Department of Health and Sciences, Section of Pathological Anatomy, University of Florence, 50139, Florence, Italy. 6. Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS Di Napoli", Via Mariano Semmola, 80131, Naples, Italy. 7. Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy. 8. Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy.
Abstract
PURPOSE: The aim of this single-center retrospective study is to assess whether contrast-enhanced computed tomography (CECT) radiomics analysis is predictive of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) grade based on the 2019 World Health Organization (WHO) classification and to establish a tumor grade (G) prediction model. MATERIAL AND METHODS: Preoperative CECT images of 78 patients with GEP-NENs were retrospectively reviewed and divided in two groups (G1-G2 in class 0, G3-NEC in class 1). A total of 107 radiomics features were extracted from each neoplasm ROI in CT arterial and venous phases acquisitions with 3DSlicer. Mann-Whitney test and LASSO regression method were performed in R for feature selection and feature reduction, in order to build the radiomic-based predictive model. The model was developed for a training cohort (75% of the total) and validated on the independent validation cohort (25%). ROC curves and AUC values were generated on training and validation cohorts. RESULTS: 40 and 24 features, for arterial phase and venous phase, respectively, were found to be significant in class distinction. From the LASSO regression 3 and 2 features, for arterial phase and venous phase, respectively, were identified as suitable for groups classification and used to build the tumor grade radiomic-based prediction model. The prediction of the arterial model resulted in AUC values of 0.84 (95% CI 0.72-0.97) and 0.82 (95% CI 0.62-1) for the training cohort and validation cohort, respectively, while the prediction of the venous model yielded AUC values of 0.7877 (95% CI 0.6416-0.9338) and 0.6813 (95% CI 0.3933-0.9693) for the training cohort and validation cohort, respectively. CONCLUSIONS: CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.
PURPOSE: The aim of this single-center retrospective study is to assess whether contrast-enhanced computed tomography (CECT) radiomics analysis is predictive of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) grade based on the 2019 World Health Organization (WHO) classification and to establish a tumor grade (G) prediction model. MATERIAL AND METHODS: Preoperative CECT images of 78 patients with GEP-NENs were retrospectively reviewed and divided in two groups (G1-G2 in class 0, G3-NEC in class 1). A total of 107 radiomics features were extracted from each neoplasm ROI in CT arterial and venous phases acquisitions with 3DSlicer. Mann-Whitney test and LASSO regression method were performed in R for feature selection and feature reduction, in order to build the radiomic-based predictive model. The model was developed for a training cohort (75% of the total) and validated on the independent validation cohort (25%). ROC curves and AUC values were generated on training and validation cohorts. RESULTS: 40 and 24 features, for arterial phase and venous phase, respectively, were found to be significant in class distinction. From the LASSO regression 3 and 2 features, for arterial phase and venous phase, respectively, were identified as suitable for groups classification and used to build the tumor grade radiomic-based prediction model. The prediction of the arterial model resulted in AUC values of 0.84 (95% CI 0.72-0.97) and 0.82 (95% CI 0.62-1) for the training cohort and validation cohort, respectively, while the prediction of the venous model yielded AUC values of 0.7877 (95% CI 0.6416-0.9338) and 0.6813 (95% CI 0.3933-0.9693) for the training cohort and validation cohort, respectively. CONCLUSIONS: CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.
Authors: R Garcia-Carbonero; H Sorbye; E Baudin; E Raymond; B Wiedenmann; B Niederle; E Sedlackova; C Toumpanakis; M Anlauf; J B Cwikla; M Caplin; D O'Toole; A Perren Journal: Neuroendocrinology Date: 2016-01-05 Impact factor: 4.914
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