Literature DB >> 33126173

Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms.

I S Gruzdev1, K A Zamyatina2, V S Tikhonova3, E V Kondratyev4, A V Glotov5, G G Karmazanovsky6, A Sh Revishvili7.   

Abstract

PURPOSE: To evaluate the reproducibility of textural features of pancreatic neuroendocrine neoplasms (PNENs), obtained under various CT-scanning conditions. METHODS AND MATERIALS: We included 12 patients with PNENs and 2 contrast enhanced CT (CECT): 1) from our center according to standard CT-protocol; 2) from another institution. Two radiologists independently segmented the entire neoplasm volume using a 3D region of interest by LIFEx application on the arterial phase and then copied it to the other phases. 52 texture features were calculated for each phase. As a criterion for the segmentation consistency, a value of neoplasm volume was compared using the Bland-Altman method. The Kendall concordance coefficient was calculated to assess the texture features reproducibility in three scenarios: 1) different radiologists, same CECT; 2) same radiologist, different CECT; 3) different radiologists, different CECT.
RESULTS: For the scenario 1 the neoplasm volumes (except one large PNEN) were found within two standard deviations; this indicates high consistency of the segmentation. For the first scenario, Kendall's coefficient exceeded a threshold of 0.7 for all 52 features for all CT phases. For the second and third scenario, the concordance coefficient exceeded a threshold of 0.7 in 38, 28, 42, 45 and in 36, 25, 36, 44 features for the native, arterial, venous and delayed phases, respectively.
CONCLUSION: The highest reproducibility was found in the first scenario compared to the second and third: 100 % vs. 74 % and 67 %. Reproducible texture features can be reliably used to assess the PNENs structure.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  CT; Neuroendocrine neoplasm; Pancreas; Radiomics; Reproducibility of results; Texture analysis

Mesh:

Year:  2020        PMID: 33126173     DOI: 10.1016/j.ejrad.2020.109371

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  2 in total

1.  Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer.

Authors:  Giorgio Maria Masci; Fabio Ciccarelli; Fabrizio Ivo Mattei; Damiano Grasso; Fabio Accarpio; Carlo Catalano; Andrea Laghi; Paolo Sammartino; Franco Iafrate
Journal:  Radiol Med       Date:  2022-01-23       Impact factor: 3.469

Review 2.  The impact of radiomics in diagnosis and staging of pancreatic cancer.

Authors:  Calogero Casà; Antonio Piras; Andrea D'Aviero; Francesco Preziosi; Silvia Mariani; Davide Cusumano; Angela Romano; Ivo Boskoski; Jacopo Lenkowicz; Nicola Dinapoli; Francesco Cellini; Maria Antonietta Gambacorta; Vincenzo Valentini; Gian Carlo Mattiucci; Luca Boldrini
Journal:  Ther Adv Gastrointest Endosc       Date:  2022-03-16
  2 in total

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