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Radiomics in pulmonary neuroendocrine tumours (NETs)
OBJECTIVES: The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carc...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Milan
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130162/ https://www.ncbi.nlm.nih.gov/pubmed/35538389 http://dx.doi.org/10.1007/s11547-022-01494-5 |
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author | Cozzi, Diletta Bicci, Eleonora Cavigli, Edoardo Danti, Ginevra Bettarini, Silvia Tortoli, Paolo Mazzoni, Lorenzo Nicola Busoni, Simone Pradella, Silvia Miele, Vittorio |
author_facet | Cozzi, Diletta Bicci, Eleonora Cavigli, Edoardo Danti, Ginevra Bettarini, Silvia Tortoli, Paolo Mazzoni, Lorenzo Nicola Busoni, Simone Pradella, Silvia Miele, Vittorio |
author_sort | Cozzi, Diletta |
collection | PubMed |
description | OBJECTIVES: The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases. METHODS: Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old—average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes. RESULTS: Statistically significant (p < 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only. CONCLUSIONS: CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness. |
format | Online Article Text |
id | pubmed-9130162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Milan |
record_format | MEDLINE/PubMed |
spelling | pubmed-91301622022-05-26 Radiomics in pulmonary neuroendocrine tumours (NETs) Cozzi, Diletta Bicci, Eleonora Cavigli, Edoardo Danti, Ginevra Bettarini, Silvia Tortoli, Paolo Mazzoni, Lorenzo Nicola Busoni, Simone Pradella, Silvia Miele, Vittorio Radiol Med Chest Radiology OBJECTIVES: The aim of this single-centre, observational, retrospective study is to find a correlation using Radiomics between the analysis of CT texture features of primary lesion of neuroendocrine (NET) lung cancer subtypes (typical and atypical carcinoids, large and small cell neuroendocrine carcinoma), Ki-67 index and the presence of lymph nodal mediastinal metastases. METHODS: Twenty-seven patients (11 males and 16 females, aged between 48 and 81 years old—average age of 70,4 years) with histological diagnosis of pulmonary NET with known Ki-67 status and metastases who have performed pre-treatment CT in our department were included. All examinations were performed with the same CT scan (Sensation 16-slice, Siemens). The study protocol was a baseline scan followed by 70 s delay acquisition after administration of intravenous contrast medium. After segmentation of primary lesions, quantitative texture parameters of first and higher orders were extracted. Statistics nonparametric tests and linear correlation tests were conducted to evaluate the relationship between different textural characteristics and tumour subtypes. RESULTS: Statistically significant (p < 0.05) differences were seen in post-contrast enhanced CT in multiple first and higher-order extracted parameters regarding the correlation with classes of Ki-67 index values. Statistical analysis for direct acquisitions was not significant. Concerning the correlation with the presence of metastases, one histogram feature (Skewness) and one feature included in the Gray-Level Co-occurrence Matrix (ClusterShade) were significant on contrast-enhanced CT only. CONCLUSIONS: CT texture analysis may be used as a valid tool for predicting the subtype of lung NET and its aggressiveness. Springer Milan 2022-05-10 2022 /pmc/articles/PMC9130162/ /pubmed/35538389 http://dx.doi.org/10.1007/s11547-022-01494-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Chest Radiology Cozzi, Diletta Bicci, Eleonora Cavigli, Edoardo Danti, Ginevra Bettarini, Silvia Tortoli, Paolo Mazzoni, Lorenzo Nicola Busoni, Simone Pradella, Silvia Miele, Vittorio Radiomics in pulmonary neuroendocrine tumours (NETs) |
title | Radiomics in pulmonary neuroendocrine tumours (NETs) |
title_full | Radiomics in pulmonary neuroendocrine tumours (NETs) |
title_fullStr | Radiomics in pulmonary neuroendocrine tumours (NETs) |
title_full_unstemmed | Radiomics in pulmonary neuroendocrine tumours (NETs) |
title_short | Radiomics in pulmonary neuroendocrine tumours (NETs) |
title_sort | radiomics in pulmonary neuroendocrine tumours (nets) |
topic | Chest Radiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9130162/ https://www.ncbi.nlm.nih.gov/pubmed/35538389 http://dx.doi.org/10.1007/s11547-022-01494-5 |
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