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Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma
Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologist...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149869/ https://www.ncbi.nlm.nih.gov/pubmed/35645384 http://dx.doi.org/10.3390/tomography8030098 |
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author | Crimì, Filippo Bao, Quoc Riccardo Mari, Valentina Zanon, Chiara Cabrelle, Giulio Spolverato, Gaya Pucciarelli, Salvatore Quaia, Emilio |
author_facet | Crimì, Filippo Bao, Quoc Riccardo Mari, Valentina Zanon, Chiara Cabrelle, Giulio Spolverato, Gaya Pucciarelli, Salvatore Quaia, Emilio |
author_sort | Crimì, Filippo |
collection | PubMed |
description | Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologists, considering only loco-regional LNs with a long axis (LA) ≥ 5 mm. For each nodal group, the short axis (SA), volume and SA/LA ratio of the largest LN, the sum of the SAs of all LNs, and the mean of the SA/LA ratios were plotted in ROC curves, taking the presence/absence of metastases at histopathology for reference. On a per-patient basis, the sums of the SAs of all LNs, and the sums of the SAs, volumes, and SA/LA ratios of the largest LNs in all nodal groups were also plotted, taking the presence/absence of metastatic LNs in each patient for reference. Results. Four hundred and forty-three nodal groups were harvested during surgery from 107 patients with GC, and 173 (39.1%) were metastatic at histopathology. By nodal group, the sum of the SAs showed the best Area Under the Curve (AUC), with a sensitivity/specificity of 62.4/72.6% using Youden’s index with a >8 mm cutoff. In the per-patient analysis, the sum of the SAs of all LNs in the loco-regional nodal groups showed the best AUC with a sensitivity/specificity of 65.6%/83.7%, using Youden’s index with a >39 mm cutoff. Conclusion. In patients with GC, the sum of the SAs of all the LNs at staging CT is the best predictor among dimensional LNs criteria of both metastatic invasion of the nodal group and the presence of metastatic LNs. |
format | Online Article Text |
id | pubmed-9149869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91498692022-05-31 Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma Crimì, Filippo Bao, Quoc Riccardo Mari, Valentina Zanon, Chiara Cabrelle, Giulio Spolverato, Gaya Pucciarelli, Salvatore Quaia, Emilio Tomography Article Background. The aim of this study was to identify the most accurate computed-tomography (CT) dimensional criteria of loco-regional lymph nodes (LNs) for detecting nodal metastases in gastric cancer (GC) patients. Methods. Staging CTs of surgically resected GC were jointly reviewed by two radiologists, considering only loco-regional LNs with a long axis (LA) ≥ 5 mm. For each nodal group, the short axis (SA), volume and SA/LA ratio of the largest LN, the sum of the SAs of all LNs, and the mean of the SA/LA ratios were plotted in ROC curves, taking the presence/absence of metastases at histopathology for reference. On a per-patient basis, the sums of the SAs of all LNs, and the sums of the SAs, volumes, and SA/LA ratios of the largest LNs in all nodal groups were also plotted, taking the presence/absence of metastatic LNs in each patient for reference. Results. Four hundred and forty-three nodal groups were harvested during surgery from 107 patients with GC, and 173 (39.1%) were metastatic at histopathology. By nodal group, the sum of the SAs showed the best Area Under the Curve (AUC), with a sensitivity/specificity of 62.4/72.6% using Youden’s index with a >8 mm cutoff. In the per-patient analysis, the sum of the SAs of all LNs in the loco-regional nodal groups showed the best AUC with a sensitivity/specificity of 65.6%/83.7%, using Youden’s index with a >39 mm cutoff. Conclusion. In patients with GC, the sum of the SAs of all the LNs at staging CT is the best predictor among dimensional LNs criteria of both metastatic invasion of the nodal group and the presence of metastatic LNs. MDPI 2022-04-22 /pmc/articles/PMC9149869/ /pubmed/35645384 http://dx.doi.org/10.3390/tomography8030098 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Crimì, Filippo Bao, Quoc Riccardo Mari, Valentina Zanon, Chiara Cabrelle, Giulio Spolverato, Gaya Pucciarelli, Salvatore Quaia, Emilio Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title | Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title_full | Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title_fullStr | Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title_full_unstemmed | Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title_short | Predictors of Metastatic Lymph Nodes at Preoperative Staging CT in Gastric Adenocarcinoma |
title_sort | predictors of metastatic lymph nodes at preoperative staging ct in gastric adenocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149869/ https://www.ncbi.nlm.nih.gov/pubmed/35645384 http://dx.doi.org/10.3390/tomography8030098 |
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