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Risk stratification in GIST: shape quantification with CT is a predictive factor

BACKGROUND: Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs. METHODS: A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry...

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Autores principales: Wei, Sheng-cai, Xu, Liang, Li, Wan-hu, Li, Yun, Guo, Shou-fang, Sun, Xiao-rong, Li, Wen-wu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062662/
https://www.ncbi.nlm.nih.gov/pubmed/31900704
http://dx.doi.org/10.1007/s00330-019-06561-6
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author Wei, Sheng-cai
Xu, Liang
Li, Wan-hu
Li, Yun
Guo, Shou-fang
Sun, Xiao-rong
Li, Wen-wu
author_facet Wei, Sheng-cai
Xu, Liang
Li, Wan-hu
Li, Yun
Guo, Shou-fang
Sun, Xiao-rong
Li, Wen-wu
author_sort Wei, Sheng-cai
collection PubMed
description BACKGROUND: Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs. METHODS: A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry and underwent enhanced CT examination. All lesions’ pathologic sizes were 1 to 10 cm. Points A and B were the extremities of the longest diameter (LD) of the tumor and points C and D the extremities of the small axis, which was the longest diameter perpendicular to AB. The four angles of the quadrangle ABCD were measured and each angle named by its summit (A, B, C, D). For regular lesions, we took angles A and B as big angle (BiA) and small angle (SmA). For irregular lesions, we compared A/B ratio and D/C ratio and selected the larger ratio for analysis. The chi-square test, t test, ROC analysis, and hierarchical or binary logistic regression analysis were used to analyze the data. RESULTS: The BiA/SmA ratio was an independent predictor for risk level of GISTs (p = 0.019). With threshold of BiA at 90.5°, BiA/SmA ratio at 1.35 and LD at 6.15 cm, the sensitivities for high-risk GISTs were 82.4%, 85.3%, and 83.8%, respectively; the specificities were 87.1%, 71%, and 77.4%, respectively; and the AUCs were 0.852, 0.818, and 0.844, respectively. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could (p < 0.05). Shape and Ki-67 were independent predictors of the mitotic value (p = 0.036 and p < 0.001, respectively), and the accuracy was 87.8%. CONCLUSIONS: Quantifying tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs, especially for high-risk grading and mitotic value > 5/50HPF. KEY POINTS: • The BiA/SmA ratio was an independent predictor affecting the risk level of GISTs. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could. • Shape and Ki-67 were independent predictors of the mitotic value. • The method for quantifying the tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs.
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spelling pubmed-70626622020-03-23 Risk stratification in GIST: shape quantification with CT is a predictive factor Wei, Sheng-cai Xu, Liang Li, Wan-hu Li, Yun Guo, Shou-fang Sun, Xiao-rong Li, Wen-wu Eur Radiol Computed Tomography BACKGROUND: Tumor shape is strongly associated with some tumor’s genomic subtypes and patient outcomes. Our purpose is to find the relationship between risk stratification and the shape of GISTs. METHODS: A total of 101 patients with primary GISTs were confirmed by pathology and immunohistochemistry and underwent enhanced CT examination. All lesions’ pathologic sizes were 1 to 10 cm. Points A and B were the extremities of the longest diameter (LD) of the tumor and points C and D the extremities of the small axis, which was the longest diameter perpendicular to AB. The four angles of the quadrangle ABCD were measured and each angle named by its summit (A, B, C, D). For regular lesions, we took angles A and B as big angle (BiA) and small angle (SmA). For irregular lesions, we compared A/B ratio and D/C ratio and selected the larger ratio for analysis. The chi-square test, t test, ROC analysis, and hierarchical or binary logistic regression analysis were used to analyze the data. RESULTS: The BiA/SmA ratio was an independent predictor for risk level of GISTs (p = 0.019). With threshold of BiA at 90.5°, BiA/SmA ratio at 1.35 and LD at 6.15 cm, the sensitivities for high-risk GISTs were 82.4%, 85.3%, and 83.8%, respectively; the specificities were 87.1%, 71%, and 77.4%, respectively; and the AUCs were 0.852, 0.818, and 0.844, respectively. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could (p < 0.05). Shape and Ki-67 were independent predictors of the mitotic value (p = 0.036 and p < 0.001, respectively), and the accuracy was 87.8%. CONCLUSIONS: Quantifying tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs, especially for high-risk grading and mitotic value > 5/50HPF. KEY POINTS: • The BiA/SmA ratio was an independent predictor affecting the risk level of GISTs. LD could not effectively distinguish between intermediate-risk and high-risk GISTs, but BiA could. • Shape and Ki-67 were independent predictors of the mitotic value. • The method for quantifying the tumor shape has better predictive efficacy than LD in predicting the risk level and mitotic value of GISTs. Springer Berlin Heidelberg 2020-01-03 2020 /pmc/articles/PMC7062662/ /pubmed/31900704 http://dx.doi.org/10.1007/s00330-019-06561-6 Text en © The Author(s) 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Computed Tomography
Wei, Sheng-cai
Xu, Liang
Li, Wan-hu
Li, Yun
Guo, Shou-fang
Sun, Xiao-rong
Li, Wen-wu
Risk stratification in GIST: shape quantification with CT is a predictive factor
title Risk stratification in GIST: shape quantification with CT is a predictive factor
title_full Risk stratification in GIST: shape quantification with CT is a predictive factor
title_fullStr Risk stratification in GIST: shape quantification with CT is a predictive factor
title_full_unstemmed Risk stratification in GIST: shape quantification with CT is a predictive factor
title_short Risk stratification in GIST: shape quantification with CT is a predictive factor
title_sort risk stratification in gist: shape quantification with ct is a predictive factor
topic Computed Tomography
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062662/
https://www.ncbi.nlm.nih.gov/pubmed/31900704
http://dx.doi.org/10.1007/s00330-019-06561-6
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