Noninvasive prediction model for diagnosing gastrointestinal stromal tumors using contrast-enhanced harmonic endoscopic ultrasound

In Rae Cho, Jun Chul Park, Yun Ho Roh, Soo In Choi, Jeung Eun Lee, Eun Hye Kim, Sung Kwan Shin, SangKil Lee, Yongchan Lee

Research output: Contribution to journalArticle

Abstract

Background & Aims: Subepithelial tumors (SETs) are difficult to diagnose accurately without invasive pathological confirmation. We created a noninvasive prediction model for diagnosing gastrointestinal stromal tumors (GISTs) using contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS). Methods: We retrospectively reviewed 176 patients who underwent CEH-EUS from October 2011 to August 2017. Seventy patients with a diagnosis of GIST (n = 37) or leiomyoma (n = 33) were included. The long-to-short axis ratio (LSR) and enhancement patterns (vascularity, diffuse enhancement) on CEH-EUS were assessed. Logistic regression and classification and regression tree (CART) analyses were performed. Results: The mean age of all patients was 54.9 ± 13.68 years. The GIST group showed significantly higher rates of positive vascularity (81.1% vs. 15.2%, p < 0.001) and diffuse enhancement (51.4% vs. 15.2%, p = 0.001), and lower LSR (1.30 vs. 1.76, p < 0.001). In multivariate logistic regression, positive vascularity (odds ratio [OR] 27.765, 95% confidence interval [CI] 5.336–144.458) and low LSR (OR 18.940, 95% CI 3.623–99.007) were independent predictors of GIST. A noninvasive prediction model for GISTs was developed using the CART model, by allocating patients according to statistically significant variables. Conclusions: The LSR and vascularity of SETs on CEH-EUS can be used as parameters for a noninvasive prediction model of GISTs. This model may be helpful in the early identification and treatment of GISTs.

Original languageEnglish
Pages (from-to)985-992
Number of pages8
JournalDigestive and Liver Disease
Volume51
Issue number7
DOIs
Publication statusPublished - 2019 Jul 1

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Gastrointestinal Stromal Tumors
Logistic Models
Odds Ratio
Confidence Intervals
Leiomyoma
Neoplasms
Regression Analysis

All Science Journal Classification (ASJC) codes

  • Hepatology
  • Gastroenterology

Cite this

Cho, In Rae ; Park, Jun Chul ; Roh, Yun Ho ; Choi, Soo In ; Lee, Jeung Eun ; Kim, Eun Hye ; Shin, Sung Kwan ; Lee, SangKil ; Lee, Yongchan. / Noninvasive prediction model for diagnosing gastrointestinal stromal tumors using contrast-enhanced harmonic endoscopic ultrasound. In: Digestive and Liver Disease. 2019 ; Vol. 51, No. 7. pp. 985-992.
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title = "Noninvasive prediction model for diagnosing gastrointestinal stromal tumors using contrast-enhanced harmonic endoscopic ultrasound",
abstract = "Background & Aims: Subepithelial tumors (SETs) are difficult to diagnose accurately without invasive pathological confirmation. We created a noninvasive prediction model for diagnosing gastrointestinal stromal tumors (GISTs) using contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS). Methods: We retrospectively reviewed 176 patients who underwent CEH-EUS from October 2011 to August 2017. Seventy patients with a diagnosis of GIST (n = 37) or leiomyoma (n = 33) were included. The long-to-short axis ratio (LSR) and enhancement patterns (vascularity, diffuse enhancement) on CEH-EUS were assessed. Logistic regression and classification and regression tree (CART) analyses were performed. Results: The mean age of all patients was 54.9 ± 13.68 years. The GIST group showed significantly higher rates of positive vascularity (81.1{\%} vs. 15.2{\%}, p < 0.001) and diffuse enhancement (51.4{\%} vs. 15.2{\%}, p = 0.001), and lower LSR (1.30 vs. 1.76, p < 0.001). In multivariate logistic regression, positive vascularity (odds ratio [OR] 27.765, 95{\%} confidence interval [CI] 5.336–144.458) and low LSR (OR 18.940, 95{\%} CI 3.623–99.007) were independent predictors of GIST. A noninvasive prediction model for GISTs was developed using the CART model, by allocating patients according to statistically significant variables. Conclusions: The LSR and vascularity of SETs on CEH-EUS can be used as parameters for a noninvasive prediction model of GISTs. This model may be helpful in the early identification and treatment of GISTs.",
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Noninvasive prediction model for diagnosing gastrointestinal stromal tumors using contrast-enhanced harmonic endoscopic ultrasound. / Cho, In Rae; Park, Jun Chul; Roh, Yun Ho; Choi, Soo In; Lee, Jeung Eun; Kim, Eun Hye; Shin, Sung Kwan; Lee, SangKil; Lee, Yongchan.

In: Digestive and Liver Disease, Vol. 51, No. 7, 01.07.2019, p. 985-992.

Research output: Contribution to journalArticle

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AU - Cho, In Rae

AU - Park, Jun Chul

AU - Roh, Yun Ho

AU - Choi, Soo In

AU - Lee, Jeung Eun

AU - Kim, Eun Hye

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AU - Lee, Yongchan

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AB - Background & Aims: Subepithelial tumors (SETs) are difficult to diagnose accurately without invasive pathological confirmation. We created a noninvasive prediction model for diagnosing gastrointestinal stromal tumors (GISTs) using contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS). Methods: We retrospectively reviewed 176 patients who underwent CEH-EUS from October 2011 to August 2017. Seventy patients with a diagnosis of GIST (n = 37) or leiomyoma (n = 33) were included. The long-to-short axis ratio (LSR) and enhancement patterns (vascularity, diffuse enhancement) on CEH-EUS were assessed. Logistic regression and classification and regression tree (CART) analyses were performed. Results: The mean age of all patients was 54.9 ± 13.68 years. The GIST group showed significantly higher rates of positive vascularity (81.1% vs. 15.2%, p < 0.001) and diffuse enhancement (51.4% vs. 15.2%, p = 0.001), and lower LSR (1.30 vs. 1.76, p < 0.001). In multivariate logistic regression, positive vascularity (odds ratio [OR] 27.765, 95% confidence interval [CI] 5.336–144.458) and low LSR (OR 18.940, 95% CI 3.623–99.007) were independent predictors of GIST. A noninvasive prediction model for GISTs was developed using the CART model, by allocating patients according to statistically significant variables. Conclusions: The LSR and vascularity of SETs on CEH-EUS can be used as parameters for a noninvasive prediction model of GISTs. This model may be helpful in the early identification and treatment of GISTs.

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