Evaluation of risk classifications for gastrointestinal stromal tumor using multi-parameter Magnetic Resonance analysis
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Evaluation of risk classifications for gastrointestinal stromal tumor using multi‑parameter Magnetic Resonance analysis Tao Zheng1 · Juan Du1 · Linsha Yang1 · Yanchao Dong2 · Zhanqiu Wang1 · Defeng Liu1 · Shuo Wu1 · Qinglei Shi3 · Xiaohan Wang1 · Lanxiang Liu1 Received: 14 August 2020 / Revised: 29 September 2020 / Accepted: 7 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Background Gastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the gastrointestinal tract. At present, it is generally believed that the prognosis of GIST is closely related to its risk classification. It may add value to correctly diagnose and evaluate the risk of invasion using a noninvasive imaging examination prior to surgery. MRI has the advantages of multiple parameters and high soft tissue resolution, which may be the potential method to preoperatively evaluate the risk of GIST. Purpose To retrospectively evaluate the diagnostic accuracy of multi-parameter MR analysis for preoperative risk classification of GIST. Materials and methods In this 6-year retrospective study, full MRI examination was performed on all 60 GIST cases confirmed classified by pathology, including 35 cases of very low-to-low-risk GIST and 25 cases of intermediate-to-high-risk GIST. Dynamic contrast-enhanced T1- and T2-weighted images, and apparent diffusion coefficient (ADC) maps were reviewed independently by two radiologists blinded to pathologic results. Volume, ADC ratio, three wash-in indexes (WII) were calculated and compared using t-test or Kruskal–Wallis nonparametric test. Sensitivity and specificity analyses were performed to calculate diagnostic accuracy using ROC analyses. Differences were considered significant at p 10.0 >5 >5 ≤5
Primary tumor site Any Any Gastric Any Gastric Tumor rupture Any Any Any Nongastric Nongastric
Statistical analysis All patient data were retrospectively acquired through reviews of medical records, imaging and pathology reports. SPSS (version 19.0, IBM SPSS) and MedCalc (version 11.3.0.0, MedCalc Software) statistical software were used to analyze the data. Continuous variables were expressed as mean ± standard deviation. Categorical data were expressed as frequency (percentage). Inter-observer agreement between the two radiologists was assessed using the intraclass correlation coefficient (ICC). ICCs greater than 0.75 were considered as “good consistency”. The Chi-squared test or Fisher’s exact probability method were used to compare the counting data (sex, position, shape). T-tests or Kruskal–Wallis nonparametric tests were used to compare the measurement data (age, volume, ADCr, WII) according to the normality of the data and the homogeneity of the variances. ROC analysis was used to determine the differential diagnostic ability of different indicators in the two GIST groups. The optimal cutoff value was defined as the value of a parameter when the sum of sensitivity and specificity was maximized. p values less than 0.05 were c
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