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A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics

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Abstract

Purpose

To construct and validate a radiomics feature model based on computed tomography (CT) images and clinical characteristics to predict the microsatellite instability (MSI) status of gastric cancer patients before surgery.

Methods

We retrospectively collected the upper abdominal or the entire abdominal-enhanced CT scans of 189 gastric cancer patients before surgery. The patients underwent postoperative gastric cancer MSI status testing, and the dates of their radiologic images and clinicopathological data were from January 2015 to August 2021. These 189 patients were divided into a training set (n = 90) and an external validation set (n = 99). The patients were divided by MSI status into the MSI-high (H) arm (30 and 33 patients in the training set and external validation set, respectively) and MSI-low/stable (L/S) arm (60 and 66 patients in the training set and external validation set, respectively). In the training set, the clinical characteristics and tumor radiologic characteristics of the patients were extracted, and the tenfold cross-validation method was used for internal validation of the training set. The external validation set was used to assess its generalized performance. A receiver-operating characteristic (ROC) curve was plotted to assess the model performance, and the area under the curve (AUC) was calculated.

Results

The AUC of the radiomics model in the training set and external validation set was 0.8228 [95% confidence interval (CI) 0.7355–0.9101] and 0.7603 [95% CI 0.6625–0.8581], respectively, showing that the constructed radiomics model exhibited satisfactory generalization capabilities. The accuracy, sensitivity, and specificity of the training dataset were 0.72, 0.63, and 0.77, respectively. The accuracy, sensitivity, and specificity of the external validation dataset were 0.67, 0.79, and 0.60, respectively. Statistical analysis was carried out on the clinical data, and there was statistical significance for the tumor site and age (p < 0.05). MSI-H gastric cancer was mostly seen in the gastric antrum and older patients.

Conclusions

Radiomics markers based on CT images and clinical characteristics have the potential to be a non-invasive auxiliary diagnostic tool for preoperative assessment of gastric cancer MSI status, and they can aid in clinical decision-making and improve patient outcomes.

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Abbreviations

MSI:

Microsatellite instability

CT:

Computed tomography

AUC:

Area under the curve

ROC:

Receiver operating curve

NCCN:

National comprehensive cancer network

MMR:

Mismatch repair

MSI-H:

MSI-high frequency

MSI-L:

MSI-low frequency

MSS:

MSI stability

VOI:

Volume of interest

ROI:

Region of interest

DNA:

Deoxyribonucleic acid

FDA:

Food and Drug Administration

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

GLDM:

Gray-level dependence matrix

NGTDM:

Neighborhood gray-tone difference matrix

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Authors and Affiliations

  1. Department of Radiology, The People’s Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China

    Xiuqun Liang & Ying Liu

  2. Department of Radiology, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China

    Yinbo Wu

  3. Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310007, Zhejiang, China

    Danping Yu & Zhi Li

  4. Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China

    Chencui Huang

Authors
  1. Xiuqun Liang
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  2. Yinbo Wu
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  3. Ying Liu
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  4. Danping Yu
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  6. Zhi Li
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Correspondence to Zhi Li.

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Liang, X., Wu, Y., Liu, Y. et al. A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics. Abdom Radiol 47, 2036–2045 (2022). https://doi.org/10.1007/s00261-022-03507-3

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  • DOI: https://doi.org/10.1007/s00261-022-03507-3

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