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Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors

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Abstract

Purpose

The pathological risk degree of gastrointestinal stromal tumors (GISTs) has become an issue of great concern. Computed tomography (CT) is beneficial for showing adjacent tissues in detail and determining metastasis or recurrence of GISTs, but its function is still limited. Radiomics has recently shown a great potential in aiding clinical decision-making. The purpose of our study is to develop and validate CT-based radiomics models for GIST risk stratification.

Methods

Three hundred and sixty-six patients clinically suspected of primary GISTs from January 2013 to February 2018 were retrospectively enrolled, among which data from 140 patients were eventually analyzed after exclusion. Data from patient CT images were partitioned based on the National Institutes of Health Consensus Classification, including tumor segmentation, radiomics feature extraction and selection. A radiomics model was then proposed and validated.

Results

The radiomics signature demonstrated discriminative performance for advanced and nonadvanced GISTs with an area under the curve (AUC) of 0.935 [95% confidence interval (CI) 0.870–1.000] and an accuracy of 90.2% for validation cohort. The radiomics signature demonstrated favorable performance for the risk stratification of GISTs with an AUC of 0.809 (95% CI 0.777–0.841) and an accuracy of 67.5% for the validation cohort. Radiomics analysis could capture features of the four risk categories of GISTs. Meanwhile, this CT-based radiomics signature showed good diagnostic accuracy to distinguish between nonadvanced and advanced GISTs, as well as the four risk stratifications of GISTs.

Conclusion

Our findings highlight the potential of a quantitative radiomics analysis as a complementary tool to achieve an accurate diagnosis for GISTs.

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Funding

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Authors and Affiliations

  1. Department of Radiology, Cangzhou Central Hospital, No. 16 Xinhua West Road, Cangzhou, 061000, China

    Lijing Zhang, Liqing Kang, Guoce Li & Shujing Yu

  2. Department of Pathology, Cangzhou Central Hospital, Cangzhou, 061000, China

    Xin Zhang

  3. GE Healthcare, Shanghai, 210000, China

    Jialiang Ren & Zhongqiang Shi

  4. Department of Radiology, Cangzhou Renmin Hospital, Cangzhou, 061000, China

    Jiayue Li

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  1. Lijing Zhang
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  2. Liqing Kang
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  4. Xin Zhang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by LK, GL, XZ, JR, ZS, JL and SY. The first draft of the manuscript was written by LZ, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lijing Zhang.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (Cangzhou Central Hospital + ER3N) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Zhang, L., Kang, L., Li, G. et al. Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol med 125, 465–473 (2020). https://doi.org/10.1007/s11547-020-01138-6

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  • DOI: https://doi.org/10.1007/s11547-020-01138-6

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