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Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging

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Abstract

Purpose

Identify radiomics features associated with progression-free survival (PFS) and develop a predictive model for accurate PFS prediction in liver metastatic gastrointestinal stromal tumor patients (GIST).

Methods

This multi-center retrospective study involved a comprehensive review of clinical and imaging data pertaining to 211 patients with gastrointestinal stromal tumors (GIST) from Center A and B. A total of 147 patients with hepatic metastatic GIST were included, with 102 cases as the training set and 45 cases as the external validation set. Radiomics features were extracted from non-enhanced MR images, specifically T2WI, DWI, and ADC, and relevant features were selected through LASSO-Cox regression. A radiomics nomogram model was then constructed using multivariable Cox regression analysis to effectively predict PFS. The models performance were evaluated with the concordance index (C-index).

Results

The median age of the patients was 53 years, with 82 males and 65 females. A total of 21 radiomics features were selected to generate the radiomics signature. Radiomics signature slightly outperformed the clinical model but without significant difference (P > 0.05). Integrated radiomics signature with clinical features to build a nomogram, which exhibited high predictive performance in both training (C-index 0.757, 95% CI 0.692–0.822) and validation cohorts (C-index 0.718, 95% CI 0.618–0.818). Nomogram significantly outperformed the clinical model (P = 0.002 for training cohort, P < 0.001 for validation cohort). Stable long-term predictions shown by time-dependent ROC analysis (AUC 0.765–0.919 in training, 0.766–0.893 in validation). Multivariable Cox regression confirmed radiomics signature as an independent prognostic factor for preoperative survival prediction in hepatic metastatic GIST patients (HR = 3.973).

Conclusion

Radiomics signature is valuable for predicting PFS in metastatic GIST patients. Integrating imaging features and clinical factors into a comprehensive nomogram improves accuracy and effectiveness of survival prognosis, guiding personalized treatment strategies.

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Acknowledgements

This research was supported by Qinhuangdao Key Research and Development of Science and Technology Support Project (202301A272).

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Author notes

  1. Linsha Yang and Duo Zhang have contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, The First Hospital of Qinhuangdao, Qinhuangdao, People’s Republic of China

    Linsha Yang, Tao Zheng & Defeng Liu

  2. Department of Medical Imaging, Baoding No. 1 Central Hospital, Baoding, People’s Republic of China

    Duo Zhang

  3. Medical Imaging Center, Chongqing Yubei District People’s Hospital, Chongqing, People’s Republic of China

    Yuan Fang

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  1. Linsha Yang
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Contributions

TZ, DL and YF designed and coordinated the study, LY and DZ carried out experiment and data process, and drafted the manuscript. All authors gave final approval for publication.

Corresponding authors

Correspondence to Defeng Liu or Yuan Fang.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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This retrospective study involved human participants. This retrospective study that incorporates anonymous data was approved by the Ethics Committee of two hospitals and the need for informed consent was waived.

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Yang, L., Zhang, D., Zheng, T. et al. Predicting the progression-free survival of gastrointestinal stromal tumors after imatinib therapy through multi-sequence magnetic resonance imaging. Abdom Radiol 49, 801–813 (2024). https://doi.org/10.1007/s00261-023-04093-8

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  • DOI: https://doi.org/10.1007/s00261-023-04093-8

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