European journal of radiology · 2025
Computed tomography features of gastric cancer with deficient mismatch repair: Radiologic-pathologic correlation.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Li Jia-Zheng, Li Yan-Ling, Zhao Xin-Ya, Wang Yin-Kui, Li Yan-Li, Liu Yi-Ting, Wei Yi-Yuan, Li Xiao-Ting, Li Zhen-Hui, Sun Yu, Tang Lei, Li Zi-Yu
Аннотация
OBJECTIVES: Our study aimed to identify CT features of locally advanced gastric cancer (LAGC) associated with deficient mismatch repair (dMMR), explore the underlying pathological basis, and develop a morphology-based model for dMMR prediction.
METHODS: This multicenter, retrospective analysis included 397 patients with LAGC treated at three institutions between January 2016 and January 2022. Mismatch repair (MMR) status was determined by immunohistochemical analysis of postoperative specimens. The patients were divided into training and external validation groups. CT features of primary tumours and lymph nodes associated with MMR status were identified, and the underlying pathological basis for these features were explored. Multivariable logistic analysis was used to identify independent CT features and to develop a morphology-based model.
RESULTS: Tumours with dMMR were characterized by increased thickness (p = 0.04), location in the lower stomach (p < 0.001), heterogeneous enhancement patterns (p = 0.02), a well-defined margin (p < 0.001), the presence of mushroom sign (p < 0.001), and the presence of abnormal lymph nodes (p = 0.07). Pathologically, tumours with dMMR exhibited extensive lymphocytic infiltration and solid growth with a pushing border, consistent with the distinctive CT features observed. Multivariable logistic analysis identified lower tumour location, well-defined tumour margin, and mushroom sign as independent predictors of dMMR. The morphology-based model achieved AUC values of 0.73-0.74 for dMMR prediction in the training and external validation groups.
CONCLUSIONS: LAGC with dMMR showed distinct CT features. A morphology-based model constructed from these CT features has the potential to predict dMMR status for LAGC.
METHODS: This multicenter, retrospective analysis included 397 patients with LAGC treated at three institutions between January 2016 and January 2022. Mismatch repair (MMR) status was determined by immunohistochemical analysis of postoperative specimens. The patients were divided into training and external validation groups. CT features of primary tumours and lymph nodes associated with MMR status were identified, and the underlying pathological basis for these features were explored. Multivariable logistic analysis was used to identify independent CT features and to develop a morphology-based model.
RESULTS: Tumours with dMMR were characterized by increased thickness (p = 0.04), location in the lower stomach (p < 0.001), heterogeneous enhancement patterns (p = 0.02), a well-defined margin (p < 0.001), the presence of mushroom sign (p < 0.001), and the presence of abnormal lymph nodes (p = 0.07). Pathologically, tumours with dMMR exhibited extensive lymphocytic infiltration and solid growth with a pushing border, consistent with the distinctive CT features observed. Multivariable logistic analysis identified lower tumour location, well-defined tumour margin, and mushroom sign as independent predictors of dMMR. The morphology-based model achieved AUC values of 0.73-0.74 for dMMR prediction in the training and external validation groups.
CONCLUSIONS: LAGC with dMMR showed distinct CT features. A morphology-based model constructed from these CT features has the potential to predict dMMR status for LAGC.