Zhongguo shi yan xue ye xue za zhi · 2025
[Predictive Value of MIC Typing for IDH1/2 Mutations in Patients with Acute Myeloid Leukemia].
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Chen Hui-Juan, Shen Yang-Ling, Guo Yan-Ting, Zhou Yi-Fang, Miao Ying-Jie, Dong Wei-Min, Gu Wei-Ying
Аннотация
OBJECTIVE: To investigate the predictive value of morphology, immunology, and cytogenetics for isocitrate dehydrogenase 1 and 2 (IDH1/2) gene mutation in newly diagnosed acute myeloid leukemia (AML) patients.
METHODS: The clinical data of 186 newly diagnosed AML patients (except M3 subtype) in the First People's Hospital of Changzhou were retrospectively analyzed, and the variables associated with IDH1/2 mutation in patients were screened using LASSO regression to construct a multivariate logistic regression analysis model. The Bootstrap method was used for internal validation of the model and nomograms were used to visualize the model, and receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model.
RESULTS: A total of 60 AML patients had IDH1/2 mutation at initial diagnosis. LASSO regression screened 9 predictive variables associated with IDH1/2 mutation, including CD7, CD56, CD11b, CD15, CD64, HLA-DR, platelet count≥50×109/L, isolated +8 and normal karyotype. The nomogram and ROC curve were plotted based on the above 9 variables. The area under the ROC curve (AUC) of the training set and the validation set were 0.871 and 0.806, respectively. Internal validation showed that the nomogram had good predictive ability.
CONCLUSION: The prediction model based on MIC typing constructed in this study has a good predictive ability for the presence of IDH1/2 mutations in newly diagnosed AML patients and has important clinical application value when the gene mutation detection results are unavailable.
METHODS: The clinical data of 186 newly diagnosed AML patients (except M3 subtype) in the First People's Hospital of Changzhou were retrospectively analyzed, and the variables associated with IDH1/2 mutation in patients were screened using LASSO regression to construct a multivariate logistic regression analysis model. The Bootstrap method was used for internal validation of the model and nomograms were used to visualize the model, and receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model.
RESULTS: A total of 60 AML patients had IDH1/2 mutation at initial diagnosis. LASSO regression screened 9 predictive variables associated with IDH1/2 mutation, including CD7, CD56, CD11b, CD15, CD64, HLA-DR, platelet count≥50×109/L, isolated +8 and normal karyotype. The nomogram and ROC curve were plotted based on the above 9 variables. The area under the ROC curve (AUC) of the training set and the validation set were 0.871 and 0.806, respectively. Internal validation showed that the nomogram had good predictive ability.
CONCLUSION: The prediction model based on MIC typing constructed in this study has a good predictive ability for the presence of IDH1/2 mutations in newly diagnosed AML patients and has important clinical application value when the gene mutation detection results are unavailable.