Journal of translational internal medicine · 2025
Artificial intelligence-based predictive model for relapse in acute myeloid leukemia patients following haploidentical hematopoietic cell transplantation.
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Fan Shuang, Hong Haoyang, Lu Shengye, Wen Qi, Hong Shenda, Zhang Xiaohui, Xu Lanping, Wang Yu, Yan Chenhua, Chen Huan, Chen Yuhong, Han Wei, Wang Fengrong, Wang Jingzhi, Huang Xiaojun, Mo Xiaodong
Аннотация
BACKGROUND AND OBJECTIVES: Relapse is one of the most critical causes of transplant failure in patients with acute myeloid leukemia (AML) receiving haploidentical-related donor (HID) hematopoietic stem cell transplantation (HSCT). We aimed to develop an artificial intelligence (AI)-based predictive model for post-transplant relapse in patients with AML receiving HID HSCT.
METHODS: This study included patients with consecutive AML (aged ≥ 12 years) receiving HID HSCT in complete remission (CR). We randomly selected 70% of the entire population (n = 665) as the training cohort for developing the model and nomogram, which were both evaluated using data from the remaining 30% of the patients (validation cohort, n = 286). Furthermore, the model was validated in an independent cohort (n = 213) and in the clinical practice of five experienced clinicians.
RESULTS: Five variables (AML risk category, number of courses of induction chemotherapy for first CR, disease status, measurable residual disease before HSCT, and blood group disparity) were included in the final model (i.e., PKU-AML model). The concordance index of the nomogram was 0.707. The Hosmer-Lemeshow test showed a good fit for this model (P = 0.205). The calibration curve was close to the ideal diagonal line, and decision curve analysis showed a significantly better net benefit for this model. The reliability of our prediction nomogram was demonstrated in a validation cohort, an independent cohort, and in clinical practice.
CONCLUSIONS: Our PKU-AML model can predict the relapse of patients with AML receiving HID HSCT in CR, providing an effective tool for the early prediction and timely management of post-transplant relapse.
METHODS: This study included patients with consecutive AML (aged ≥ 12 years) receiving HID HSCT in complete remission (CR). We randomly selected 70% of the entire population (n = 665) as the training cohort for developing the model and nomogram, which were both evaluated using data from the remaining 30% of the patients (validation cohort, n = 286). Furthermore, the model was validated in an independent cohort (n = 213) and in the clinical practice of five experienced clinicians.
RESULTS: Five variables (AML risk category, number of courses of induction chemotherapy for first CR, disease status, measurable residual disease before HSCT, and blood group disparity) were included in the final model (i.e., PKU-AML model). The concordance index of the nomogram was 0.707. The Hosmer-Lemeshow test showed a good fit for this model (P = 0.205). The calibration curve was close to the ideal diagonal line, and decision curve analysis showed a significantly better net benefit for this model. The reliability of our prediction nomogram was demonstrated in a validation cohort, an independent cohort, and in clinical practice.
CONCLUSIONS: Our PKU-AML model can predict the relapse of patients with AML receiving HID HSCT in CR, providing an effective tool for the early prediction and timely management of post-transplant relapse.