Research Papers:

Prognostic nomogram for previously untreated adult patients with acute myeloid leukemia

Zhuojun Zheng, Xiaodong Li, Yuandong Zhu, Weiying Gu, Xiaobao Xie and Jingting Jiang _

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Oncotarget. 2016; 7:71526-71535. https://doi.org/10.18632/oncotarget.12245

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Zhuojun Zheng1,2,3,4,*, Xiaodong Li2,3,4,5,*, Yuandong Zhu1, Weiying Gu1, Xiaobao Xie1, Jingting Jiang2,3,4

1Department of Hematology, The Third Affiliated Hospital of Soochow University, China

2Department of Tumor Biological Treatment, The Third Affiliated Hospital of Soochow University, China

3Cancer Immunotherapy Engineering Research Center of Jiangsu Province, China

4Institute of Cell Therapy, Soochow University, China

5Department of Oncology, The Third Affiliated Hospital of Soochow University, China

*Co-first authors

Correspondence to:

Jingting Jiang, email: [email protected]

Keywords: acute myeloid leukemia, nomogram, prognosis, prediction, TCGA

Received: March 29, 2016     Accepted: September 19, 2016     Published: September 26, 2016


This study was designed to perform an acceptable prognostic nomogram for acute myeloid leukemia. The clinical data from 311 patients from our institution and 165 patients generated with Cancer Genome Atlas Research Network were reviewed. A prognostic nomogram was designed according to the Cox’s proportional hazard model to predict overall survival (OS). To compare the capacity of the nomogram with that of the current prognostic system, the concordance index (C-index) was used to validate the accuracy as well as the calibration curve. The nomogram included 6 valuable variables: age, risk stratifications based on cytogenetic abnormalities, status of FLT3-ITD mutation, status of NPM1 mutation, expression of CD34, and expression of HLA-DR. The C-indexes were 0.71 and 0.68 in the primary and validation cohort respectively, which were superior to the predictive capacity of the current prognostic systems in both cohorts. The nomogram allowed both patients with acute myeloid leukemia and physicians to make prediction of OS individually prior to treatment.

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