Prognostic nomogram for adult patients with acute myeloid leukemia

Chen, Cunte; Wang, Peipei; Wang, Caixia

doi:10.1097/md.0000000000015804

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Besides, the AUC values obtained by internal and external verification were high (0.71–0.9), and the C-index values were >0.8, which revealed excellent consistency and high predictability. In addition, the calibration curve basically coincided, revealing high accuracy of the prediction model, which had higher predictability and accuracy than the prediction models constructed by Chen Cun te, Molica Stefano et al [ 16 , 17 ] Consequently, the Nomogram prediction model can provide a favorable prognosis assessment system for children B-ALL patients.…”

Section: Discussionmentioning

confidence: 87%

“…The direct deletion of a large number of cases due to the missing of few data should also be avoided, which might result in waste of resources and severe bias in the results. [ 15 , 16 ] Patients were excluded if the follow-up was lost, or any of the survival events data were missing, or more than 1% clinical data were missing. Finally, 197 and 1119 children were enrolled from the stage I and stage II children, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The Nomogram prediction model can well deal with this problem. Currently, intuitive clinical prediction models for multiple cancer types have been constructed based on the databases, such as the Nomogram prediction models for AML and chronic lymphoblastic leukemia (CLL), [ 16 – 18 ] which can favorably predict the survival rate and guide the clinical treatment and prognosis improvement. Nonetheless, there is no prediction model specific to children B-ALL so far.…”

Section: Introductionmentioning

confidence: 99%

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The establishment and evaluation of a new model for the prediction of Children B-ALL based on TARGET

Gao¹,

Chen²

2020

Medicine

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B lymphocytic leukemia (B-ALL) is a hematopoietic malignant disease characterized by an accumulation of early B cells. This study aimed to construct a children B-ALL Nomogram prediction model based on Therapeutically Applicable Research to Generate Effective Treatments database, so as to further guide clinical diagnose and treatment. Clinical data related to children B-ALL were collected from the TARGET database, among which, the stage II clinical data were used as the prediction model, while the stage I clinical data were utilized as the external verification model. The stage II clinical factors were analyzed through Lasso regression analysis to screen the risk factors for the construction of Nomogram prediction model. In addition, the model prediction capacity and accuracy were verified internally and externally using the ROC curve, C-index and calibration curve, respectively. A total of 1316 B-ALL children were enrolled in this study. Lasso regression analysis revealed that, Age, Gender, WBC, CNSL, MRD29, BMR, CNS R, BCR-ABL1, BMA29, DS, and DI were the important prognostic risk factors. The C-index values of internal and external verification models were 0.870 and 0.827, respectively, revealing the ideal model discriminating capacity. Besides, the calibration curve had high contact ratio, which suggested favorable consistency between the incidence predicted by the model and the actual incidence. Moreover, the AUC values of the ROC curve were 0.858, 0.787, 0.898, and 0.867, respectively, indicating high model prediction accuracy in predicting the 3- and 5-year survival rates of children with B-ALL.The Nomogram prediction model plotted in this study exhibits favorable prediction capacity and clinical practicability for the survival rate of B-ALL children, which contributes to patients screening and clinical intervention.

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Section: Discussionmentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The establishment and evaluation of a new model for the prediction of Children B-ALL based on TARGET

Gao¹,

Chen²

2020

Medicine

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“…The 2018 AML incidence estimates from SEER (4) are <1.23 per 100,000 in the <40-year-old population, 10.92 per 100,000 in the ≥60-year-old population and 20.89 per 100,000 in the ≥75-year-old population in the USA. The adult AML population is comprised largely of the elderly patients with AML (5).…”

Section: Introductionmentioning

confidence: 99%

Cytotoxic effect of CLL‑1 CAR‑T cell immunotherapy with PD‑1 silencing on relapsed/refractory acute myeloid leukemia

Lin

Zhang

et al. 2021

Mol Med Rep

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The activation of chimeric antigen receptor (CAR)-T cells can lead to persistently high levels of programmed cell death 1 (PD-1) antigen and eventually causes the exhaustion of T cells. The effectiveness of CAR-T cells targeting C-type lectin-like molecule-1 (CLL-1) combined with PD-1 silencing therapy for acute myeloid leukemia (AML) was evaluated in the present study. CLL-1 levels in primary AML bone marrow samples was examined using flow cytometric analysis. We designed a CLL-1 CAR-T, containing CLL-1-specific single-chain variable fragment, CD28, OX40, CD8 hinge and TM and CD3-ζ signaling domains. CLL-1 CAR-T with PD-1 silencing was constructed. It was confirmed that CLL-1 is expressed on the surface of AML cells. CLL-1 CAR-T showed specific lysing activity against CLL-1 + AML cells. PD-1 silencing enhanced the killing ability of CLL-1 CAR-T. Furthermore, it was found that CAR-T derived from healthy donor T cells was more effective in killing THP-1 cells (a human acute monocytic leukemia cell line) than those from patient-derived T cells. These results indicated that CLL-1 CAR-T and PD-1 knockdown CLL-1 CAR-T could be used as a potential immunotherapy to treat relapsed or refractory AML.

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“…Therefore, it is essential to understand the prognostic factors for AML outcomes for effective planning of treatment and rehabilitation modalities. While there have been few studies translating the prognostic factors to predictive models on AML, they have focused on adult patients and none have used machine learning specifically for predicting pediatric patient survival [10,11].…”

Section: Introductionmentioning

confidence: 99%

Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool

Das

Mishra

Mishra³

et al. 2020

Preprint

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Background: Acute myeloid leukemia (AML) accounts for a fifth of childhood leukemia. Although survival rates for AML have greatly improved over the past few decades, they vary depending on demographic and AML type factors. Objectives: To predict the five-year survival among pediatric AML patients using machine learning algorithms and deploy the best performing algorithm as an online survival prediction tool. Materials and methods: Pediatric patients (0 to 14 years) with a microscopically confirmed AML were extracted from the Surveillance Epidemiology and End Results (SEER) database (2000-2016) and split into training and test datasets (80/20 ratio). Four machine learning algorithms (logistic regression, support vector machine, gradient boosting and K nearest neighbor) and a deep neural network algorithm were trained on features to predict five-year survival. Performances of the algorithms were compared and the best performing algorithm was deployed as an online prediction tool. Results: A total of 2,140 patients met our inclusion criteria. The gradient boosting algorithm was the best performer in terms of discrimination and predictive ability. It was deployed as the online survival prediction tool named OSPAM-C (https://ashis-das.shinyapps.io/ospam/). Conclusions: Our study provides a framework for the development and deployment of an online survival prediction tool for pediatric patients with AML. While external validation is needed, our survival prediction tool presents an opportunity to inform clinical decision-making for AML patients.

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Prognostic nomogram for adult patients with acute myeloid leukemia

Cited by 6 publications

References 25 publications

The establishment and evaluation of a new model for the prediction of Children B-ALL based on TARGET

The establishment and evaluation of a new model for the prediction of Children B-ALL based on TARGET

Cytotoxic effect of CLL‑1 CAR‑T cell immunotherapy with PD‑1 silencing on relapsed/refractory acute myeloid leukemia

Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool

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