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357 results on '"Liu, Zhenfang"'

3. Patient Versus Physician Perspective in the Management of Chronic Myeloid Leukemia During Treatment with Tyrosine Kinase Inhibitors

Author

Chen, Hong, Wen, Yan, Zeng, Yun, Lin, Lie, Sun, Bihong, Zhu, Hongqian, He, Huiqing, Wang, Xiaotao, Zou, Waiyi, Zheng, Caifeng, Zheng, Liling, Huang, Jinxiong, Pang, Liping, Huang, Jixian, Zhang, Yuming, Lin, Haiqing, Liu, Zelin, Zhu, Wanshou, Wang, Qiang, Zhou, Xuan, Liu, Xiaoli, Qu, Hong, Liu, Zhenfang, Du, Xin, and Xu, Na

Published
2024
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11. Comparison of the Efficacy Among Nilotinib, Dasatinib, Flumatinib and Imatinib in Newly Diagnosed Chronic-Phase Chronic Myeloid Leukemia Patients: A Real-World Multi-Center Retrospective Study

Author

Zhang, Xiaoshuai, Xu, Na, Yang, Yunfan, Lin, Hai, Liu, Bingcheng, Du, Xin, Liu, Xiaoli, Liang, Rong, Chen, Chunyan, Huang, Jian, Zhu, Huanling, Pan, Ling, Wang, Xiaodong, Li, Guohui, Liu, Zhuogang, Zhang, Yanqing, Liu, Zhenfang, Hu, Jianda, Liu, Chunshui, Li, Fei, Yang, Wei, Meng, Li, Han, Yanqiu, Lin, Li'e, Zhao, Zhenyu, Tu, Chuanqing, Zheng, Caifeng, Bai, Yanliang, Zhou, Zeping, Chen, Suning, Qiu, Huiying, Yang, Lijie, Sun, Xiuli, Sun, Hui, Zhou, Li, Liu, Zelin, Wang, Danyu, Guo, Jianxin, Pang, Liping, Zeng, Qingshu, Suo, Xiaohui, Zhang, Weihua, Zheng, Yuanjun, Zhang, Yanli, Li, Weiming, and Jiang, Qian

Published
2024
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13. Validation of the imatinib-therapy failure model

Author

Zhang, Xiaoshuai, Gale, Robert Peter, Liu, Bingcheng, Huang, Jian, Zhang, Yanli, Du, Xin, Weng, Jianyu, Li, Weiming, Xu, Na, Liu, Xiaoli, Chen, Chunyan, Lin, Hai, Li, Guohui, Liang, Rong, Liu, Zhuogang, Wang, Xiaodong, Zhang, Yanqing, Han, Yanqiu, Liu, Chunshui, Hu, Jianda, Lin, Lie, Yang, Wei, Liu, Zhenfang, Meng, Li, Tu, Chuanqing, Zheng, Caifeng, Zhou, Zeping, Bai, Yanliang, Qiu, Huiying, Chen, Suning, Li, Fei, Guo, Jianxin, Liu, Zelin, Sun, Hui, Zhou, Li, Feng, Ru, Sun, Xiuli, Huang, Xiaojun, and Jiang, Qian

Published
2023
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14. A predictive model for therapy failure in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitor therapy

Author

Zhang, Xiaoshuai, Liu, Bingcheng, Huang, Jian, Zhang, Yanli, Xu, Na, Gale, Robert Peter, Li, Weiming, Liu, Xiaoli, Zhu, Huanling, Pan, Ling, Yang, Yunfan, Lin, Hai, Du, Xin, Liang, Rong, Chen, Chunyan, Wang, Xiaodong, Li, Guohui, Liu, Zhuogang, Zhang, Yanqing, Liu, Zhenfang, Hu, Jianda, Liu, Chunshui, Li, Fei, Yang, Wei, Meng, Li, Han, Yanqiu, Lin, Li’e, Zhao, Zhenyu, Tu, Chuanqing, Zheng, Caifeng, Bai, Yanliang, Zhou, Zeping, Chen, Suning, Qiu, Huiying, Yang, Lijie, Sun, Xiuli, Sun, Hui, Zhou, Li, Liu, Zelin, Wang, Danyu, Guo, Jianxin, Pang, Liping, Zeng, Qingshu, Suo, Xiaohui, Zhang, Weihua, Zheng, Yuanjun, Huang, Xiaojun, and Jiang, Qian

Published
2024
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27. A multi-center retrospective comparison between systemic mastocytosis with t(8;21) AML and KIT mutant t(8;21) AML

Author

Zhang, Zhibo, primary, Yin, Jia, additional, Lian, Guoli, additional, Bao, Xiebing, additional, Hu, Meng, additional, Liu, Zhenfang, additional, Yu, Yuan, additional, Mi, Ruihua, additional, Zuo, Yabei, additional, Shi, Pengcheng, additional, Zheng, Weiyan, additional, Jiang, Qian, additional, Chao, Hongying, additional, Xiao, Peifang, additional, Yu, Weijuan, additional, Han, Yanqiu, additional, Wu, Yu, additional, Zeng, Yan, additional, Wu, Depei, additional, Yang, Xiaofei, additional, and Chen, Suning, additional

Published
2024
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32. Patient Versus Physician Perspective in the Management of Chronic Myeloid Leukemia During Treatment with Tyrosine Kinase Inhibitors

Author

Chen, Hong, primary, Wen, Yan, additional, Zeng, Yun, additional, Lin, Lie, additional, Sun, Bihong, additional, Zhu, Hongqian, additional, He, Huiqing, additional, Wang, Xiaotao, additional, Zou, Waiyi, additional, Zheng, Caifeng, additional, Zheng, Liling, additional, Huang, Jinxiong, additional, Pang, Liping, additional, Huang, Jixian, additional, Zhang, Yuming, additional, Lin, Haiqing, additional, Liu, Zelin, additional, Zhu, Wanshou, additional, Wang, Qiang, additional, Zhou, Xuan, additional, Liu, Xiaoli, additional, Qu, Hong, additional, Liu, Zhenfang, additional, Du, Xin, additional, and Xu, Na, additional

Published
2023
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38. Does cleavage stage morphology increase the discriminatory power of prediction in blastocyst transfer outcome?

Author

Liu, Zhenfang, Cai, Jiali, Liu, Lanlan, Ouyang, Ling, Chen, Jinghua, Yang, Chao, Chen, Kaijie, Yang, Xiaolian, Ren, Jianzhi, and Jiang, Xiaoming

Subjects
*BLASTOCYST, *MORPHOLOGY, *BIRTH rate, *EMBRYOS, *PREDICTION models, *FORECASTING
Abstract

Purpose: To evaluate the contribution of the cleavage stage morphological parameters to the prediction of blastocyst transfer outcomes. Methods: A retrospective study was conducted on 8383 single-blastocyst transfer cycles including 2246 fresh and 6137 vitrified-warmed cycles. XGboost, LASSO, and GLM algorithms were employed to establish models for assessing the predictive value of the cleavage stage morphological parameters in transfer outcomes. Four models were developed using each algorithm: all-in model with or without day 3 morphology and embryo quality-only model with or without day 3 morphology. Results: The live birth rate was 48.04% in the overall cohort. The AUCs of the models with the algorithm of XGboost were 0.83, 0.82, 0.63, and 0.60; with LASSO were 0.66, 0.66, 0.61, and 0.60; and with GLM were 0.66, 0.66, 0.61, and 0.60 respectively. In models 1 and 2, female age, basal FSH, peak E2, endometrial thickness, and female BMI were the top five critical features for predicting live birth; In models 3 and 4, the most crucial factor was blastocyst formation on D5 rather than D6. In model 3, incorporating cleavage stage morphology, including early cleavage, D3 cell number, and fragmentation, was significantly associated with successful live birth. Additionally, the live birth rates for blastocysts derived from on-time, slow, and fast D3 embryos were 49.7%, 39.5%, and 52%, respectively. Conclusions: The value of cleavage stage morphological parameters in predicting the live birth outcome of single blastocyst transfer is limited. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?

Author

Cai, Jiali, Jiang, Xiaoming, Liu, Lanlan, Liu, Zhenfang, Chen, Jinghua, Chen, Kaijie, Yang, Xiaolian, and Ren, Jianzhi

Subjects
HUMAN in vitro fertilization, MEDICAL societies, CONSTRUCTION projects, CHINESE people, PREDICTION models
Abstract

STUDY QUESTION What was the performance of different pretreatment prediction models for IVF, which were developed based on UK/US population (McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model), in wider populations? SUMMARY ANSWER For a patient in China, the published pretreatment prediction models based on the UK/US population provide similar discriminatory power with reasonable AUCs and underestimated predictions. WHAT IS KNOWN ALREADY Several pretreatment prediction models for IVF allow patients and clinicians to estimate the cumulative probability of live birth in a cycle before the treatment, but they are mostly based on the population of Europe or the USA, and their performance and applicability in the countries and regions beyond these regions are largely unknown. STUDY DESIGN, SIZE, DURATION A total of 26 382 Chinese patients underwent oocyte pick-up cycles between January 2013 and December 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS UK/US model performance was externally validated according to the coefficients and intercepts they provided. Centre-specific models were established with XGboost, Lasso, and generalized linear model algorithms. Discriminatory power and calibration of the models were compared as the forms of the AUC of the Receiver Operator Characteristic and calibration curves. MAIN RESULTS AND THE ROLE OF CHANCE The AUCs for McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model were 0.69 (95% CI 0.68–0.69), 0.67 (95% CI 0.67–0.68), 0.69 (95% CI 0.68–0.69), and 0.67 (95% CI 0.67–0.68), respectively. The centre-specific yielded an AUC of 0.71 (95% CI 0.71–0.72) with key predictors including age, duration of infertility, and endocrine parameters. All external models suggested underestimation. Among the external models, the rescaled McLernon 2022 model demonstrated the best calibration (Slope 1.12, intercept 0.06). LIMITATIONS, REASONS FOR CAUTION The study is limited by its single-centre design and may not be representative elsewhere. Only per-complete cycle validation was carried out to provide a similar framework to compare different models in the sample population. Newer predictors, such as AMH, were not used. WIDER IMPLICATIONS OF THE FINDINGS Existing pretreatment prediction models for IVF may be used to provide useful discriminatory power in populations different from those on which they were developed. However, models based on newer more relevant datasets may provide better calibrations. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Natural Science Foundation of China [grant number 22176159], the Xiamen Medical Advantage Subspecialty Construction Project [grant number 2018296], and the Special Fund for Clinical and Scientific Research of Chinese Medical Association [grant number 18010360765]. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Published
2024
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40. PB1968: IMPACTS ON OUTCOMES AND A PREDICTIVE MODEL OF SEVERE THROMBOCYTOPENIA IN PATIENTS WITH CHRONIC PHASE CHRONIC MYELOID LEUKEMIA RECEIVING INITIAL NILOTINIB-THERAPY

Author

LI, Ziyu, primary, Jiang, Qian, additional, Zhang, Xiaoshuai, additional, Zhang, Yanli, additional, Xu, Na, additional, Zhu, Huanling, additional, Lin, Hai, additional, LI, Weiming, additional, Du, Xin, additional, Liang, Rong, additional, LI, Fei, additional, Chen, Chunyan, additional, Liu, Bingcheng, additional, Wang, Xiaodong, additional, Yang, Lijie, additional, Liu, Zhenfang, additional, Pang, Liping, additional, Liu, Zhuogang, additional, Sun, Xiuli, additional, Bai, Yanliang, additional, Meng, LI, additional, and Chen, Suning, additional

Published
2023
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48. Using UK/ASRM models and local model to predict the prognosis of IVF in China: a retrospective study

Author

Jianzhi Ren, Jiali Cai, Xiaoming Jiang, Lanlan Liu, Liu Zhenfang, Jinghua Chen, Kaijie Chen, and Xiaolian Yang

Abstract

Objective: To validate four pretreatment prediction models which were developed based on UK/US population (McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model) and compare them with a model developed based on local data. Design: Retrospective development and validation of prediction models. Setting: University-affiliated hospital Patient(s): A total of 26035 Chinese patients underwent OPU cycles between January 2013 and December 2020. Methods: UK/US model performance was externally validated. Local models were established with XGboost, Lasso, and generalized linear model algorithms. Discriminatory power and calibration of the models were compared. Main Outcome Measure(s): The areas under the curves (AUC) of the Receiver Operator Characteristic (ROC) and calibration curves. Result(s): The AUCs for McLernon 2016 model, Luke model, Dhillon model, and McLernon 2022 model were 0.68 (95% CI: 0.675-0.689), 0.67 (95% CI: 0.666-0.68), 0.685((95% CI: 0.678-0.692), and 0.673((95% CI: 0.666-0.68), respectively. The local model yielded an AUC of 0.709(95% CI: 0.702-0.716) with key predictors including age, duration of infertility, and endocrine parameters. All external models suggested underestimation. Among the external models, the rescaled McLernon 2022 model demonstrated the best calibration (slope 1.12, intercept 0.06). Conclusion(s): A patient in China may find the published pretreatment prediction models based on UK/US population provide similar discriminatory power with reasonable AUCs and underestimated predictions.

Published
2023

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