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Prediction of prognosis in breast cancer patients using lapatinib based on real-world evidence
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Ze YU1a, Xuan YE2, Chun-ming LÜ1b, Jin-yuan ZHANG3, Xin HAO4, Rui-wen WANG4, Qing ZHAI2, Fei GAO3
Chinese Journal of New Drugs and Clinical Remedies | 2024, 43(1) : 44 - 50
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Chinese Journal of New Drugs and Clinical Remedies | 2024, 43(1): 44-50
Original Article
Prediction of prognosis in breast cancer patients using lapatinib based on real-world evidence
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Ze YU1a, Xuan YE2, Chun-ming LÜ1b, Jin-yuan ZHANG3, Xin HAO4, Rui-wen WANG4, Qing ZHAI2, Fei GAO3
Affiliations
  • 1a.Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, SHANGHAI 201203, China
  • 1b.Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, SHANGHAI 201203, China
  • 2.Fudan University Shanghai Cancer Center, SHANGHAI 200032, China
  • 3.Beijing Medicinovo Technology Co. Ltd., BEIJING 100071, China
  • 4.Dalian Medicinovo Technology Co. Ltd., Dalian LIAONING 116000, China
Published: 2024-01-25 doi: 10.14109/j.cnki.xyylc.2024.01.09
Outline
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AIM

Based on real world data and machine learning technology, a predictive model of progression free survival (PFS) of patients with breast cancer treated with lapatinib was constructed.

METHODS

A retrospective collection of 150 patients admitted to the Fudan University Shanghai Cancer Center from July 2016 to June 2017 was conducted. The outcome indicator of the prediction model was whether the patient’s PFS was ≤ 1 year. Using sequential forward selection algorithms for feature selection, and comparing the predictive performance of 9 algorithms for building models, including extreme gradient boost (XGBoost), classification boost (CatBoost), random forest (RF), light gradient boost (LightGBM), gradient boost decision tree (GBDT), logistic regression (LR), support vector regression (SVR), artificial neural network (ANN), and TabNet.

RESULTS

Important variables included medication regimen, age, frequency of chemotherapy, anthracycline drugs, platinum drugs, estrogen receptor, disease stage, and number of metastatic sites. The XGBoost model had the best prediction performance, with a prediction accuracy of 93% and a recall rate of 87% for PFS≤1 year. And a prediction accuracy was 71%, and a recall rate was 83% for PFS > 1 year.

CONCLUSION

The performance and robustness of the prognosis prediction model for patients with breast cancer treated with lapatinib established are good, which can provide a better auxiliary decision-making basis for clinical treatment of breast cancer.

lapatinib  /  breast neoplasms  /  machine learning  /  real world study
Ze YU, Xuan YE, Chun-ming LÜ, Jin-yuan ZHANG, Xin HAO, Rui-wen WANG, Qing ZHAI, Fei GAO. Prediction of prognosis in breast cancer patients using lapatinib based on real-world evidence[J]. Chinese Journal of New Drugs and Clinical Remedies, 2024 , 43 (1) : 44 -50 . DOI: 10.14109/j.cnki.xyylc.2024.01.09
Year 2024 volume 43 Issue 1
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Article Info
doi: 10.14109/j.cnki.xyylc.2024.01.09
  • Receive Date:2023-08-31
  • Online Date:2026-03-19
  • Published:2024-01-25
Article Data
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History
  • Received:2023-08-31
  • Accepted:2023-10-27
Funding
Affiliations
    1a.Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, SHANGHAI 201203, China
    1b.Experiment Center for Science and Technology, Shanghai University of Traditional Chinese Medicine, SHANGHAI 201203, China
    2.Fudan University Shanghai Cancer Center, SHANGHAI 200032, China
    3.Beijing Medicinovo Technology Co. Ltd., BEIJING 100071, China
    4.Dalian Medicinovo Technology Co. Ltd., Dalian LIAONING 116000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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