How platinum-induced nephrotoxicity occurs? Machine learning prediction in non-small cell lung cancer patients

Huang, Shih-Hui; Chu, Chao-Yu; Hsu, Yu‐Chia; Wang, San-Yuan; Kuo, Li-Na; Bai, Kuan‐Jen; Yu, Ming-Chih; Chang, Jer‐Hwa; Liu, Eugene H.; Chen, Hsiang-Yin

doi:10.1016/j.cmpb.2022.106839

Cited by 6 publications

(1 citation statement)

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“…Compared with the classical screening method, Lasso can effectively avoid the in uence of factors such as different orders of magnitude, different units and possible collinearity between variables [26] . To screen candidate variables, we opted for Lasso regression over classic single factor regression, using a 1 standard deviation penalty coe cient lambda (λ) as the screening parameter to prevent the exclusion of relatively unimportant variables [7,27,28] . The LASSO algorithm was executed using the "glmmet" R package, while the logistic regression model was constructed using the "glm" R package [20] .…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a LASSO Prediction Model for cisplatin induced nephrotoxicity: A Case-Control Study in China

Zhang,

Luo,

Fan

et al. 2023

Preprint

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Background: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy. Method: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the test set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike's information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the validation set to evaluate the model's performance. Result: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the test and validation sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Conclusion: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

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Section: Discussionmentioning

confidence: 99%