Volume 24 Issue 3
Mar.  2026
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Article Navigation >  Chinese Journal of General Practice  > 2026  >  24(3): 411-414
PAN Kunlei, ZHENG Qingxiu, HUANG Yuenuo, LIU Gang. Construction and validation of a predictive model for the efficacy of neoadjuvant chemotherapy in patients with non-small cell lung cancer based on serum PI3K, AKT, and mTOR levels[J]. Chinese Journal of General Practice, 2026, 24(3): 411-414. doi: 10.16766/j.cnki.issn.1674-4152.004408
Citation: PAN Kunlei, ZHENG Qingxiu, HUANG Yuenuo, LIU Gang. Construction and validation of a predictive model for the efficacy of neoadjuvant chemotherapy in patients with non-small cell lung cancer based on serum PI3K, AKT, and mTOR levels[J]. Chinese Journal of General Practice, 2026, 24(3): 411-414. doi: 10.16766/j.cnki.issn.1674-4152.004408
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Construction and validation of a predictive model for the efficacy of neoadjuvant chemotherapy in patients with non-small cell lung cancer based on serum PI3K, AKT, and mTOR levels

doi: 10.16766/j.cnki.issn.1674-4152.004408

  • Department of Respiratory and Critical Care Medicine, Wenzhou Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Wenzhou, Zhejiang 325000, China

Funds:

 2024ZL995

  • Received Date: 2025-05-23
    Available Online: 2026-06-02
    Abstract
  •   Objective  There are individual differences in the efficacy of neoadjuvant therapy for non-small cell lung cancer (NSCLC). Phosphatidylinositol-3-kinase (PI3K)/serine-threonine kinase (Akt)/mammalian target of rapamycin (mTOR) pathway is a key regulatory pathway for the occurrence and development of NSCLC. The aim of this study is to explore the efficacy of the prediction model based on the above serum indicators to guide clinical treatment decisions.  Methods  A total of 200 patients with NSCLC were selected from those admitted to Wenzhou Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University from January 2024 to April 2025. They were randomly divided into a training set (n=141) and a validation set (n= 59) in a 7∶3 ratio. Biochemical tests were conducted before chemotherapy. LASSO regression was employed to screen for characteristic variables associated with the efficacy of neoadjuvant therapy in NSCLC patients. A predictive model was then constructed based on the random forest algorithm. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA), and was externally validated on the independent validation set.  Results  The variables related to the efficacy of neoadjuvant chemotherapy in NSCLC patients included the maximum diameter of the tumor, degree of differentiation, PI3K mRNA, AKT mRNA, mTOR mRNA, and carcinoembryonic antigen(CEA). A random forest model was constructed based on the variables related to the non-response of neoadjuvant chemotherapy in NSCLC patients screened out by LASSO regression, and the AUC values in the training set and the validation set were 0.889 and 0.801, respectively. The Brier scores in the training set and validation set random forest models were 0.091 and 0.110 respectively. The DCA curve revealed that, in the training set, the curve was above the "None" line within the threshold probability range of 0.10 to 0.76, while in the validation set, it remained above the "None" line from 0.10 to 0.58. The performance of the training set was superior to that of the validation set, adhering to the golden rule of model validation.  Conclusion  The random forest model established based on serum levels of PI3K, AKT, and mTOR demonstrates high predictive efficacy for the response to neoadjuvant chemotherapy in patients with NSCLC, thereby facilitating clinical decision-making.

     

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