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Background <p>Whether metabolic parameters on PET/CT are noninvasive alternatives to programmed cell death ligand 1 (PD-L1) expression and tumor mutational burden (TMB) in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) remains unknown. We identified predictors and developed PET/CT-based nomogram models for predicting PD-L1 expression and TMB, and tested the usefulness of models for stratifying immunotherapy responses.</p> Methods <p>We enrolled 305 patients with non-small cell lung cancer from January 2017 to April 2024 as the primary cohort to investigate independent predictors of PD-L1 expression and TMB for ADC (<i>n</i> = 183) and SCC (<i>n</i> = 122). Clinicopathological characteristics, semantic CT features, and PET metabolic parameters were reviewed and analyzed. Significant predictors of ADC biomarkers were identified, and ADC were randomly assigned 7:3 to the training (<i>n</i> = 128) and validation (<i>n</i> = 55) cohorts to develop and validate predictive models for PD-L1 expression, TMB, and the combination of both. Clinical, SUL<sub>max</sub>, and clinical-SUL<sub>max</sub> combined models were constructed using logistic regression analysis. Model discrimination, calibration, and clinical usefulness were assessed. An independent test cohort (<i>n</i> = 29) with ADC receiving neoadjuvant immunotherapy was used to validate the models in stratifying pathologic responses.</p> Results <p>PD-L1 expression did not differ significantly between SCC and ADC; however, the TMB was significantly higher in SCC (10 vs. 5 mutations/Mb, <i>p</i> &lt; 0.001). In the analysis of ADC, PD-L1 expression was associated with clinical stage, differentiation, and <i>EGFR</i> mutation status (<i>p</i> &lt; 0.05). TMB was associated with age, gender, smoking history, and <i>EGFR</i> mutation status (<i>p</i> &lt; 0.05). SUL<sub>max</sub> was an independent predictor of both PD-L1-Pos and TMB-High (<i>p</i> &lt; 0.001). The clinical-SUL<sub>max</sub> combined models for predicting both PD-L1 expression and TMB demonstrated great performance (AUC = 0.805 for PD-L1-Pos and TMB-High; AUC = 0.798 for PD-L1-Neg and TMB-Low). The usefulness of models in stratifying pathologic responses was confirmed on the ADC test cohort receiving neoadjuvant immunotherapy (<i>p</i> = 0.035 for PD-L1-Pos and TMB-High model; <i>p</i> = 0.001 for PD-L1-Neg and TMB-Low model). However, no significant predictors were identified to develop models in the analysis of SCC.</p> Conclusion <p>SUL<sub>max</sub> is an independent predictor of both PD-L1-Pos and TMB-High in ADC. The clinical-SUL<sub>max</sub> combined models effectively predicted PD-L1-Pos and TMB-High status, as well as PD-L1-Neg and TMB-Low status in ADC, indicating the clinical utility in patient selection and immunotherapy response stratification.</p>

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18F-FDG PET/CT for prediction of PD-L1 expression and tumor mutational burden in non-small cell lung cancer: biomarkers prediction models development and immunotherapy responses verification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月28日

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  • Qian Zhang,
  • Pei Yuan,
  • Zewei Zhang,
  • Jianming Ying,
  • Lei Guo,
  • Ning Li,
  • Shuhang Wang,
  • Jing Li,
  • Ying Liu,
  • Wei Guo,
  • Xiuli Tao,
  • Ning Wu

摘要

Background

Whether metabolic parameters on PET/CT are noninvasive alternatives to programmed cell death ligand 1 (PD-L1) expression and tumor mutational burden (TMB) in adenocarcinoma (ADC) and squamous cell carcinoma (SCC) remains unknown. We identified predictors and developed PET/CT-based nomogram models for predicting PD-L1 expression and TMB, and tested the usefulness of models for stratifying immunotherapy responses.

Methods

We enrolled 305 patients with non-small cell lung cancer from January 2017 to April 2024 as the primary cohort to investigate independent predictors of PD-L1 expression and TMB for ADC (n = 183) and SCC (n = 122). Clinicopathological characteristics, semantic CT features, and PET metabolic parameters were reviewed and analyzed. Significant predictors of ADC biomarkers were identified, and ADC were randomly assigned 7:3 to the training (n = 128) and validation (n = 55) cohorts to develop and validate predictive models for PD-L1 expression, TMB, and the combination of both. Clinical, SULmax, and clinical-SULmax combined models were constructed using logistic regression analysis. Model discrimination, calibration, and clinical usefulness were assessed. An independent test cohort (n = 29) with ADC receiving neoadjuvant immunotherapy was used to validate the models in stratifying pathologic responses.

Results

PD-L1 expression did not differ significantly between SCC and ADC; however, the TMB was significantly higher in SCC (10 vs. 5 mutations/Mb, p < 0.001). In the analysis of ADC, PD-L1 expression was associated with clinical stage, differentiation, and EGFR mutation status (p < 0.05). TMB was associated with age, gender, smoking history, and EGFR mutation status (p < 0.05). SULmax was an independent predictor of both PD-L1-Pos and TMB-High (p < 0.001). The clinical-SULmax combined models for predicting both PD-L1 expression and TMB demonstrated great performance (AUC = 0.805 for PD-L1-Pos and TMB-High; AUC = 0.798 for PD-L1-Neg and TMB-Low). The usefulness of models in stratifying pathologic responses was confirmed on the ADC test cohort receiving neoadjuvant immunotherapy (p = 0.035 for PD-L1-Pos and TMB-High model; p = 0.001 for PD-L1-Neg and TMB-Low model). However, no significant predictors were identified to develop models in the analysis of SCC.

Conclusion

SULmax is an independent predictor of both PD-L1-Pos and TMB-High in ADC. The clinical-SULmax combined models effectively predicted PD-L1-Pos and TMB-High status, as well as PD-L1-Neg and TMB-Low status in ADC, indicating the clinical utility in patient selection and immunotherapy response stratification.