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Purpose <p>Non-small cell lung cancer (NSCLC) remains a major clinical challenge, with Programmed death-ligand 1 (PD-L1) expression serving as a crucial biomarker to guide immunotherapy. However, its current assessment through invasive biopsies may not capture tumor heterogeneity. This study explores the feasibility of a CT-based radiomics approach, combined with machine learning (ML), as a potential non-invasive virtual biopsy to predict high PD-L1 expression (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\geq \)</EquationSource></InlineEquation>50%) in NSCLC patients.</p> Methods <p>Contrast-enhanced CT scans from 55 patients with histologically confirmed NSCLC were retrospectively analyzed. Radiomic features were extracted from tumor volumes, and multiple ML classifiers were trained and evaluated through repeated stratified k-fold cross-validation.</p> Results <p>Among the models evaluated, the Support Vector Machine (SVM) classifier demonstrated the best performance, achieving a median accuracy of 0.77 (quartiles: 0.66–0.82) and an area under the curve (AUC) of 0.83 (0.63–0.92). Feature importance analysis using SHAP (Shapley Additive Explanations) revealed that texture features were the most informative in predicting PD-L1 expression levels. Notably, the integration of clinical data did not improve model performance, highlighting the dominant predictive value of radiomic features alone.</p> Conclusion <p>Our findings support the feasibility of CT-based radiomics as a potential tool for virtual biopsy to identify NSCLC patients with high PD-L1 expression (<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\geq \)</EquationSource></InlineEquation>50%), potentially serving as a complementary or alternative tool to tissue biopsy, especially in cases where biopsy is contraindicated or insufficient.</p>

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CT-based radiomics as a non-invasive virtual biopsy for high PD-L1 expression prediction in non-small cell lung cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Michela Destito,
  • Caterina Battaglia,
  • Paolo Zaffino,
  • Giulio Caridà,
  • Maria Cucè,
  • Alessandro Pullano,
  • Martina Frangipane,
  • Maria Francesca Spadea,
  • Domenico Laganà,
  • Pierfrancesco Tassone,
  • Pierosandro Tagliaferri,
  • Carlo Cosentino

摘要

Purpose

Non-small cell lung cancer (NSCLC) remains a major clinical challenge, with Programmed death-ligand 1 (PD-L1) expression serving as a crucial biomarker to guide immunotherapy. However, its current assessment through invasive biopsies may not capture tumor heterogeneity. This study explores the feasibility of a CT-based radiomics approach, combined with machine learning (ML), as a potential non-invasive virtual biopsy to predict high PD-L1 expression (\(\geq \)50%) in NSCLC patients.

Methods

Contrast-enhanced CT scans from 55 patients with histologically confirmed NSCLC were retrospectively analyzed. Radiomic features were extracted from tumor volumes, and multiple ML classifiers were trained and evaluated through repeated stratified k-fold cross-validation.

Results

Among the models evaluated, the Support Vector Machine (SVM) classifier demonstrated the best performance, achieving a median accuracy of 0.77 (quartiles: 0.66–0.82) and an area under the curve (AUC) of 0.83 (0.63–0.92). Feature importance analysis using SHAP (Shapley Additive Explanations) revealed that texture features were the most informative in predicting PD-L1 expression levels. Notably, the integration of clinical data did not improve model performance, highlighting the dominant predictive value of radiomic features alone.

Conclusion

Our findings support the feasibility of CT-based radiomics as a potential tool for virtual biopsy to identify NSCLC patients with high PD-L1 expression (\(\geq \)50%), potentially serving as a complementary or alternative tool to tissue biopsy, especially in cases where biopsy is contraindicated or insufficient.