What question did this study set out to answer?

This research aims to evaluate the effectiveness of radiomics from 18F-FDG PET/CT in predicting PD-L1 status and histological types in NSCLC.

March 21, 2026Open Access

Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer using 18F-FDG PET/CT radiomics

Key Points

This research aims to evaluate the effectiveness of radiomics from 18F-FDG PET/CT in predicting PD-L1 status and histological types in NSCLC.
Retrospective analysis of pre-treatment 18F-FDG PET/CT scans from 115 NSCLC patients.
Extraction of radiomic features from primary tumors along with clinical variables.
Application of PCA and hierarchical clustering for class separability assessment.
Training of multiple machine learning classifiers including random forest for predictive modeling.
Evaluation of model performance using AUC-ROC metrics.
Random forest model using radiomic features achieved an AUC-ROC of 0.83 for PD-L1 prediction.
Combination feature model showed an AUC-ROC of 0.81, outperforming naïve-only models (AUC-ROC ~ 0.5).
Hierarchical clustering indicated significant separation of PD-L1-positive and negative tumors.
For histological subtype classification, the best RF model achieved an AUC-ROC of 0.76.
PCA showed partial separability between adenocarcinoma and squamous cell carcinoma.

Abstract

Radiomics analysis of 18F-FDG PET/CT enables extraction of high-dimensional quantitative features that may serve as imaging biomarkers for tumor biology. In particular, in non-small cell lung cancer (NSCLC), such features could potentially predict immune checkpoint biomarker status, such as PD-L1 expression, and histological subtype without the need for invasive tissue sampling. We retrospectively analyzed pre-treatment 18F-FDG PET/CT scans of 115 patients with histologically confirmed NSCLC (adenocarcinoma or squamous cell carcinoma) and known PD-L1 status. Radiomic features were extracted from primary tumors and combined with basic clinical variables (“naïve features”). Principal component analysis (PCA) and hierarchical clustering were performed to assess separability between the selected classes representing either PD-L1 subtype or histology. Multiple machine learning classifiers, including random forest (RF), were trained using radiomic-only, naïve-only, and combined feature sets. Model performance was evaluated by area under the receiver operating characteristic curve (AUC-ROC). For PD-L1 prediction, the RF model trained on radiomic features achieved the highest accuracy (AUC-ROC = 0.83, 95% CI = 0.75 - 0.91), outperforming the combined feature model (AUC-ROC = 0.81, 95% CI = 0.73 - 0.89) and naïve-only generalized linear and RF models (AUC-ROC ~ 0.5). Heatmap analysis of selected radiomic features showed significant clustering of PD-L1-positive and -negative tumors (Fisher’s exact p = 7 × 10− 4). For histological subtype classification, the best-performing RF model achieved an AUC-ROC of 0.76 using radiomic features. Although PCA demonstrated partial separation between adenocarcinoma and squamous cell carcinoma (PC1 = 38%, PC2 = 21%), the hierarchical clustering led to the significant class separation (p = 0.0234). Our pilot project indicated that 18F-FDG PET/CT radiomics enables accurate non-invasive prediction of PD-L1 expression and provides moderate discrimination between major NSCLC histological subtypes. Clinical variables alone have negligible predictive value for PD-L1 status. Radiomic models may complement histopathology, particularly when tissue sampling is limited or repeated biomarker assessment is required. Prospective, multi-center validation is needed to confirm generalizability and facilitate clinical translation.

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