Dual-layer spectral detector CT radiomic features to predict programmed cell death ligand 1 expression in invasive
Hua Wang1,2, Yaqiong Ma3, Huaxin Li1
1Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Clinical Research Center for Cancer, Tianjin, 300060, China.
Objectives:
This prospective study aimed to develop and validate integrated nomograms that combine preoperative radiomic features from dual-layer spectral detector CT (DLCT) with key postoperative pathological information for the prediction of programmed cell death ligand 1 (PD-L1) expression in invasive lung adenocarcinoma.
Methods:
The study included 191 participants with invasive lung adenocarcinoma who underwent preoperative thoracic contrast-enhanced DLCT scans and PD-L1 expression testing. Radiomic features were extracted from various DLCT images. Least absolute shrinkage and selection operator was used to derive radscores for PD-L1 expression (tumor proportion score ≥1% was defined as PD-L1 positivity). Nomograms were developed by integrating radscores with clinicopathological characteristics through logistic regression analysis. Performance was assessed using receiver operator characteristic (ROC) curves, area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
Results:
Three sets of radiomic nomograms, based on iodine map (IM), virtual non-contrast (VNC), and conventional images (PCI), along with pathological stages (pTNM), were developed. The IM nomogram exhibited superior performance in both training (AUC = 0.791) and validation (AUC = 0.737) sets. Calibration and DCA confirmed the IM nomogram's consistency and clinical utility.
Conclusions:
The IM nomogram demonstrated potential for individualized prediction of PD-L1 expression in invasive lung adenocarcinoma and identify the candidates who may benefit from immunotherapy.
Advances In Knowledge:
The nomogram based on dual-layer spectral detector CT can predict the probability of PD-L1 expression in invasive lung adenocarcinoma and may help personalized immunotherapy decisions.
