Quantitative spectral parameters of photon-counting detector CT for noninvasive prediction of PD-L1 expression in
Jiazhong Ren1, Zheng Fu1, Linfeng Li2
1Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No. 440 Jiyan Road, Huaiyin District, Jinan, Shandong Province, China.
Purpose:
This study aimed to investigate the value of quantitative photon-counting computed tomography (PCCT) spectral parameters for the non-invasive assessment and prediction of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC).
Methods:
This single-center retrospective study enrolled 63 NSCLC patients who underwent pretherapeutic PCCT. Pretherapeutic PD-L1 expression was evaluated using immunohistochemistry and classified into three groups: negative (TPS < 1%), intermediate (1%-49%), and high (≥ 50%). The arterial and venous-phase CT values (A/V-40 keV, A/V-70 keV, and A/V-100 keV) and iodine concentration of the lesions were measured on 40, 70, and 100 keV monoenergetic images. Normalized iodine concentration and the slope of the spectral curve (λHU) were calculated accordingly. Spearman and Pearson correlation, receiver operating characteristic (ROC), univariate and multivariate logistic regression analyses were conducted to assess the correlations, predictive performance, and the independent predictive value of PCCT parameters for PD-L1 expression.
Results:
Age and gender demonstrated statistically differences among the groups (P = 0.036; P = 0.008). A-40 keV and A-λHU also exhibited significant differences. Both parameters were negatively correlated with PD-L1 expression (A-λHU: rs = - 0.270, P = 0.032; A-40 keV: rs = - 0.279, P = 0.027). Multivariate logistic regression confirmed that age and A-λHU as independent predictors of intermediate and high PD-L1 expression, respectively (OR = 1.014 and 0.031; both P < 0.05). ROC analysis revealed that the clinical model, PCCT model, and combined model demonstrated favorable predictive performance for high PD-L1 expression, with area under the curve values of 0.762 (95%CI: 0.637-0.860), 0.846 (95% CI: 0.733-0.925), and 0.889 (95% CI: 0.785-0.954), respectively (all P < 0.001).
Conclusions:
Age and A-λHU are valuable predictors of PD-L1 expression in NSCLC patients before treatment. The model, combining PCCT spectral parameters and clinical features, may serve as a non-invasive tool for individualized treatment in NSCLC.
