Platelet-derived growth factor receptors (PDGFRs), recognized as key oncogenic drivers in hepatocellular carcinoma (HCC), play crucial roles in regulating cancer cell proliferation, differentiation and migration. Although PDGFRβ is implicated in HCC progression, highly selective PDGFRβ inhibitors are still scarce. Herein, we report the discovery of novel and selective PDGFRβ inhibitors A42, A43, A45, and A46 through structure-based optimization. Among these, A45 showed the highest selectivity, with a selectivity factor of 20.7. Based on cellular antiproliferative activity and cytotoxicity profiles, the moderately selective compound A42 was selected for further investigation. A42 significantly inhibited the proliferation, colony formation and migration of HCC cells and induced substantial apoptosis. Furthermore, A42 demonstrated a favorable safety profile and promising pharmacokinetic properties with an oral bioavailability of 43.47% and significantly inhibited tumor growth in the Huh-7 xenograft tumor model. Collectively, these results suggest that A42 may serve as a promising therapeutic candidate for HCC treatment.

Accurately predicting progression-free survival (PFS) in patients with advanced lung squamous cell carcinoma (LUSC) receiving immunotherapy remains a clinical challenge. Radiomics has emerged as a promising non-invasive approach; however, its application in this specific patient population remains relatively limited. A total of 129 patients were retrospectively enrolled. Radiomics features were extracted from baseline positron emission tomography/computed tomography (PET/CT) images. Feature reproducibility was evaluated using intraclass correlation coefficients (ICC), and only features with ICC > 0.75, indicating good reproducibility, were retained. A three-step feature selection strategy, including univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox modeling, was applied to construct the radiomics signature. Model performance was evaluated using time-dependent receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and bootstrap resampling. Additionally, a 3D convolutional neural network (CNN) model was explored for comparative analysis. The final model achieved a 2-year AUC of 0.673. The relatively stable AUC values across time points (1-year: 0.670; 2-year: 0.673; 3-year: 0.651) suggest that the model primarily captures baseline risk stratification rather than strong time-dependent variation. Patients in the high-risk group exhibited a significantly increased risk of progression compared with those in the low-risk group (HR = 4.36, 95

OBJECTIVE:To develop and evaluate a noninvasive imaging framework based on 18F-FDG PET/CT for estimating programmed death-ligand 1 (PD-L1) expression status in non-small cell lung cancer (NSCLC) by integrating habitat radiomics and 2.5D deep features. MATERIALS AND METHODS:This retrospective two-center study included 224 patients with pathologically confirmed NSCLC who underwent pretreatment 18F-FDG PET/CT and PD-L1 immunohistochemistry. Patients from Hospital 1 were randomly assigned to a training cohort (n = 138) and a test cohort (n = 42), while patients from Hospital 2 served as an independent external validation cohort (n = 44). PD-L1 expression was dichotomized as low (TPS < 50%) or high (TPS ≥ 50%). Radiomics features were extracted from PET and CT images, and habitat subregions were generated using K-means clustering. 2.5D deep features were extracted using a ResNet-50-based feature-extraction strategy with maximum-intensity projections. Prediction models were constructed using support vector machine, XGBoost, logistic regression, random forest, and artificial neural network classifiers, and were evaluated using the area under the receiver operating characteristic curve with 95% confidence intervals, accuracy, sensitivity, specificity, precision, F1 score, and balanced accuracy. SHAP was applied to enhance the visualization and interpretability of the models. RESULTS:Habitat radiomics demonstrated superior discriminative performance compared with conventional radiomics across cohorts, particularly in the external validation cohort (AUC = 0.840 vs 0.794). The 2.5D deep-feature model achieved the highest discriminative ability in external validation (AUC = 0.918), indicating favorable generalizability. The 2.5D deep-feature model achieved the highest external-validation AUC and F1 score among the representative models (AUC = 0.918; F1 score = 0.783). The combined model achieved the highest AUC in the test cohort (AUC = 0.894) and showed favorable sensitivity in external validation (0.769), although its external-validation AUC and F1 score were slightly lower than those of the 2.5D deep-feature model. SHAP analysis revealed that deep features and habitat radiomics features reflecting intratumoral heterogeneity were the primary contributors to model predictions, whereas conventional metabolic parameters showed limited impact. CONCLUSION:In conclusion, the proposed 18F-FDG PET/CT-based framework integrating habitat radiomics and 2.5D deep features showed promising performance for noninvasive estimation of PD-L1 expression status in NSCLC. The combined model demonstrated balanced predictive performance across the test and external validation cohorts, suggesting its potential as a candidate imaging biomarker. Further validation in larger multicenter studies is warranted. However, further prospective validation incorporating immunotherapy-response and survival outcomes is required before clinical application.

Platelet-derived growth factor receptors (PDGFRs), recognized as key oncogenic drivers in hepatocellular carcinoma (HCC), play crucial roles in regulating cancer cell proliferation, differentiation and migration. Although PDGFR beta is implicated in HCC progression, highly selective PDGFR beta inhibitors are still scarce. Herein, we report the discovery of novel and selective PDGFR beta inhibitors A42, A43, A45, and A46 through structure-based optimization. Among these, A45 showed the highest selectivity, with a selectivity factor of 20.7. Based on cellular antiproliferative activity and cytotoxicity profiles, the moderately selective compound A42 was selected for further investigation. A42 significantly inhibited the proliferation, colony formation and migration of HCC cells and induced substantial apoptosis. Furthermore, A42 demonstrated a favorable safety profile and promising pharmacokinetic properties with an oral bioavailability of 43.47% and significantly inhibited tumor growth in the Huh-7 xenograft tumor model. Collectively, these results suggest that A42 may serve as a promising therapeutic candidate for HCC treatment.

Telomere repeat-binding factor 2 (TRF2) is a crucial component of the shelterin complex, commonly overexpressed in osteosarcoma (OS) and positively correlated with its progression. To date, effective TRF2 inhibitors for in vivo applications remain limited. In this study, a series of Flavokavain B derivatives were designed and synthesized, and their TRF2 inhibition and antitumor activity were evaluated. Among the tested compounds, the active compound F2 showed remarkable inhibition of TRF2 expression, along with potent antiproliferative activity in U2OS and MG63 cells, with IC50 values of 5.28 μM and 1.52 μM, respectively. Moreover, F2 significantly suppressed OS cell proliferation and induced apoptosis by accelerating telomere shortening and loss due to TRF2 inhibition. Mechanically, F2 selectively inhibited TRF2 protein expression and telomeric localization by directly binding to the TRF2TRFH domain. Furthermore, F2 demonstrated strong antitumor efficacy with minimal toxicity in an MG63-derived xenograft mouse model. These findings demonstrate that F2 is a promising drug candidate for the treatment of osteosarcoma.

BACKGROUND:The presence of bone marrow involvement (BMI) in patients with diffuse large B-cell lymphoma (DLBCL) has a significant impact on treatment plans and prognosis, but clinical diagnosis is difficult. The purpose of this study was to evaluate the utility of PET/CT in the assessment of BMI and prognosis in newly diagnosed DLBCL. PATIENTS AND METHODS:This retrospective study included 57 eligible DLBCL patients who underwent bone marrow biopsy (BMB) and PET/CT prior to any treatment initiation. Increased FDG uptake in the bone marrow on PET/CT scans was indicative of BMI positivity, with such instances not attributable to benign findings. If BMB yielded positive results, or if the marrow uptake resolved concurrently with other lymphoma lesions during PET/CT monitoring, the diagnosis of BMI was established. The evaluation of bone marrow status via PET/CT involved both visual analysis and a quantitative index, specifically the ratio of maximum standardized uptake values of bone marrow to liver (BLR). Factors associated with 2-year progression-free survival (PFS) was analyzed utilizing the Cox proportional hazards regression model. RESULTS:34 patients were diagnosed with BMI. PET/CT demonstrated superior accuracy (93.0% vs. 75.4%) and sensitivity (94.1% vs. 58.8%) compared to BMB. During the follow-up period, 15 patients experienced disease progression. Survival analysis identified Eastern Cooperative Oncology Group performance status (ECOG PS), BLR, and PET/CT bone marrow status as the sole independent predictors of PFS (p = 0.010, 0.002, and 0.015, respectively). CONCLUSIONS:PET/CT played an important role in evaluating BMI and predicting PFS in newly diagnosed DLBCL.

Fibroblast growth factor receptors (FGFRs) play a critical role in the regulation of cancer cell proliferation, differentiation, and migration. However, the development of acquired resistance to FGFR inhibitors remains a major challenge in treating non-small cell lung cancer (NSCLC), particularly due to mutations at the gatekeeper residue. In this study, we report the discovery of a series of irreversible FGFR inhibitors targeting gatekeeper mutations in FGFR1-3, utilizing a 2,4,5-trisubstituted pyrimidine scaffold. Through rational design, structure-activity relationship optimization, and pharmacokinetic evaluation, compound ng 12l emerged as a promising candidate. It demonstrated potent inhibition of FGFR1-3 gatekeeper mutations in vitro along with favorable pharmacokinetic properties. The efficacy of 12l in targeting FGFR1 gatekeeper mutations was confirmed in assays using L6-FGFR1V561M/F cells. Furthermore, in xenograft models using both H1581 and L6-FGFR1V561M cells, 12l exhibited robust anti-tumor activity with minimal toxicity. These findings position 12l as a promising therapeutic agent for overcoming gatekeeper-mediated resistance in NSCLC.

RATIONALE AND OBJECTIVES:To develop and validate predictive models based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics and a clinical model for differentiating invasive adenocarcinoma (IAC) from non-invasive ground-glass nodules (GGNs) in early-stage lung cancer. MATERIALS AND METHODS:A total of 164 patients with GGNs histologically confirmed as part of the lung adenocarcinoma spectrum (including both invasive and non-invasive subtypes) who underwent preoperative 18F-FDG PET/CT and surgery. Radiomic features were extracted from PET and CT images. Models were constructed using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Five predictive models (CT, PET, PET/CT, Clinical, Combined) were evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Statistical comparisons were performed using DeLong's test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS:The Combined model, integrating PET/CT radiomic features with the clinical model, achieved the highest diagnostic performance (AUC: 0.950 in training, 0.911 in test). It consistently showed superior IDI and NRI across both cohorts and significantly outperformed the clinical model (DeLong p = 0.027), confirming its enhanced predictive power through multimodal integration. A clinical nomogram was constructed from the final model to support individualized risk stratification. CONCLUSION:Integrating PET/CT radiomic features with a clinical model significantly enhances the preoperative prediction of GGN invasiveness. This multimodal image data may assist in preoperative risk stratification and support personalized surgical decision-making in early-stage lung adenocarcinoma.

Background:In non-small cell lung cancer (NSCLC), accurate lymph node staging is vital for prognosis and treatment planning. However, positron emission tomography/computed tomography (PET/CT) is limited by false positives, and morphology-based criteria lack reliability. This study aimed to develop and validate a PET/CT-based deep learning radiomics (DLR) approach to distinguish benign from malignant lymph nodes. Methods:A total of 217 hypermetabolic lymph nodes from 185 NSCLC patients were retrospectively analyzed. Radiomics and DenseNet121-based deep network features were extracted from PET/CT images. Clinical and imaging variables were selected using logistic regression (LR), correlation analysis, and recursive feature elimination (RFE). Nine machine learning models were trained and externally validated; diagnostic performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results:Our study demonstrated that the artificial neural network (ANN) and extra trees (ET) models exhibited superior diagnostic performance in identifying suspected malignant lymph nodes in NSCLC patients. Specifically, the ANN achieved an AUC of 0.865, sensitivity of 66.7%, and accuracy of 82.0% on the test set, while the ET model performed best in the external validation set with an AUC of 0.865, sensitivity of 76.9%, and accuracy of 80.4%. Tumor location, lymph node long-to-short axis (L/S) ratio, and bilateral hilar 18F-fluorodeoxyglucose (FDG) uptake were significant predictors of nodal status. Correlation analysis showed that deep learning and radiomics features are complementary, suggesting their integration can significantly improve lung cancer diagnostic accuracy. Conclusions:The PET/CT-based DLR model accurately differentiates benign from malignant lymph nodes, outperforming conventional methods. Combining DenseNet121-derived features with radiomics improves staging accuracy and aids personalized treatment planning.

Objective This study was conducted to explore the differential diagnostic value of PET/computed tomography (PET/CT) combined with high-resolution computed tomography (HRCT) in predicting the invasiveness of ground-glass nodules (GGNs). Materials and methods This retrospective analysis included 67 patients (mean age 62.5 ± 8.4, including 45 females and 22 males) with GGNs who underwent preoperative 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT and HRCT examinations between January 2018 and October 2022. Based on the postoperative pathological results of lung adenocarcinoma, the patients were classified into two groups: invasive adenocarcinoma (IAC) and non-IAC. Besides, the clinical and imaging information of these patients was collected. HRCT signs include the existence of air bronchial signals, vascular convergence, pleural indentation, lobulation, and spiculation. Moreover, the diameter of solid components (D Solid ), diameter of ground-glass nodules (D GGN ), and computed tomography values of ground-glass nodules (CT GGN ) were measured concurrently. Furthermore, the mean standardized uptake value, maximal standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis were assessed during PET/CT. Associations between invasiveness and these factors were evaluated using univariate and multivariate analyses. Results The results of logistic regression analysis demonstrated that D GGN , D Solid , consolidation tumor ratio (CTR), CT GGN , and SUVmax were independent predictors in the IAC group. The combined diagnosis based on these five predictors revealed that area under the curve was 0.825. Conclusion The D GGN , D Solid , CTR, CT GGN , and SUVmax in GGNs were independent predictors of IAC, and combining 18 F-FDG PET/CT metabolic parameters with HRCT may improve the predictive value of pathological classification in lung adenocarcinoma.

Telomere repeat binding factor 2 (TRF2), a critical element of the shelterin complex, plays a vital role in the maintenance of genome integrity. TRF2 overexpression is found in a wide range of malignant cancers, whereas its down-regulation could cause cell death. Despite its potential role, the selectively small-molecule inhibitors of TRF2 and its therapeutic effects on liver cancer remain largely unknown. Our clinical data combined with bioinformatic analysis demonstrated that TRF2 is overexpressed in liver cancer and that high expression is associated with poor prognosis. Flavokavain B derivative FKB04 potently inhibited TRF2 expression in liver cancer cells while having limited effects on the other five shelterin subunits. Moreover, FKB04 treatment induced telomere shortening and increased the amounts of telomere-free ends, leading to the destruction of T-loop structure. Consequently, FKB04 promoted liver cancer cell senescence without modulating apoptosis levels. In corroboration with these findings, FKB04 inhibited tumor cell growth by promoting telomeric TRF2 deficiency-induced telomere shortening in a mouse xenograft tumor model, with no obvious side effects. These results demonstrate that TRF2 is a potential therapeutic target for liver cancer and suggest that FKB04 may be a selective small-molecule inhibitor of TRF2, showing promise in the treatment of liver cancer.

X-ray repair cross-complementing 2 (XRCC2), a critical protein in homologous recombination (HR), plays a significant role in the occurrence, progression, and drug resistance of colorectal cancer (CRC). In this study, a series of xanthohumol C derivatives were synthesized, and their anticancer activity was evaluated. The results revealed that A33 demonstrated the potent anticancer activity and effectively inhibited the proliferation of CRC cells in vitro. Mechanistic investigations revealed that A33 suppressed the transcription and expression of XRCC2, resulting in cell cycle delay and the accumulation of DNA damage, ultimately leading to cell proliferation inhibition. Furthermore, A33 displayed high safety, favorable bioavailability (F = 37.51 %), and potent tumor growth inhibition in vivo, which highlighting its potential as a candidate for the development of novel anti-CRC therapies.

Purpose This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). The model's interpretability was enhanced using Shapley additive explanations (SHAP). Methods A total of 248 NSCLC patients who underwent preoperative PET/CT scans were included and divided into training, test, and external validation sets. Radiomics features were extracted from segmented tumor regions on PET/CT images, and deep learning features were generated using the ResNet50 architecture. Feature selection was performed using minimum-redundancy maximum-relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) algorithm. Four models—clinical, radiomics, deep learning radiomics (DL_radiomics), and combined model—were constructed using the XGBoost algorithm and evaluated based on diagnostic performance metrics, including area under the receiver operating characteristic curve (AUC), accuracy, F1 score, sensitivity, and specificity. Shapley Additive exPlanations (SHAP) was used for model interpretability. Results The combined model achieved the highest AUC in the test set (AUC=0.853), outperforming the clinical (AUC=0.758), radiomics (AUC=0.831), and DL_radiomics (AUC=0.834) models. Decision curve analysis (DCA) demonstrated that the combined model offered greater clinical net benefits. SHAP was used for global interpretation, and the summary plot indicated that the features ct_original_glrlm_LongRunHighGrayLevelEmphasis, and pet_gradient_glcm_lmc1 were the most important for the model’s predictions. Conclusion The combined model, combining clinical, radiomics, and deep learning features from PET/CT, significantly improved the accuracy of LNM prediction in NSCLC patients. SHAP-based interpretability provided valuable insights into the model's decision-making process, enhancing its potential clinical application for preoperative decision-making in NSCLC.

Purpose:To evaluate the value of positron emission tomography/computed tomography (PET/CT) combined with high-resolution CT (HRCT) in determining the degree of differentiation of lung adenocarcinoma.Methods:From January 2018 to January 2022, 88 patients with solid density nodules that are lung adenocarcinoma were surgically treated. All patients were examined using HRCT and PET/CT before surgery. During HRCT, two independent observers assessed the presence of lobulation, spiculation, pleural indentation, vascular convergence, and air bronchial signs (bronchial distortion and bronchial disruption). The diameter and CT value of the nodules were measured simultaneously. During PET/CT, the maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the nodules were measured. The risk factors of pathological classification were predicted by logistic regression analysis.Results:All 88 patients (mean age 60 ± 8 years; 44 males and 44 females) were evaluated. The average nodule size was 2.6 ± 1.1 cm. The univariate analysis showed that carcinoembryonic antigen (CEA), pleural indentation, vascular convergence, bronchial distortion, and higher SUVmax were more common in poor differentiated lung adenocarcinoma, and in the multivariate analysis, pleural indentation, vascular convergence, and SUVmax were predictive factors. The combined diagnosis using these three factors showed that the area under the curve (AUC) was 0.735.Conclusion:SUVmax >6.99 combined with HRCT (pleural indentation sign and vascular convergence sign) is helpful to predict the differentiation degree of lung adenocarcinoma dominated by solid density.

Aberrant FGFR4 signaling has been implicated in the development of several cancers, making FGFR4 a promising target for cancer therapy. Several FGFR4-selective inhibitors have been developed, yet none of them have been approved. Herein, we report a novel series of 1,6-naphthyridine-2-one derivatives as potent and selective inhibitors targeting FGFR4 kinase. Preliminary structure-activity relationship analysis was conducted. The screening cascades revealed that 19g was the preferred compound among the prepared series. 19g demonstrated excellent kinase selectivity and substantial cytotoxic effect against all tested colorectal cancer cell lines. 19g induced significant tumor inhibition in a HCT116 xenograft mouse model without any apparent toxicity. Notably, 19g exhibited excellent potency in disrupting the phosphorylation of FGFR4 and downstream signaling proteins mediated by FGF18 and FGF19. Compound 19g might be a potential antitumor drug candidate for the treatment of colorectal cancer.

Purpose: To evaluate the value of positron emission tomography/computed tomography (PET/CT) combined with high-resolution CT (HRCT) in determining the degree of differentiation of lung adenocarcinoma.Methods: From January 2018 to January 2022, 88 patients with solid density nodules that are lung adenocarcinoma were surgically treated.All patients were examined using HRCT and PET/CT before surgery.During HRCT, two independent observers assessed the presence of lobulation, spiculation, pleural indentation, vascular convergence, and air bronchial signs (bronchial distortion and bronchial disruption).The diameter and CT value of the nodules were measured simultaneously.During PET/CT, the maximum standard uptake value(SUVmax), mean standard uptake value(SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the nodules were measured.The risk factors of pathological classification were predicted by logistic regression analysis.Results: All 88 patients (mean age 60±8 years; 44 males and 44 females) were evaluated.The average nodule size was 2.6±1.1cm.The univariate analysis showed that carcinoembryonic antigen (CEA), pleural indentation, vascular convergence, bronchial distortion, and higher SUVmax were more common in poor differentiated lung adenocarcinoma, and in the multivariate analysis, pleural indentation, vascular convergence, and SUVmax were predictive factors.The combined diagnosis using these three factors shows that the area under the curve (AUC) is 0.735.Conclusion: SUVmax > 6.99 combined with HRCT (pleural indentation sign and vascular convergence sign) is helpful to predict the differentiation degree of lung adenocarcinoma dominated by solid density.

目的 本文回顾性分析预测进展期肺腺癌患者EGFR突变状态.方法 选取治疗前行PET/CT检查的进展期肺腺癌患者176例.PET/CT代谢参数为:SUVmaxT(原发灶SUVmax)、TLGT(原发病灶的糖酵解总量)、SUVmaxWBR(依据RECIST 1.1标准选取病灶的最大SUVmax)及TLGWBR(依据RECIST 1.1标准选取的所有病灶TLG总和).分析SUVmaxT、SUVmaxWBR 、TLGT 、TLGwBR及其他临床病理因素在单因素及多因素logistic回归预测进展期肺腺癌EGFR突变中的价值.结果 显示:TLGWBR(≥222.6)、血浆CEA及ECOG评分预测是进展期肺腺癌EGFR突变的独立预测因素,TLGWBR(≥222.6)独立预测EGFR突变的ROC曲线下面积为0.58,联合其他临床病理参数预测EGFR突变的曲线下面积为0.799.结论 PET/CT代谢参数TLGWBR (≥222.6)作为最新的代谢指标,是预测进展期肺腺癌EGFR突变状态独立预测因素,联合其他临床病理参数具有较高预测EGFR突变的价值.

The aim of this study was to retrospectively analyze F-18-FDG positron emission tomography/computed tomography (F-18-FDG PET/CT) metabolic variables, programmed death-ligand 1 (PD-L1) and phosphorylated signal transducer and activator of transcription 3 (p-STAT3) tumor expression, and other factors as predictors of disease-free survival (DFS) in patients with lung adenocarcinoma (LUAD) (stage IA-IIIA) who underwent surgical resection. We still lack predictor of immune checkpoint (programmed cell death-1 [PD-1]/PD-L1) inhibitors. Herein, we investigated the correlation between metabolic parameters from F-18-FDG PET/CT and PD-L1 expression in patients with surgically resected LUAD. Seventy-four patients who underwent F-18-FDG PET/CT prior to treatment were consecutively enrolled. The main F-18-FDG PET/CT-derived variables were primary tumor maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Surgical tumor specimens were analyzed for PD-L1 and p-STAT3 expression using immunohistochemistry. Correlations between immunohistochemistry results and F-18-FDG PET/CT-derived variables were compared. Associations of PD-L1 and p-STAT3 tumor expression, F-18-FDG PET/CT-derived variables, and other factors with DFS in resected LUAD were evaluated. All tumors were FDG-avid. The cutoff values of low and high SUVmax, MTV, and TLG were 12.60, 14.87, and 90.85, respectively. The results indicated that TNM stage, PD-L1 positivity, and high F-18-FDG PET/CT metabolic volume parameters (TLG >= 90.85 or MTV >= 14.87) were independent predictors of worse DFS in resected LUAD. No F-18-FDG metabolic parameters associated with PD-L1 expression were observed (chi-square test), but we found that patients with positive PD-L1 expression have significantly higher SUVmax (P = .01), MTV (P = .00), and TLG (P = .00) than patients with negative PD-L1 expression. F-18-FDG PET/CT metabolic volume parameters (TLG >= 90.85 or MTV >= 14.87) were more helpful in prognostication than the conventional parameter (SUVmax), PD-L1 expression was an independent predictor of DFS in patients with resected LUAD. Metabolic parameters on F-18-FDG PET/CT have a potential role for F-18-FDG PET/CT in selecting candidate LUAD for treatment with checkpoint inhibitors.