Correlation between combining 18F–FDG PET/CT metabolic parameters and other clinical features and ALK or ROS1 fusion in patients with non-small-cell lung cancer

The Potential of Basal F-18-FDG PET/CT in Evaluating Prognosis and Benefit From Adjuvant Chemotherapy After Tumor Resection of Stage IB(T2, ≤ 3 cm With VPI, N0, M0)NSCLC

OBJECTIVES

To investigated whether the basal F-18-FDG PET/CT could evaluate the prognosis or the benefit from adjuvant chemotherapy after surgery of patients with early-stage NSCLC with visceral pleural invasion.

MATERIALS AND METHODS

A total of 116 patients with stage IB (T2, ≤ 3 cm with VPI, N0, M0) NSCLC underwent tumor resection and F-18-FDG PET/CT 1-3 weeks before surgery and were followed up for 1-79 months after surgery. SUVpeak, SUVmax, SUVmean, MTV, and TLG of tumors were obtained. The primary and secondary endpoints were progression-free survival (PFS) and overall survival (OS), respectively. ROC curve analysis, Cox regression test, and the Kaplan-Meier method were used for statistical analysis.

RESULTS

High SUVs, TLG, and MTV were associated with postoperative progression of NSCLC (the area under the ROC curve: 0.695 to 0.750, P < .001). The increase of SUVs, TLG or MTV was associated with short postoperative PFS (P < .001) while an increase in TLG (P = .016) or MTV (P = .018) was associated with short postoperative OS. TLG > 16.81 was an independent indicator of both the short PFS (HR = 5.534, P = .002) and the short OS (HR = 5.075, P = .031). Further, adjuvant chemotherapy was associated with longer PFS in NSCLCs with TLG > 16.81 (treated vs. untreated: 63 vs. 52 months; HR = 2.242, P = .022) rather than those with TLG ≤ 16.81.

CONCLUSION

SUV-based parameters on F-18-FDG PET/CT have the potential to evaluate the prognosis and benefit from adjuvant chemotherapy after tumor resection in stage IB (T2, ≤ 3 cm with VPI, N0, M0) NSCLC and therefore may be helpful for lung cancer treatment.

Transcriptomic analysis reveals transcription factors implicated in radon-induced lung carcinogenesis

Background

Radon, a potent carcinogen, is a significant catalyst for lung cancer development. However, the molecular mechanisms triggering radon-induced lung cancer remain elusive.

Methods

Utilizing a radon exposure concentration of 20,000 Bq/m3 for 20 min/session, malignant transformation was induced in human bronchial epithelial cells (BEAS-2B).

Results

Radon-exposed cells derived from passage 25 (BEAS-2B-Rn) exhibited enhanced proliferation and increased colony formation. Analysis of differential gene expression (DEG) through transcription factors revealed 663 up-regulated and 894 down-regulated genes in radon-exposed cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed significant alterations in the malignant transformation pathway of cells, including those related to cancer and the PI3K/AKT signaling pathway. A PPI network analysis indicated a significant association of oncogenes, such as CCND1, KIT, and GATA3, with lung cancer among differentially expressed genes. In addition, the stability of the housekeeping gene was determined through RT-qPCR analysis, which also confirmed the results of transcriptome analysis.

Conclusions

The results suggest that transcription factors may play a pivotal role in conferring a survival advantage to radon-exposed cells. This is achieved by malignant transformation of human bronchial epithelial cells into lung carcinogenesis cell phenotypes.

Combination of 18F-FDG PET/CT and convex probe endobronchial ultrasound elastography for intrathoracic malignant and benign lymph nodes prediction

Background

Positron emission tomography–computed tomography (PET/CT) and convex probe endobronchial ultrasound (CP-EBUS) elastography are important diagnostic methods in predicting intrathoracic lymph nodes (LNs) metastasis, but a joint analysis of the two examinations is still lacking. This study aimed to compare the diagnostic efficiency of the two methods and explore whether the combination can improve the diagnostic efficiency in differentiating intrathoracic benign LNs from malignant LNs.

Materials and Methods

LNs examined by EBUS-guided transbronchial needle aspiration (EBUS-TBNA) and PET/CT from March 2018 to June 2019 in Shanghai Chest Hospital were retrospectively analyzed as the model group. Four PET/CT parameters, namely, maximal standardized uptake value mean standardized uptake value (SUVmean), SUVmean, metabolic tumor volume (MTV), and tumor lesion glycolysis (TLG); four quantitative elastography indicators (stiff area ratio, mean hue value, RGB, and mean gray value); and the elastography grading score of targeted LNs were analyzed. A prediction model was constructed subsequently and the dataset from July to November 2019 was used to validate the diagnostic capability of the model.

Results

A total of 154 LNs from 135 patients and 53 LNs from 47 patients were enrolled in the model and validation groups, respectively. Mean hue value and grading score were independent malignancy predictors of elastography, as well as SUVmax and TLG of PET/CT. In model and validation groups, the combination of PET/CT and elastography demonstrated sensitivity, specificity, positive and negative predictive values, and accuracy for malignant LNs diagnosis of 85.87%, 88.71%, 91.86%, 80.88%, and 87.01%, and 94.44%, 76.47%, 89.47%, 86.67%, and 88.68%, respectively. Moreover, elastography had better diagnostic accuracies than PET/CT in both model and validation groups (85.71% vs. 79.22%, 86.79% vs. 75.47%).

Conclusion

EBUS elastography demonstrated better efficiency than PET/CT and the combination of the two methods had the best diagnostic efficacy in differentiating intrathoracic benign from malignant LNs, which may be helpful for clinical application.

PET/CT imaging for evaluation of multimodal treatment efficacy and toxicity in advanced NSCLC-current state and future directions

PURPOSE

The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of advanced NSCLC, leading to a string of approvals in recent years. Herein, a narrative review on the role of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in the ever-evolving treatment landscape of advanced NSCLC is presented.

METHODS

This comprehensive review will begin with an introduction into current treatment paradigms incorporating ICIs; the evolution of CT-based criteria; moving onto novel phenomena observed with ICIs and the current state of hybrid imaging for diagnosis, treatment planning, evaluation of treatment efficacy and toxicity in advanced NSCLC, also taking into consideration its limitations and future directions.

CONCLUSIONS

The advent of ICIs marks the dawn of a new era bringing forth new challenges particularly vis-à-vis treatment response assessment and observation of novel phenomena accompanied by novel systemic side effects. While FDG PET/CT is widely adopted for tumor volume delineation in locally advanced disease, response assessment to immunotherapy based on current criteria is of high clinical value but has its inherent limitations. In recent years, modifications of established (PET)/CT criteria have been proposed to provide more refined approaches towards response evaluation. Not only a comprehensive inclusion of PET-based response criteria in prospective randomized controlled trials, but also a general harmonization within the variety of PET-based response criteria is pertinent to strengthen clinical implementation and widespread use of hybrid imaging for response assessment in NSCLC.

Utility of Volumetric Metabolic Parameters on Preoperative FDG PET/CT for Predicting Tumor Lymphovascular Invasion in Non-Small Cell Lung Cancer

Background: Lymphovascular invasion (LVI) is an adverse prognostic indicator in non-small cell lung cancer (NSCLC) and serves as an indication for postoperative adjuvant chemotherapy recommendation after resection. Objective: To assess the utility of clinicopathologic factors and volumetric metabolic parameters from preoperative FDG PET/CT in predicting primary tumor LVI in NSCLC. Methods: This retrospective study included 161 patients (mean age, 61.8±8.1 years; 111 men, 50 women) with surgically-confirmed NSCLC who underwent preoperative FDG PET/CT between January 2018 and November 2020. Two nuclear medicine physicians used software to place automated volumes of interest delineating each tumor to record metabolic indices (SUVmax, SUVmean, and metabolictumor volume [MTV]), which in turn were used to calculate total lesion glycolysis (TLG). Measurements were first performed independently to determine interobserver agreement using intraclass correlation coefficients (ICCs) and then repeated in consensus. Associations of clinicopathologic and metabolic parameters with tumor LVI status were assessed using t test, Mann-Whitney U test, and chi-squared test. Diagnostic performance was assessed using ROC analysis. Multivariable logistic regression analysis was performed to identify independent predictors of tumor LVI. Results: A total of 23.6% (38/161) of patients had LVI. Interobserver agreement was ICC=1.000 for SUVmax, ICC=0.997 for SUVmean, and 0.999 for MTV. Tumors with LVI, compared with tumors without LVI, exhibited higher SUVmax (15.4±5.9 vs 11.7±7.5, p=.006), SUVmean (6.0±1.6 vs 5.1±2.0, p=.009), MTV (median 15.8 cm3 vs 5.5 cm3, p<.001), and TLG (median 88.8 vs 24.5, p<.001). Among the metabolic parameters, AUC was highest for MTV (0.704), with optimal MTV cutoff of 6.4 cm3 yielding sensitivity 92.1% (35/38), specificity 56.1% (69/123), PPV 39.3% (35/89), and NPV 95.8% (69/72) for LVI. Independent predictors (p<.05) of LVI were MTV (≥6.4 cm3, odds ratio [OR]=6.5), N1 (OR=6.4) or N2 (OR=4.0) disease, and T2 disease (OR=3.6). These factors combined achieved AUC of 0.854 for LVI. Conclusion: The volumetric metabolic parameter MTV from preoperative FDG PET/CT is an independent predictor of tumor LVI in NSCLC. Clinical Impact: Further studies are warranted to assess the potential role of preoperative prediction of LVI using FDG PET/CT to help guide clinical decision making in NSCLC.

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

Objectives

Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.

Methods

Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.

Results

A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.

Conclusions

This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

The potential of 18F-FDG PET/CT in predicting PDL1 expression status in pulmonary lesions of untreated stage IIIB-IV non-small-cell lung cancer

OBJECTIVES

To investigate the potential of 2-deoxy-2(¹⁸F)fluoro-d-glucose (¹⁸F-FDG) combined positron emission tomography and computed tomography (PET/CT) in predicting programmed cell death ligand-1 (PDL1) expression status in pulmonary lesions of advanced non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

This retrospective study includes 133 untreated stage IIIB-IV NSCLC patients who underwent pulmonary lesion biopsy for PDL1 immunochemistry 1-4 weeks after ¹⁸F-FDG PET/CT scanning, randomly assigned to cohorts for modelling and validation of PDL1 expression predictors. Mean and maximum standard uptake values (pSUVmean and pSUVmax), metabolic tumour volume (pMTV), and total lesion glycolysis (pTLG) of primary lesions were determined. PDL1 expression in pulmonary lesions (pPDL1) was determined using tumour proportion score (TPS), and pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % were considered as pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively.

RESULTS

pSUVmean and pSUVmax values were increased with the increase of pPDL1 levels, whereas pMTV and pTLG values were not associated with pPDL1 levels. In the modelling cohort, we found that pSUVmax rather than pSUVmean was an independent predictor for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, whereas pSUVmax < 14.4, 14.4-17.5, and > 17.5 were suggested as predictors for pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (odds ratio: 4.82, 3.92, and 4.45, respectively; P = 0.002, 0.021, and 0.020, respectively). In the validation cohort, pSUVmax < 14.4, 14.4-17.5, and > 17.5 showed significantly high probabilities of being pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively (P = 0.006). The accuracies of pSUVmax < 14.4, 14.4-17.5, and > 17.5 predicting pPDL1-negative, pPDL1-moderate, and pPDL1-strong, respectively, in validation cohort, were 66.7 %, 75.8 %, and 84.8 %, respectively.

CONCLUSION

pSUVmax on ¹⁸F-FDG PET/CT is a potential biomarker for pPDL1 TPS < 1%, 1-49 %, and ≥ 50 % in untreated stage IIIB-IV NSCLC, and therefore may be helpful for determining immunotherapeutic strategy for advanced NSCLC.

Evaluating Solid Lung Adenocarcinoma Anaplastic Lymphoma Kinase Gene Rearrangement Using Noninvasive Radiomics Biomarkers

Purpose

To develop a radiogenomics classifier to assess anaplastic lymphoma kinase (ALK) gene rearrangement status in pretreated solid lung adenocarcinoma noninvasively.

Materials and Methods

This study consisted of 140 consecutive pretreated solid lung adenocarcinoma patients with complete enhanced CT scans who were tested for both EGFR mutations and ALK status. Pre-contrast CT and standard post-contrast CT radiogenomics machine learning classifiers were designed as two separate classifiers. In each classifier, dataset was randomly split into training and independent testing group on a 7:3 ratio, accordingly subjected to a 5-fold cross-validation. After normalization, best feature subsets were selected by Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) or recursive feature elimination (RFE), whereupon a radiomics classifier was built with support vector machine (SVM). The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

In classifier one, 98 cases were selected as training data set, 42 cases as independent testing data set. In classifier two, 87 cases were selected as training data set, 37 cases as independent testing data set. Both classifiers extracted 851 radiomics features. The top 25 pre-contrast features and top 19 post-contrast features were selected to build optimal ALK+ radiogenomics classifiers accordingly. The accuracies, AUCs, sensitivity, specificity, PPV, and NPV of pre-contrast CT classifier were 78.57%, 80.10% (CI: 0.6538–0.9222), 71.43%, 82.14%, 66.67%, and 85.19%, respectively. Those results of standard post-contrast CT classifier were 81.08%, 82.85% (CI: 0.6630–0.9567), 76.92%, 83.33%, 71.43%, and 86.96%.

Conclusion

Solid lung adenocarcinoma ALK+ radiogenomics classifier of standard post-contrast CT radiomics biomarkers produced superior performance compared with that of pre-contrast one, suggesting that post-contrast CT radiomics should be recommended in the context of solid lung adenocarcinoma radiogenomics AI. Standard post-contrast CT machine learning radiogenomics classifier could help precisely identify solid adenocarcinoma ALK rearrangement status, which may act as a pragmatic and cost-efficient substitute for traditional invasive ALK status test.