The diagnostic and predictive efficacy of 18F-FDG PET/CT metabolic parameters for EGFR mutation status in non-small-cell lung cancer: A meta-analysis

Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma

BACKGROUND

To introduce a three-dimensional convolutional neural network (3D CNN) leveraging transfer learning for fusing PET/CT images and clinical data to predict EGFR mutation status in lung adenocarcinoma (LADC).

METHODS

Retrospective data from 516 LADC patients, encompassing preoperative PET/CT images, clinical information, and EGFR mutation status, were divided into training (n = 404) and test sets (n = 112). Several deep learning models were developed utilizing transfer learning, involving CT-only and PET-only models. A dual-stream model fusing PET and CT and a three-stream transfer learning model (TS_TL) integrating clinical data were also developed. Image preprocessing includes semi-automatic segmentation, resampling, and image cropping. Considering the impact of class imbalance, the performance of the model was evaluated using ROC curves and AUC values.

RESULTS

TS_TL model demonstrated promising performance in predicting the EGFR mutation status, with an AUC of 0.883 (95%CI = 0.849-0.917) in the training set and 0.730 (95%CI = 0.629-0.830) in the independent test set. Particularly in advanced LADC, the model achieved an AUC of 0.871 (95%CI = 0.823-0.919) in the training set and 0.760 (95%CI = 0.638-0.881) in the test set. The model identified distinct activation areas in solid or subsolid lesions associated with wild and mutant types. Additionally, the patterns captured by the model were significantly altered by effective tyrosine kinase inhibitors treatment, leading to notable changes in predicted mutation probabilities.

CONCLUSION

PET/CT deep learning model can act as a tool for predicting EGFR mutation in LADC. Additionally, it offers clinicians insights for treatment decisions through evaluations both before and after treatment.

Predictive value of 18F-FDG PET/CT radiomics for EGFR mutation status in non-small cell lung cancer: a systematic review and meta-analysis

Objective

This study aimed to evaluate the value of 18F-FDG PET/CT radiomics in predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis.

Methods

The PubMed, Embase, Cochrane Library, Web of Science, and CNKI databases were searched from the earliest available date to June 30, 2023. The meta-analysis was performed using the Stata 15.0 software. The methodological quality and risk of bias of included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score criteria. The possible causes of heterogeneity were analyzed by meta-regression.

Results

A total of 17 studies involving 3763 non-small cell lung cancer patients were finally included. We analyzed 17 training cohorts and 10 validation cohorts independently. Within the training cohort, the application of 18F-FDG PET/CT radiomics in predicting EGFR mutations in NSCLC demonstrated a sensitivity of 0.76 (95% CI: 0.70-0.81) and a specificity of 0.78 (95% CI: 0.74-0.82), accompanied by a positive likelihood ratio of 3.5 (95% CI:3.0-4.2), a negative likelihood ratio of 0.31 (95% CI: 0.24-0.39), a diagnostic odds ratio of 11.0 (95% CI: 8.0-16.0), and an area under the curve (AUC) of 0.84 (95% CI: 0.80-0.87). In the validation cohort, the values included a sensitivity of 0.76 (95% CI: 0.67-0.83), a specificity of 0.75 (95% CI: 0.68-0.80), a positive likelihood ratio of 3.0 (95% CI:2.4-3.8), a negative likelihood ratio of 0.32 (95% CI: 0.24-0.44), a diagnostic odds ratio of 9 (95% CI: 6-15), and an AUC of 0.82 (95% CI: 0.78-0.85). The average Radiomics Quality Score (RQS) across studies was 10.47 ± 4.72. Meta-regression analysis identifies the application of deep learning and regions as sources of heterogeneity.

Conclusion

18F-FDG PET/CT radiomics may be useful in predicting mutation status of the EGFR gene in non-small cell lung cancer.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42022385364.

Prognostic significance of integrating total metabolic tumor volume and EGFR mutation status in patients with lung adenocarcinoma

Background

The objective of this study was to investigate the prognostic significance of total metabolic tumor volume (TMTV) derived from baseline 18F-2-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), in conjunction with epidermal growth factor receptor (EGFR) mutation status, among patients with lung adenocarcinoma (LUAD).

Methods

We performed a retrospective analysis on 141 patients with LUAD (74 males, 67 females, median age 67 (range 34-86)) who underwent 18F-FDG PET/CT and had their EGFR mutation status determined. Optimal cutoff points for TMTV were determined using time-dependent receiver operating characteristic curve analysis. The survival difference was compared using Cox regression analysis and Kaplan‒Meier curves.

Results

The EGFR mutant patients (n = 79, 56.0%) exhibited significantly higher 2-year progression-free survival (PFS) and overall survival (OS) rates compared to those with EGFR wild-type (n = 62, 44.0%), with rates of 74.2% vs 69.2% (P = 0.029) and 86.1% vs 67.7% (P = 0.009), respectively. The optimal cutoff values of TMTV were 36.42 cm3 for PFS and 37.51 cm3 for OS. Patients with high TMTV exhibited significantly inferior 2-year PFS and OS, with rates of 22.4% and 38.1%, respectively, compared to those with low TMTV, who had rates of 85.8% and 95.0% (both P < 0.001). In both the EGFR mutant and wild-type groups, patients exhibiting high TMTV demonstrated significantly inferior 2-year PFS and OS compared to those with low TMTV. In multivariate analysis, EGFR mutation status (hazard ratio, HR, 0.41, 95% confidence interval, CI [0.18-0.94], P = 0.034) and TMTV (HR 8.08, 95% CI [2.34-28.0], P < 0.001) were independent prognostic factors of OS, whereas TMTV was also an independent prognosticator of PFS (HR 2.59, 95% CI [1.30-5.13], P = 0.007).

Conclusion

Our study demonstrates that the integration of TMTV on baseline 18F-FDG PET/CT with EGFR mutation status improves the accuracy of prognostic evaluation for patients with LUAD.

Role of multiple dual-phase 18F-FDG PET/CT metabolic parameters in differentiating adenocarcinomas from squamous cell carcinomas of the lung

Purpose

To evaluate the ability of multiple dual-phase 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters to distinguish the histological subtypes of non-small cell lung cancer (NSCLC).

Methods

Data from 127 patients with non-small cell lung cancer who underwent preoperative dual-phase 18F-FDG PET/CT scanning at the PET-CT center of our hospital from December 2020 to October 2021 were collected, and the metabolic parameters of their primary lesions were measured and analyzed retrospectively. Intraclass correlation coefficients (ICC) were calculated for consistency between readers. Metabolic parameters in the early (SUVpeak, SUVmean, SUVmin, SUVmax, MTV, and TLG) and delayed phases (dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, dpMTV, and dpTLG) were calculated. We drew receiver operating characteristic (ROC) curves to compare the differences in different metabolic parameters between the adenocarcinoma (AC) and squamous cell carcinoma (SCC) groups and evaluated the ability of different metabolic parameters to distinguish AC from SCC.

Results

Inter-reader agreement, as assessed by the intraclass correlation coefficient (ICC), was good (ICC = 0.71, 95% CI:0.60-0.79). The mean MTV, SUVmax, TLG, SUVpeak, SUVmean, dpSUVmax, dpTLG, dpSUVpeak, dpSUVmean, and dpSUVmin of the tumors were significantly higher in SCC lesions than in AC lesions (P = 0.049, < 0.001, 0.016, < 0.001, 0.001, < 0.001, 0.018, < 0.001, 0.001, and 0.001, respectively). The diagnostic efficacy of the metabolic parameters in 18F-FDG PET/CT for differentiating adenocarcinoma from squamous cell carcinoma ranged from high to low as follows: SUVpeak (AUC = 0.727), SUVmax (AUC = 0.708), dpSUVmax (AUC = 0.699), dpSUVpeak (AUC = 0.698), TLG (AUC = 0.695), and dpTLG (AUC = 0.692), SUVmean (AUC = 0.690), dpSUVmean (AUC = 0.687), dpSUVmin (AUC = 0.680), SUVmin (AUC = 0.676), and MTV (AUC = 0.657).

Conclusions

Squamous cell carcinoma of the lung had higher mean MTV, SUVmax, TLG, SUVpeak, SUVmean, SUVmin, dpSUVpeak, dpSUVmean, dpSUVmin, dpSUVmax, and dpTLG than AC, which can be helpful tools in differentiating between the two. The metabolic parameters of the delayed phase (2 h after injection) 18F-FDG PET/CT did not improve the diagnostic efficacy in distinguishing lung AC from SCC. Conventional dual-phase 18F-FDG PET/CT is not recommended.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

BACKGROUND

This study aims to construct radiomics models based on [¹⁸F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models.

METHODS

A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.

RESULTS

Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).

CONCLUSIONS

Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

Predictive value of intratumor metabolic and heterogeneity parameters on [18F]FDG PET/CT for EGFR mutations in patients with lung adenocarcinoma

PURPOSE

This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC).

MATERIALS AND METHODS

A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [¹⁸F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model.

RESULTS

108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter.

CONCLUSIONS

EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.

18F-FDG PET as an imaging biomarker for the response to FGFR-targeted therapy of cancer cells via FGFR-initiated mTOR/HK2 axis

Rationale: The overall clinical response to FGFR inhibitor (FGFRi) is far from satisfactory in cancer patients stratified by FGFR aberration, the current biomarker in clinical practice. A novel biomarker to evaluate the therapeutic response to FGFRi in a non-invasive and dynamic manner is thus greatly desired. Methods: Six FGFR-aberrant cancer cell lines were used, including four FGFRi-sensitive ones (NCI-H1581, NCI-H716, RT112 and Hep3B) and two FGFRi-resistant ones (primary for NCI-H2444 and acquired for NCI-H1581/AR). Cell viability and tumor xenograft growth analyses were performed to evaluate FGFRi sensitivities, accompanied by corresponding ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake assay. mTOR/PLCγ/MEK-ERK signaling blockade by specific inhibitors or siRNAs was applied to determine the regulation mechanism. Results: FGFR inhibition decreased the in vitro accumulation of ¹⁸F-FDG only in four FGFRi-sensitive cell lines, but in neither of FGFRi-resistant ones. We then demonstrated that FGFRi-induced transcriptional downregulation of hexokinase 2 (HK2), a key factor of glucose metabolism and FDG trapping, via mTOR pathway leading to this decrease. Moreover, ¹⁸F-FDG PET imaging successfully differentiated the FGFRi-sensitive tumor xenografts from primary or acquired resistant ones by the tumor ¹⁸F-FDG accumulation change upon FGFRi treatment. Of note, both ¹⁸F-FDG tumor accumulation and HK2 expression could respond the administration/withdrawal of FGFRi in NCI-H1581 xenografts correspondingly. Conclusion: The novel association between the molecular mechanism (FGFR/mTOR/HK2 axis) and radiological phenotype (¹⁸F-FDG PET uptake) of FGFR-targeted therapy was demonstrated in multiple preclinical models. The adoption of ¹⁸F-FDG PET biomarker-based imaging strategy to assess response/resistance to FGFR inhibition may benefit treatment selection for cancer patients.

Predictive value of multiple metabolic and heterogeneity parameters of 18F-FDG PET/CT for EGFR mutations in non-small cell lung cancer

OBJECTIVES

To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and ¹⁸F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve.

RESULTS

The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter.

CONCLUSION

Among the metabolic and heterogeneity parameters of ¹⁸F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.

New PET/CT criterion for predicting lymph node metastasis in resectable advanced (stage IB-III) lung cancer: The standard uptake values ratio of ipsilateral/contralateral hilar nodes

Background

The aim of the present study was to use surgical and histological results to develop a simple noninvasive technique to improve nodal staging using preoperative PET/CT in patients with resectable lung cancer.

Methods

Preoperative PET/CT findings (pStage IB–III 182 patients) and pathological diagnoses after surgical resection were evaluated. Using PET/CT images to determine the standardized uptake value (SUV) ratio, the SUV_max of a contralateral hilar lymph node (on the side of the chest opposite to the primary tumor) was measured simultaneously. The I/C‐SUV ratio was calculated as ipsilateral hilar node SUV/contralateral hilar node SUV. Receiver operating characteristic (ROC) curves were then used to analyze those data.

Results

Based on ROC analyses, the cutoff I/C‐SUV ratio for diagnosis of lymph node metastasis was 1.34. With a tumor ipsilateral lymph node SUV_max ≥2.5, an IC‐SUV ratio ≥1.34 had the highest accuracy for predicting N1/N2 metastasis; the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of nodal staging were 60.66, 85.11, 84.09, 62.5 and 71.29%, respectively.

Conclusions

When diagnosing nodal stage, a lymph node I/C‐SUV ratio ≥1.34 can be an effective criterion for determining surgical indications in advanced lung cancer.

The role of metabolic ecosystem in cancer progression — metabolic plasticity and mTOR hyperactivity in tumor tissues

Despite advancements in cancer management, tumor relapse and metastasis are associated with poor outcomes in many cancers. Over the past decade, oncogene-driven carcinogenesis, dysregulated cellular signaling networks, dynamic changes in the tissue microenvironment, epithelial-mesenchymal transitions, protein expression within regulatory pathways, and their part in tumor progression are described in several studies. However, the complexity of metabolic enzyme expression is considerably under evaluated. Alterations in cellular metabolism determine the individual phenotype and behavior of cells, which is a well-recognized hallmark of cancer progression, especially in the adaptation mechanisms underlying therapy resistance. In metabolic symbiosis, cells compete, communicate, and even feed each other, supervised by tumor cells. Metabolic reprogramming forms a unique fingerprint for each tumor tissue, depending on the cellular content and genetic, epigenetic, and microenvironmental alterations of the developing cancer. Based on its sensing and effector functions, the mechanistic target of rapamycin (mTOR) kinase is considered the master regulator of metabolic adaptation. Moreover, mTOR kinase hyperactivity is associated with poor prognosis in various tumor types. In situ metabolic phenotyping in recent studies highlights the importance of metabolic plasticity, mTOR hyperactivity, and their role in tumor progression. In this review, we update recent developments in metabolic phenotyping of the cancer ecosystem, metabolic symbiosis, and plasticity which could provide new research directions in tumor biology. In addition, we suggest pathomorphological and analytical studies relating to metabolic alterations, mTOR activity, and their associations which are necessary to improve understanding of tumor heterogeneity and expand the therapeutic management of cancer.