Predictive significance of standardized uptake value parameters of FDG-PET in patients with non-small cell lung carcinoma

Correlation between 18-FDG standardized uptake value and tumor grade in patients with resectable non-small cell lung cancer

Background

Positron-emission tomography (PET) is widely used for staging lung cancer. Although a correlation between the fluorodeoxyglucose standardized uptake value (SUV) and the histologic grade of the tumor has been shown in several studies, little is known about the impact of different clinical variables on this correlation. This study aimed to evaluate the correlation between tumor SUV and tumor grade in a large cohort of patients and to analyse the impact of clinical factors on this correlation.

Methods

This retrospective cohort study including patients with non-small cell lung cancer age 18-90 years, with clinical stage I-IVA, who underwent curative-intent lung resection.

Results

Data from 726 patients was included in this study. There was a strong correlation between SUV and primary tumor grade in the whole cohort (P<0.001), which was significant in both sexes (P<0.001) and in all selected age groups (P<0.001-0.03). There was a significant SUV-grade correlation for the right upper and left lower lobes, as well as for the central location in the right lung (P<0.001, P=0.005 and P=0.04, respectively). Moreover, a significant SUV-grade correlation was found for squamous cell cancer and adenocarcinoma (P<0.001 and P=0.01, respectively), and for T1-T3 factors (P<0.001, P=0.006, P=0.005 respectively).

Conclusions

In patients with resectable lung cancer, a significant correlation was observed between the SUV of the primary tumor and its grade. This correlation was maintained for both sexes, age groups, most common histological types and T factors T1-T3.

Use of PET/CT to detect myocardial inflammation and the risk of malignant arrhythmia in chronic Chagas disease

BACKGROUND

Chagas heart disease (CHD) is characterized by progressive myocardial inflammation associated with myocardial fibrosis and segmental abnormalities that may lead to malignant ventricular arrhythmia and sudden cardiac death. This arrhythmia might be related to the persistence of parasitemia or inflammation in the myocardium in late-stage CHD. Positron emission tomography/computed tomography (PET/CT) has been used to detect myocardial inflammation in non-ischemic cardiomyopathies, such as sarcoidosis, and might be useful for risk prediction in patients with CHD.

METHODS AND RESULTS

Twenty-four outpatients with chronic CHD were enrolled in this prospective cross-sectional study between May 2019 and March 2022. The patients were divided into two groups: those with sustained ventricular tachycardia and/or aborted sudden cardiac death who required implantable cardioverter-defibrillators, and those with the same stages of CHD and no complex ventricular arrhythmia. Patients underwent 18F-fluorodeoxyglucose (18F-FDG) and 68Ga-DOTATOC PET/CT, and blood samples were collected for qualitative parasite assessment by polymerase chain reaction. Although similar proportions of patients with and without complex ventricular arrhythmia showed 18F-FDG and 68Ga-DOTATOC uptake, 68Ga-DOTATOC corrected SUVmax was higher in patients with complex arrhythmia (3.4 vs 1.7; P = .046), suggesting that inflammation could be associated with the presence of malignant arrhythmia in the late stages of CHD. We also detected Trypanosoma cruzi in both groups, with a nonsignificant trend of increased parasitemia in the group with malignant arrhythmia (66.7% vs 33.3%).

CONCLUSION

18F-FDG and 68Ga-DOTATOC uptake on PET/CT may be useful for the detection of myocardial inflammation in patients with Chagas cardiomyopathy, and 68Ga-DOTATOC uptake may be associated with the presence of malignant arrhythmia, with potential therapeutic implications.

A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer

Objective

The aim of this study was to develop a machine learning-based automatic analysis method for the diagnosis of early-stage lung cancer based on positron emission tomography/computed tomography (PET/CT) data.

Methods

A retrospective cohort study was conducted using PET/CT data from 187 cases of non-small cell lung cancer (NSCLC) and 190 benign pulmonary nodules. Twelve PET and CT features were used to train a diagnosis model. The performance of the machine learning-based PET/CT model was tested and validated in two separate cohorts comprising 462 and 229 cases, respectively.

Results

The standardized uptake value (SUV) was identified as an important biochemical factor for the early stage of lung cancer in this model. The PET/CT diagnosis model had a sensitivity and area under the curve (AUC) of 86.5% and 0.89, respectively. The testing group comprising 462 cases showed a sensitivity and AUC of 85.7% and 0.87, respectively, while the validation group comprising 229 cases showed a sensitivity and AUC of 88.4% and 0.91, respectively. Additionally, the proposed model improved the clinical discrimination ability for solid pulmonary nodules (SPNs) in the early stage significantly.

Conclusion

The feature data collected from PET/CT scans can be analyzed automatically using machine learning techniques. The results of this study demonstrated that the proposed model can significantly improve the accuracy and positive predictive value (PPV) of SPNs at the early stage. Furthermore, this algorithm can be optimized into a robotic and less biased PET/CT automatic diagnosis system.

Prediction of Distant Metastases After Stereotactic Body Radiation Therapy for Early Stage NSCLC: Development and External Validation of a Multi-Institutional Model

INTRODUCTION

Distant metastases (DMs) are the primary driver of mortality for patients with early stage NSCLC receiving stereotactic body radiation therapy (SBRT), yet patient-level risk is difficult to predict. We developed and validated a model to predict individualized risk of DM in this population.

METHODS

We used a multi-institutional database of 1280 patients with cT1-3N0M0 NSCLC treated with SBRT from 2006 to 2015 for model development and internal validation. A Fine and Gray (FG) regression model was built to predict 1-year DM risk and compared with a random survival forests model. The higher performing model was evaluated on an external data set of 130 patients from a separate institution. Discriminatory performance was evaluated using the time-dependent area under the curve (AUC). Calibration was assessed graphically and with Brier scores.

RESULTS

The FG model yielded an AUC of 0.71 (95% confidence interval [CI]: 0.57-0.86) compared with the AUC of random survival forest at 0.69 (95% CI: 0.63-0.85) in the internal test set and was selected for further testing. On external validation, the FG model yielded an AUC of 0.70 (95% CI: 0.57-0.83) with good calibration (Brier score: 0.08). The model identified a high-risk patient subgroup with greater 1-year DM rates in the internal test (20.0% [3 of 15] versus 2.9% [7 of 241], p = 0.001) and external validation (21.4% [3 of 15] versus 7.8% [9 of 116], p = 0.095). A model nomogram and online application was made available.

CONCLUSIONS

We developed and externally validated a practical model that predicts DM risk in patients with NSCLC receiving SBRT which may help select patients for systemic therapy.

Correlation of Transcriptomics and FDG-PET SUVmax Indicates Reciprocal Expression of Stemness-Related Transcription Factor and Neuropeptide Signaling Pathways in Glucose Metabolism of Ewing Sarcoma

BACKGROUND

In Ewing sarcoma (EwS), long-term treatment effects and poor survival rates for relapsed or metastatic cases require individualization of therapy and the discovery of new treatment methods. Tumor glucose metabolic activity varies significantly between patients, and FDG-PET signals have been proposed as prognostic factors. However, the biological basis for the generally elevated but variable glucose metabolism in EwS is not well understood.

METHODS

We retrospectively included 19 EwS samples (17 patients). Affymetrix gene expression was correlated with maximal standardized uptake value (SUVmax) using machine learning, linear regression modelling, and gene set enrichment analyses for functional annotation.

RESULTS

Expression of five genes correlated (MYBL2, ELOVL2, NETO2) or anticorrelated (FAXDC2, PLSCR4) significantly with SUVmax (adjusted p-value ≤ 0.05). Additionally, we identified 23 genes with large SUVmax effect size, which were significantly enriched for "neuropeptide Y receptor activity (GO:0004983)" (adjusted p-value = 0.0007). The expression of the members of this signaling pathway (NPY, NPY1R, NPY5R) anticorrelated with SUVmax. In contrast, three transcription factors associated with maintaining stemness displayed enrichment of their target genes with higher SUVmax: RNF2, E2F family, and TCF3.

CONCLUSION

Our large-scale analysis examined comprehensively the correlations between transcriptomics and tumor glucose utilization. Based on our findings, we hypothesize that stemness may be associated with increased glucose uptake, whereas neuroectodermal differentiation may anticorrelate with glucose uptake.

FDG-PET/CT tumor to liver SUV ratio (TLR), tumor SUVmax, and tumor size: can this help in differentiating squamous cell carcinoma from adenocarcinoma of the lung?

Background

PET/CT plays an essential role in the diagnosis, staging, and follow-up of lung cancer. We aimed to assess the ability of PET/CT to differentiate between adenocarcinomas (AC) and squamous cell carcinomas (SCC) of the lung using tumor size, tumor maximum standardized uptake value (SUVmax), lymph nodes SUVmax, and tumor to liver SUV ratio (TLR).

Results

A total of 60 patients pathologically proved to have non-small cell lung cancer either AC or SCC were retrospectively evaluated. The mean tumor size, SUVmax of the tumor, and TLR were significantly higher in SCC lesions compared to AC lesions. The mean SCC tumoral size was 7.96 ± 2.18 cm compared to 5.66 ± 2.57 cm in AC lesions (P = 0.008). The mean tumor SUVmax in SCC lesions was 18.95 ± 8.3 compared to 12.4 ± 7.55 in AC lesions (P = 0.04). While the mean TLR of SCC lesions was 10.32 ± 4.03 compared to 7.36 ± 4.61 in AC lesions (P = 0.028). All three parameters showed the same sensitivity (75%), while TLR showed the highest specificity (77.78%) followed by tumor size (76.47%) and then SUVmax of the tumor (72.22%).

Conclusions

SCC of the lung has a higher mean tumor size, SUVmax of the tumor, and TLR as compared to AC which can be helpful tools in differentiation between them using PET/CT.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction.

Findings

1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features.

Interpretation

CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care.

Funding

NIH NHLBI training grant (5T35HL094308-12, John Sollee).

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Backgrounds

Epidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline ¹⁸F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.

Methods

A sum of 313 NSCLC patients with pre-treatment ¹⁸F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.

Results

Three hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.

Conclusions

The PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

Comparison of 68Ga-DOTATOC and 18F-FDG Thoracic Lymph Node and Pulmonary Lesion Uptake Using PET/CT in Postprimary Tuberculosis

Tuberculosis (TB) remains one of the world's leading infectious cause of morbidity and mortality. Positron emission tomography (PET) associated with computed tomography (CT) allows a structural and metabolic evaluation of TB lesions, being an excellent noninvasive alternative for understanding its pathogenesis. DOTATOC labeled with gallium-68 (68Ga-DOTATOC) can bind to somatostatin receptors present in activated macrophages and lymphocytes, cells with a fundamental role in TB pathogenesis. We describe 68Ga-DOTATOC uptake distribution and patterns in thoracic lymph nodes (LN) and pulmonary lesions (PL) in immunocompetent patients with active postprimary TB, analyze the relative LN/PL uptake, and compare this two tracer's uptake. High uptake of both radiotracers in PL and LN was demonstrated, with higher LN/PL ratio on 68Ga-DOTATOC (P < 0.05). Considering that LN in immunocompetent patients are poorly studied, 68Ga-DOTATOC can contribute to the understanding of the complex immunopathogenesis of TB.

Prognostic impact of maximum standardized uptake value on 18 F-FDG PET/CT imaging of the primary lung lesion on survival in advanced non-small cell lung cancer: A retrospective study

Background

Positron emission tomography/computed tomography (PET/CT) has been recognized for diagnosing and staging lung cancer, but the prognostic value of standardized uptake value (SUV) on ¹⁸F‐FDG PET/CT imaging in patients with advanced non‐small cell lung cancer (NSCLC) remains controversial.

Methods

We performed a retrospective analysis of patients with advanced NSCLC who had undergone ¹⁸F‐FDG PET/CT before systemic treatment between June 2012 and June 2016. The relationship between the maximum SUV (SUVmax) of the pulmonary lesion and lesion size was evaluated via Spearman's correlation analysis. We collected patients' clinical and pathological data. Univariate and multivariate analyses were performed to analyze the factors influencing survival.

Results

We included 157 patients with advanced NSCLC. Among these, 135 died, 13 survived, and nine were lost to follow‐up (median follow‐up period, 69 months). SUVmax was correlated with lesion size and was significantly greater for tumors ≥3 cm than for tumors <3 cm (10.2 ± 5.4 vs. 5.6 ± 3.3, t = −6.709, p = 0.000). Univariate analysis showed that survival was associated with gender, tumor size, epidermal growth factor receptor gene mutation or anaplastic lymphoma kinase rearrangement, SUVmax of the primary lung lesion, and treatment lines. Multivariate analysis showed a significant correlation between SUVmax of the primary lung lesion and survival. The mortality risk of patients with SUVmax ≤6 was 35% lower than that of patients with SUVmax >6 (HR = 0.651, 95% confidence interval, 0.436–0.972; Wald value, 4.400; p = 0.036).

Conclusions

The SUVmax of the primary lung lesion on PET/CT is significantly correlated with survival in treatment‐naive patients with advanced NSCLC.

Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations

Objectives

To investigate the development and validation of a radiomics nomogram based on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma mutation(s) in the EGFR gene.

Methods

A cohort of 109 (77/32 in training/validation cohort) consecutive lung adenocarcinoma patients with an EGFR mutation was enrolled in this study. A total of 1672 radiomic features were extracted from PET and CT images, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the radiomic features and construct the radiomics nomogram for the estimation of overall survival (OS), which was then assessed with respect to calibration and clinical usefulness. Patients with an EGFR mutation were divided into high- and low- risk groups according to their nomogram score. The treatment strategy for high- and low-risk groups was analyzed using Kaplan–Meier analysis and a log-rank test.

Results

The C-index of the radiomics nomogram for the prediction of OS in lung adenocarcinoma in patients with an EGFR mutation was 0.840 and 0.803 in the training and validation cohorts, respectively. Distant metastasis [(Hazard ratio, HR),1.80], metabolic tumor volume (MTV, HR, 1.62), and rad score (HR, 17.23) were the independent risk factors for patients with an EGFR mutation. The calibration curve showed that the predicted survival time was remarkably close to the actual time. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Targeted therapy for patients with high-risk EGFR mutations attained a greater benefit than other therapies (p < 0.0001), whereas the prognoses of the two therapies were similar in the low-risk group (p = 0.85).

Conclusions

Development and validation of a radiomics nomogram based on PET/CT radiomic features combined with clinicopathological factors may guide targeted therapy for patients with lung adenocarcinoma with EGFR mutations. This is conducive to the advancement of precision medicine.

Is there any improvement in clinical staging with 18F-FDG PET/CT compared to surgical staging in cases of lung cancer?

OBJECTIVE

¹⁸F-Fluorine fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging is considered the standard imaging modality for patients with non-small cell lung carcinoma. The aim of this study was to compare clinical staging (cTNM) performed with ¹⁸F-FDG PET/CT and surgical staging (sTNM) in patients with non-small cell carcinoma treated with surgery.

MATERIAL AND METHODS

We performed a retrospective analysis of 99 surgical patients with non-small cell carcinoma who underwent ¹⁸F-FDG PET/CT examination. Semiquantitative measures were calculated from the primary lesions and mediastinal lymph nodes. Findings of cTNM were compared with final surgical-pathological evaluation. Subjects were divided into two groups as postsurgical cTNM changed and cTNM unchanged. Patients in the cTNM changed group were further classified as postsurgical upstaged (US) and downstaged (DS). Results of the US patients were compared with the results of the remaining patients consisting of cTNM unchanged and DS to evaluate the predictable roles of semiquantitative parameters for postsurgical upstaging. To determine mediastinal tumoral involvement, cut-off values were obtained from calculated semiquantitative results of FDG uptakes in lymph nodes. A p value<0.05 was considered statistically significant.

RESULTS

Subjects were aged 40-82 years with a mean age of 64.78±8.70 years. Classification agreement was observed in 43 patients (43%) and in 57%, postsurgical stage migration was seen. Concurrence of cTNM and sTNM was more pronounced in the T1 and N0 subsets which were 84% and 74%, respectively. The lowest concurrence was observed in N1 classification followed by T4 and N2 (1%, 50% and 58%, respectively). Change in T staging occurred in 20 of 56 (36%), in N staging 22 of 56 (39%) and change in T and N in 14 patients (25%). Distribution of US and DS patients in the cTNM changed group was 43% (24 of 56) and 57% (32 of 56), respectively. Results of semiquantitative measures were significantly higher in US patients than the results of the group consisting of DS patients and cTNM unchanged patients, for all parameters. Cut-off value calculated from mediastinal uptakes was most specific for metastases in MTV (metabolic tumor volume) with an acceptable sensitivity (90% and 67%, respectively).

CONCLUSIONS

The concordance between cTNM and sTNM was better in staging T category compared to N stations. Semiquantitative measures of primary tumor may play a role in predicting postsurgical upstaging. Taking MTV into consideration in the mediastinal region may be more valuable than other parameters in the assessment of nodal involvement.

Usefulness of the maximum standardized uptake value for the diagnosis and staging of patients with cervical cancer undergoing positron emission tomography/computed tomography

Cervical cancer recently has become more common among younger women in Japan. Diagnosing early-stage cancer is straightforward using cervical cytodiagnosis and histological diagnosis. However, postmenopausal endophytic cervical cancer and skip lesions in cervical adenocarcinoma are difficult to detect. We compared the maximum standardized uptake value (SUVmax) of 18F-fluorodeoxy-glucose positron emission tomography/computed tomography (PET/CT) for primary staging of cervical cancer and evaluated the relationship of the imaging findings to prognosis.This was a retrospective study of 38 patients with cervical cancer who underwent PET/CT. Patients were grouped according to disease stage, and the mean SUVmax, overall survival, and progression-free survival (PFS) were evaluated.The mean SUVmax was significantly different between patients with stage ≤I and ≥II diseases among those with squamous (P > .001) and glandular (P = .023) lesions. With an SUVmax of receiver operating characteristic curves as the optimal cutoff value, the log-rank test for PFS revealed a statistically significant difference between the 2 disease stages (P = .020 and P = .016, respectively).SUVmax is useful to differentiate between stage ≤I and ≥II cervical cancer. SUVmax may be useful for the prognostic evaluation of disease recurrence in patients with cervical cancer.