Despite the recent advances in lung cancer biology, molecular pathology, and treatment, this malignancy remains the leading cause of cancer-related death worldwide and non-small cell lung cancer (NSCLC) is the most common form found at diagnosis. Accurate staging of the disease is a fundamental prognostic factor that correctly predicts progression-free (PFS) and overall survival (OS) of NSCLC patients. However, outcome of patients within each TNM staging group can change widely highlighting the need to identify additional prognostic biomarkers to better stratify patients on the basis of risk. 18F-FDG PET/CT plays an essential role in staging, evaluation of treatment response, and tumoral target delineation in NSCLC patients. Moreover, a number of studies showed the prognostic role of imaging parameters derived from PET images, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). These parameters represent three-dimensional PET-based measurements providing information on both tumor volume and metabolic activity and previous studies reported their ability to predict OS and PFS of NSCLC patients. This review will primarily focus on the studies that showed the prognostic and predictive role of MTV and TLG in NSCLC patients, addressing also their potential utility in the new era of immunotherapy of NSCLC.

The aim of our study was to predict the occurrence of distant metastases in non-small-cell lung cancer (NSCLC) patients using machine learning methods and texture analysis of ¹⁸F-labeled 2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography {[¹⁸F]FDG PET/CT} images. In this retrospective and single-center study, we evaluated 79 patients with advanced NSCLC who had undergone [¹⁸F]FDG PET/CT scan at diagnosis before any therapy. Patients were divided into two independent training (n = 44) and final testing (n = 35) cohorts. Texture features of primary tumors and lymph node metastases were extracted from [¹⁸F]FDG PET/CT images using the LIFEx program. Six machine learning methods were applied to the training dataset using the entire panel of features. Dedicated selection methods were used to generate different combinations of five features. The performance of selected machine learning methods applied to the different combinations of features was determined using accuracy, the confusion matrix, receiver operating characteristic (ROC) curves, and area under the curve (AUC). A total of 104 and 78 lesions were analyzed in the training and final testing cohorts, respectively. The support vector machine (SVM) and decision tree methods showed the highest accuracy in the training cohort. Seven combinations of five features were obtained and introduced in the models and subsequently applied to the training and final testing cohorts using the SVM and decision tree. The accuracy and the AUC of the decision tree method were higher than those obtained with the SVM in the final testing cohort. The best combination of features included shape sphericity, gray level run length matrix_run length non-uniformity (GLRLM_RLNU), Total Lesion Glycolysis (TLG), Metabolic Tumor Volume (MTV), and shape compacity. The combination of these features with the decision tree method could predict the occurrence of distant metastases with an accuracy of 74.4% and an AUC of 0.63 in NSCLC patients.

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from ¹⁸F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent ¹⁸F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTV_TOT) and whole-body Total Lesion Glycolysis (TLG_WB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTV_TOT, TLG_WB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTV_TOT (p = 0.0050), TLG_WB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTV_TOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTV_TOT ≤ 89.5 mL had higher OS than those with MTV_TOT > 89.5 mL (p = 0.0063). Combining CoV and MTV_TOT, patients with CoV ≤ 0.38 and MTV_TOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients.

Gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) constitute an ideal target for radiolabeled somatostatin analogs. The theragnostic approach is able to combine diagnosis and therapy by the identification of a molecular target that can be diagnosed and treated with the same radiolabeled compound. During the last years, advances in functional imaging with the introduction of somatostatin analogs and peptide receptor radionuclide therapy, have improved the diagnosis and treatment of GEP-NENs. Moreover, PET/CT imaging with ¹⁸F-FDG represents a complementary tool for prognostic evaluation of patients with GEP-NENs. In the field of personalized medicine, the theragnostic approach has emerged as a promising tool in diagnosis and management of patients with GEP-NENs. The aim of this review is to summarize the current evidence on diagnosis and management of patients with GEP-NENs, focusing on the theragnostic approach.

Objectives: The aim of the present study was to test whether a parameter reflecting tumor dissemination (Dmax), derived from basal 18F-FDG PET/CT, may predict clinical outcome in patients with advanced non-small-cell lung cancer (NSCLC). Methods: A total of 78 patients (55 men, 23 women) with stage III and IV NSCLC who had undergone whole-body 18F-FDG PET/CT scan at diagnosis were included in this study. Imaging parameters of primary lung tumors along with total MTV (MTV_TOT) and whole-body TLG (TLG_WB) of all malignant lesions were determined. Moreover, the largest distance between two 18F-FDG avid lesions (Dmax) in each patient was measured. Univariate and multivariate analyses of clinical and imaging variables were performed followed by overall survival (OS) curves. Results: A total of 441 lesions were analyzed, including 78 primary tumors, 174 metastatic lymph nodes, and 189 distant metastases. In primary tumors, the average values of SUVmax, SUVmean, MTV, and TLG were 11.80 ± 5.73, 5.37 ± 2.09, 60.61 ± 102.57 mL, and 340.36 ± 558.40 g, respectively. The mean value of Dmax was 29.98 ± 20.98 cm, whereas the average values of MTV_TOT and TLG_WB were 155.90 ± 176.94 mL and 851.08 ± 1032.17 g, respectively. In the univariate analysis, OS was predicted by MTV_TOT (p = 0.0145), TLG_WB (p = 0.0518), Dmax (p = 0.0031), and stage (p = 0.0130), whereas in the multivariate analysis, only Dmax was retained in the model (χ² = 7.3130, p = 0.0068). In particular, a high Dmax value indicates a worse prognosis. Moreover, the combination of Dmax with MTV_TOT was able to improve the prognostic stratification of patients with advanced stages of NSCLC. Conclusions: Dmax, by reflecting tumor dissemination throughout the body, can predict overall survival in NSCLC patients.

Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of ¹⁸F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone ¹⁸F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTV_TOT) and Whole-Body TLG (TLG_WB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan–Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTV_TOT (p = 0.0007), TLG_WB (p = 0.0129) and stage (p = 0.0122). Using Kaplan–Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients.