Combination of positron emission tomography/computed tomography and chest thin-layer high-resolution computed tomography for evaluation of pulmonary nodules: Correlation with imaging features, maximum standardized uptake value, and pathology

CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [18F]FDG PET uptake values

PURPOSE

Utilizing [18F]Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography ([18F]FDG PET/CT) scans on primary colon cancer (CC) patients including with liver metastases (LM), we aimed to determine the relationship between structural CT radiomic features and metabolic PET standard uptake value (SUV) in these patients.

MATERIAL AND METHOD

A retrospective analysis was performed on 60 patients with primary CC, of which 40 had liver metastases that were more than 2 cm in diameter. [18F]FDG PET/CT was used to calculate SUVmax, and 42 CT radiomic characteristics were extracted from non-enhanced CT images. Tumors were manually segmented on fused PET/CT scans by two experienced nuclear medicine physicians. Sixty primary CC and forty LM lesions were segmented accordingly. In the cases of multiple LM lesions, the lesion with the largest diameter was chosen for segmentation. In a univariate analysis approach, we used Spearman correlation with multiple testing correction (Benjamini-Hutchberg false discovery rate (FDR), α = 0.05) to ascertain the relationship between SUVmax and CT radiomic features.

RESULT

Twenty-two (52.3%) and twenty-six (61.9%) CT radiomic features were found to be significantly correlated with SUVmax values of primary CC (n = 60) and LM (n = 40) lesions, respectively (FDR-corrected p value < 0.05 and 0.6 < |ρ| < 1). GLCM_homogeneity (ρ = 0.839), GLCM_dissimilarity (ρ = - 0.832), GLZLM_ZLNU (ρ = 0.827), and GLCM_contrast (ρ = - 0.815) were the 4 features most correlated with SUVmax in CC. On the other hand, in LM, the 4 features most correlated with SUVmax were GLRLM_LRHGE (ρ = 0.859), GLRLM_LRE (ρ = 0.859), GLRLM_LRLGE (ρ = 0.857), and GLRLM_RP (ρ = - 0.820).

CONCLUSION

We investigated the relationship between SUVmax of preoperative primary CC lesions and their LM with CT radiomic features. We found some CT radiomic features having relationships with the metabolic characteristics of lesions. This work suggests that non-invasive predictive imaging biomarkers for precision medicine can be derived from CT radiomic.

Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule

This study aims to evaluate the reliability of qualitative and semiquantitative parameters of ¹⁸F-FDG PET-CT, and eventually a correlation between them, in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. A total of 146 patients were retrospectively studied according to their pre-test probability of malignancy (all patients were intermediate risk), based on radiological features and risk factors, and qualitative and semiquantitative parameters, such as SUVmax, SUVmean, TLG, and MTV, which were obtained from the FDG PET-CT scan of such patients before diagnosis. It has been observed that visual analysis correlates well with the risk of malignancy in patients with SPN; indeed, only 20% of SPNs in which FDG uptake was low or absent were found to be malignant at the cytopathological examination, while 45.45% of SPNs in which FDG uptake was moderate and 90.24% in which FDG uptake was intense were found to be malignant. The same trend was observed evaluating semiquantitative parameters, since increasing values of SUVmax, SUVmean, TLG, and MTV were observed in patients whose cytopathological examination of SPN showed the presence of lung cancer. In particular, in patients whose SPN was neoplastic, we observed a median (MAD) SUVmax of 7.89 (±2.24), median (MAD) SUVmean of 3.76 (±2.59), median (MAD) TLG of 16.36 (±15.87), and a median (MAD) MTV of 3.39 (±2.86). In contrast, in patients whose SPN was non-neoplastic, the SUVmax was 2.24 (±1.73), SUVmean 1.67 (±1.15), TLG 1.63 (±2.33), and MTV 1.20 (±1.20). Optimal cut-offs were drawn for semiquantitative parameters considered predictors of malignancy. Nodule size correlated significantly with FDG uptake intensity and with SUVmax. Finally, age and nodule size proved significant predictors of malignancy. In conclusion, considering the pre-test probability of malignancy, qualitative and semiquantitative parameters can be considered reliable tools in patients with SPN, since cut-offs for SUVmax, SUVmean, TLG, and MTV showed good sensitivity and specificity in predicting malignancy.

Derivation and validation of a 18F-FDG PET/CT scoring model to predict malignant pleural effusion

OBJECTIVE

To develop an 18F-fluorodeoxyglucose PET/computed tomography (CT) scoring model based on metabolic and radiologic findings of the pleura and fluid to identify malignant pleural effusion.

METHODS

The PET and CT findings from patients with pleural effusion in the derivation dataset were used to develop a scoring model. Then, the diagnostic accuracy of the predictive score was verified by the validation dataset.

RESULTS

Eight parameters independently predicting malignancy were retained in the scoring model, including pleural nodules or masses (4 points), focal pleural thickening (2 points), absence of pleural loculation (2 points), thickness of mediastinal pleura involvement ≥0.5 cm (2 points), maximum standardized uptake value (SUVmax) of mediastinal pleura involvement ≥2.3 (2 points), thickness of nonmediastinal pleura involvement ≥0.5 cm (1 point), SUVmax of nonmediastinal pleura involvement ≥3.0 (1 point) and fluid SUVmax ≥1.6 (1 point). The operating characteristics of the PET/CT score were 0.958 area under the curve (AUC), 88.6% sensitivity, 91.2% specificity, 10.09 positive likelihood ratio and 0.13 negative likelihood ratio, with 6 points as the threshold. These values in the validation dataset were 0.947, 91.7%, 88.4%, 7.91 and 0.094, respectively. No difference was found in AUCs between the derivation and validation datasets (z = 0.517, P = 0.697). The negative predictive value was 99.4% in the score from 0 to 2, and the positive predictive value was 98.3% for patients with score between 9 and 15.

CONCLUSIONS

The PET/CT scoring model is a valuable strategy to help physicians to distinguish malignant-benign pleural effusion and stratify patients who will benefit from invasive procedures.

The Added Value of [18F]FDG PET/CT in the Management of Invasive Fungal Infections

Anatomy-based imaging methods are the usual imaging methods used in assessing invasive fungal infections (IFIs). [¹⁸F]FDG PET/CT has also been used in the evaluation of IFIs. We assessed the added value of [¹⁸F]FDG PET/CT when added to the most frequently used anatomy-based studies in the evaluation of IFIs. The study was conducted in two University Medical Centers in the Netherlands. Reports of [¹⁸F]FDG PET/CT and anatomy-based imaging performed within two weeks of the [¹⁸F]FDG PET/CT scan were retrieved, and the presence and sites of IFI lesions were documented for each procedure. We included 155 [¹⁸F]FDG PET/CT scans performed in 73 patients. A total of 216 anatomy-based studies including 80 chest X-rays, 89 computed tomography studies, 14 magnetic resonance imaging studies, and 33 ultrasound imaging studies were studied. The anatomy-based studies were concordant with the [¹⁸F]FDG PET/CT for 94.4% of the scans performed. [¹⁸F]FDG PET/CT detected IFI lesions outside of the areas imaged by the anatomy-based studies in 48.6% of the scans. In 74% of the patients, [¹⁸F]FDG PET/CT added value in the management of the IFIs.

[18F-FDG PET/CT manifestations of massive type active pulmonary tuberculosis and its differentiation from lung cancer]

OBJECTIVE

To investigate 18F-FDG PET/CT manifestations of massive type active tuberculosis and lung cancer and the differential diagnosis of the two diseases based on 18F-FDG PET/CT findings.

METHODS

We retrospectively collected the data from 74 patients with active tuberculosis and 64 patients with lung cancer, whose lesions presented as solid masses on CT. The demographic and clinical data of the patients, 18F-FDG PET characteristics including SUVmax, 18F-FDG uptake (higher than mediastinal blood pool or not), radioactive defect within the lesion, and the CT findings including the lesion size, signs of cavity, vacuoles, lobulation, smooth border, and mediastinal/lung window ratio (M/L ratio) of the lesions were analyzed. Univariate and multivariate analyses were used to compare the variables between the two groups, and a logistic regression model was established for differentiation of the two diseases. The diagnostic efficiency was evaluated by area under the receiver-operating characteristic (ROC) curve analysis.

RESULTS

No significant differences were found in the quantitative index (SUVmax >2.5 or not) or in the qualitative index (uptake of lesion higher than mediastinal blood pool or not) in PET between massive type active tuberculosis and lung cancer (P>0.05). Univariate analysis revealed that SUVmax, 18F-FDG uptake of the lesion, age, lesion size, signs of cavity, or M/L ratio were not significantly different (P>0.05), but gender, signs of radioactive defect, vacuoles, smooth border and lobulation were significantly different (P < 0.05) between the two diseases. Multivariate analysis showed that gender, signs of radioactive defect, smooth border and lobulation of the lesion were independent factors for discrimination of the two diseases (P < 0.05). A risk prediction model for active tuberculosis was established based on logistic regression analysis: P=1/(1+e-x), X=-0.530+1.978×gender+3.343×radioactive defect +2.846×smooth border-2.116×lobulation. For diagnosis of active tuberculosis, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of this model were 78.4%, 92.2%, 84.8%, 92.1%, and 78.7%, respectively.

CONCLUSIONS

The combined analysis of gender, signs of radioactive defect, smooth border and lobulation of the lesions is useful for discriminating massive type active tuberculosis from lung cancer in the majority of the patients, whereas 18F-FDG uptake alone has only limited value for a differential diagnosis.

Solitary Pulmonary Nodule: Morphological Effects on Metabolic Activity Assessment

Objectives

We aimed to evaluate the effects of morphological characteristics of the solitary pulmonary nodules (SPN) on metabolic activity assessment. To the best of our knowledge, this is the first study to compare the volumetric metabolic activity parameters according to the morphologic parameters of the nodules.

Methods

In this retrospective study, ¹⁸F-FDG positron emission tomography and computed tomography scans performed between 2011 and 2018 were evaluated by a nuclear and diagnostic radiologist. One hundred thirteen patients with SPNs with biopsy-proven diagnosis were included. SPNs were classified as solid, partially solid (PS), and ground glass opacity (GGO).

Results

SPN diameter, SUV_max, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and density were significantly higher in the malignant group. SUV_max, MTV, TLG increased in direct proportion to the diameter. There was no a significant difference between GGO, PS, and solid nodules in terms of SUV_max values. MTV and TLG values increased in parallel with the density of the nodules, but this increase was only significant in the malignant group. There was a significant difference between SPNs <2 cm and SPNs ≥2 cm in terms of MTV, while there was no difference in terms of SUV_max. The cut-off value determined by the ROC curve was found to be 4.39 for SUV_max, 7.33 mL for MTV and 31.88 g for TLG. The cut-off values for SUV_max of solid and subsolid nodules were close to each other, but cut-off values for MTV and TLG were higher in solid nodules.

Conclusion

SUV_max, MTV, and TLG are affected by diameter and attenuation. We suggest using different MTV and TLG cut-off values for solid and subsolid nodules, but we suggest using same SUV_max values. MTV can be a more reliable parameter than SUV_max in prediction of malignancy in smaller nodules.

Pulmonary Lymphangitic Carcinomatosis: Diagnostic Performance of High-Resolution CT and 18F-FDG PET/CT in Correlation with Clinical Pathologic Outcome

The rationale of this study was to investigate the performance of high-resolution CT (HRCT) versus ¹⁸F-FDG PET/CT for the diagnosis of pulmonary lymphangitic carcinomatosis (PLC). Methods: In this retrospective institution-approved study, 94 patients addressed for initial staging of lung cancer with suspicion of PLC were included. Using double-blind analysis, we assessed the presence of signs favoring PLC on HRCT (smooth or nodular septal lines, subpleural nodularity, peribronchovascular thickening, satellite nodules, lymph node enlargement, and pleural effusion). ¹⁸F-FDG PET/CT images were reviewed to qualitatively evaluate peritumoral uptake and to quantify tracer uptake in the tumoral and peritumoral areas. Histology performed on surgical specimens served as the gold standard for all patients. Results: Among 94 included patients, 73% (69/94) had histologically confirmed PLC. Peribronchovascular thickening, lymph node involvement, and increased peritumoral uptake were more often present in patients with PLC (P < 0.009). Metabolic variables, including tumor SUV_max, SUV_mean, metabolic tumor volume, and total lesion glycolysis, as well as peritumoral SUV_max, SUV_mean, and their respective ratios to background, were significantly higher in the PLC group than in the non-PLC group (P ≤ 0.0039). Sensitivity, specificity, and area under the receiver-operating-characteristic curve for peribronchovascular thickening (69%, 83%, and 0.76, respectively; 95% confidence interval [95%CI], 0.67-0.85) and increased peritumoral uptake (94%, 84%, and 0.89, respectively; 95%CI, 0.81-0.97) were similar (P = 0.054). For detecting PLC, sensitivity, specificity, and area under the receiver-operating-characteristic curve were significantly higher, at 97%, 92%, and 0.98, respectively (95%CI, 0.96-1.00), for peritumoral SUV_max and 94%, 88%, and 0.96, respectively (95%CI, 0.92-1.00), for peritumoral SUV_mean (all P ≤ 0.025). Conclusion: Qualitative evaluation of ¹⁸F-FDG PET/CT and HRCT perform similarly for the diagnosis of PLC, with both being outperformed by ¹⁸F-FDG PET/CT quantitative parameters.