Can dynamic imaging, using <sup>18</sup>F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

Marin, Aleksander; Murchison, John T; Skwarski, K; Tavares, Adriana; Fletcher, Alison; Wallace, William; Salapura, Vladka; Beek, Edwin Jacques Rudolph van; Mirsadraee, Saeed

doi:10.2478/raon-2021-0024

Cited by 13 publications

(5 citation statements)

References 51 publications

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“…Some of these preliminary studies did not show significant differences in the contrast between malignant and benign lesions. However, Ki, a marker of FDG metabolism, seems to be more suitable for predicting tumor cellular function and aggressiveness than SUV, a static FDG uptake marker in lung and breast cancer [97][98][99][100][101][102][103][104][105]. As many PET centers have had the chance to apply their up-to-date technology for parametric FDG analysis, these preliminary results will be confirmed in many oncology areas.…”

Section: Use Of Temporal Changementioning

confidence: 86%

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

et al. 2023

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Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) has been commonly used in many oncological areas. High-resolution PET permits a three-dimensional analysis of FDG distributions on various lesions in vivo, which can be applied for tissue characterization, risk analysis, and treatment monitoring after chemoradiotherapy and immunotherapy. Metabolic changes can be assessed using the tumor absolute FDG uptake as standardized uptake value (SUV) and metabolic tumor volume (MTV). In addition, tumor heterogeneity assessment can potentially estimate tumor aggressiveness and resistance to chemoradiotherapy. Attempts have been made to quantify intratumoral heterogeneity using radiomics. Recent reports have indicated the clinical feasibility of a dynamic FDG PET-computed tomography (CT) in pilot cohort studies of oncological cases. Dynamic imaging permits the assessment of temporal changes in FDG uptake after administration, which is particularly useful for differentiating pathological from physiological uptakes with high diagnostic accuracy. In addition, several new parameters have been introduced for the in vivo quantitative analysis of FDG metabolic processes. Thus, a four-dimensional FDG PET-CT is available for precise tissue characterization of various lesions. This review introduces various new techniques for the quantitative analysis of FDG distribution and glucose metabolism using a four-dimensional FDG analysis with PET-CT. This elegant study reveals the important role of tissue characterization and treatment strategies in oncology.

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Section: Use Of Temporal Changementioning

confidence: 86%

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

et al. 2023

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“…Huang et al [16] concluded that in a small group of patients (N = 34), K i can better identify benign and malignant solitary pulmonary nodules (0.004 vs 0.023 ml/g/min, P = 0.0034) in areas (Taiwan) with a high prevalence of granulomatous disease. Aleksander et al [17] revealed that the lung malignancy group has higher K i values than the benign group (0.0230 ± 0.0155 vs.…”

Section: Discussionmentioning

confidence: 99%

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Wumener,

Zhang,

Zang

et al. 2023

Preprint

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Introduction: This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic 18F-fluorodeoxyglucose (FDG) positron emission tomography/CT (PET/CT) in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status. Methods: We included 147 patients with lung lesions who were enrolled to perform FDG PET/CT dynamic + static imaging with informed consent. Based on the results of puncture and postoperative pathology, the patients were divided into benign and malignant groups, adenocarcinoma (AC) and squamous carcinoma (SCC) groups and EGFR-positive (EGFR+) and EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartment modeling using in-house Matlab software. The standardized uptake values (SUV) analysis from conventional static data. Differences in each metabolic parameter among the groups were analyzed. Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analyses were performed on each parameter to compare the diagnostic effects among the differentiated group. P<0.05 were considered statistically significant for all tests. Results: In the malignant group (N=124), the SUVmax, k2, k3, and Ki were higher than the benign group (N=23), and all have good performance in the differential diagnosis (P<0.05, respectively). In the AC group (N=88), the SUVmax, k3, and Ki were lower than in the SCC group, and the differences were statistically significant (P<0.05, respectively). For ROC analysis, when the Ki cut-off value of 0.0250 ml/g/min have better diagnostic specificity than SUVmax (0.999 vs 0.70). In AC group, 48 patients underwent EGFR testing. In the EGFR (+) group (N=31), the average Ki (0.0279±0.0153 ml/g/min) was lower than EGFR (-) group (N=17, 0.0405±0.0199 ml/g/min), and the differences were statistically significant (P<0.05). However, the SUVmax, and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P＞0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min for predicting EGFR status, a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523-0.802]. Conclusion: When the cut-off value of Ki was 0.0250 ml/g/min, there was a more specificity than SUVmax for the differential diagnosis of lung cancer, although both methods were specific. The Ki has a good diagnostic value in the prediction of the EGFR status. For patients for whom EGFR testing is not available, dynamic imaging may become an important non-invasive screening tool.

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“…CTPI can be used to evaluate the perfusion and vascularity of lesions in the lung and many other organs. [21][22][23][24][25][26][27][28] Wang et al 21 reported that parameters of CTPI (including BF, BV and PS but not MTT) of SPNs were significantly correlated with SPNs' microvessel density (MVD) and luminal vascular parameters such as luminal vascular number (LVN), luminal vascular area (LVA), and luminal vascular perimeter (LVP). Huang et al 22 analyzed the CT perfusion parameters (BF, BV, MTT, and PS) and the microvessel parameters (MVD, LVN, LVA, and LVP) in the non-small cell lung cancer (NSCLC) patients with and without lymph node metastasis.…”

Section: Ctpi In Evaluating Spnsmentioning

confidence: 99%

Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study

Yan

Fan

et al. 2023

Radiology and Oncology

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Background Solitary pulmonary nodules (SPNs) are one of the most common chest computed tomography (CT) abnormalities clinically. We aimed to investigate the value of non-contrast enhanced CT (NECT), contrast enhanced CT (CECT), CT perfusion imaging (CTPI), and dual- energy CT (DECT) used for differentiating benign and malignant SPNs with a multi-institutional and prospective study. Patients and methods Patients with 285 SPNs were scanned with NECT, CECT, CTPI and DECT. Differences between the benign and malignant SPNs on NECT, CECT, CTPI, and DECT used separately (NECT combined with CECT, DECT, and CTPI were methods of A, B, and C) or in combination (Method A + B, A + C, B + C, and A + B + C) were compared by receiver operating characteristic curve analysis. Results Multimodality CT imaging showed higher performances (sensitivities of 92.81% to 97.60%, specificities of 74.58% to 88.14%, and accuracies of 86.32% to 93.68%) than those of single modality CT imaging (sensitivities of 83.23% to 85.63%, specificities of 63.56% to 67.80%, and accuracies of 75.09% to 78.25%, all p < 0.05). Conclusions SPNs evaluated with multimodality CT imaging contributes to improving the diagnostic accuracy of benign and malignant SPNs. NECT helps to locate and evaluate the morphological characteristics of SPNs. CECT helps to evaluate the vascularity of SPNs. CTPI using parameter of permeability surface and DECT using parameter of normalized iodine concentration at the venous phase both are helpful for improving the diagnostic performance.

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Can dynamic imaging, using ¹⁸F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

Cited by 13 publications

References 51 publications

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study

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Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?

Cited by 13 publications

References 51 publications

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

Four-dimensional quantitative analysis using FDG-PET in clinical oncology

The value of dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting EGFR mutations

Multimodality CT imaging contributes to improving the diagnostic accuracy of solitary pulmonary nodules: a multi-institutional and prospective study

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Can dynamic imaging, using ¹⁸F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules?