Accuracy of 18F-FDG in Detecting Stage I Lung Adenocarcinomas According to IASLC/ATS/ERS Classification

[18F]F-FAPI-42 PET dynamic imaging characteristics and multiparametric quantification of lung cancer: an exploratory study using uEXPLORER PET/CT

PURPOSE

To explore the dynamic and parametric characteristics of [18F]F-FAPI-42 PET/CT in lung cancers.

METHODS

Nineteen participants with newly diagnosed lung cancer underwent 60-min dynamic [18F]F-FAPI-42 PET/CT. Time-activity curves (TAC) were generated for tumors and normal organs, with kinetic parameters (K1, K2, K3, K4, Ki) calculated. A new parameter, the K ratio (K1 + K3)/(K2 + K4), was introduced to measure net uptake efficiency.

RESULTS

In primary tumor (PT), [18F]F-FAPI-42 uptake showed a gradual increase followed by a plateau, contrasting with organs like the thyroid and pancreas, which showed rapid uptake and continuous washout. Compared to non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) lesions reached the plateau earlier (11 min vs. 14 min) but had a lower uptake. During the plateau phase, [18F]F-FAPI-42 demonstrated slight washout in SCLC, whereas its uptake increased slightly in NSCLC. Lymph node and distant metastases exhibited similar TAC profiles to primary tumors. Kinetic modeling revealed that an irreversible two-compartment model (irre-2TCM) best represented the pharmacokinetics of [18F]F-FAPI-42 in lung cancer, whereas re-2TCM was better suited for the pancreas and thyroid. Lower K1, K2, K3 and K4 were observed in PT compared to those in the pancreas and thyroid (P < 0.05), however, the K ratio in PT was found to be 2-3 times higher. SCLC had lower Ki and SUVmean than NSCLC (P < 0.05). Kinetic parameter differences were also observed between PT and metastatic lesions. Larger metastatic lymph nodes exhibited higher K1, Ki, and K ratio than smaller ones.

CONCLUSION

Lung cancers exhibit distinct [18F]F-FAPI-42 dynamic and kinetic characteristics compared to the thyroid gland and pancreas. Differences were also observed between SCLC and NSCLC, primary and metastatic lesions, as well as larger versus smaller lesions. These findings provide valuable insights into the in vivo pharmacokinetics of [18F]F-FAPI-42, potentially improving the diagnosis of lung cancer.

TRIAL REGISTRATION

ChiCTR2100045757. Registered April 24, 2021 retrospectively registered, http//www.chictr.org.cn.

Imaging of Lung Cancer Staging: TNM 9 Updates

Imaging plays a key role in clinical staging of lung cancer and guiding therapy. A thorough understanding of the staging system including the nomenclature and updates is necessary to tailor treatment plans and optimize patient care. The 9th edition of the Tumor, Node, Metastasis staging system for lung cancer has no changes for T classification and subdivides N2 and M1c categories. In nodal staging, N2 splits into N2a, ipsilateral mediastinal single station involvement and N2b, ipsilateral mediastinal multiple stations involvement. In the staging of multiple extrathoracic metastases, M1c splits into M1c1, multiple extrathoracic metastases in one organ system and M1c2, multiple extrathoracic metastases in multiple organ systems. Awareness of the proposed changes in TNM-9 staging classification is essential to provide methodical and accurate imaging interpretation.

Diagnostic Potential of Supplemental Static and Dynamic 68Ga-FAPI-46 PET for Primary 18F-FDG-Negative Pulmonary Lesions

PET using 68Ga-labeled fibroblast activation protein (FAP) inhibitors (FAPIs) holds high potential for diagnostic imaging of various malignancies, including lung cancer (LC). However, 18F-FDG PET is still the clinical gold standard for LC imaging. Several subtypes of LC, especially lepidic LC, are frequently 18F-FDG PET-negative, which markedly hampers the assessment of single pulmonary lesions suggestive of LC. Here, we evaluated the diagnostic potential of static and dynamic 68Ga-FAPI-46 PET in the 18F-FDG-negative pulmonary lesions of 19 patients who underwent surgery or biopsy for histologic diagnosis after PET imaging. For target validation, FAP expression in lepidic LC was confirmed by FAP immunohistochemistry. Methods: Hematoxylin and eosin staining and FAP immunohistochemistry of 24 tissue sections of lepidic LC from the local tissue bank were performed and analyzed visually. Clinically, 19 patients underwent static and dynamic 68Ga-FAPI-46 PET in addition to 18F-FDG PET based on individual clinical indications. Static PET data of both examinations were analyzed by determining SUVmax, SUVmean, and tumor-to-background ratio (TBR) against the blood pool, as well as relative parameters (68Ga-FAPI-46 in relation to18F-FDG), of histologically confirmed LC and benign lesions. Time-activity curves and dynamic parameters (time to peak, slope, k 1, k 2, k 3, and k 4) were extracted from dynamic 68Ga-FAPI-46 PET data. The sensitivity and specificity of all parameters were analyzed by calculating receiver-operating-characteristic curves. Results: FAP immunohistochemistry confirmed the presence of strongly FAP-positive cancer-associated fibroblasts in lepidic LC. LC showed markedly elevated 68Ga-FAPI-46 uptake, higher TBRs, and higher 68Ga-FAPI-46-to-18F-FDG ratios for all parameters than did benign pulmonary lesions. Dynamic imaging analysis revealed differential time-activity curves for LC and benign pulmonary lesions: initially increasing time-activity curves with a decent slope were typical of LC, and steadily decreasing time-activity curve indicated benign pulmonary lesions, as was reflected by a significantly increased time to peak and significantly smaller absolute values of the slope for LC. Relative 68Ga-FAPI-46-to-18F-FDG ratios regarding SUVmax and TBR showed the highest sensitivity and specificity for the discrimination of LC from benign pulmonary lesions. Conclusion: 68Ga-FAPI-46 PET is a powerful new tool for the assessment of single 18F-FDG-negative pulmonary lesions and may optimize patient stratification in this clinical setting.

Impact of FDG PET Standardized Uptake Value in Resected Clinical Stage IA Non-Small Cell Lung Cancer

BACKGROUND

A significant proportion of patients with clinical stage IA non-small cell lung cancer (NSCLC) experience will recurrence and decreased survival after surgery. This study examined the impact of preoperative primary tumor positron emission tomography (PET) scan maximum standardized uptake value (SUVmax) on oncologic outcomes after surgery.

METHODS

This was a retrospective review of 251 patients who underwent surgical treatment of clinical stage IA NSCLC at an academic medical center (2005-2014). Patients were classified according to PET SUVmax level (low vs high) for analysis of upstaging, tumor recurrence, and overall survival.

RESULTS

Median SUVmax values were higher in squamous cell carcinoma than in adenocarcinoma (median 3.3 vs 7.2; P < .0001). There were 109 (43.4%) patients in the SUVmax low group and 142 (56.6%) in the SUVmax high group. Patients with SUVmax high had larger tumors. SUVmax high was associated with higher rates of nodal upstaging (16.2% vs 4.6% in SUVmax low; P = .004), particularly in N1 nodes. SUVmax high was independently associated with nodal upstaging (adjusted odds ratio, 3.95; 95% CI, 1.36-11.46; P = .011). SUVmax high was associated with time to recurrence (hazard ratio, 1.62; 95% CI, 1.03-2.54; P = .036), but this association was lost on multivariable analysis (hazard ratio, 1.52; 95% CI, 0.91-2.54; P = .106). SUVmax was not associated with overall survival.

CONCLUSIONS

Preoperative PET SUVmax level is strongly associated with nodal upstaging, particularly in N1 nodes, in patients with clinical stage IA NSCLC who undergo resection. PET SUVmax should be regarded as a risk factor when considering candidacy for sublobar resections and in future trials involving patients with stage I NSCLC.

Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma

OBJECTIVES

To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma.

METHODS

Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without.

RESULTS

PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit.

CONCLUSION

18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.

Lung Cancer Staging: Imaging and Potential Pitfalls

Lung cancer is the leading cause of cancer deaths in men and women in the United States. Accurate staging is needed to determine prognosis and devise effective treatment plans. The International Association for the Study of Lung Cancer (IASLC) has made multiple revisions to the tumor, node, metastasis (TNM) staging system used by the Union for International Cancer Control and the American Joint Committee on Cancer to stage lung cancer. The eighth edition of this staging system includes modifications to the T classification with cut points of 1 cm increments in tumor size, grouping of lung cancers associated with partial or complete lung atelectasis or pneumonitis, grouping of tumors with involvement of a main bronchus regardless of distance from the carina, and upstaging of diaphragmatic invasion to T4. The N classification describes the spread to regional lymph nodes and no changes were proposed for TNM-8. In the M classification, metastatic disease is divided into intra- versus extrathoracic metastasis, and single versus multiple metastases. In order to optimize patient outcomes, it is important to understand the nuances of the TNM staging system, the strengths and weaknesses of various imaging modalities used in lung cancer staging, and potential pitfalls in image interpretation.

Histology of the Primary Tumor Correlates with False Positivity of Integrated 18F-FDG-PET/CT Lymph Node Staging in Resectable Lung Cancer Patients

This study aimed to evaluate the diagnostic accuracy and false positivity rate of lymph node (LN) staging assessed by integrated 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG-PET/CT) in patients with operable lung cancer to the tumor histology. In total, 129 consecutive patients with non-small-cell lung cancer (NSCLC) undergoing anatomical lung resections were included. Preoperative LN staging was evaluated in the relationship to the histology of the resected specimens (group 1: lung adenocarcinoma/LUAD; group 2: squamous cell carcinoma/SQCA). Statistical analysis was performed by the Mann-Whitney U-test, the chi² test, and binary logistic regression analysis. To establish an easy-to-use algorithm for the identification of LN false positivity, a decision tree including clinically meaningful parameters was generated. In total, 77 (59.7%) and 52 (40.3%) patients were included in the LUAD and SQCA groups, respectively. SQCA histology, non-G1 tumors, and tumor SUVmax > 12.65 were identified as independent predictors of LN false positivity in the preoperative staging. The corresponding ORs and their 95% CIs were 3.35 [1.10-10.22], p = 0.0339; 4.60 [1.06-19.94], p = 0.0412; and 2.76 [1.01-7.55], and p = 0.0483. The preoperative identification of false-positive LNs is an important aspect of the treatment regimen for patients with operable lung cancer; thus, these preliminary findings should be further evaluated in larger patient cohorts.

Usefulness of pyruvate dehydrogenase-E1α expression to determine SUVmax cut-off value of [18F]FDG-PET for predicting lymph node metastasis in lung cancer

A more accurate cut-off value of maximum standardized uptake value (SUVmax) in [¹⁸F]fluorodeoxyglucose positron emission tomography/computed tomography ([¹⁸F]FDG-PET/CT) is necessary to improve preoperative nodal staging in patients with lung cancer. Overall, 223 patients with lung cancer who had undergone [¹⁸F]FDG-PET/CT within 2 months before surgery were enrolled. The expression of glucose transporter-1, pyruvate kinase-M2, pyruvate dehydrogenase-E1α (PDH-E1α), and carbonic anhydrase-9 was evaluated by immunohistochemistry. Clinicopathological background was retrospectively investigated. According to PDH-E1α expression in primary lesion, a significant difference (p = 0.021) in SUVmax of metastatic lymph nodes (3.0 with PDH-positive vs 4.5 with PDH-negative) was found, but not of other enzymes. When the cut-off value of SUVmax was set to 2.5, the sensitivity and specificity were 0.529 and 0.562, respectively, and the positive and negative predictive values were 0.505 and 0.586, respectively. However, when the cut-off value of SUVmax was set according to PDH-E1α expression (2.7 with PDH-positive and 3.2 with PDH-negative), the sensitivity and specificity were 0.441 and 0.868, respectively, and the positive and negative predictive values were 0.738 and 0.648, respectively. The SUVmax cut-off value for metastatic lymph nodes depends on PDH-E1α expression in primary lung cancer. The new SUVmax cut-off value according to PDH-E1α expression showed higher specificity for [¹⁸F]FDG-PET in the diagnosis of lymph node metastasis.