FDG PET-CT for solitary pulmonary nodule and lung cancer: Literature review

Low FDG uptake in lung metastasis despite high FDG uptake in a primary adenoid cystic carcinoma of a sublingual gland

Adenoid cystic carcinoma is a rare malignant tumor that primarily occurs in the salivary glands. There are few reports of sublingual gland adenoid cystic carcinoma with lung metastases on which 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) was performed. We report the case of a 57-year-old Japanese woman with an adenoid cystic carcinoma of the sublingual gland with lung metastases in whom the FDG uptake of the lung metastasis was low despite high FDG uptake in the primary lesion. The pathological examination revealed that solid components were more visible and the Ki-67 index was more positive in the primary lesion compared to the metastatic lesion. We speculate that differences in tumor growth ability might have resulted in the differences in FDG uptake. This case demonstrates that significant differences might occur in the FDG uptake between primary and metastatic tumors.

Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: a predictive model for identifying false-negative results

BACKGROUND

Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results.

MATERIALS AND METHODS

From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy.

RESULTS

The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851.

CONCLUSIONS

In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

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.

Feasibility and limitations of deep learning-based coronary calcium scoring in PET-CT: a comparison with coronary calcium score CT

OBJECTIVE

This study aimed to determine the feasibility and limitations of deep learning-based coronary calcium scoring using positron emission tomography-computed tomography (PET-CT) in comparison with coronary calcium scoring using ECG-gated non-contrast-enhanced cardiac computed tomography (CaCT).

MATERIALS AND METHODS

A total of 215 individuals who underwent both CaCT and PET-CT were enrolled in this retrospective study. The Agatston method was used to calculate the coronary artery calcium scores (CACS) from CaCT, PET-CT(reader), and PET-CT(AI) to analyse the effect of using different modalities and AI-based software on CACS measurement. The total CACS and CACS classified according to the CAC-DRS guidelines were compared between the three sets of CACS. The differences, correlation coefficients, intraclass coefficients (ICC), and concordance rates were analysed. Statistical significance was set at p < 0.05.

RESULTS

The correlation coefficient of the total CACS from CaCT and PET-CT(reader) was 0.837, PET-CT(reader) and PET-CT(AI) was 0.894, and CaCT and PET-CT(AI) was 0.768. The ICC of CACS from CaCT and PET-CT(reader) was 0.911, PET-CT(reader) and PET-CT(AI) was 0.958, and CaCT and PET-CT(AI) was 0.842. The concordance rate between CaCT and PET-CT(AI) was 73.8%, with a false-negative rate of 37.3% and a false-positive rate of 4.4%. Age and male sex were associated with an increased misclassification rate.

CONCLUSIONS

Artificial intelligence-assisted CACS measurements in PET-CT showed comparable results to CACS in coronary calcium CT. However, the relatively high false-negative results and tendency to underestimate should be of concern.

CLINICAL RELEVANCE STATEMENT

Application of automated calcium scoring to PET-CT studies could potentially select patients at high risk of coronary artery disease from among cancer patients known to be susceptible to coronary artery disease and undergoing routine PET-CT scans.

KEY POINTS

• Cancer patients are susceptible to coronary disease, and PET-CT could be potentially used to calculate coronary artery calcium score (CACS). • Calcium scoring using artificial intelligence in PET-CT automatically provides CACS with high ICC to CACS in coronary calcium CT. • However, underestimation and false negatives of CACS calculation in PET-CT should be considered.

Risk factors of pneumothorax in computed tomography guided lung nodule marking using autologous blood: a retrospective study

BACKGROUND

To investigate the risk factors of pneumothorax of using computed tomography (CT) guidance to inject autologous blood to locate isolated lung nodules.

METHODS

In the First Hospital of Putian City, 92 cases of single small pulmonary nodules were retrospectively analyzed between November 2019 and March 2023. Before each surgery, autologous blood was injected, and the complications of each case, such as pneumothorax and pulmonary hemorrhage, were recorded. Patient sex, age, position at positioning, and nodule type, size, location, and distance from the visceral pleura were considered. Similarly, the thickness of the chest wall, the depth and duration of the needle-lung contact, the length of the positioning procedure, and complications connected to the patient's positioning were noted. Logistics single-factor and multi-factor variable analyses were used to identify the risk factors for pneumothorax. The multi-factor logistics analysis was incorporated into the final nomogram prediction model for modeling, and a nomogram was established.

RESULTS

Logistics analysis suggested that the nodule size and the contact depth between the needle and lung tissue were independent risk factors for pneumothorax.

CONCLUSION

The factors associated with pneumothorax after localization are smaller nodules and deeper contact between the needle and lung tissue.

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

A retrospective study of 68Ga-FAPI PET/CT in differentiating the nature of pulmonary lesions

Purpose

This study aimed to investigate the characteristics of various pulmonary lesions as revealed by 68Ga-FAPI PET/CT and to determine the utility of 68Ga-FAPI PET/CT in distinguishing the nature of these pulmonary lesions.

Methods

A retrospective analysis was conducted on 99 patients with pulmonary lesions, who were categorized into three distinct groups: primary lung tumors (G1), metastatic lung tumors (G2), and benign lesions (G3). Each participant underwent a 68Ga-FAPI PET/CT scan. Among these groups, variables such as the Tumor/Background Ratio (TBR), Maximum Standardized Uptake Value (SUVmax), and the true positive rate of the lesions were compared. Furthermore, the FAPI uptake in nodular-like pulmonary lesions (d<3cm) and those with irregular borders was evaluated across the groups. A correlation analysis sought to understand the relationship between FAPI uptake in primary and pulmonary metastatic lesions.

Results

The study's participants were composed of 52 males and 47 females, with an average age of 56.8 ± 13.2 years. A higher uptake and detection rate for pulmonary lesions were exhibited by Group G1 compared to the other groups (SUVmax [G1 vs. G2 vs. G3: 9.1 ± 4.1 vs. 6.1 ± 4.1 vs. 5.3 ± 5.8], P<0.05; TBR [G1 vs. G2 vs. G3: 6.2 ± 2.4 vs. 4.1 ± 2.2 vs. 3.2 ± 2.7], P<0.01; true positive rate 95.1% vs. 88% vs. 75.6%]. In nodular-like lung lesions smaller than 3 cm, G1 showed a significantly higher FAPI uptake compared to G2 and G3 (SUVmax [G1 vs. G2 vs. G3: 8.8 ± 4.3 vs. 5.2 ± 3.2 vs. 4.9 ± 6.1], P<0.01; TBR [G1 vs. G2 vs. G3: 5.7 ± 2.7 vs. 3.7 ± 2.1 vs. 3.3 ± 4.4], P<0.05). Both G1 and G2 demonstrated significantly elevated FAPI agent activity in irregular-bordered pulmonary lesions when compared to G3 (SUVmax [G1 vs. G2 vs. G3: 10.9 ± 3.3 vs. 8.5 ± 2.7 vs. 4.6 ± 2.7], P<0.01; TBR [G1 vs. G2 vs. G3: 7.2 ± 2.1 vs. 6.4 ± 1.3 vs. 3.2 ± 2.4], P<0.01). A positive correlation was identified between the level of 68Ga-FAPI uptake in primary lesions and the uptake in pulmonary metastatic lesions within G2 (r=0.856, P<0.05).

Conclusion

68Ga-FAPI PET/CT imaging proves to be of significant value in the evaluation of pulmonary lesions, offering distinctive insights into their nature.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model

OBJECTIVE

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor.

MATERIALS AND METHODS

We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness.

RESULTS

Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P  < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P  < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively.

CONCLUSION

Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

BACKGROUND

Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs.

METHODS

Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data.

RESULTS

This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85).

CONCLUSIONS

These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

Decreased levels of sex hormones in females with solitary pulmonary nodules were risk factors for malignancy

OBJECTIVE

The purpose of this research was to detect the relationship between the levels of sex hormones in females with solitary pulmonary nodules (SPNs) and their potential malignancies.

METHODS

A total of 187 consecutive patients with pathologically confirmed SPNs by chest CT were enrolled in our study. They were divided into two groups based on the pathologic findings of SPNs after surgery: benign and malignant SPNs. Progesterone (P), estradiol (E2), and testosterone (T) levels in the two groups were measured. Meanwhile, we used binary logistic regression analysis to analyze the risk factors for SPNs.

RESULTS

Of these 187 patients, 73 had benign SPNs, while 114 had malignant SPNs. We found that the levels of progesterone (P), estradiol (E2), and testosterone (T) were decreased significantly in patients with malignant SPNs compared to patients with benign SPNs (all P < 0.05). Multivariate logistic regression analysis revealed that second-hand smoke, burr sign, lobulation sign, pleural traction sign, vascular convergence sign, vacuole sign, and ≥ 1 cm nodules were independent risk factors for malignant pulmonary nodules in females.

CONCLUSIONS

Decreased levels of sex hormones in females were associated with malignant pulmonary nodules, suggesting that they can contribute to the diagnosis of lung cancer.

Is 18F-fluorodeoxyglucose PET recommended for small lung nodules? CT findings of 18F-fluorodeoxyglucose non-avid lung cancer

OBJECTIVES

To determine the image characteristics associated with low 18F-FDG (18F-fluorodeoxyglucose) avidity among 8-15 mm solid lung cancer.

METHODS

Patients satisfying the following criteria were included: underwent surgery between January 2014 and December 2019 for lung cancer, presented 8-15 mm nodule without measurable ground glass component on preoperative CT, and underwent 18F-FDG PET before resection. Image characteristics, including air bronchogram, concave shape, pleural attachment, and background emphysema, were evaluated by two board-certified radiologists. The Mann-Whitney U test was used to compare maximum standardized uptake (SUVmax) values from 18F-FDG PET images.

RESULTS

The analysis included 235 patients. The SUVmax values of lesions with air bronchogram and concave shape were significantly lower than the SUVmax values of lesions without these features (median: 1.55 vs 2.56 and 1.66 vs 2.45, both P < .001), whereas lesions arising from emphysematous lungs had significantly higher SUVmax values than lesions arising from non-emphysematous lungs (2.90 vs 1.69, P < .001). No significant differences were detected between lesions attached and not attached to pleura. The interobserver agreement was almost perfect for air bronchograms and background emphysema (κ = 0.882 and 0.927, respectively), and 89.7% of lesions with air bronchograms and arising from non-emphysematous lungs showed SUVmax values below 2.5.

CONCLUSIONS

Among 8-15 mm solid lung cancer, the presence of air bronchograms and concave shape and the absence of background emphysema were associated with low 18F-FDG accumulation.

ADVANCES IN KNOWLEDGE

18F-FDG PET can be misleading in differentiating certain type of small solid lung cancer.

Liquid biopsy for precision diagnostics and therapeutics

Liquid biopsy (LB) has emerged as a highly promising and non-invasive diagnostic approach, particularly in the field of oncology, and has garnered interest in various medical disciplines. This technique involves the examination of biomolecules released into physiological fluids, such as urine samples, blood, and cerebrospinal fluid (CSF). The analysed biomolecules included circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), cell-free DNA (cfDNA), exosomes, and other cell-free components. In contrast to conventional tissue biopsies, LB provides minimally invasive diagnostics, offering invaluable insights into tumor characteristics, treatment response, and early disease detection. This Review explores the contemporary landscape of technologies and clinical applications in the realm of LB, with a particular emphasis on the isolation and analysis of ctDNA and/or cfDNA. Various methodologies have been employed, including droplet digital polymerase chain reaction (DDP), BEAMing (beads, emulsion, amplification, and magnetics), TAm-Seq (tagged-amplicon deep sequencing), CAPP-Seq (cancer personalized profiling by deep sequencing), WGBS-Seq (whole genome bisulfite sequencing), WES (whole exome sequencing), and WGS (whole-genome sequencing). Additionally, CTCs have been successfully isolated through biomarker-based cell capture, employing both positive and negative enrichment strategies based on diverse biophysical and other inherent properties. This approach also addresses challenges and limitations associated with liquid biopsy techniques, such as sensitivity, specificity, standardization and interpretability of findings. This review seeks to identify the current technologies used in liquid biopsy samples, emphasizing their significance in identifying tumor markers for cancer detection, prognosis, and treatment outcome monitoring.

PET/CT-aided biopsy of lung lesions enhances diagnostic efficacy, especially for lesions >3cm

Objectives

The purpose of this study was to compare the diagnostic efficacy of PET/CT-aided CT-guided and routine CT-guided transthoracic needle biopsy for lung lesions.

Methods

A total of 458 patients with suspicious lung lesions were referred for CT-guided biopsy, with 227 patients assigned to the PET/CT group and 231 patients assigned to the CT group. The clinical characteristics and diagnostic yield were compared between the two groups. Furthermore, conducting subgroup analysis to evaluate the differences of diagnostic success or failure between the two groups.

Results

The sensitivity and diagnostic accuracy rate differed significantly (P = 0.035, P = 0.048). In the PET/CT group, the values were 95.7% and 96.3%, respectively, while in the CT group, they were 90.1% and 91.9%. When considering non-diagnostic cases, the overall diagnostic success rate increased markedly in PET/CT group (93.0% vs. 83.1%, P = 0.001). In our subgroup analysis, the PET/CT group demonstrated superiority in detecting lesions larger than 3 cm (OR, 4.81; 95CI%, 2.03 - 11.36), while showing a moderate effect in lesions smaller than 3 cm (OR, 1.09; 95CI%, 0.42 - 2.81). Significant effect modification was observed in large lesions in the PET/CT group (P for interaction = 0.023).

Conclusions

18F-FDG-PET/CT enhances the diagnostic efficacy of CT-guided transthoracic needle biopsy for lung lesions, and the incremental value can be modified by lesion size, particularly when the diameter is larger than 3 cm.

Primary biliary cholangitis presenting with granulomatous lung disease misdiagnosed as lung cancer: A case report

BACKGROUND

There are few cases of pulmonary granulomatous changes secondary to primary biliary cirrhosis (PBC). No case of granulomatous lung disease secondary to PBC misdiagnosed as lung cancer had been reported.

CASE SUMMARY

A middle-aged woman presented with lung nodules and was misdiagnosed with lung cancer by positron emission tomography/computed tomography. She underwent left lobectomy, and the pathology of the nodules showed granulomatous inflammation, which was then treated with antibiotics. However, a new nodule appeared. Further investigation with lung biopsy and liver serology led to the diagnosis of PBC, and chest computed tomography indicated significant reduction in the pulmonary nodule by treatment with methylprednisolone and ursodeoxycholic acid.

CONCLUSION

Diagnosis of pulmonary nodules requires integrating various clinical data to avoid unnecessary pulmonary lobectomy.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.

Lung CT stabilization with high-frequency non-invasive ventilation (HF-NIV) and breath-hold (BH) in lung nodule assessment by PET/CT

PURPOSE

To evaluate the effect of lung stabilization using high-frequency non-invasive ventilation (HF-NIV) and breath-hold (BH) techniques on lung nodule detection and texture assessment in PET/CT compared to a free-breathing (FB) standard lung CT acquisition in PET/CT.

MATERIALS AND METHODS

Six patients aged 65 ± 7 years, addressed for initial assessment of at least one suspicious lung nodule with 18F-FDG PET/CT, underwent three consecutive lung PET/CT acquisitions with FB, HF-NIV and BH. Lung nodules were assessed on all three CT acquisitions of the PET/CT and characterized for any size, volume and solid/sub-solid nature.

RESULTS

BH detected a significantly higher number of nodules (n = 422) compared to HF-NIV (n = 368) and FB (n = 191) (p < 0.001). The mean nodule size (mm) was 2.4 ± 2.1, 2.6 ± 1.9 and 3.2 ± 2.4 in BH, HF-NIV and FB, respectively, for long axis and 1.5 ± 1.3, 1.6 ± 1.2 and 2.1 ± 1.7 in BH, HF-NIV and FB, respectively, for short axis. Long- and short-axis diameters were significantly different between BH and FB (p < 0.001) and between HF-NIV and FB (p < 0.001 and p = 0.008), but not between BH and HF-NIV. A trend for higher volume was shown in FB compared to BH (p = 0.055) and HF-NIV (p = 0.068) without significant difference between BH and HF-NIV (p = 1). We found a significant difference in detectability of sub-solid nodules between the three acquisitions, with BH showing a higher number of sub-solid nodules (n = 128) compared to HF-NIV (n = 72) and FB (n = 44) (p = 0.002).

CONCLUSION

We observed a higher detection rate of pulmonary nodules on CT under BH or HF-NIV conditions applied to PET/CT than with FB. BH and HF-NIV demonstrated comparable texture assessment and performed better than FB in assessing size and volume. BH showed a better performance for detecting sub-solid nodules compared to HF-NIV and FB. The addition of BH or HF-NIV to PET/CT can help improve the detection and texture characterization of lung nodules by CT, therefore improving the accuracy of oncological lung disease assessment. The ease of use of BH and its added value should prompt its use in routine practice.

Differentiating primary from secondary lung cancer with FDG PET/CT and extra-pulmonary tumor grade

OBJECTIVE

Assess feasibility of differentiating primary from secondary lung cancer in patients with a solid solitary malignant pulmonary lesion (SMPL) and a previously resected extrapulmonary tumor.

METHODS

Patients with pathology proven primary or secondary lung cancer from a solitary pulmonary lesion and known histopathology of extrapulmonary tumor were included. Patients with a small pulmonary lesion size, multiple malignant pulmonary nodules or an active infectious/inflammatory process were excluded. Extrapulmonary tumor grade was categorized as low, intermediate and high and was matched to FDG uptake intensity of SMPL, with FDG uptake range (SMPL/Liver SUVmax) of <0.9 for low, 0.91-1.99 for intermediate and >2.0 for high extrapulmonary tumor grade.

RESULTS

Of 274 patients, 62 met the study criteria. 46 are primary and 16 are secondary lung cancer. There are 19 low, 27 intermediate and 16 high grade extrapulmonary tumors. Mean SMPL SUVmax is 8.2 ± 4.5 and SMPL/liver SUVmax is 2.4 ± 1.4. There are 37 cases (60%) with mismatched results (e.g., low FDG SMPL with intermediate or high grade extrapulmonary tumor or vice versa) and 25 matched cases (40%) that are inconclusive (e.g., low FDG with low tumor grade or high FDG with high tumor grade). Of the mismatched cases, we correctly predicted 30 cases (81%) as primary lung cancers.

CONCLUSION

A mismatch between the SMPL SUVmax and the extrapulmonary tumor grade could be used to differentiate a primary lung cancer from a metastasis with reasonable accuracy. Our preliminary results support the hypothesis that FDG uptake intensity of a metastatic pulmonary lesion mirrors the tumor aggressiveness of its extrapulmonary neoplasm of origin.

FAPI Compared with FDG PET/CT for Diagnosis of Primary and Metastatic Lung Cancer

Background The radiotracer fluorine 18 (18F)-labeled fibroblast activation protein inhibitor (FAPI) has shown promise for visualizing several types of cancer, but the accuracy of 18F-FAPI compared with 18F-fluorodeoxyglucose (FDG) for the detection of lung cancer remains uncertain. Purpose To evaluate the effectiveness of 18F-FAPI-based PET/CT imaging for the diagnosis of primary and metastatic lung cancer lesions as compared with 18F-FDG PET/CT. Materials and Methods In this secondary analysis of a prospective trial, consecutively recruited patients from a single center with pathologically confirmed lung cancer were prospectively enrolled from December 2020 to April 2022 and underwent paired 18F-FAPI and 18F-FDG PET/CT examinations at intervals of more than 20 hours and within 7 days of each other. Histopathologic and clinical follow-up results were used as reference standards for final diagnoses. 18F-FAPI and 18F-FDG uptake were compared using the McNemar test or paired Student t test. Diagnostic accuracy was compared between the two techniques by using the McNemar χ2 test. Results Sixty-eight participants (median age, 63 years [IQR, 58-68 years; range, 42-79 years]; 46 male [68%]) were evaluated. Compared with the mean tumor-to-background ratio (TBR) for FDG uptake, TBR for FAPI uptake was lower in primary lung tumors (25.3 ± 14.0 [SD] vs 32.1 ± 21.1; P < .001) but higher in metastatic lymph nodes (7.5 ± 6.6 vs 5.9 ± 8.6; P < .001) and bone metastases (8.6 ± 5.4 vs 4.3 ± 2.3; P < .001). For diagnostic accuracy in a total of 548 lesions in 68 participants, compared with 18F-FDG PET/CT, 18F-FAPI PET/CT demonstrated a higher sensitivity (99% [392 of 397 lesions] vs 87% [346 of 397]; P < .001), specificity (93% [141 of 151 lesions] vs 79% [120 of 151]; P = .004), accuracy (97% [533 of 548 lesions] vs 85% [466 of 548]; P < .001), and negative predictive value (97% [141 of 146 lesions] vs 70% [120 of 171 lesions]; P < .001), but there was no evidence of a difference for positive predictive value (98% [392 of 402 lesions] vs 92% [346 of 377 lesions]; P = .57). Conclusion 18F-FAPI PET/CT may be superior to 18F-FDG PET/CT for detecting lung cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Zukotynski and Gerbaudo in this issue.

The Role of Lymphadenectomy in Early-Stage NSCLC

Lung cancer remains the leading cause of cancer-related death worldwide. The involvement of lymph nodes by the tumor has a strong impact on survival of patients. For this reason, lymphadenectomy plays a crucial role in the staging and prognosis of NSCLC, to define the most appropriate therapeutic strategies concerning the stage of the disease. To date, the benefit, in terms of survival, of the different extents of lymphadenectomy remains controversial in the scientific community. It is recognized that metastatic involvement of mediastinal lymph nodes in lung cancer is one of the most significant prognostic factors, in terms of survival, and it is therefore mandatory to identify patients with lymph node metastases who may benefit from adjuvant therapies, to prevent distant disease and local recurrences. The purpose of this review is to evaluate the role of lymphadenectomy in early-stage NSCLC in terms of efficacy and accuracy, comparing systematic, sampling, and lobe-specific lymph node dissection and analyzing the existing critical issue, through a search of the most relevant articles published in the last decades.

The effect of butylscopolamine on [18F]FDG uptake in the gastrointestinal tract is negligible and regionally variable

BACKGROUND

Butylscopolamine (or hyoscine butylbromide, trade name Buscopan^®) is occasionally administered as a premedication to reduce non-specific FDG uptake in the gastrointestinal tract based on its antiperistaltic effect. To date, there are no consistent recommendations for its use. The aim of this study was to quantify the reduction in intestinal and non-intestinal uptake by butylscopolamine administration and to derive relevance for clinical evaluation.

RESULTS

458 patients (PET/CT for lung cancer) were retrospectively reviewed. 218 patients with butylscopolamine and 240 patients without butylscopolamine had comparable characteristics. While the SUV_mean in the gullet/stomach and small intestine was significantly reduced with butylscopolamine, the colon and rectum/anus showed no difference. The liver and salivary glands showed a reduced SUV_mean, while skeletal muscle and blood pool were unaffected. An effect of butylscopolamine was particularly evident in men and patients under 65 years of age. There was no difference in the perceived confidence in the assessment of intestinal findings in the subjective evaluation, although in the butylscopolamine group further diagnostics appeared advisable more frequently.

CONCLUSIONS

Butylscopolamine reduces gastrointestinal FDG accumulation only in selected segments and, despite a significant effect, only to a small extent. A general recommendation for the use of butylscopolamine cannot be derived from these results, its use for specific issues could be considered individually.

The diagnostic and prognostic role of combined [18F]FDG and [68Ga]-DOTA-peptides PET/CT in primary pulmonary carcinoids: a multicentric experience

OBJECTIVES

In the present retrospective multicentric study, we combined [68Ga]-DOTA-peptides and [18F]FDG-PET/CT findings aiming to investigate their capability to differentiate typical (TC) and atypical pulmonary carcinoids (AC) and their prognostic role.

METHODS

From three centers, 61 patients were retrospectively included. Based on a dual tracer combination we classified PET scans as score 1, [18F]FDG- and [68Ga]-DOTA-peptides negative; score 2, [68Ga]-DOTA-peptides positive and [18F]FDG-negative; score 3, [68Ga]-DOTA-peptides negative and [18F]FDG-positive; score 4, both tracers positive. Moreover, for each patient, the ratios of SUVmax on [68Ga]-DOTA-PET to that on [18F]FDG-PET were calculated (SUVr).

RESULTS

Thirty-five patients had a final diagnosis of TC. Twenty-two TC (57%) had positive [68Ga]-DOTA-peptides PET; instead, 21/26 (81%) AC had positive [18F]FDG-PET/CT. On dual-tracer analysis, scores 1, 2, 3 and 4 were 13%, 20%, 43% and 24% for all populations; 17%, 26%, 20% and 37% for TC; 8%, 11%, 73% and 8% for AC. Median SUVr was significantly higher in TC than AC (6.4 vs. 0.4, p = 0.011). The best value of SUVr to predict the final diagnosis was 1.05 (AUC 0.889). Relapse or progression of disease happened in 17 patients (11 affected by AC) and death in 10 cases (7 AC). AC diagnosis, positive [18F]FDG-PET, negative DOTA-PET and dual tracer score were significantly correlated with PFS (p = 0.013, p = 0.033, p = 0.029 and p = 0.019), while only AC diagnosis with OS (p = 0.022).

CONCLUSION

PET/CT findings had also a prognostic role in predicting PFS. Dual-tracer PET behavior may be used to predict the nature of pulmonary carcinoids and select the most appropriate management.

KEY POINTS

• Combination of [18F]FDG and [68Ga]-DOTA-peptides PET/CT results may help to differentiate between atypical and typical lung carcinoids. • The SUVmax ratio between [18F]FDG and [68Ga]-DOTA-peptides PET may help to differentiate between atypical and typical lung carcinoids. • Histotype and PET/CT features have a prognostic impact on PFS.

Diagnostic performance of diffusion-weighted imaging versus 18F-FDG PET/CT in differentiating pulmonary lesions: an updated meta-analysis of comparative studies

OBJECTIVE

To compare the diagnostic accuracy of diffusion-weighted imaging (DWI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for differentiating pulmonary nodules and masses.

METHODS

We systematically searched six databases, including PubMed, EMBASE, the Cochrane Library, and three Chinese databases, to identify studies that used both DWI and PET/CT to differentiate pulmonary nodules. The diagnostic performance of DWI and PET/CT was compared and pooled sensitivity and specificity were calculated along with 95% confidence intervals (CIs). The Quality Assessment of Diagnostic Accuracy Studies 2 was used to assess the quality of the included studies, and STATA 16.0 software was utilized to perform statistical analysis.

RESULTS

Overall, 10 studies that enrolled a total of 871 patients with 948 pulmonary nodules were included in this meta-analysis. DWI had greater pooled sensitivity (0.85 [95% CI 0.77-0.90]) and specificity (0.91 [95% CI 0.82-0.96]) than PET/CT (sensitivity, 0.82 [95% CI 0.70-0.90]); specificity, (0.81, [95% CI 0.72-0.87]). The area under the curve of DWI and PET/CT were 0.94 (95% CI 0.91-0.96) and 0.87 (95% CI 0.84-0.90) (Z = 1.58, P > 0.05), respectively. The diagnostic odds ratio of DWI (54.46, [95% CI 17.98-164.99]) was superior to that of PET/CT (15.77, [95% CI 8.19-30.37]). The Deeks' funnel plot asymmetry test showed no publication bias. The Spearman correlation coefficient test revealed no significant threshold effect. Lesion diameter and reference standard could be potential causes for the heterogeneity of both DWI and PET/CT studies, and quantitative or semi-quantitative parameters used would be a potential source of bias for PET/CT studies.

CONCLUSION

As a radiation-free technique, DWI may have similar performance compare with PET/CT in differentiating malignant pulmonary nodules or masses from benign ones.

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Objective

To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).

Materials and methods

Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.

Results

Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p &lt; 0.001) and external validation (p &lt; 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.

Conclusion

Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Invasive mucinous adenocarcinoma of the lung: clinicopathological features, 18F-FDG PET/CT findings, and survival outcomes

OBJECTIVE

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of lung adenocarcinoma. This study aimed to retrospectively evaluate the clinicopathological features, ¹⁸F-FDG PET/CT findings, and prognosis of IMA of the lung, as well as to investigate the associations among these variables, to improve the management of such patients.

METHODS

Clinicopathological and ¹⁸F-FDG PET/CT characteristics of 72 patients with pathologically confirmed IMA of the lung were retrospectively collected and investigated, and their predictive efficacy on progression-free survival (PFS) was evaluated.

RESULTS

The median age of the enrolled 72 patients was 61 years (range, 26-79 years), and the male-to-female ratio was 1:1.25. According to the radiological morphology of IMA, solidary nodule/mass type (n = 59, 81.9%) was the most common, followed by GGO type (n = 8, 11.1%) and pneumonia type (n = 5, 6.9%). Lobulated or spiculated margin and pleural traction were the most common radiological signs. The median SUVmax of IMA lesions was 3.0, ranging from 0.5 to 23.1. Higher SUVmax was observed in IMA with non-GGO type, clinical symptom, advanced stage, lobulated margin, pleural traction or spread through air spaces (STAS) (P < 0.05). Moreover, higher SUVmax was related to larger tumor size in non-pneumonia-type IMA (r = 0.708, P < 0.001). The median PFS was 21.3 months, and the 12-, 24- and 36-month PFS rates were 89.8%, 83.3% and 75.5%, respectively. A poorer PFS was significantly associated with SUVmax ≥ 3, advanced stage and STAS.

CONCLUSION

¹⁸F-FDG PET/CT combined with clinicopathological characteristics can aid the diagnosis and prognostic evaluation of lung IMA, which could provide guidance for the appropriate management of such patients.

Preoperative PET-SUVmax and volume based PET parameters of the primary tumor fail to predict nodal upstaging in early-stage lung cancer

OBJECTIVES

Accurate nodal staging is of utmost importance in patients with lung cancer. FDG-PET/CT imaging is now part of the routine staging. Despite thorough preoperative staging nodal upstaging still occurs in early-stage lung cancer. However, the predictive value of preoperative PET metrics of the primary tumor on nodal upstaging remains to be unexplored. Our aim was to assess the association of these preoperative PET-parameters with nodal upstaging in histologically confirmed lung adenocarcinoma and squamous cell carcinoma.

METHODS

From January 2016 to November 2018, 500 patients with pT1-T2/cN0 lung cancer received an anatomical resection with curative intent. 171 patients with adenocarcinoma and squamous cell carcinoma and available PET-CTs were retrospectively included. We analyzed the the association of nodal upstaging with preoperative PET-SUVmax and metabolic PET metrics including total lesion glycolysis (TLG) and metabolic tumor volume (MTV) with different defined thresholds.

RESULTS

High values of preoperative PET-SUVmax of the primary tumor were associated with squamous cell carcinoma (p < 0.0001) and with larger tumors (p < 0.0001). Increased preoperative C-reactive protein levels (<1mg/dL) correlated significantly with high preoperative PET-SUVmax values (p < 0.0001). No significant relationship between PET-SUVmax and lactate dehydrogenase activity (p = 0.6818), white blood cell count (p = 0.7681), gender (p = 0.1115) or age (p = 0.9284) was observed. Nodal upstaging rate was 14.0 % with 8.8 % N1 and 5.3 % N2 upstaging. Tumor size (p = 0.0468) and number of removed lymph nodes (p = 0.0461) were significant predictors of nodal upstaging but no significant association was found with histology or PET parameters. Of note, increased MTV - regardless of the threshold - tended to associate with nodal upstaging.

CONCLUSION

Early-stage lung cancer patients with squamous histology and T2 tumors presented increased preoperative PET-SUVmax values. Nevertheless, beyond tumor size and number of removed lymph nodes neither SUVmax nor metabolic PET parameters MTV and TLG were significant predictors of nodal upstaging.

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.

First-, Second-, and Third-Generation Radiolabeled Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Positron Emission Tomography: State of the Art, a Systematic Review

Introduction: Lung cancer (LC) is a leading cause of death among men and women, with non-small cell LC (NSCLC) accounting for a substantial portion of the histopathological spectrum and epidermal growth factor receptor (EGFR) mutations being correlated with its manifestation and evolution. Positron emission tomography (PET)/computed tomography has been the most widely used instrument to assess and monitor LC in a noninvasive way, including EGFR-mutated NSCLC, and its course during therapy, indicating to the referring physician the response to ongoing treatment or the lack of it. This systematic review aims to evaluate the feasibility and safety of radiolabeled EGFR tyrosine kinase inhibitors (TKis) in PET in clinical practice. Materials and Methods: From 1999 to April 2022 a Medline search was conducted on four different databases such as PubMed, Cochrane Library, Scopus, and Web of Sciences. Clinical studies were assessed by Quality Assessment of Diagnostic accuracy Studies-2 (QUADAS-2) and preclinical studies were also reported in this review. Results: Nine clinical studies were QUADAS-2 assessed and risk-of-bias assessment, and it turned out acceptable as two out of eight studies had low risk of bias in all four domains for risk-of-bias assessment, and the other four studies had three low-risk domains. The overall assessment for applicability risks was low. Conclusions: Radiolabeled EGFR-TKis in PET are a valid tool in identifying patients who may benefit from TKi therapy and who may not as a means to start an effective treatment. Although the number of clinical studies conducted so far is meager, these new PET tracers are already proving to be very useful in clinical settings as patient prognosis can be better assessed.

Morphologic Severity of Atypia Is Predictive of Lung Cancer Diagnosis

In cytologic analysis of lung nodules, specimens classified as atypia cannot be definitively diagnosed as benign or malignant. Atypia patients are typically subject to additional procedures to obtain repeat samples, thus delaying diagnosis. We evaluate morphologic categories predictive of lung cancer in atypia patients. This retrospective study stratified patients evaluated for primary lung nodules based on cytologic diagnoses. Atypia patients were further stratified based on the most severe verbiage used to describe the atypical cytology. Logistic regressions and receiver operator characteristic curves were performed. Of 129 patients with cytologic atypia, 62.8% later had cytologically or histologically confirmed lung cancer and 37.2% had benign respiratory processes. Atypia severity significantly predicted final diagnosis even while controlling for pack years and modified Herder score (p = 0.012). Pack years, atypia severity, and modified Herder score predicted final diagnosis independently and while adjusting for covariates (all p < 0.001). This model generated a significantly improved area under the curve compared to pack years, atypia severity, and modified Herder score (all p < 0.001) alone. Patients with severe atypia may benefit from repeat sampling for cytologic confirmation within one month due to high likelihood of malignancy, while those with milder atypia may be followed clinically.

Evaluation of data-driven respiratory gating for subcentimeter lesions using digital PET/CT system and three-axis motion phantom

Introduction.The application of data-driven respiratory gating (DDG) for subcentimeter lesions with respiratory movement remains poorly understood. Hence, this study aimed to clarify DDG application for subcentimeter lesions and the ability of digital Positron emission tomography/computed tomography (PET/CT) system combined with DDG to detect these lesions under three-axis respiration.Methods.Discovery MI PET/CT system and National Electrical Manufacturers Association (NEMA) body phantom with Micro Hollow Sphere (4, 5, 6, 8, 10, and 13 mm) were used. The NEMA phantom was filled with¹⁸F-FDG solutions of 42.4 and 5.3 kBq/ml for each hot sphere and background region. The 3.6 s cycles of three-axis respiratory motion were reproduced using the motion platform UniTraQ. The PET data acquisition was performed in stationary and respiratory-moving states. The data were reconstructed in three PET groups: stationary (NM-PET), no gating with respiratory movement (NG-PET), and DDG gating with respiratory movement (DDG-PET) groups. For image quality, percent contrast (Q_H); maximum, peak, and mean standardized uptake value (SUV); background region; and detectability index (DI) were evaluated in each PET group. Visual assessment was also conducted.Results.The groups with respiratory movement had deteriorated Q_Hand SUVs compared with NM-PET. Compared with NG-PET, DDG-PET has significantly improved Q_Hand SUVs in spheres above 6 mm. The background region showed no significant difference between groups. The SUVmax, SUVpeak, and Q_Hvalues of 8 mm sphere were highest in NM-PET, followed by DDG-PET and NG-PET. In visual assessment, the spheres above 6 mm were detected in all PET groups. DDG application did not detect new lesions, but it increased DI and visual score.Conclusions. The application of principal component analysis (PCA)-based DDG algorithm improves both image quality and quantitative SUVs in subcentimeter lesions measuring above 6 mm. Although DDG application cannot detect new subcentimeter lesions, it increases the visual indices.

Solitary Pulmonary Capillary Hemangioma: CT and PET-CT Features with Clinicopathologic Correlation

The aim of this study was to evaluate the CT and PET-CT features of solitary pulmonary capillary hemangioma (SPCH) with clinicopathologic correlations. This retrospective study included 17 patients with histologically proven SPCH from four tertiary institutions. The clinical, pathological and imaging findings of SPCH were reviewed. The CT features assessed included lesion location, size, density, contour, margin, enhancement, presence of air bronchogram, perivascular lucency and pleural retraction, and 18F-fluorodeoxyglucose uptake on PET-CT. Changes in the size during the follow-up period were also evaluated. Imaging features were correlated with the clinicopathologic findings. The mean age of the patients was 47 years (range 30–60 years). All SPCHs were incidentally detected during screening CT examinations (n = 13, 76%) or during cancer work-up (n = 4, 24%). Most SPCHs appeared as part-solid nodules (n = 15, 88%), the remaining appeared as a pure ground-glass nodule or a pure solid nodule, respectively. Most had smooth contours (n = 16, 94%), while one had a lobulated contour. Nine SPCHs (53%) showed ill-defined margins. Air bronchogram was present in ten (59%) SPCHs, and perivascular lucency in two (12%). All SPCHs exhibited hypoattenuation on contrast-enhanced CT and hypometabolism on PET-CT. During the follow-up period (mean 14.8 ± 17.7 months), the lesions showed no change in size or density in ten SPCHs (59%), decreased or fluctuation in size and density in three (18%). SPCH is often incidentally detected in young and middle-aged adults, commonly as an ill-defined part-solid nodule that may accompany air bronchogram, perivascular lucency, and fluctuation in size or density on CT and hypometabolism on PET-CT.

Transesophageal Endoscopic Ultrasound Fine Needle Biopsy for the Diagnosis of Mediastinal Masses: A Retrospective Real-World Analysis

Background: Endoscopic ultrasound (EUS) plays an important role in the diagnosis and staging of thoracic disease. Our report studies the diagnostic performance and clinical impact of EUS fine needle aspiration (FNA) in a homogenous cohort of patients according to the distribution of the enlarged MLNs or pulmonary masses. Methods: We retrospectively reviewed the diagnostic performance of 211 EUS-FNA in 200 consecutive patients with enlarged or PET-positive MLNs and para-mediastinal masses who were referred to our oncological center between January 2019 and May 2020. Results: The overall sensitivity of EUS-FNA was 85% with a corresponding negative predictive value (NPV) of 56% and an accuracy of 87.5%. The sensitivity and accuracy in patients with abnormal MLNs were 81.1% and 84.4%, respectively. In those with para-mediastinal masses, sensitivity and accuracy were 96.4% and 96.8%. The accuracy for both masses and lymph nodes was 100%, and in the LAG (left adrenal gland), it was 66.6%. Conclusions: Our results show that, in patients with suspected mediastinal masses, EUS-FNA is an accurate technique to evaluate all reachable mediastinal nodal stations, including station 5.

Preoperative computed tomography-guided localization for multiple pulmonary nodules: comparison of methylene blue and coil

Background

Preoperative computed tomography (CT)-guided localization has been used to guide the video-assisted thoracoscopic surgery (VATS) sublobar (wedge or segmental) resection for pulmonary nodules (PNs). We aimed to assess the relative efficacy and safety of CT-guided methylene blue (MB)- and coil-based approaches to the preoperative localization of multiple PNs (MPNs).

Methods

Between January 2015 and December 2020, 31 total cases suffering from MPNs at our hospital underwent CT-guided localization and subsequent VATS resection in our hospital, of whom 15 and 16 respectively underwent MB localization (MBL) and coil localization (CL). The clinical effectiveness and complication rates were compared between 2 groups.

Results

The PN- and patient-based technical success rates in the MBL group were both 100%, whereas in the CL group they were 97.2% (35/36) and 93.8% (15/16), respectively, with no substantial discrepancies between groups. Patients in the MBL group illustrated a substantially shorter CT-guided localization duration compared with the CL group (18 min vs. 29.5 min, P < 0.001). Pneumothorax rates (P = 1.000) and lung hemorrhage (P = 1.000) were comparable in both groups. In the MBL and CL groups, the median interval between localization and VATS was 1 h and 15.5 h, respectively (P < 0.001). One-stage VATS sublobar resection of the target nodules was successfully performed in all patients from both groups.

Conclusion

Both CT-guided MBL and CL can be readily and safely utilized for preoperative localization in individuals who had MPNs, with MBL being correlated with a shorter localization duration compared with CL.

Automated Classification of PET‐CT Lesions in Lung Cancer: An Independent Validation Study

Introduction

Recently, a tool called the positron emission tomography (PET)‐assisted reporting system (PARS) was developed and presented to classify lesions in PET/computed tomography (CT) studies in patients with lung cancer or lymphoma. The aim of this study was to validate PARS with an independent group of lung‐cancer patients using manual lesion segmentations as a reference standard, as well as to evaluate the association between PARS‐based measurements and overall survival (OS).

Methods

This study retrospectively included 115 patients who had undergone clinically indicated (18F)‐fluorodeoxyglucose (FDG) PET/CT due to suspected or known lung cancer. The patients had a median age of 66 years (interquartile range [IQR]: 61–72 years). Segmentations were made manually by visual inspection in a consensus reading by two nuclear medicine specialists and used as a reference. The research prototype PARS was used to automatically analyse all the PET/CT studies. The PET foci classified as suspicious by PARS were compared with the manual segmentations. No manual corrections were applied. Total lesion glycolysis (TLG) was calculated based on the manual and PARS‐based lung‐tumour segmentations. Associations between TLG and OS were investigated using Cox analysis.

Results

PARS showed sensitivities for lung tumours of 55.6% per lesion and 80.2% per patient. Both manual and PARS TLG were significantly associated with OS.

Conclusion

Automatically calculated TLG by PARS contains prognostic information comparable to manually measured TLG in patients with known or suspected lung cancer. The low sensitivity at both the lesion and patient levels makes the present version of PARS less useful to support clinical reading, reporting and staging.

Explainable Machine Learning Solution for Observing Optimal Surgery Timings in Thoracic Cancer Diagnosis

In this paper, we introduce an AI-based procedure to estimate and assist in choosing the optimal surgery timing, in the case of a thoracic cancer diagnostic, based on an explainable machine learning model trained on a knowledge base. This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon, and it might not be confirmed by the histopathological exam. Therefore, we propose a pipeline of automatic processing steps with the purpose of inferring the prospective result of the histopathologic exam, generating an explanation of why this inference holds, and finally, evaluating it against the conclusive opinion of an experienced surgeon. To obtain an accurate practical result, the training dataset is labeled manually by the thoracic surgeon, creating a training knowledge base that is not biased towards clinical practice. The resulting intelligent system benefits from both the precision of a classical expert system and the flexibility of deep neural networks, and it is supposed to avoid, at maximum, any possible human misinterpretations and provide a factual estimate for the proper timing for surgical intervention. Overall, the experiments showed a 7% improvement on the test set compared with the medical opinion alone. To enable the reproducibility of the AI system, complete handling of a case study is presented from both the medical and technical aspects.

A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions

Background

This is a pilot study of radiomics based on ⁶⁸Ga-NOTA-PRGD2 [NOTA-PEG4-E[c(RGDfK)]2)] and ¹⁸F-FDG PET/CT to (i) evaluate the diagnostic efficacy of radiomics features of ⁶⁸Ga-NOTA-PRGD2 PET in the differential diagnosis of benign and malignant pulmonary space-occupying lesions and (ii) compare the diagnostic efficacy of multi-modality and multi-probe images.

Methods

We utilized a dataset of 48 patients who participated in ⁶⁸Ga-NOTA-PRGD2 PET/CT and ¹⁸F-FDG PET/CT clinical trials to extract image features and evaluate their diagnostic efficacy in the differentiation of benign and malignant lesions by the Mann-Whitney U test. After feature selection with sequential forward selection, random forest models were developed with tenfold cross-validation. The diagnostic performance of models based on different image features was visualized by receiver operating characteristic (ROC) curves and compared by permutation tests.

Results

Fourteen of the ⁶⁸Ga-NOTA-PRGD2 PET features between benign and malignant pulmonary space-occupying lesions had significant differences (P<0.05, Mann-Whitney U test). Eighteen of the ⁶⁸Ga-NOTA-PRGD2 PET features demonstrated higher AUC values than all CT features in the differential diagnosis of pulmonary lesions. The AUC value (0.908) ​​of the three-modal feature model was significantly higher (P<0.05, permutation test) than those of the single- and dual-modal models.

Conclusion

⁶⁸Ga-NOTA-PRGD2 PET features have better diagnostic capacity than CT features for pulmonary space-occupying lesions. The combination of multi-modality and multi-probe images can improve the diagnostic efficiency of models. Our preliminary clinical hypothesis of using radiomics based on ⁶⁸Ga-NOTA-PRGD2 PET images and multimodal images as a diagnostic tool warrants further validation in a larger multicenter sample size.

Random Walk Algorithm-Based Computer Tomography (CT) Image Segmentation Analysis Effect of Spiriva Combined with Symbicort on Immunologic Function of Non-Small-Cell Lung Cancer

The objective of this research was to explore the effect of the treatment regimen of Spiriva combined with Symbicort on the immune function of non-small-cell lung cancer (NSCLC) based on computed tomography (CT) imaging features. An automatic CT image segmentation algorithm (RW-CT) was constructed based on random walk (RW) and image segmentation technology. The image segmentation algorithm based on the Toboggan method (C-CT) was introduced to compare with the traditional RW algorithm. 60 subjects were divided into four groups: a Chinese combined with Western medicine group (treated with Spiriva combined with Symbicort, group C+W), a Chinese medicine group (treated with Spiriva, group C), a Western medicine group (treated with Symbicort, group W), and a model group for control (group M). The results show that the Dice coefficient of the RW-CT algorithm was obviously larger than that of the C-CT algorithm and the RW algorithm, while the Hausdorff distance (HD) of the RW-CT algorithm was much smaller than that of the other two algorithms ( $P<0.05$ ). The proportion of positive cells of hypoxia-inducible factor-1α (HIF-1α) in group C+W was the least (15%-23%), followed by the group W (21%-29%) and the group C (28%-37%), and that in the group M was the highest (39%-49%). There was a remarkable difference in the immunohistochemical scores (HIS) of vascular endothelial growth factor (VEGF) in the tumor tissues between group C+W and the group M ( $H=14.200$ , $P=0.001$ ), but there was no great difference from the group C and the group W ( $P>0.05$ ). There was a notable difference in the IHS of vascular endothelial factor recepto-2 (VEGFR-2) between the group C+W medication group and the group M ( $H=12.800$ , $P=0.002$ ), and there was no statistical difference between the group C and W ( $P>0.05$ ). In short, the RW-CT constructed based on RW was better than the traditional algorithms for CT image segmentation. The Spiriva combined with Symbicort could effectively inhibit the expression of VEGF, VEGFR-2, and HIF-1α in NSCLC and promote the immunologic function of the body.

Mediastinal lymphnode positivity clinical scoring system for lung adenocarsinoma-mediastinal lymph node evaluation and staging

Aim: The study-cohort aims to assess PET-CT's correlation with adenocarcinomas' subtypes and propose a scoring system for mediastinal lymph nodes staging. Material and Method: The patient cohort is a multicenter, retrospective analysis of 268 patient that underwent surgery for NSCLC adenocarcinoma. Preoperative PET-CT results for mediastinal lymph node staging was pathologically confirmed on tissue specimens obtained at anatomical resection. Statistical evaluation of PET CT, radiological and pathological outcomes were performed on all subgroups. Results: The low FDG affinity in the lepidic pattern was statistically significant in the study (p

Polymyositis as a paraneoplastic syndrome of a patient with primary pulmonary lymphoepithelioma-like carcinoma: a case report and literature review

Background

Pulmonary lymphoepithelioma-like carcinoma (LELC) is a rare type of non-small cell lung cancer, which mostly occurred in non-smoking Asian populations. The prognosis of this tumor is better than other lung cancers. Polymyositis, a kind of idiopathic inflammatory myopathies, may negatively affect the prognosis of patients with lung cancer as a paraneoplastic syndrome (PNPS). LELC is seldomly accompanied by PNPS, thus the treatment strategy and prognosis should be discussed.

Case presentation

We report a 49-year-old female patient who was hospitalized for “symmetric limb weakness and pain for more than 2 months”. Glucocorticoid-based anti-inflammatory therapy had been performed for over 3 weeks before the patient was hospitalized, however, in vain. The result of serum autoimmune antibody showed Anti-nRNP/Sm ( +). The serum level of myoglobin, lactate dehydrogenase and creatine kinase elevated significantly. An electromyogram revealed peripheral nerves injury and myogenic damages. Imaging showed a mass in the posterior basal segment of the left lung. A percutaneous transthoracic needle biopsy was performed and the pathological result was LELC. The patient was diagnosed with pulmonary LELC accompanied by polymyositis. Positron emission tomography-computed tomography (PET-CT) showed only ipsilateral hilar and mediastinal lymph nodes metastasis. Video-assisted thoracoscopic left lower lobectomy and systematic mediastinal lymphadenectomy were performed. The postoperative pathological stage was T2N2M0, IIIA (UICC 8th), and the patient received adjuvant chemotherapy and subsequent radiotherapy. The patient was followed up for 5 months with no recurrence of tumor and the limb weakness and pain were relieved apparently after the successful comprehensive treatment of her primary tumor.

Conclusion

Pulmonary LELC is a rare subtype of non-small cell lung cancer seldomly accompanied by PNPS. Though polymyositis is associated with lung cancer, it is easy to ignore this relationship when a patient is diagnosed with LELC in the clinic. Surgery based comprehensive treatment of primary tumor can lead to a prospective prognosis in pulmonary LELC patients with PNPS. And successful treatment of pulmonary LELC can also improve symptoms of PNPS.

Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter

Background

To investigate the value of ¹⁸F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm).

Methods

A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).

Results

According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93–0.97) in the training group and 0.94 (95% CI: 0.89–0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.

Conclusions

The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00895-x.

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Methods

The imaging data of 55 patients with chest CT plain scan in the Xuancheng People's Hospital were collected retrospectively. The data of each patient included lung window reconstruction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance.

Results

Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were 80.32% ± 5.91%, 79.83% ± 6.12%, and 80.17% ± 5.89%, respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all P > 0.05).

Conclusion

The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.

Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images

This study aimed to explore the effect of deep learning models on lung CT image lung parenchymal segmentation (LPS) and the application value of CT image texture features in the diagnosis of peripheral non-small-cell lung cancer (NSCLC). Data of peripheral lung cancer (PLC) patients was collected retrospectively and was divided into peripheral SCLC group and peripheral NSCLC group according to the pathological examination results, ResNet50 model and feature pyramid network (FPN) algorithm were undertaken to improve the Mask-RCNN model, and after the MaZda software extracted the texture features of the CT images of PLC patients, the Fisher coefficient was used to reduce the dimensionality, and the texture features of the CT images were analyzed and compared. The results showed that the average Dice coefficients of the 2D CH algorithm, Faster-RCNN, Mask-RCNN, and the algorithm proposed in the validation set were 0.882, 0.953, 0.961, and 0.986, respectively. The accuracy rates were 88.3%, 93.5%, 94.4%, and 97.2%. The average segmentation speeds in lung CT images were 0.289 s/sheet, 0.115 s/sheet, 0.108 s/sheet, and 0.089 s/sheet. The improved deep learning model showed higher accuracy, better robustness, and faster speed than other algorithms in the LPS of CT images. In summary, deep learning can achieve the LPS of CT images and show excellent segmentation efficiency. The texture parameters of GLCM in CT images have excellent differential diagnosis performance for NSCLC and SCLC and potential clinical application value.

Pulmonary functional imaging (PFI): A historical review and perspective

PFI Pulmonary Functional Imaging (PFI) refers to visualization and measurement of ventilation, perfusion, gas flow and exchange as well as biomechanics. In this review, we will highlight the historical development of PFI, describing recent advances and listing the various techniques for PFI offered per modality. Challenges PFI is facing and requirements for PFI from a clinical point of view will be pointed out. Hereby the review is meant as an introduction to PFI.

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.

[Pulmonary nodules and pneumonia : A diagnostic guideline]

Hintergrund

Das konventionelle Röntgenbild zählt zu den am häufigsten durchgeführten radiologischen Untersuchungen. Seine Interpretation gehört zu den Grundkenntnissen jedes Radiologen.

Fragestellung

Ziel dieses Artikels ist es, häufige Zeichen und Muster der Pneumonie sowie Merkmale von Pseudoläsionen im konventionellen Röntgenbild zu erkennen und einen diagnostischen Leitfaden für junge Radiologen zu schaffen.

Methoden

Analyse aktueller Studien und Daten sowie eine Übersicht der häufigsten Zeichen und Muster im konventionellen Röntgenbild.

Ergebnisse

Die Kenntnis über häufige Zeichen und Muster im Röntgenbild bietet eine Hilfestellung in der Diagnostik und kann hinweisend für die Ursache einer Infektion sein. Häufig sind diese Zeichen jedoch unspezifisch und sollten daher immer in klinische Korrelation gesetzt werden. In der Detektion und Beurteilung von pulmonalen Rundherden gewinnt die Computertomographie (CT) durch ihre deutlich höhere Sensitivität in der Primärdiagnostik immer mehr an Bedeutung.

Schlussfolgerung

Das konventionelle Röntgenbild bildet weiterhin eine führende Rolle in der Primärdiagnostik; der Radiologe sollte jedoch die Limitationen des konventionellen Bildes kennen.

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.

Prognostic Radiological Tools for Clinical Stage IA Pure Solid Lung Cancer

In this study, we analyzed prognostic radiological tools and surgical outcomes for radiologically pure solid adenocarcinomas (AD) and squamous cell carcinoma (SQ) in clinical stage IA. We retrospectively investigated 130 patients who underwent surgical resections. We assessed the predictive risk factors for recurrence and pathological lymph node metastasis (LNM). There was no statistical difference in recurrence free survival (RFS) or cancer-specific survival (CSS) between AD and SQ groups (p = 0.642 and p = 0.403, respectively). In the whole cohort, tumor size on lung window and mediastinal settings, and tumor disappearance ratio using high-resolution computed tomography (HRCT) were not prognostic parameters (p = 0.127, 0.066, and 0.082, respectively). The maximal standardized uptake value (SUVmax) using positron emission tomography-CT was associated with recurrence (p = 0.016). According to the receiver operating characteristic curve, the cut-off value of SUVmax for recurrence was 4.6 (p = 0.016). The quantitative continuous variables using any radiological tools were not associated with LNM. However, tumor diameter on mediastinal setting ≥8 mm with SUVmax ≥2.4 could be a risk factor for LNM. Pure solid AD and SQ were equivalent for the RFS and CSS. SUVmax was useful to predict recurrence. The tumor diameter on a mediastinal setting and SUVmax were useful in predicting pathological LNM.

Metabolic active tumour volume quantified on [18F]FDG PET/CT further stratifies TNM stage IV non-small cell lung cancer patients

PURPOSE

This study aimed to assess whether the whole body metabolic active tumour volume (MTV_WB), quantified on staging [¹⁸F]FDG PET/CT, could further stratify stage IV non-small cell lung cancer (NSCLC) patients.

METHODS

A group of 160 stage IV NSCLC patients, submitted to staging [¹⁸F]FDG PET/CT between July 2010 and May 2020, were retrospectively evaluated. MTV_WB was quantified. Univariate and multivariate Cox regressions were carried out to assess correlation with overall survival (OS). C-statistic was used to test predictive power. Kaplan-Meier survival curves with Log-Rank tests were performed to compute statistical differences between strata from dichotomized variables and to calculate the estimated mean survival times (EMST). Survival rates at 1 and 5 years were calculated.

RESULTS

MTV_WB was a statistically significant predictor of OS on univariate (p < 0.0001) and multivariate analyses (p < 0.0001). The multivariate model with MTV_WB (Cindex ± SE = 0.657 ± 0.024) worked significantly better as an OS predictor than the cTNM model (Cindex ± SE = 0.544 ± 0.028) (p = 0.003). An EMST of 29.207 ± 3.627(95% CI 22.099-36.316) months and an EMST of 10.904 ± 1.171(95% CI 8.609-13.199) months (Log-Rank p < 0.0001) were determined for patients with MTV_WB < 104.3 and MTV_WB ≥ 104.3, respectively. In subsamples of stage IVA (cut-off point = 114.5) and IVB patients (cut-off point = 191.1), statistically significant differences between EMST were also reported, with p-values of 0.0001 and 0.0002, respectively. In both substages and in the entire cohort, patients with MTV_WB ≥ cut-off points had lower EMST and survival rates.

CONCLUSION

Baseline MTV_WB, measured on staging [¹⁸F]FDG PET/CT, further stratifies stage IV NSCLC patients. This parameter is an independent predictor of OS and provides valuable prognostic information over the 8th edition of cTNM staging.

Impact of the 18F-FDG-PET/MRI on Metastatic Staging in Patients with Hepatocellular Carcinoma: Initial Results from 104 Patients

Optimal HCC therapeutic management relies on accurate tumor staging. Our aim was to assess the impact of ¹⁸F-FDG-WB-PET/MRI on HCC metastatic staging, compared with the standard of care CT-CAP/liver MRI combination, in patients with HCC referred on a curative intent or before transarterial radioembolization. One hundred and four consecutive patients followed for HCC were retrospectively included. The WB-PET/MRI was compared with the standard of care CT-CAP/liver MRI combination for HCC metastatic staging, with pathology, followup, and multidisciplinary board assessment as a reference standard. Thirty metastases were identified within 14 metastatic sites in 11 patients. The sensitivity of WB-PET/MRI for metastatic sites and metastatic patients was significantly higher than that of the CT-CAP/liver MRI combination (respectively 100% vs. 43%, p = 0.002; and 100% vs. 45%, p = 0.01). Metastatic sites missed by CT-CAP were bone (n = 5) and distant lymph node (n = 3) in BCLC C patients. For the remaining 93 nonmetastatic patients, three BCLC A patients identified as potentially metastatic on the CT-CAP/liver MRI combination were correctly ruled out with the WB-PET/MRI without significant increase in specificity (100% vs. 97%; p = 0.25). The WB-PET/MRI may improve HCC metastatic staging and could be performed as a “one-stop-shop” examination for HCC staging with a significant impact on therapeutic management in about 10% of patients especially in locally advanced HCC.