By correcting the effect of tumor size on metabolic activity, the maximum standardized uptake value-to-tumor size (SUVmax:tumor size) ratio on fluorodeoxyglucose F18 positron emission tomography (18F-FDG PET)/computed tomography (CT) scans can be a prognostic parameter of non-small cell lung cancer (NSCLC). The current study evaluates the prognostic value of SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans in patients with NSCLC. Furthermore, the SUVmax:tumor size ratio is compared with other established PET parameters.

This study included 108 patients with NSCLC who underwent pretreatment 18F-FDG PET/CT scans and curative lung surgery. The associations between the SUVmax:tumor size ratio and other conventional PET parameters were investigated. The recurrence-free survival according to the SUVmax:tumor size ratio was also analyzed. In addition, the SUVmax:tumor size ratio was compared according to postoperative pathologic findings.

In total, 72 (66.7%) of the 108 participants presented with adenocarcinoma (ADC). Nineteen (17.6%) patients experienced recurrence during a median follow-up period of 32.3 months. The median SUV max:tumor size ratio was 2.37 (1.23 for ADCs and 3.90 for other histologic types). The SUVmax:tumor size ratio was associated with SUVmax and mean SUV, as well as metabolic tumor volume and total lesion glycolysis. Patients with an SUVmax:tumor size ratio higher than the median had a worse recurrence outcome than those with an SUVmax:tumor size ratio lower than the median. Participants with ADC who presented with lymphovascular invasion had a higher SUVmax:tumor size ratio than those without. The presence of lymph node metastasis and advanced histologic grade were associated with a high SUVmax:tumor size ratio in patients with ADC.

The SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans was associated with aggressive tumor behavior and poor outcome in NSCLCs, particularly ADC.

The SUVmax:tumor size ratio on pretreatment 18F-FDG PET/CT scans has a prognostic value in patients with NSCLCs, especially ADC.

To assess feasibility of lung cancer screening, we analysed lung lesion detectability simulating low-dose and convolutional neural network (CNN) denoised [18F]-FDG PET/CT reconstructions.

Retrospectively, we analysed lung lesions on full statistics and decimated [18F]-FDG PET/CT. Reduced count PET data were emulated according to various percentage levels of total. Full and reduced statistics datasets were denoised using a CNN algorithm trained to recreate full statistics PET. Two readers assessed a detectability score from 3 to 0 for each lesion. The resulting detectability score and quantitative measurements were compared between full statistics and the different decimation levels (100%, 30%, 5%, 2%, 1%) with and without denoising.

We analysed 141 lung lesions from 49 patients across 588 reconstructions. The dichotomised lung lesion malignancy score was significantly different from 10% decimation without denoising (p < 0.029) and from 5% decimation with denoising (p < 0.001). Compared to full statistics, detectability score distribution differed significantly from 2% decimation without denoising (p < 0.001) and from 5% decimation with denoising (p < 0.001). Detectability scores at same decimation levels with or without denoising differed significantly at 10%, 2%, and 1% decimation (p < 0.019); dichotomised scores did not differ significantly. Denoising significantly increased the proportion of lung lesion scores with a high diagnostic confidence (3 and 0) (p < 0.038).

Lung lesion detectability was preserved down to 30% of injected activity without denoising and to 10% with denoising. These results support the feasibility of reduced-activity [18F]-FDG PET/CT as a potential tool for lung lesion detection. Further studies are warranted to compare this approach with low-dose CT in screening settings.

This study aims to develop and evaluate deep learning models for the detection and classification of hypermetabolic lung lesions into four categories: benign, lung cancer, pulmonary lymphoma, and metastasis. These categories are defined by their pathological origin, clinical relevance, and therapeutic implications.

A lesion localisation model was first developed using manually annotated PET/CT images. For classification, a multi-dimensional joint network was employed, incorporating both image patches and two-dimensional projections. Classification performance was quantified by metrics like accuracy, and compared to that of a radiomics model. Additionally, false-positive segmentations were manually reviewed and analysed for clinical evaluation.

The study retrospectively included 647 cases (409 males/238 females) over more than 8 years from five centres, divided into an internal dataset (426 cases from Shanghai Ruijin Hospital), an external test set I (151 cases from four other institutions), and an external test set II (70 cases from a new imaging device). The localisation model achieved detection rates of 81.19%, 75.48%, and 77.59% on the internal, external test set I, and external test set II, respectively. The classification model outperformed the radiomics approach, with area-under-curves of 88.4%, 80.7%, and 66.6%, respectively. Most false-positive segmentations were clinically acceptable, corresponding to suspicious lesions in adjacent regions, particularly lymph nodes.

Deep learning models based on PET/CT imaging can effectively detect, segment, and classify hypermetabolic lung lesions, and identify suspicious adjacent lesions. These results highlight the potential of artificial intelligence in clinical decision-making and lung disease diagnosis.

Unexplained lung shadows are challenging in respiratory medicine, with both infectious and non-infectious etiologies. Lung biopsy is definitive but invasive, prompting a need for non-invasive alternatives. Metagenomic next-generation sequencing (mNGS) of bronchoalveolar lavage fluid (BALF) is emerging as a promising diagnostic tool.

We retrospectively analyzed 105 patients with unexplained lung shadows, collecting general information, mNGS results from BALF, and clinical diagnosis. We evaluated mNGS's diagnostic performance by comparing with final diagnosis.

mNGS showed good diagnostic performance in differentiating infectious from non-infectious causes. The specificity and accuracy for bacteria and fungi exceeded 90%, while the sensitivity and precision for fungi were lower than for bacteria. Atypical pathogens were frequently identified, especially in mixed infections.

mNGS of BALF is efficient in diagnosing infectious and non-infectious causes of unexplained lung shadows. While effective for bacteria and fungi detection, the sensitivity and precision for fungi are lower.

Fibroblast activation protein (FAP), selectively expressed on activated fibroblasts in proliferating tissues, is emerging as a promising target in oncology. In lung cancer, the leading cause of cancer-related deaths worldwide, [18F]FDG PET/CT has set the bar high and earned widespread recognition in clinical guidelines for its essential role in staging and follow-up. Yet, FAP-targeted imaging agents like FAPI PET/CT have demonstrated significant potential due to their high tumor specificity, rapid tracer uptake, and low background activity. This review focuses on the role of FAPI PET/CT in lung cancer, highlighting its applications in staging, biomarker evaluation, and clinical management. FAP expression correlates with cancer associated fibroblast-driven tumorigenesis in lung cancer, showing higher expression in nonsmall cell lung cancer (NSCLC) than in small cell lung cancer (SCLC) subtypes. Studies reveal that FAPI PET/CT provides comparable or superior detection rates for primary tumors and metastases compared to [18F]FDG PET/CT, particularly in brain, pleural, and bone lesions. It also enhances accuracy in lymph node staging, influencing disease management by enabling surgical resection in cases misclassified by [18F]FDG PET/CT. Despite these advantages, several challenges remain, such as differentiating benign from malignant lesions, assessing FAPI's prognostic implications or its role in treatment response monitoring. Future directions include exploring FAPI-based theranostics, standardizing radiopharmaceuticals, and conducting well-designed, adequately powered prospective trials. FAPI PET/CT represents a transformative diagnostic tool, complementing or potentially surpassing [18F]FDG PET/CT in precision lung cancer care.

Lung cancer is the leading cause of cancer-related deaths worldwide, with invasive non-mucinous adenocarcinoma (INMA) being the most common type and carrying a poor prognosis. In 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histological grading system, which offers more precise prognostic assessments by combining the proportions of major and high-grade histological patterns. Accurate identification of lung INMA grading is crucial for clinical diagnosis, treatment planning, and prognosis evaluation. Currently, non-invasive imaging methods (such as CT, PET/CT, and MRI) are increasingly being studied to predict the new grading of lung INMA, showing promising application prospects. This review outlines the establishment and prognostic efficiency of the new IASLC grading system, highlights the application and latest progress of non-invasive imaging techniques in predicting lung INMA grading, and discusses their role in personalized treatment of lung INMA and future research directions. CRITICAL RELEVANCE STATEMENT: The new IASLC grading system has important prognostic implications for patients with lung invasive non-mucinous adenocarcinoma (INMA), and non-invasive imaging methods can be used to predict it, thereby improving patient prognoses. KEY POINTS: The new IASLC grading system more accurately prognosticates for patients with lung INMA. Preoperative prediction of the new grading is challenging because of the complexity of INMA subtypes. It is feasible to apply non-invasive imaging methods to predict the new IASLC grading system.

Inflammatory disorders historically have been difficult to monitor with conventional PET imaging due to limitations including radiation exposure, lack of validated imaging biomarkers, low spatial resolution, and long acquisition durations. However, the recent development of long-axial field-of-view (LAFOV) PET/CT scanners may allow utilization of novel noninvasive biomarkers to diagnose, predict outcomes, and monitor therapeutic response of inflammatory conditions. LAFOV PET scanners can image most of the human body (if not the entire body) simultaneously in one bed position, with improved signal collection efficiency compared to conventional PET scanners. This allows for imaging with shorter acquisition durations, decreased injected radiotracer dose, prolonged uptake times, or a combination of any of these. In addition, LAFOV PET scanners enable whole-body dynamic imaging. Altogether, these intrinsically superior capabilities in assessing both local and systemic diseases, have allowed these scanners to make increasingly significant contributions to the assessment of inflammatory conditions. This review aims to further explore the role and benefits of LAFOV scanners for imaging various inflammatory conditions while addressing future developments and challenges faced by this technology.

To determine the learning curve for double-port video-assisted thoracoscopic (VATS) lung segmentectomy performed by the same surgical team in our center.

We retrospectively collected clinical data from 193 patients who underwent double-port video-assisted thoracoscopic lung segmentectomy from March 2017 to March 2023. The operative time (OT) was analyzed using the cumulative sum (CUSUM) method, and two stages of the learning curve were obtained. Propensity score matching (PSM) was performed for age, sex, underlying disease, and single-segment resection via radius matching. The OT, estimated amount of intraoperative blood loss, and other complications were analyzed.

We generated a graph of the CUSUM of the OT and found that the learning curve could be differentiated into two stages: the learning stage (1st to 95th surgery) and the proficiency stage (96th to 193rd surgery). Before PSM, there were significant differences in the OT, extent of lymph node station dissection, amount of drainage on the day of surgery, amount of drainage on the first postoperative day, estimated amount of intraoperative blood loss, and length of hospital stay after surgery. There were no significant differences in the average amount of drainage 3 days after surgery, postoperative tube time, or number of intraoperative revolutions. However, after PSM, there were significant differences in the OT, number of lymph node stations removed, amount of drainage on the day of surgery, and amount of drainage on the first postoperative day. There were no significant differences in the estimated amount of intraoperative blood loss, length of hospital stay after surgery, average amount of drainage for 3 days after surgery, postoperative tube time, or number of intraoperative revolutions.

In our center, the learning curve for double-port video-assisted thoracoscopic lung segmentectomy transitions from the learning stage to the proficiency stage when the number of surgical cases reaches 95. There were significant differences in the OT, number of lymph node stations removed, amount of drainage on the day of surgery, and amount of drainage on the first postoperative day.

Imaging methods are fundamental tools to detect and diagnose lung diseases, monitor their treatment and detect possible complications. Each modality, starting from classical chest radiographs and computed tomography, as well as the ever more popular and easily available thoracic ultrasound, magnetic resonance imaging and nuclear medicine methods, and new techniques such as photon counting computed tomography, radiomics and application of artificial intelligence, has its strong and weak points, which we should be familiar with to properly choose between the methods and interpret their results. In this review, we present the indications, strengths and main limitations of methods for chest imaging.

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846-1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice.

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.

The diagnosis of solitary pulmonary nodules has always been a difficult and important point in clinical research, especially granulomatous nodules (GNs) with lobulation and spiculation signs, which are easily misdiagnosed as malignant tumors. Therefore, in this study, we utilised a CT deep learning (DL) model to distinguish GNs with lobulation and spiculation signs from solid lung adenocarcinomas (LADCs), to improve the diagnostic accuracy of preoperative diagnosis.

420 patients with pathologically confirmed GNs and LADCs from three medical institutions were retrospectively enrolled. The regions of interest in non-enhanced CT (NECT) and venous contrast-enhanced CT (VECT) were identified and labeled, and self-supervised labels were constructed. Cases from institution 1 were randomly divided into a training set (TS) and an internal validation set (IVS), and cases from institutions 2 and 3 were treated as an external validation set (EVS). Training and validation were performed using self-supervised transfer learning, and the results were compared with the radiologists' diagnoses.

The DL model achieved good performance in distinguishing GNs and LADCs, with area under curve (AUC) values of 0.917, 0.876, and 0.896 in the IVS and 0.889, 0.879, and 0.881 in the EVS for NECT, VECT, and non-enhanced with venous contrast-enhanced CT (NEVECT) images, respectively. The AUCs of radiologists 1, 2, 3, and 4 were, respectively, 0.739, 0.783, 0.883, and 0.901 in the (IVS) and 0.760, 0.760, 0.841, and 0.844 in the EVS.

A CT DL model showed great value for preoperative differentiation of GNs with lobulation and spiculation signs from solid LADCs, and its predictive performance was higher than that of radiologists.

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.

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.

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.

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

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.

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).

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.

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.

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.

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.

• 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.

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

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.

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

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

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.

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.

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.

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).

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

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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

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

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.

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.

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.

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

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.

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.

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.

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).

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.

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.

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.

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

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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).

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.

• 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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.