Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis

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

OBJECTIVES

18F-fluorodeoxyglucose (FDG) PET/CT has been widely used for the differential diagnosis of cancer. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may make it difficult to differentiate between benign and malignant lesions. It is crucial to find reliable quantitative metabolic parameters to further support the diagnosis. This study aims to evaluate the value of the quantitative metabolic parameters derived from dynamic FDG PET/CT in the differential diagnosis of lung cancer and predicting epidermal growth factor receptor (EGFR) mutation status.

METHODS

We included 147 patients with lung lesions to perform FDG PET/CT dynamic plus static imaging with informed consent. Based on the results of the postoperative pathology, the patients were divided into benign/malignant groups, adenocarcinoma (AC)/squamous carcinoma (SCC) groups, and EGFR-positive (EGFR+)/EGFR-negative (EGFR-) groups. Quantitative parameters including K1, k2, k3, and Ki of each lesion were obtained by applying the irreversible two-tissue compartmental modeling using an in-house Matlab software. The SUV analysis was performed based on conventional static scan data. Differences in each metabolic parameter among the group were analyzed. Wilcoxon rank-sum test, independent-samples T-test, and receiver-operating characteristic (ROC) analysis were performed to compare the diagnostic effects among the differentiated groups. P < 0.05 were considered statistically significant for all statistical tests.

RESULTS

In the malignant group (N = 124), the SUVmax, k2, k3, and Ki were higher than the benign group (N = 23), and all had-better performance in the differential diagnosis (P < 0.05, respectively). In the AC group (N = 88), the SUVmax, k3, and Ki were lower than in the SCC group, and such differences were statistically significant (P < 0.05, respectively). For ROC analysis, Ki with cut-off value of 0.0250 ml/g/min has better diagnostic specificity than SUVmax (AUC = 0.999 vs. 0.70). In AC group, 48 patients further underwent EGFR testing. In the EGFR (+) group (N = 31), the average Ki (0.0279 ± 0.0153 ml/g/min) was lower than EGFR (-) group (N = 17, 0.0405 ± 0.0199 ml/g/min), and the difference was significant (P < 0.05). However, SUVmax and k3 did not show such a difference between EGFR (+) and EGFR (-) groups (P>0.05, respectively). For ROC analysis, the Ki had a cut-off value of 0.0350 ml/g/min when predicting EGFR status, with a sensitivity of 0.710, a specificity of 0.588, and an AUC of 0.674 [0.523-0.802].

CONCLUSION

Although both techniques were specific, Ki had a greater specificity than SUVmax when the cut-off value was set at 0.0250 ml/g/min for the differential diagnosis of lung cancer. At a cut-off value of 0.0350 ml/g/min, there was a 0.710 sensitivity for EGFR status prediction. If EGFR testing is not available for a patient, dynamic imaging could be a valuable non-invasive screening method.

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.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Local imaging to interpret tumor size in F18 fluorodeoxyglucose positron emission tomography/CT in lung cancers

OBJECTIVE

This study aimed to determine the thoracic and extra-thoracic extension of the disease in patients diagnosed with lung cancer and who had whole-body F18-fluorodeoxyglucose positron emission tomography/CT imaging and to investigate whether there is a relationship between tumor size and extrathoracic spread.

METHODS

A total of 308 patients diagnosed with lung cancer were included in this study. These 308 patients were first classified as group 1 (SPN 30 mm>longest lesion diameter ≥10 mm) and group 2 (lung mass (longest lesion diameter ≥30 mm), and then the same patients were classified as group 3 (nodular diameter of ≤20 mm) and group 4 (nodular size of >20 mm). Group 1 was compared with group 2 in terms of extrathoracic metastases. Similarly, group 3 was compared with group 4 in terms of frequency of extrathoracic metastases. F18 fluorodeoxyglucose positron emission tomography/CT examination was used to detect liver, adrenal, bone, and supraclavicular lymph node metastasis, besides extrathoracic metastasis.

RESULTS

Liver, bone, and extrathoracic metastasis in group 1 was statistically lower than in group 2 (p<0.001, p<0.01, and p=0.03, respectively). Liver, extrathoracic, adrenal, and bone metastasis in group 3 was statistically lower than that in group 4 (p<0.001, p=0.01, and p=0.04, p<0.01, respectively). The extrathoracic extension was observed in only one patient in group 3. In addition, liver, adrenal, and bone metastases were not observed in group 3 patients.

CONCLUSION

Positron emission tomography/CT may be more appropriate for cases with a nodule diameter of ≤20 mm. Performing local imaging in patients with a nodule diameter of ≤20 mm could reduce radiation exposure and save radiopharmaceuticals used in positron emission tomography/CT imaging.

Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation

BACKGROUND

Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation.

RESEARCH QUESTION

Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models?

STUDY DESIGN AND METHODS

Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots.

RESULTS

Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23.

INTERPRETATION

The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

[Prevention, Diagnosis, Therapy, and Follow-up of Lung Cancer - Interdisciplinary Guideline of the German Respiratory Society and the German Cancer Society - Abridged Version]

The current S3 Lung Cancer Guidelines are edited with fundamental changes to the previous edition based on the dynamic influx of information to this field:The recommendations include de novo a mandatory case presentation for all patients with lung cancer in a multidisciplinary tumor board before initiation of treatment, furthermore CT-Screening for asymptomatic patients at risk (after federal approval), recommendations for incidental lung nodule management , molecular testing of all NSCLC independent of subtypes, EGFR-mutations in resectable early stage lung cancer in relapsed or recurrent disease, adjuvant TKI-therapy in the presence of common EGFR-mutations, adjuvant consolidation treatment with checkpoint inhibitors in resected lung cancer with PD-L1 ≥ 50%, obligatory evaluation of PD-L1-status, consolidation treatment with checkpoint inhibition after radiochemotherapy in patients with PD-L1-pos. tumor, adjuvant consolidation treatment with checkpoint inhibition in patients withPD-L1 ≥ 50% stage IIIA and treatment options in PD-L1 ≥ 50% tumors independent of PD-L1status and targeted therapy and treatment option immune chemotherapy in first line SCLC patients.Based on the current dynamic status of information in this field and the turnaround time required to implement new options, a transformation to a "living guideline" was proposed.

Lung cancer screening using low-dose CT and FDG-PET in liver transplant recipients

To address the feasibility of implementing a lung cancer screening program in liver transplant recipients (LTR) targeted to detect early-stage lung cancer one hundred twenty-four LTR (89% male, 59.8+/-8.8 y old), who entered the lung cancer screening program at our hospital were reviewed. The results of the diagnostic algorithm using low-dose CT and F-18-fluorodeoxyglycose positron emission tomography (FDG-PET) were analyzed. Lung cancer was detected in 12 LTR (9.7%), most of which corresponded to the non-small cell subtype. Two of the 12 lung cancers were detected in the baseline study (prevalence of 1.6%), whereas 10 patients were diagnosed with lung cancer in the follow-up (incidence of 8.1%). Considering all cancers, 10 of 12 (83.3%) were diagnosed at stage I, one cancer was diagnosed at stage IIIA, and another one at stage IV. The sensitivity, specificity, diagnostic accuracy, and positive and negative predictive values of F-18-fluorodeoxyglycose positron emission tomography to detect malignancy in our cohort were 81.8%,100%, 99.3%, 100%, and 99.3%, respectively. A carefully followed multidisciplinary lung cancer screening algorithm in LTR that includes F-18-fluorodeoxyglycose positron emission tomography and low-dose CT allows lung cancer to be diagnosed at an early stage while reducing unnecessary invasive procedures.

Quantitative parameters of static imaging and fast kinetics imaging in 18F-FDG total-body PET/CT for the assessment of histological feature of pulmonary lesions

Background

To investigate the value of quantitative parameters related to static imaging and fast kinetics imaging of total-body (TB) 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in differentiating benign from malignant pulmonary lesions and squamous cell carcinoma (SCC) from adenocarcinoma (AC) and to analyze the correlation of each parameter with the Ki-67 index.

Methods

A total of 108 patients with pulmonary lesions from July 2021 to May 2022 in the Henan Provincial People's Hospital, China, were consecutively recruited for TB 18F-FDG PET/CT in this prospective study. Static imaging parameters maximum standardized uptake value (SUVmax) and fast kinetics imaging parameters transport constant (K1), rate constants (k2), time delay (td), and fractional blood volume (vb) were calculated and compared. The area under the receiver operating characteristic (ROC) curve (AUC), Delong test, Logistic regression analyses, and Pearson correlation were used to assess diagnostic efficacy, find independent predictors and analyse correlations respectively.

Results

Malignant lesions had higher SUVmax and K1 and lower vb than benign lesions, and SCC had higher SUVmax and K1 and lower td and vb than AC (all P<0.05). For the differentiation of benign and malignant lesions, SUVmax, K1, and vb were independent predictors, and AUC (SUVmax + K1+ vb) =0.909 (95% CI: 0.839-0.956), AUC (SUVmax) =0.883 (95% CI: 0.807-0.937), AUC (K1) =0.810 (95% CI: 0.723-0.879), and AUC (vb) =0.746 (95% CI: 0.653-0.825), where AUC (SUVmax + K1+ vb) was significantly different from AUC (K1), AUC (vb) (Z=3.006, 3.965, all P<0.05). For the differentiation of SCC and AC, SUVmax, K1, td, and vb were independent predictors, and AUC (SUVmax + K1+ td + vb) =0.946 (95% CI: 0.840-0.991), AUC (SUVmax) =0.818 (95% CI: 0.680-0.914), AUC (K1) =0.770 (95% CI: 0.626-0.879), AUC (vb) =0.737 (95% CI: 0.590-0.853), and AUC (td) =0.669 (95% CI: 0.510-0.791), where AUC (SUVmax + K1+ td + vb) was significantly different from AUC (SUVmax), AUC (K1), AUC (vb), and AUC (td) (Z=2.269, 2.821, 2.848, and 3.276, all P<0.05). SUVmax and K1 were moderately and mildly positively correlated with the Ki-67 index (r=0.541, 0.452, all P<0.05), respectively.

Conclusions

Quantitative parameters of static imaging and fast kinetics imaging in 18F-FDG total-body PET/CT can be used to differentiate benign from malignant pulmonary lesions and SCC from AC and to assess Ki-67 expression.

Differentiating Lung Nodules Due to Coccidioides from Those Due to Lung Cancer Based on Radiographic Appearance

BACKGROUND

Coccidioidomycosis (cocci) is an endemic fungal disease that can cause asymptomatic or post-symptomatic lung nodules which are visible on chest CT scanning. Lung nodules are common and can represent early lung cancer. Differentiating lung nodules due to cocci from those due to lung cancer can be difficult and lead to invasive and expensive evaluations.

MATERIALS AND METHODS

We identified 302 patients with biopsy-proven cocci or bronchogenic carcinoma seen in our multidisciplinary nodule clinic. Two experienced radiologists who were blinded to the diagnosis read the chest CT scans and identified radiographic characteristics to determine their utility in differentiating lung cancer nodules from those due to cocci.

RESULTS

Using univariate analysis, we identified several radiographic findings that differed between lung cancer and cocci infection. We then entered these variables along with age and gender into a multivariate model and found that age, nodule diameter, nodule cavitation, presence of satellite nodules and radiographic presence of chronic lung disease differed significantly between the two diagnoses. Three findings, cavitary nodules, satellite nodules and chronic lung disease, have sufficient discrimination to potentially be useful in clinical decision-making.

CONCLUSIONS

Careful evaluation of the three obtained radiographic findings can significantly improve our ability to differentiate benign coccidioidomycosis infection from lung cancer in an endemic region for the fungal disease. Using these data may significantly reduce the cost and risk associated with distinguishing the cause of lung nodules in these patients by preventing unnecessary invasive studies.

Dual targeting PET tracer [68Ga]Ga-FAPI-RGD in patients with lung neoplasms: a pilot exploratory study

Rationale: Early discovery, accurate diagnosis, and staging of lung cancer is essential for patients to receive appropriate treatment. PET/CT has become increasingly recognized as a valuable imaging modality for these patients, but there remains room for improvement in PET tracers. We aimed to evaluate the feasibility of using [⁶⁸Ga]Ga-FAPI-RGD, a dual-targeting heterodimeric PET tracer that recognizes both fibroblast activation protein (FAP) and integrin α_vβ₃ for detecting lung neoplasms, by comparing it with [¹⁸F]FDG and single-targeting tracers [⁶⁸Ga]Ga-RGD and [⁶⁸Ga]Ga-FAPI. Methods: This was a pilot exploratory study of patients with suspected lung malignancies. All 51 participants underwent [⁶⁸Ga]Ga-FAPI-RGD PET/CT, of which: 9 participants received dynamic scans, 44 participants also underwent [¹⁸F]FDG PET/CT scan within two weeks, 9 participants underwent [⁶⁸Ga]Ga-FAPI PET/CT scan and 10 participants underwent [⁶⁸Ga]Ga-RGD PET/CT scan. The final diagnosis was made based on histopathological analyses and clinical follow-up reports. Results: Among those who underwent dynamic scans, the uptake of pulmonary lesions increased over time. The optimal timepoint for a PET/CT scan was identified to be 2 h post-injection. [⁶⁸Ga]Ga-FAPI-RGD had a higher detection rate of primary lesions than [¹⁸F]FDG (91.4% vs. 77.1%, p < 0.05), higher tumor uptake (SUVmax, 6.9 ± 5.3 vs. 5.3 ± 5.4, p < 0.001) and higher tumor-to-background ratio (10.0 ± 8.4 vs. 9.0 ± 9.1, p < 0.05), demonstrated better accuracy in mediastinal lymph node evaluation (99.7% vs. 90.9%, p < 0.001), and identified more metastases (254 vs. 220). There was also a significant difference between the uptake of [⁶⁸Ga]Ga-FAPI-RGD and [⁶⁸Ga]Ga-RGD of primary lesions (SUVmax, 5.8 ± 4.4 vs. 2.3 ± 1.3, p < 0.001). Conclusion: In our small scale cohort study, [⁶⁸Ga]Ga-FAPI-RGD PET/CT gave a higher primary tumor detection rate, higher tracer uptake, and improved detection of metastases compared with [¹⁸F]FDG PET/CT, and [⁶⁸Ga]Ga-FAPI-RGD also had advantages over [⁶⁸Ga]Ga-RGD and was non-inferior to [⁶⁸Ga]Ga-FAPI. We thus provide proof-of-concept for using [⁶⁸Ga]Ga-FAPI-RGD PET/CT for diagnosing lung cancer. With the stated advantages, the dual-targeting FAPI-RGD should also be explored for therapeutic use in future studies.

High risk lung nodule: A multidisciplinary approach to diagnosis and management

Pulmonary nodules are often discovered incidentally during CT scans performed for other reasons. While the vast majority of nodules are benign, a small percentage may represent early-stage lung cancer with the potential for curative treatments. With the growing use of CT for both clinical purposes and lung cancer screening, the number of pulmonary nodules detected is expected to increase substantially. Despite well-established guidelines, many nodules do not receive proper evaluation due to a variety of factors, including inadequate coordination of care and financial and social barriers. To address this quality gap, novel approaches such as multidisciplinary nodule clinics and multidisciplinary boards may be necessary. As pulmonary nodules may indicate early-stage lung cancer, it is crucial to adopt a risk-stratified approach to identify potential lung cancers at an early stage, while minimizing the risk of harm and expense associated with over investigation of low-risk nodules. This article, authored by multiple specialists involved in nodule management, delves into the diagnostic approach to lung nodules. It covers the process of determining whether a patient requires tissue sampling or continued surveillance. Additionally, the article provides an in-depth examination of the various biopsy and therapeutic options available for malignant lung nodules. The article also emphasizes the significance of early detection in reducing lung cancer mortality, especially among high-risk populations. Furthermore, it addresses the creation of a comprehensive lung nodule program, which involves smoking cessation, lung cancer screening, and systematic evaluation and follow-up of both incidental and screen-detected nodules.

Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors

BACKGROUND

Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis.

RESEARCH QUESTION

What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model?

STUDY DESIGN AND METHODS

Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model.

RESULTS

In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold.

INTERPRETATION

The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.

False Positive Positron Emission Tomography/Computed Tomography (PET/CT) Requiring Biopsy for Proper Staging of Lung Cancer

Lung cancer is the leading cause of cancer death in women in developed countries. Staging is crucial in determining the treatment modality. Different treatment modalities for lung cancer include surgery, radiation therapy, and chemotherapy. PET/CT is the most sensitive and accurate modality for detecting hilar, mediastinal, and metastatic disease except in the brain. PET/CT scan often upstages the disease. PET/CT has also been shown to have false positive results. We present the case of a 72-year-old female who had a false positive finding on PET/CT, which would have changed the management process and outcome of her disease.

Glycolytic Plasticity of Metastatic Lung Cancer Captured by Noninvasive 18F-FDG PET/CT and Serum 1H-NMR Analysis: An Orthotopic Murine Model Study

We aim to establish a noninvasive diagnostic platform to capture early phenotypic transformation for metastasis using 18F-FDG PET and 1H-NMR-based serum metabolomics. Mice with implantation of NCI-H460 cells grew only primary lung tumors in the localized group and had both primary and metastatic lung tumors in the metastatic group. The serum metabolites were analyzed using 1H-NMR at the time of PET/CT scan. The glycolysis status and cell proliferation were validated by Western blotting and staining. A receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of SUVmean and serum metabolites in metastasis. In the metastatic mice, the SUVmean of metastatic tumors was significantly higher than that of primary lung tumors in PET images, which was supported by elevated glycolytic protein expression of HK2 and PKM2. The serum pyruvate level in the metastatic group was significantly lower than that in the localized group, corresponding to increased pyruvate-catalyzed enzyme and proliferation rates in metastatic tumors. In diagnosing localized or metastatic tumors, the areas under the ROC curves of SUVmean and pyruvate were 0.92 and 0.91, respectively, with p < 0.05. In conclusion, the combination of 18F-FDG PET and 1H-NMR-based serum metabolomics demonstrated the feasibility of a glycolytic platform for diagnosing metastatic lung cancers.

Current investigative modalities for detecting and staging lung cancers: a comprehensive summary

This narrative review compares the advantages and drawbacks of imaging and other investigation modalities which currently assist with lung cancer diagnosis and staging, as well as those which are not routinely indicated for this. We examine plain film radiography, computed tomography (CT) (alone, as well as in conjunction with positron emission tomography (PET)), magnetic resonance imaging (MRI), ultrasound, and newer techniques such as image-guided bronchoscopy (IGB) and robotic bronchoscopy (RB). While a chest X-ray is the first-line imaging investigation in patients presenting with symptoms suggestive of lung cancer, it has a high positive predictive value (PPV) even after negative X-ray findings, which calls into question its value as part of a potential national screening programme. CT lowers the mortality for high-risk patients when compared to X-ray and certain scoring systems, such as the Brock model can guide the need for further imaging, like PET-CT, which has high sensitivity and specificity for diagnosing solitary pulmonary nodules as malignant, as well as for assessing small cell lung cancer spread. In practice, PET-CT is offered to everyone whose lung cancer is to be treated with a curative intent. In contrast, MRI is only recommended for isolated distant metastases. Similarly, ultrasound imaging is not used for diagnosis of lung cancer but can be useful when there is suspicion of intrathoracic lymph node involvement. Ultrasound imaging in the form of endobronchial ultrasonography (EBUS) is often used to aid tissue sampling, yet the diagnostic value of this technique varies widely between studies. RB is another novel technique that offers an alternative way to biopsy lesions, but further research on it is necessary. Lastly, thoracic surgical biopsies, particularly minimally invasive video-assisted techniques, have been used increasingly to aid in diagnosis and staging.

Lung cancer histopathological image classification using wavelets and AlexNet

Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.

Performance and Prospects of [68Ga]Ga-FAPI PET/CT Scans in Lung Cancer

Fibroblast activation protein (FAP) could be a promising target for tumor imaging and therapy, as it is expressed in >90% of epithelial cancers. A high level of FAP-expression might be associated with worse prognosis in several cancer types, including lung cancer. FAPI binds this protein and allows for labelling to Gallium-68, as well as several therapeutic radiopharmaceuticals. As FAP is only expressed at insignificant levels in adult normal tissue, FAPI provides a highly specific tumor-marker for many epithelial cancers. In this review, current information on the use of [68Ga]Ga-FAPI PET/CT in lung cancer is presented. [68Ga]Ga-FAPI shows a high uptake (standardized uptake value = SUVmax) and tumor-to-background ratio (TBR) in primary lung cancer lesions, as well as in metastatic lesions of other tumor types located in the lung and in lung cancer metastases located throughout the body. Where a comparison was made to [18F]FDG PET/CT, [68Ga]Ga-FAPI showed a similar or higher SUVmax and TBR. In brain and bone metastases, [68Ga]Ga-FAPI PET/CT outperformed [18F]FDG PET/CT. In addition to this strong diagnostic performance, a possible prognostic value of [68Ga]Ga-FAPI PET/CT in lung cancer is proposed.

Dynamic FDG-PET imaging for differentiating metastatic from non-metastatic lymph nodes of lung cancer

OBJECTIVES

18F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer.

METHODS

One hundred and eight patients with pulmonary nodules were enrolled to perform 18F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K1, k2, k3 and Ki and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was computed subsequently as a separate marker. We further divided the LNs into mediastinal LNs (N=82) and pulmonary hilar LNs (N=53). Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analysis was performed on each parameter to compare the diagnostic efficacy in differentiating lymph node metastases from inflammatory uptake. P<0.05 were considered statistically significant.

RESULTS

Among the 135 FDG-avid LNs confirmed by pathology, 49 LNs were non-metastatic, and 86 LNs were metastatic. LN-SUVmax, MBP-SUVmax, LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter couldn't well differentiate metastatic from non-metastatic LNs (P>0.05). However, LN-SUVmax/PT-SUVmax have good performance in the differential diagnosis of non-metastatic and metastatic LNs (P=0.039). Dynamic metabolic parameters in addition to k3, the parameters including K1, k2, Ki, and Ki/K1, on the other hand, have good performance in the differential diagnosis of metastatic and non-metastatic LNs (P=0.045, P=0.001, P=0.001, P=0.001, respectively). For ROC analysis, the metabolic parameters Ki (AUC of 0.672 [0.579-0.765], sensitivity 0.395, specificity 0.918) and Ki/K1 (AUC of 0.673 [0.580-0.767], sensitivity 0.570, specificity 0.776) have good performance in the differential diagnosis of metastatic from non-metastatic LNs than SUVmax (AUC of 0.596 [0.498-0.696], sensitivity 0.826, specificity 0.388), included the mediastinal region and pulmonary hilar region.

CONCLUSION

Compared with SUVmax, quantitative parameters such as K1, k2, Ki and Ki/K1 showed promising results for differentiation of metastatic and non-metastatic LNs with high uptake. The Ki and Ki/K1 had a high differential diagnostic value both in the mediastinal region and pulmonary hilar region.

Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules

Objectives

This study aimed to evaluate the ability of ¹⁸fluorine-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN).

Methods

Data of 48 patients with SPN detected on ¹⁸F-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC).

Results

The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs ~0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%).

Conclusion

Machine learning based on ¹⁸F-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.

18F-FSPG PET imaging for the evaluation of indeterminate pulmonary nodules

Background

¹⁸F-fluorodeoxyglucose (FDG) PET/CT is recommended for evaluation of intermediate-risk indeterminate pulmonary nodules (IPNs). While highly sensitive, the specificity of FDG remains suboptimal for differentiating malignant from benign nodules, particularly in areas where fungal lung diseases are prevalent. Thus, a cancer-specific imaging probe is greatly needed. In this study, we tested the hypothesis that a PET radiotracer (S)-4-(3-[¹⁸F]-fluoropropyl)-L-glutamic acid (FSPG) improves the diagnostic accuracy of IPNs compared to ¹⁸F-FDG PET/CT.

Methods

This study was conducted at a major academic medical center and an affiliated VA medical center. Twenty-six patients with newly discovered IPNs 7-30mm diameter or newly diagnosed lung cancer completed serial PET/CT scans utilizing ¹⁸F-FDG and ¹⁸F-FSPG, without intervening treatment of the lesion. The scans were independently reviewed by two dual-trained diagnostic radiology and nuclear medicine physicians. Characteristics evaluated included quantitative SUVmax values of the pulmonary nodules and metastases.

Results

A total of 17 out of 26 patients had cancer and 9 had benign lesions. ¹⁸F-FSPG was negative in 6 of 9 benign lesions compared to 7 of 9 with ¹⁸F-FDG. ¹⁸F-FSPG and ¹⁸F-FDG were positive in 14 of 17 and 12 of 17 malignant lesions, respectively. ¹⁸F-FSPG detected brain and intracardiac metastases missed by ¹⁸F-FDG PET in one case, while ¹⁸F-FDG detected a metastasis to the kidney missed by ¹⁸F-FSPG.

Conclusion

In this pilot study, there was no significant difference in overall diagnostic accuracy between ¹⁸F-FSPG and ¹⁸F-FDG for the evaluation of IPNs and staging of lung cancer. Additional studies will be needed to determine the clinical utility of this tracer in the management of IPNs and lung cancer.

The impact of the lung EDRN-CVC on Phase 1, 2, & 3 biomarker validation studies

The Early Detection Research Network’s (EDRN) purpose is to discover, develop and validate biomarkers and imaging methods to detect early-stage cancers or at-risk individuals. The EDRN is composed of sites that fall into four categories: Biomarker Developmental Laboratories (BDL), Biomarker Reference Laboratories (BRL), Clinical Validation Centers (CVC) and Data Management and Coordinating Centers. Each component has a crucial role to play within the mission of the EDRN. The primary role of the CVCs is to support biomarker developers through validation trials on promising biomarkers discovered by both EDRN and non-EDRN investigators. The second round of funding for the EDRN Lung CVC at Vanderbilt University Medical Center (VUMC) was funded in October 2016 and we intended to accomplish the three missions of the CVCs: To conduct innovative research on the validation of candidate biomarkers for early cancer detection and risk assessment of lung cancer in an observational study; to compare biomarker performance; and to serve as a resource center for collaborative research within the Network and partner with established EDRN BDLs and BRLs, new laboratories and industry partners. This report outlines the impact of the VUMC EDRN Lung CVC and describes the role in promoting and validating biological and imaging biomarkers.

Evaluating the Patient With a Pulmonary Nodule: A Review

IMPORTANCE

Pulmonary nodules are identified in approximately 1.6 million patients per year in the US and are detected on approximately 30% of computed tomographic (CT) images of the chest. Optimal treatment of an individual with a pulmonary nodule can lead to early detection of cancer while minimizing testing for a benign nodule.

OBSERVATIONS

At least 95% of all pulmonary nodules identified are benign, most often granulomas or intrapulmonary lymph nodes. Smaller nodules are more likely to be benign. Pulmonary nodules are categorized as small solid (<8 mm), larger solid (≥8 mm), and subsolid. Subsolid nodules are divided into ground-glass nodules (no solid component) and part-solid (both ground-glass and solid components). The probability of malignancy is less than 1% for all nodules smaller than 6 mm and 1% to 2% for nodules 6 mm to 8 mm. Nodules that are 6 mm to 8 mm can be followed with a repeat chest CT in 6 to 12 months, depending on the presence of patient risk factors and imaging characteristics associated with lung malignancy, clinical judgment about the probability of malignancy, and patient preferences. The treatment of an individual with a solid pulmonary nodule 8 mm or larger is based on the estimated probability of malignancy; the presence of patient comorbidities, such as chronic obstructive pulmonary disease and coronary artery disease; and patient preferences. Management options include surveillance imaging, defined as monitoring for nodule growth with chest CT imaging, positron emission tomography-CT imaging, nonsurgical biopsy with bronchoscopy or transthoracic needle biopsy, and surgical resection. Part-solid pulmonary nodules are managed according to the size of the solid component. Larger solid components are associated with a higher risk of malignancy. Ground-glass pulmonary nodules have a probability of malignancy of 10% to 50% when they persist beyond 3 months and are larger than 10 mm in diameter. A malignant nodule that is entirely ground glass in appearance is typically slow growing. Current bronchoscopy and transthoracic needle biopsy methods yield a sensitivity of 70% to 90% for a diagnosis of lung cancer.

CONCLUSIONS AND RELEVANCE

Pulmonary nodules are identified in approximately 1.6 million people per year in the US and approximately 30% of chest CT images. The treatment of an individual with a pulmonary nodule should be guided by the probability that the nodule is malignant, safety of testing, the likelihood that additional testing will be informative, and patient preferences.

A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Cystic Primary Lung Cancer: Evolution of Computed Tomography Imaging Morphology Over Time

PURPOSE

Primary lung cancers associated with cystic airspaces are increasingly being recognized; however, there is a paucity of data on their natural history. We aimed to evaluate the prevalence, pathologic, and imaging characteristics of cystic lung cancer in a regional thoracic surgery center with a focus on the evolution of computed tomography morphology over time.

MATERIALS AND METHODS

Consecutive patients referred for potential surgical management of primary lung cancer between January 2016 and December 2018 were included. Clinical, imaging, and pathologic data were collected at the time of diagnosis and at the time of the oldest computed tomography showing the target lesion. Descriptive analysis was carried out.

RESULTS

A total of 441 cancers in 431 patients (185 males, 246 females), median age 69.6 years (interquartile range: 62.6 to 75.3 y), were assessed. Overall, 41/441 (9.3%) primary lung cancers were cystic at the time of diagnosis. The remaining showed solid (67%), part-solid (22%), and ground-glass (2%) morphologies. Histopathology of the cystic lung cancers at diagnosis included 31/41 (76%) adenocarcinomas, 8/41 (20%) squamous cell carcinomas, 1/41 (2%) adenosquamous carcinoma, and 1/41 (2%) unspecified non-small cell lung carcinoma. Overall, 8/34 (24%) cystic cancers at the time of diagnosis developed from different morphologic subtype precursor lesions, while 8/34 (24%) cystic precursor lesions also transitioned into part-solid or solid cancers at the time of diagnosis.

CONCLUSIONS

This study demonstrates that cystic airspaces within lung cancers are not uncommon, and may be seen transiently as cancers evolve. Increased awareness of the spectrum of cystic lung cancer morphology is important to improve diagnostic accuracy and lung cancer management.

A rare etiology of pulmonary nodules

Introduction

Pulmonary nodules are a frequent finding on chest imaging studies, with differential including multiple benign entities, but malignancy is often also a concern. Computed Tomography (CT) and Fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET) scans have improved the characterization of pulmonary nodules. However, many nodules remain indeterminate and require periodic monitoring. Here we report two nodular pulmonary amyloidosis cases as a rare etiology of enlarging pulmonary nodules with FDG avidity.

Case presentation

Case 1: 75-year-old woman with a history of asthma, emphysema, bronchiectasis, and a 48 pack-year smoking history was found to have subcentimeter groundglass pulmonary nodules in the right lower lobe (RLL). Follow-up imaging demonstrated an increased solid component of a RLL bulla associated with mild FDG uptake on PET scan. A CT-guided biopsy revealed amyloid deposition. Case 2: 77-year-old man with a history of interstitial lung disease, asbestos exposure, prior tobacco use, and atrial fibrillation treated with amiodarone was found to have a 1.6cm RLL nodule. Follow-up imaging identified an interval increase to 2.0cm associated with moderate FDG uptake on PET scan. Transthoracic biopsy identified amyloid deposition.

Discussion

Nodular pulmonary amyloidosis is a rare form of amyloidosis which may present as an enlarging pulmonary nodule with FDG avidity, raising concern for malignancy. A CT-guided biopsy is a safe way to establish a diagnosis. Recent studies have demonstrated an association between nodular pulmonary amyloidosis and marginal zone lymphomas, which warrants longitudinal follow-up for evolution to lymphoproliferative disorder.

Macro-vasculature and positron emission tomography (PET) standardized uptake value in lung cancer patients

PURPOSE

To investigate the relationship between macro-vasculature features and the standardized uptake value (SUV) of positron emission tomography (PET), which is a surrogate for the metabolic activity of a lung tumor.

METHODS

We retrospectively analyzed a cohort of 90 lung cancer patients who had both chest CT and PET-CT examinations before receiving cancer treatment. The SUVs in the medical reports were used. We quantified three macro-vasculature features depicted on CT images (i.e., vessel number, vessel volume, and vessel tortuosity) and several tumor features (i.e., volume, maximum diameter, mean diameter, surface area, and density). Tumor size (e.g., volume) was used as a covariate to adjust for possible confounding factors. Backward stepwise multiple regression analysis was performed to develop a model for predicting PET SUV from the relevant image features. The Bonferroni correction was used for multiple comparisons.

RESULTS

PET SUV was positively correlated with vessel volume (R = 0.44, p<0.001) and vessel number (R = 0.44, p<0.001) but not with vessel tortuosity (R = 0.124, p >0.05). After adjusting for tumor size, PET SUV was significantly correlated with vessel tortuosity (R = 0.299, p = 0.004) and vessel number (R = 0.224, p = 0.035), but only marginally correlated with vessel volume (R = 0.187, p = 0.079). The multiple regression model showed a performance with an R-Squared of 0.391 and an adjusted R-Squared of 0.355 (p<0.001).

CONCLUSIONS

Our investigations demonstrate the potential relationship between macro-vasculature and PET SUV and suggest the possibility of inferring the metabolic activity of a lung tumor from chest CT images. This article is protected by copyright. All rights reserved.

Current Evidence for Stereotactic Body Radiotherapy in Lung Metastases

Lung metastases are the second most common malignant neoplasms of the lung. It is estimated that 20–54% of cancer patients have lung metastases at some point during their disease course, and at least 50% of cancer-related deaths occur at this stage. Lung metastases are widely accepted to be oligometastatic when five lesions or less occur separately in up to three organs. Stereotactic body radiation therapy (SBRT) is a noninvasive, safe, and effective treatment for metastatic lung disease in carefully selected patients. There is no current consensus on the ideal dose and fractionation for SBRT in lung metastases, and it is the subject of study in ongoing clinical trials, which examines different locations in the lung (central and peripheral). This review discusses current indications, fractionations, challenges, and technical requirements for lung SBRT.

Clinical value of 3'-deoxy-3'-[18F]fluorothymidine-positron emission tomography for diagnosis, staging and assessing therapy response in lung cancer

Lung cancer has the highest mortality rate of any tumour type. The main driver of lung tumour growth and development is uncontrolled cellular proliferation. Poor patient outcomes are partly the result of the limited range of effective anti-cancer therapies available and partly due to the limited accuracy of biomarkers to report on cell proliferation rates in patients. Accordingly, accurate methods of diagnosing, staging and assessing response to therapy are crucial to improve patient outcomes. One effective way of assessing cell proliferation is to employ non-invasive evaluation using 3'-deoxy-3'-[¹⁸F]fluorothymidine ([¹⁸F]FLT) positron emission tomography [¹⁸F]FLT-PET. [¹⁸F]FLT, unlike the most commonly used PET tracer [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG), can specifically report on cell proliferation and does not accumulate in inflammatory cells. Therefore, this radiotracer could exhibit higher specificity in diagnosis and staging, along with more accurate monitoring of therapy response at early stages in the treatment cycle. This review summarises and evaluates published studies on the clinical use of [¹⁸F]FLT to diagnose, stage and assess response to therapy in lung cancer.

Risk stratification of indeterminate pulmonary nodules

PURPOSE OF REVIEW

Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions.

RECENT FINDINGS

The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials.

SUMMARY

Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.

Revising Conventional Wisdom About Histoplasmosis in the United States

Studies performed during the 1940s-1960s continue to serve as the foundation of the epidemiology of histoplasmosis given that many knowledge gaps persist regarding its geographic distribution, prevalence, and burden in the United States. We explore 3 long-standing, frequently cited, and somewhat incomplete epidemiologic beliefs about histoplasmosis: (1) histoplasmosis is the most common endemic mycosis in the United States, (2) histoplasmosis is endemic to the Ohio and Mississippi River Valleys, and (3) histoplasmosis is associated with bird or bat droppings. We also summarize recent insights about the clinical spectrum of histoplasmosis and changes in underlying conditions associated with the severe forms. Continuing to identify prevention opportunities will require better epidemiologic data, better diagnostic testing, and greater awareness about this neglected disease among health care providers, public health professionals, and the general public.

Role of the Thoracic Radiologist in the Evaluation and Management of Solid and Subsolid Lung Nodules

In this review, the authors describe the imaging characteristics of solid and subsolid nodules as well as their management recommendations including the use of image-guided percutaneous biopsy and preoperative coil localization. Using case presentations, they offer practical management tips for the most commonly encountered nodule nodules in a thoracic surgical practice.

A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans

Simple Summary

The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging.

Abstract

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (S_t, N = 145), validation (S_v, N = 145), and independent validation (S_iv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on S_t, S_v, and S_iv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in S_v that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on S_v.

Surgery or Non-surgical Treatment of ≤8 mm Non-small Cell Lung Cancer: A Population-Based Study

Background: Timing for intervention of small indeterminate pulmonary nodules has long been a topic of debate given the low incidence of malignancy and difficulty in obtaining a definite preoperative diagnosis. We sought to determine survival outcomes of surgical and non-surgical managements in non-small cell lung cancer (NSCLC) ≤8 mm, which may provide a reference for prospective decision-making for patients with suspected NSCLC. Method: A total of 1,652 patients with Stage IA NSCLC ≤8 mm were identified from the Surveillance, Epidemiology, and End Results (SEER) database and categorized into surgery and non-surgery groups. Chi-square test, t-test and Mann-Whitney U test were used to compare the baseline characteristics between groups. Survival curves were depicted using Kaplan-Meier method and compared by log-rank test. Cox proportional hazard model was used for univariate and multivariate analyses. Adjustment of confounding factors between groups was performed by propensity score matching. Results: The surgery and non-surgery groups included 1,438 and 208 patients, respectively. Patients in surgery group demonstrated superior survival outcome than patients in non-surgery group both before [overall survival (OS): HR, 16.22; 95% CI, 11.48-22.91, p < 0.001; cancer-specific survival (CSS): HR, 49.6; 95% CI, 31.09-79.11, p < 0.001] and after (OS: HR, 3.12; 95% CI, 2.40-4.05, p < 0.001; CSS: HR, 3.85; 95% CI, 2.74-5.40, p < 0.001) propensity score matching. The 30-day mortality rates were 3.1 and 12.0% in surgery and non-surgery groups, respectively. Multivariate analysis suggested age, sex, race, tumor size, grade, pathological stage were all independent prognostic factors in patients with ≤8 mm NSCLC. A comparison of surgical resections revealed a survival superiority of lobectomy over sub-lobectomy. In terms of CSS, no statistically significant difference was found between segmentectomy and wedge resection. Conclusion: The current SEER database showed better prognosis of surgical resection than non-surgical treatment in patients with ≤8 mm NSCLC. However, the factors that should be essentially included in the proper propensity-matched analysis, such as comorbidity, cardiopulmonary function and performance status were unavailable and the true superiority or inferiority should be examined further by ongoing randomized trial, especially comparing surgery and stereotactic body irradiation.

Mediastinal lymphadenopathy: a practical approach

Introduction: Mediastinal lymphadenopathy is secondary to various benign and malignant etiologies. There is a variation in the underlying cause in different demographic settings. The initial clue to the presence of enlarged mediastinal lymph nodes is through thoracic imaging modalities. Malignancy (Lung cancer, lymphoma, and extrathoracic cancer) and granulomatous conditions (sarcoidosis and tuberculosis) are the most common causes. For a confident diagnosis, the clinician must choose from several available options and integrate the clinical, radiological, and pathology findings. An accurate diagnosis is necessary for optimal management.Areas covered: We performed a search of the PUBMED database to identify relevant articles on the causes, imaging modalities, and interventional modalities to diagnose these conditions. We discuss a practical approach toward the evaluation of a patient with mediastinal lymphadenopathy.Expert opinion: Mediastinal lymphadenopathy is a commonly encountered clinical problem. Treating physicians need to be aware of the clinico-radiological manifestations of the common diagnostic entities. Selecting an appropriate tissue diagnosis modality is crucial, with an intent to use the least invasive technique with good diagnostic yield. Endosonographic modalities (EBUS-TBNA, EUS-FNA, and EUS-B-FNA) have emerged as the cornerstone to most patients' diagnosis. An accurate diagnosis translates into favorable treatment outcomes.

Early diagnosis of lung cancer: which is the optimal choice?

The prognosis of lung cancer patients with different clinical stages is significantly different. The 5-year survival of stage IA groups can exceed 90%, while patients with stage IV can be less than 10%. Therefore, early diagnosis is extremely important for lung cancer patients. This research focused on various diagnosis methods of early lung cancer, including imaging screening, bronchoscopy, and emerging potential liquid biopsies, as well as volatile organic compounds, autoantibodies, aiming to improve the early diagnosis rate and explore feasible and effective early diagnosis strategies.

Inflammatory Lesions Mimicking Chest Malignancy: CT, Bronchoscopy, EBUS, and PET Evaluation From an Oncology Referral Center

Infective and inflammatory diseases can mimic malignancy of the lung. Granulomatous inflammations are common causes of pulmonary nodule, mass, or nodal disease. Systemic infection or inflammation also commonly involves the lung that may raise suspicion of a malignant process. Even in patients with a known malignancy, inflammatory diseases can simulate new metastasis or disease progression. Knowledge of the imaging features of these diseases is essential to prevent missed or overdiagnosis of malignancy. Radiologists also need to be familiar with the scope and limitations of bronchoscopy, endobronchial ultrasound, PET-CT, and biopsy to guide clinical management. In this review, we discuss the imaging features and diagnostic approach of common mimickers of chest malignancy that involve the chest wall, pleura, lung parenchyma, and mediastinal nodes.

State of the art of 18F-FDG PET/CT application in inflammation and infection: a guide for image acquisition and interpretation

AIM

The diagnosis, severity and extent of a sterile inflammation or a septic infection could be challenging since there is not one single test able to achieve an accurate diagnosis. The clinical use of 18F-fluorodeoxyglucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) imaging in the assessment of inflammation and infection is increasing worldwide. The purpose of this paper is to achieve an Italian consensus document on [¹⁸F]FDG PET/CT or PET/MRI in inflammatory and infectious diseases, such as osteomyelitis (OM), prosthetic joint infections (PJI), infective endocarditis (IE), prosthetic valve endocarditis (PVE), cardiac implantable electronic device infections (CIEDI), systemic and cardiac sarcoidosis (SS/CS), diabetic foot (DF), fungal infections (FI), tuberculosis (TBC), fever and inflammation of unknown origin (FUO/IUO), pediatric infections (PI), inflammatory bowel diseases (IBD), spine infections (SI), vascular graft infections (VGI), large vessel vasculitis (LVV), retroperitoneal fibrosis (RF) and COVID-19 infections.

METHODS

In September 2020, the inflammatory and infectious diseases focus group (IIFG) of the Italian Association of Nuclear Medicine (AIMN) proposed to realize a procedural paper about the clinical applications of [¹⁸F]FDG PET/CT or PET/MRI in inflammatory and infectious diseases. The project was carried out thanks to the collaboration of 13 Italian nuclear medicine centers, with a consolidate experience in this field. With the endorsement of AIMN, IIFG contacted each center, and the pediatric diseases focus group (PDFC). IIFG provided for each team involved, a draft with essential information regarding the execution of [¹⁸F]FDG PET/CT or PET/MRI scan (i.e., indications, patient preparation, standard or specific acquisition modalities, interpretation criteria, reporting methods, pitfalls and artifacts), by limiting the literature research to the last 20 years. Moreover, some clinical cases were required from each center, to underline the teaching points. Time for the collection of each report was from October to December 2020.

RESULTS

Overall, we summarized 291 scientific papers and guidelines published between 1998 and 2021. Papers were divided in several sub-topics and summarized in the following paragraphs: clinical indications, image interpretation criteria, future perspectivess and new trends (for each single disease), while patient preparation, image acquisition, possible pitfalls and reporting modalities were described afterwards. Moreover, a specific section was dedicated to pediatric and PET/MRI indications. A collection of images was described for each indication.

CONCLUSIONS

Currently, [¹⁸F]FDG PET/CT in oncology is globally accepted and standardized in main diagnostic algorithms for neoplasms. In recent years, the ever-closer collaboration among different European associations has tried to overcome the absence of a standardization also in the field of inflammation and infections. The collaboration of several nuclear medicine centers with a long experience in this field, as well as among different AIMN focus groups represents a further attempt in this direction. We hope that this document will be the basis for a "common nuclear physicians' language" throughout all the country.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40336-021-00445-w.

Pulmonary Langerhans Cell Histiocytosis Presenting as a Solitary Pulmonary Nodule on a Lung Cancer Screening CT

Pulmonary Langerhans cell histiocytosis (PLCH) is a rare inflammatory condition that mostly affects lungs in smokers. On imaging, it usually presents as multiple, upper lobe predominant, solid, and cavitary nodules, but presentation as solitary pulmonary nodule (SPN) is rare. We describe a case of SPN seen on low-dose lung cancer screening CT (LDCT) that was FDG avid on PET/CT. Given concern for malignancy, lobectomy was planned if intraoperative frozen section was consistent with malignancy. Lobectomy was performed based on frozen section; however, on formal pathology review, the nodule was ultimately found to be PLCH. This case illustrates an atypical presentation of PLCH as a solitary nodule. Furthermore, it helps demonstrate how rare etiologies (like PLCH) may be more frequently encountered and should be considered in the differential diagnosis for solitary lung nodules, especially in the era of lung cancer screening.

Artificial Intelligence Tools for Refining Lung Cancer Screening

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

Stereotactic body radiation therapy for empirically treated hypermetabolic lung lesions: a single-institutional experience identifying the Charlson score as a key prognostic factor

Background

Though pathologic evidence for non-small cell lung cancer (NSCLC) is preferred, many patients do not receive a biopsy prior to treatment with stereotactic body radiation therapy (SBRT). This study seeks to analyze the overall survival (OS), local control, and toxicity rates for such patients.

Methods

This retrospective review included patients empirically treated with SBRT for presumed non-metastatic NSCLC at a single institution. Inclusion criteria included a hypermetabolic pulmonary lesion noted on positron emission tomography (PET) imaging but no pathological evidence of NSCLC. Patients with another known metastatic tumor were excluded. Statistical analysis was conducted with Cox proportional hazards analysis, univariate analysis, and the Kaplan-Meier method.

Results

Ninety-one treatments in 90 unique patients met inclusion criteria. Patients were a median 77.9 years at the start of treatment and had a median Charlson score of 7. Pre-treatment standardized uptake value (SUV) was a median 4.5 and 1.5 after treatment. At a median follow-up of 12.9 months, 36-month local control of 91.3% was achieved. Twenty-four-month OS and progression-free survival were 65.4% and 44.8%, respectively. On univariate analysis, biologically effective dose (BED) ≥120 Gy was predictive of improved OS (P=0.001), with 36-month OS of 50.5% for patients with BED ≥120 Gy and only 31.6% for patients with BED <120 Gy. On Kaplan-Meier analysis, Charlson score ≥9 was predictive of decreased OS (P=0.04), and BED ≥120 Gy trended towards improved OS (P=0.08). Thirty-two cases of grade <3 toxicity were reported, and only two cases of grade 3 morbidity (fatigue) were noted.

Conclusions

Local control rates for empiric SBRT treatment for hypermetabolic, non-metastatic NSCLC are similar to those for biopsied NSCLC. OS is primarily dependent on a patient’s overall health status, which can be accurately assessed with the Charlson score. BED ≥120 Gy may also contribute to improved OS.

Mediastinal Masses: 18F-FDG-PET/CT Features Based on the International Thymic Malignancy Interest Group Classification

Imaging plays a key role in the management of mediastinal masses. In an effort to standardize the analysis of the mediastinum, the International Thymic Malignancy Interest Group (ITMIG) has proposed a three compartments-based diagnostic classification, intended for clinicians and radiologists. Several articles have documented its usefulness to guide the diagnosis using cross-sectional imaging. Similarly, fluorine-18-radiolabeled fluorodeoxyglucose positron emission tomography combined to computed tomography (¹⁸F-FDG-PET/CT) can be useful in this setting, either as a first-line diagnostic technique, or in addition to cross-sectional imaging. In this article, which is thought as an aid for nuclear medicine physicians and radiologists, we aim to present, based on the ITMIG classification, the main mediastinal pathologies that can be observed with ¹⁸F-FDG-PET/CT, and the additional diagnostic value that can be expected from this technique. For this purpose, we segmented the mediastinum according to the ITMIG classification, and reviewed the available literature for each of the corresponding organs and/or disease. Given the importance of the clinical context for the interpretation of PET imaging, we presented each of the diseases according to: (1) their suggestive clinical context; (2) the suggestive features on nonenhanced CT (which is the standard in PET imaging); and (3) the typical ¹⁸F-FDG characteristics. The purpose of this article is to depict the main features of the most common mediastinal diseases that can be encountered with ¹⁸F-FDG-PET/CT, and to highlight its diagnostic value in this setting, alone or in combination with other imaging modalities.

Validation of Histoplasmosis Enzyme Immunoassay to Evaluate Suspicious Lung Nodules

BACKGROUND

Granulomas caused by infectious lung diseases can present as indeterminate pulmonary nodules (IPN). This study aims to validate an enzyme immunoassay (EIA) for Histoplasma immunoglobulin G (IgG) and immunoglobulin M (IgM) for diagnosing benign IPN in areas with endemic histoplasmosis.

METHODS

Prospectively collected serum samples from patients at Vanderbilt University Medical Center (VUMC [n = 204]), University of Pittsburgh Medical Center (n = 71), and University of Cincinnati (n = 51) with IPN measuring 6 to 30 mm were analyzed for Histoplasma IgG and IgM with EIA. Diagnostic test characteristics were compared with results from the VUMC pilot cohort (n = 127). A multivariable logistic regression model was developed to predict granuloma in IPN.

RESULTS

Cancer prevalence varied by cohort: VUMC pilot 60%, VUMC validation 65%, University of Pittsburgh Medical Center 35%, and University of Cincinnati 75%. Across all cohorts, 19% of patients had positive IgG titers, 5% had positive IgM, and 3% had positive both IgG and IgM. Of patients with benign disease, 33% were positive for at least one antibody. All patients positive for both IgG and IgM antibodies at acute infection levels had benign disease (n = 13), with a positive predictive value of 100%. The prediction model for granuloma in IPN demonstrated an area under the receiver-operating characteristics curve of 0.84 and Brier score of 0.10.

CONCLUSIONS

This study confirmed that Histoplasma EIA testing can be useful for diagnosing benign IPN in areas with endemic histoplasmosis in a population at high risk for lung cancer. Integrating Histoplasma EIA testing into the current diagnostic algorithm where histoplasmosis is endemic could improve management of IPN and potentially decrease unnecessary invasive biopsies.