Saliva‑microbiome‑derived signatures: expected to become a potential biomarker for pulmonary nodules (MCEPN-1)

BACKGROUND

Oral microbiota imbalance is associated with the progression of various lung diseases, including lung cancer. Pulmonary nodules (PNs) are often considered a critical stage for the early detection of lung cancer; however, the relationship between oral microbiota and PNs remains unknown.

METHODS

We conducted a 'Microbiome with pulmonary nodule series study 1' (MCEPN-1) where we compared PN patients and healthy controls (HCs), aiming to identify differences in oral microbiota characteristics and discover potential microbiota biomarkers for non-invasive, radiation-free PNs diagnosis and warning in the future. We performed 16 S rRNA amplicon sequencing on saliva samples from 173 PN patients and 40 HCs to compare the characteristics and functional changes in oral microbiota between the two groups. The random forest algorithm was used to identify PN salivary microbial markers. Biological functions and potential mechanisms of differential genes in saliva samples were preliminarily explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Cluster of Orthologous Groups (COG) analyses.

RESULTS

The diversity of salivary microorganisms was higher in the PN group than in the HC group. Significant differences were noted in community composition and abundance of oral microorganisms between the two groups. Neisseria, Prevotella, Haemophilus and Actinomyces, Porphyromonas, Fusobacterium, 7M7x, Granulicatella and Selenomonas were the main differential genera between the PN and HC groups. Fusobacterium, Porphyromonas, Parvimonas, Peptostreptococcus and Haemophilus constituted the optimal marker sets (area under curve, AUC = 0.80), which can distinguish between patients with PNs and HCs. Further, the salivary microbiota composition was significantly correlated with age, sex, and smoking history (P < 0.001), but not with personal history of cancer (P > 0.05). Bioinformatics analysis of differential genes showed that patients with PN showed significant enrichment in protein/molecular functions related to immune deficiency and energy metabolisms, such as the cytoskeleton protein RodZ, nicotinamide adenine dinucleotide phosphate dehydrogenase (NADPH) dehydrogenase, major facilitator superfamily transporters and AraC family transcription regulators.

CONCLUSIONS

Our study provides the first evidence that the salivary microbiota can serve as potential biomarkers for identifying PN. We observed a significant association between changes in the oral microbiota and PNs, indicating the potential of salivary microbiota as a new non-invasive biomarker for PNs.

TRIAL REGISTRATION

Clinical trial registration number: ChiCTR2200062140; Date of registration: 07/25/2022.

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses

RATIONALE AND OBJECTIVES

Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses.

MATERIALS AND METHODS

A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05).

CONCLUSION

The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Association of patient and health care organization factors with incidental nodule guidelines adherence: A multi-system observational study

BACKGROUND

Health care organizations are increasingly developing systems to ensure patients with pulmonary nodules receive guideline-adherent care. Our goal was to determine patient and organization factors that are associated with radiologist adherence as well as clinician and patient concordance to 2005 Fleischner Society guidelines for incidental pulmonary nodule follow-up.

MATERIALS

Trained researchers abstracted data from the electronic health record from two Veterans Affairs health care systems for patients with incidental pulmonary nodules as identified by interpreting radiologists from 2008 to 2016.

METHODS

We classified radiology reports and patient follow-up into two categories. Radiologist-Fleischner Adherence was the agreement between the radiologist's recommendation in the computed tomography report and the 2005 Fleischner Society guidelines. Clinician/Patient-Fleischner Concordance was agreement between patient follow-up and the guidelines. We calculated multivariable-adjusted predicted probabilities for factors associated with Radiologist-Fleischner Adherence and Clinician/Patient-Fleischner Concordance.

RESULTS

Among 3150 patients, 69% of radiologist recommendations were adherent to 2005 Fleischner guidelines, 4% were more aggressive, and 27% recommended less aggressive follow-up. Overall, only 48% of patients underwent follow-up concordant with 2005 Fleischner Society guidelines, 37% had less aggressive follow-up, and 15% had more aggressive follow-up. Radiologist-Fleischner Adherence was associated with Clinician/Patient-Fleischner Concordance with evidence for effect modification by health care system.

CONCLUSION

Clinicians and patients seem to follow radiologists' recommendations but often do not obtain concordant follow-up, likely due to downstream differential processes in each health care system. Health care organizations need to develop comprehensive and rigorous tools to ensure high levels of appropriate follow-up for patients with pulmonary nodules.

Development of the Korean Association for Lung Cancer Clinical Practice Guidelines: Recommendations on Radial Probe Endobronchial Ultrasound for Diagnosing Lung Cancer - An Updated Meta-Analysis

PURPOSE

Radial probe endobronchial ultrasound (RP-EBUS) accurately locates peripheral lung lesions (PLLs) during transbronchial biopsy (TBB). We performed an updated meta-analysis of the diagnostic yield of TBB for PLLs using RP-EBUS to generate recommendations for the development of the Korean Association of Lung Cancer guidelines.

MATERIALS AND METHODS

We systematically searched MEDLINE and EMBASE (from January 2013 to December 2022), and performed a meta-analysis using R software. The diagnostic yield was evaluated by dividing the number of successful diagnoses by the total lesion number. Subgroup analysis was performed to identify related factors.

RESULTS

Forty-one studies with a total of 13,133 PLLs were included. The pooled diagnostic yield of RP-EBUS was 0.72 (95% confidence interval [CI], 0.70 to 0.75). Significant heterogeneity was observed among studies (χ2=292.38, p < 0.01, I2=86.4%). In a subgroup analysis, there was a significant difference in diagnostic yield based on RP-EBUS findings (within, adjacent to, invisible), with a risk ratio of 1.45 (95% CI, 1.23 to 1.72) between within and adjacent to, 4.20 (95% CI, 1.89 to 9.32) between within and invisible, and 2.59 (95% CI, 1.32 to 5.01) between adjacent to and invisible. There was a significant difference in diagnostic yield based on lesion size, histologic diagnosis, computed tomography (CT) bronchus sign, lesion character, and location from the hilum. The overall complication rate of TBB with RP-EBUS was 6.8% (bleeding, 4.5%; pneumothorax, 1.4%).

CONCLUSION

Our study showed that TBB with RP-EBUS is an accurate diagnostic tool for PLLs with good safety profiles, especially for PLLs with within orientation on RP-EBUS or positive CT bronchus sign.

Rate of incidental findings on routine preoperative computed tomography for shoulder arthroplasty

Background

Incidental findings are commonly noted in advanced imaging studies. Few data exist regarding the rate of incidental findings on computed tomography (CT) for preoperative shoulder arthroplasty planning. This study aims to identify the incidence of these findings and the rate at which they warrant further work-up to help guide orthopedic surgeons in counseling patients.

Methods

A retrospective review was performed to identify patients with available preoperative shoulder CT who subsequently underwent shoulder arthroplasty procedures at a single institution between 2015 and 2021. Data including age, sex, and smoking status were obtained. Radiology reports for CTs were reviewed for incidental findings and categorized based on location, tissue type, and/or body system. The rate of incidental findings and the rate at which further follow-up was recommended by the radiologist were determined.

Results

A total of 617 patients was identified. There were 173 incidental findings noted in 146 of these patients (23.7%). Findings ranged from pulmonary (59%), skin/soft tissue (16%), thyroid (13%), vascular (9%), spinal (2%), and abdominal (1%) areas. Of the pulmonary findings, 50% were pulmonary nodules and 47% were granulomatous disease. Overall, the final radiology report recommended further follow-up for 50% of the patients with incidental findings.

Conclusions

Incidental findings are relatively common in preoperative CTs obtained for shoulder arthroplasty, occurring in nearly one-quarter of patients. Most of these findings are pulmonary in nature. Overall, half of the patients with incidental findings were recommended for further follow-up. These results establish population data to guide orthopedic surgeons in patient counseling. Level of evidence: III.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CT-guided microcoil localization of pulmonary nodules before VATS: clinical experience in 1059 patients

OBJECTIVE

To retrospectively evaluate the efficacy and safety of CT-guided microcoil localization of pulmonary nodules before video-assisted thoracoscopic surgery (VATS).

METHODS

A total of 1059 consecutive patients with 1331 pulmonary nodules treated between July 2018 and April 2021 were included in this study. Of the 1331 nodules, 1318 were localized using the tailed method and 13 were localized using the non-tailed method. The localization technical success rate and complications of the microcoil localization procedure were assessed. Univariate and multivariate logistic regression analyses were used to determine potential risk factors for technical failure, pneumothorax, and pulmonary hemorrhage.

RESULTS

The technical success rate of the localization procedure was 98.4% (1310/1331 nodules). Nodule location in the lower lobes (p = 0.015) and need for a longer needle path (p < 0.001) were independent predictors of technical failure. All localization procedure-related complications were minor (grade 1 or 2) adverse events, with the exception of one grade 3 complication. The most common complications were pneumothorax (302/1331 nodules [22.7%]) and pulmonary hemorrhage (328/1331 nodules [24.6%]). Male sex (p = 0.001), nodule location in the middle (p = 0.003) and lower lobes (p = 0.025), need for a longer needle path (p < 0.001), use of transfissural puncture (p = 0.042), and simultaneous multiple localizations (p < 0.001) were independent risk factors for pneumothorax. Female sex (p = 0.015), younger age (p = 0.023), nodules location in the upper lobes (p = 0.011), and longer needle path (p < 0.001) were independent risk factors for pulmonary hemorrhage.

CONCLUSIONS

CT-guided microcoil localization of pulmonary nodules before VATS using either the tailed or non-tailed method is effective and safe.

CLINICAL RELEVANCE STATEMENT

CT-guided microcoil localization of pulmonary nodules before VATS resection is effective and safe when using either the tailed or non-tailed method. Nodules requiring transfissural puncture and multiple nodules requiring simultaneous localizations can also be successfully localized with this method.

KEY POINTS

• Pre-VATS CT-guided microcoil localization of pulmonary nodules by tailed or non-tailed method was effective and safe. • When the feasible puncture path was beyond the scope of wedge resection, localization could be performed using the non-tailed method. • Although transfissural puncture and simultaneous multiple localization were independent risk factors for pneumothorax, they remained clinically feasible.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.

Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China

BACKGROUND

To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system).

METHODS

Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021-June 30, 2022), SC Center (September 1, 2021-December 31, 2021), and SX Center (January 1, 2021-December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test.

RESULTS

For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P < 0.001; PSN 99.9% vs. 98.4%, P = 0.015), but the specificity (SN 51.2% > 35.1%, PSN 13.3% > 5.7%, all P < 0.001) and agreement rate (SN 81.1% > 74.5%, P < 0.001, PSN 94.6% > 92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003).

CONCLUSION

PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN.

Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study

BACKGROUND

Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate the optimal energy and mA image quality for screening assessment.

METHODS

We proposed a new model using the LUNGMAN N1 phantom multipurpose anthropomorphic chest phantom (diameters: 8, 10, and 12 mm; CT values: - 100, - 630, and - 800 HU) to evaluate the influence of changes in tube voltage and tube current on the size and density of pulmonary nodules. In the LUNGMAN N1 model, three types of simulated lung nodules representing solid tumors of different sizes were used. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used to evaluate the image quality of each scanning combination. The consistency between the calculated results based on segmentation from two physicists was evaluated using the interclass correlation coefficient (ICC).

RESULTS

In terms of nodule size, the longest diameters of ground-glass nodules (GGNs) were closest to the ground truth on the images measured at 100 kVp tube voltage, and the longest diameters of solid nodules were closest to the ground truth on the images measured at 80 kVp tube voltage. In respect to density, the CT values of GGNs and solid nodules were closest to the ground truth when measured at 80 kVp and 100 kVp tube voltage, respectively. The overall agreement demonstrates that the measurements were consistent between the two physicists.

CONCLUSIONS

Our proposed model demonstrated that a combination of 80 kVp and 140 mA scans was preferred for measuring the size of the solid nodules, and a combination of 100 kVp and 100 mA scans was preferred for measuring the size of the GGNs when performing lung cancer screening. The CT values at 80 kVp and 100 kVp were preferred for the measurement of GGNs and solid nodules, respectively, which were closest to the true CT values of the nodules. Therefore, the combination of scanning parameters should be selected for different types of nodules to obtain more accurate nodal data.

CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies.

MATERIALS AND METHODS

PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity.

RESULTS

In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs.

CONCLUSION

CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.

Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules

PURPOSE

To develop a comprehensive multi-classification model that combines radiomics and clinic-radiological features to accurately predict the invasiveness and differentiation of pulmonary adenocarcinoma nodules.

METHODS

A retrospective analysis was conducted on a cohort comprising 500 patients diagnosed with lung adenocarcinoma between January 2020 and December 2022. The dataset included preoperative CT images and histological reports of adenocarcinoma in situ (AIS, n = 97), minimally invasive adenocarcinoma (MIA, n = 139), and invasive adenocarcinoma (IAC, n = 264) with well-differentiated (WIAC, n = 99), moderately differentiated (MIAC, n = 84), and poorly differentiated IAC (PIAC, n = 81). The patients were classified into two groups (IAC and non-IAC) for binary classification and further divided into three and five groups for multi-classification. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm to identify the most informative radiomics and clinic-radiological features. Eight machine learning (ML) models were developed using these features, and their performance was evaluated using accuracy (ACC) and the area under the receiver-operating characteristic curve (AUC).

RESULTS

The combined model, utilizing the support vector machine (SVM) algorithm, demonstrated improved performance in the testing cohort, achieving an AUC of 0.942 and an ACC of 0.894 for the two-classification task. For the three- and five-classification tasks, the combined model employing the one versus one strategy of SVM (SVM-OVO) outperformed other models, with ACC values of 0.767 and 0.607, respectively. The AUC values for histological subtypes ranged from 0.787 to 0.929 in the testing cohort, while the Macro-AUC and Micro-AUC of the multi-classification models ranged from 0.858 to 0.896.

CONCLUSIONS

A multi-classification radiomics model combined with clinic-radiological features, using the SVM-OVO algorithm, holds promise for accurately predicting the histological characteristics of pulmonary adenocarcinoma nodules, which contributes to personalized treatment strategies for patients with lung adenocarcinoma.

[Analysis of Salivary Microbiota Characteristics in Patients With Pulmonary Nodules: A Prospective Nonrandomized Concurrent Controlled Trial]

Objective

To uncover and identify the differences in salivary microbiota profiles and their potential roles between patients with pulmonary nodules (PN) and healthy controls, and to propose new candidate biomarkers for the early warning of PN.

Methods

16S rRNA amplicon sequencing was performed with the saliva samples of 173 PN patients, or the PN group, and 40 health controls, or the HC group, to compare the characteristics, including diversity, community composition, differential species, and functional changes of salivary microbiota in the two groups. Random forest algorithm was used to identify salivary microbial markers of PN and their predictive value for PN was assessed by area under the curve (AUC). Finally, the biological functions and potential mechanisms of differentially-expressed genes in saliva samples were preliminarily investigated on the basis of predictive functional profiling of Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2).

Results

The α diversity and β diversity of salivary microbiota in the PN group were higher than those in the HC group (P<0.05). Furthermore, there were significant differences in the community composition and the abundance of oral microorganisms between the PN and the HC groups (P<0.05). Random forest algorithm was applied to identify differential microbial species. Porphyromonas, Haemophilus, and Fusobacterium constituted the optimal marker sets (AUC=0.79, 95% confidence interval: 0.71-0.86), which can be used to effectively identify patients with PN. Bioinformatics analysis of the differentially-expressed genes revealed that patients with PN showed significant enrichment in protein/molecular functions involved in immune deficiency and redox homeostasis.

Conclusion

Changes in salivary microbiota are closely associated with PN and may induce the development of PN or malignant transformation of PN, which indicates the potential of salivary microbiota to be used as a new non-invasive humoral marker for the early diagnosis of PN.

Pulmonary cryptococcosis closely mimicking lung cancer in a membranous nephropathy patient taking calcineurin inhibitor

In patients with membranous nephropathy (MN), malignancy may be either the underlying disease or results of immunosuppressive therapy which may also lead to opportunistic infections including the pulmonary cryptococcosis. On CT scan, nodule is the most common feature in pulmonary cryptococcosis and it can mimic lung cancer both clinically and radiologically. Therefore, pulmonary nodular lesions caused by cryptococcosis may be easily misdiagnosed and require unnecessary surgical treatment. As such, we herein presented an isolated subpleural solitary nodule with satellite lesion that closely mimicked lung cancer on both contrast-enhanced computed tomography (CT) scan and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET)/CT in an MN patient under long-term tacrolimus regimen. Cryptococcosis was ascertained by the finding of oval thick-walled yeast on histopathology of the lung biopsy specimen taken during the Argon-Helium cryotherapy. Further, the pulmonary lesions progressively dissipated after antifungal treatment. Arguably, our experience may help clinicians in general and nephrologists in particular with a better understanding of the cryptococcal infection manifesting as pulmonary nodule(s) in the MN patients and contribute to more efficacious differential diagnosis against the lung cancer.

Semantic characteristic grading of pulmonary nodules based on deep neural networks

BACKGROUND

Accurate grading of semantic characteristics is helpful for radiologists to determine the probabilities of the likelihood of malignancy of a pulmonary nodule. Nevertheless, because of the complex and varied properties of pulmonary nodules, assessing semantic characteristics (SC) is a difficult task.

METHOD

In this paper, we first analyze a set of important semantic characteristics of pulmonary nodules and extract the important SCs relating to pulmonary nodule malignancy by Pearson's correlation approach. Then, we propose three automatic SC grading models based on deep belief network (DBN) and a multi-branch convolutional neural network (CNN) classifier, MBCNN. The first DBN model takes grayscale and binary nodule images as the input, and the second DBN model takes grayscale nodule images and 72 features extracted from pulmonary nodules as the input.

RESULTS

Experimental results indicate that our algorithms can achieve satisfying results on semantic characteristic grading. Especially, the MBCNN can obtain higher semantic characteristic grading results with an average accuracy of 89.37%.

CONCLUSIONS

Quantitative and automatic grading of semantic characteristics proposed in this paper can assist radiologists effectively assess the likelihood of pulmonary nodules being malignant and further promote the early expectant treatment of malignant nodules.

Repeated pulmonary nodules as the primary symptom of familial hemophagocytic lymphohistiocytosis in adults: a case report and review

Pulmonary nodules are usually considered to be associated with malignant tumors and benign lesions, such as granuloma, pulmonary lymph nodes, fibrosis, and inflammatory lesions. Clinical cases of pulmonary nodules associated with hemophagocytic lymphohistiocytosis have rarely been reported. Therefore, when patients develop pulmonary nodules, the possibility of developing hemophagocytic lymphohistiocytosis is often not considered. We report the first case of familial hemophagocytic lymphohistiocytosis with recurrent pulmonary nodules as the first symptom. Our findings will hopefully provide new ideas for the diagnosis and treatment of pulmonary nodules in the future.

An unusual presentation of pulmonary nodules

A pulmonary nodule is a common radiological finding encountered in routine medical practice. It needs to be extensively evaluated as the differential diagnosis can range from sinister malignancies to fully treatable infectious causes. Here, we present a rare case of pulmonary paragonimiasis presenting as pulmonary nodule. This case report aims to sensitise the medical practitioner regarding pulmonary paragonimiasis which is completely treatable but carries a high risk of being under diagnosed.

Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods

PURPOSE

To compare the capability of CTs obtained with a silver or copper x-ray beam spectral modulation filter (Ag filter and Cu filter) and reconstructed with FBP, hybrid-type IR and deep learning reconstruction (DLR) for radiation dose reduction for lung nodule detection using a chest phantom study.

MATERIALS AND METHODS

A chest CT phantom was scanned with a 320-detector row CT with Ag filter at 0.6, 1.6 and 2.5 mGy and Cu filters at 0.6, 1.6, 2.5 and 9.6 mGy, and reconstructed with the aforementioned methods. To compare image quality of all the CT data, SNRs and CNRs for any nodule were calculated for all protocols. To compare nodule detection capability among all protocols, the probability of detection of any nodule was assessed with a 5-point visual scoring system. Then, ROC analyses were performed to compare nodule detection capability of Ag and Cu filters for each radiation dose data with the same method and of the three methods for any radiation dose data and obtained with either filter.

RESULTS

At any of the doses, SNR, CNR and area under the curve for the Ag filter were significantly higher or larger than those for the Cu filter (p < 0.05). Moreover, with DLR, those values were significantly higher or larger than all the others for CTs obtained with any of the radiation doses and either filter (p < 0.05).

CONCLUSION

The Ag filter and DLR can significantly improve image quality and nodule detection capability compared with the Cu filter and other reconstruction methods at each of radiation doses used.

Invasiveness assessment by artificial intelligence against intraoperative frozen section for pulmonary nodules ≤ 3 cm

PURPOSE

To investigate the performance of an artificial intelligence (AI) algorithm for assessing the malignancy and invasiveness of pulmonary nodules in a multicenter cohort.

METHODS

A previously developed deep learning system based on a 3D convolutional neural network was used to predict tumor malignancy and invasiveness. Dataset of pulmonary nodules no more than 3 cm was integrated with CT images and pathologic information. Receiver operating characteristic curve analysis was used to evaluate the performance of the system.

RESULTS

A total of 466 resected pulmonary nodules were included in this study. The areas under the curves (AUCs) of the deep learning system in the prediction of malignancy as compared with pathological reports were 0.80, 0.80, and 0.75 for all, subcentimeter, and solid nodules, respectively. Additionally, the AUC in the AI-assisted prediction of invasive adenocarcinoma (IA) among subsolid lesions (n = 184) was 0.88. Most malignancies that were misdiagnosed by the AI system as benign diseases with a diameter measuring greater than 1 cm (26/250, 10.4%) presented as solid nodules (19/26, 73.1%) on CT. In an exploratory analysis involving nodules underwent intraoperative pathologic examination, the concordance rate in identifying IA between the AI model and frozen section examination was 0.69, with a sensitivity of 0.50 and specificity of 0.97.

CONCLUSION

The deep learning system can discriminate malignant diseases for pulmonary nodules measuring no more than 3 cm. The AI model has a high positive predictive value for invasive adenocarcinoma with respect to intraoperative frozen section examination, which might help determine the individualized surgical strategy.

Preoperative computed tomography-guided transscapular sens-cure needle localization for pulmonary nodule located behind the scapula

BACKGROUND

Video-assisted thoracoscopic surgery (VATS) is an approach that is commonly used to resect pulmonary nodules (PNs). However, when these PNs are located behind the scapula, a transscapular access approach is generally required. In this study, the safety, efficacy, and feasibility of preoperative computed tomography (CT)-guided Sens-cure needle (SCN) localization was assessed for PNs located behind the scapula.

METHODS

From January 2020 - June 2022, a total of 122 PN patients in our hospital underwent preoperative CT-guided SCN localization and subsequent VATS resection, of whom 12 (9.8%) exhibited PNs behind the scapula necessitating a transscapular approach for this localization procedure.

RESULTS

This study included 12 patients, each of whom had one PN located behind the scapula. The CT-guided transscapular SCN localization approach was successful in all patients, and no complications near the operative site were observed. The median localization time was 12 min, and 2 (16.7%) and 1 (8.3%) patients respectively developed pneumothorax and pulmonary hemorrhage after the localization procedure was complete. Wedge resection procedures for these PNs achieved technical success in all cases. Four patients were diagnosed with invasive adenocarcinomas and subsequently accepted lobectomy and systematic lymph node dissection. The median VATS duration and the median blood loss was 80 min and 10 mL, respectively. In total, 3, 5, and 4 PNs were respectively diagnosed as benign, mini-invasive adenocarcinomas, and invasive adenocarcinomas.

CONCLUSION

Preoperative CT-guided transscapular SCN localization represents a safe, straightforward, and effective means of localizing PNs present behind the scapula.

Simulation education utilizing phantom and angle reference guide in pulmonary nodule CT localization

Objective

The incidence of sub-centimeter pulmonary nodules has been increasing along with the use of low-dose computed tomography (LDCT) as a screening tool for early lung cancer detection. In our institution, pulmonary nodule computed tomography-guided localization (PNCL) is performed preoperatively with the laser angle guided assembly (LAGA), an angle reference device. This study aims to investigate the efficacy of postgraduate education in a phantom simulation of PNCL, with or without LAGA.

Setting design

This prospective study was conducted in an academic hospital in Taiwan. Seven thoracic surgery residents and three experienced senior physicians were recruited to perform PNCL using a phantom simulation, with or without LAGA, for five nodules each and complete a questionnaire. Performance data were collected. χ2 tests, Mann-Whitney U test, univariate and multivariate linear regression were used for statistical analyses.

Results

The confidence level increased from median 7[range 1, 9] to 8, range [6,9] (p = 0.001) before and after the simulation education course. The scores of enhanced PNCL ability and course satisfaction were as high as 8 [5,9], and 9 [7,9]. LAGA enabled broader puncture angles (with 27.5° [0°,80°]; without 14° [0°, 80°], p = 0.003), a lower puncture frequency (with 1 [1,4]; without 2 [1,5], p < 0.001), and a smaller angle deviation (with 3°[ 0°,8°]; without 5°[ 0°,19°], p = 0.002). Pleural depth in millimeters was associated with increased puncture frequency (0.019[0,010,0.028]) and procedure time (0.071'[ 0.018,0.123']. The PNCL-experienced physicians performed the procedure in less time (-2.854'[-4.646',1.061']. The traverse direction toward the mediastinum diminished the frequency (toward 1[ 1,3]; away 1 [1,5], p = 0.003) and time (toward 7.5'[2',18]'; away 9'[ 3',31'], p = 0.027). The learning curve did not improve procedure performance after ten PNCL simulation rounds.

Conclusions

The phantom PNCL simulation education course increased the confidence level, enhanced residents' skill acquisition, and promoted learning satisfaction. The angle reference device helped improve the outcomes of the puncture frequency and reduced angle deviation.

Pulmonary primary meningioma: A report of two cases and review of the literature

Introduction

Primary pulmonary meningioma (PPM) is rare, mostly asymptomatic, and difficult to recognize preoperatively. In this report, we describe two new cases of PPM and provide an updated review of the literature to enhance our understanding of PPM.

Case presentation

The first patient was a 53-year-old woman with an asymptomatic solitary pulmonary nodule in the right lower lobe on chest computed tomography (CT). Wedge resection of the right lung was performed and histological features of benign PPM were pathologically confirmed, which was further supported by immunohistochemistry. The second patient was a 63-year-old man who had a left pulmonary nodule during routine physical examination. CT revealed a solid nodule in the outer basal segment of the left lower lobe. The patient underwent a wedge resection of the left lower lobe and mediastinal lymph node dissection. Postoperative pathology confirmed the presence of two tumors: one adenocarcinoma and one PPM. Both cases were confirmed to be benign PPM (grade I) by pathological examination, with histological subtypes of fibrous and psammomatous.

Conclusions

Radiological imaging is crucial for the early detection of PPM, while pathological examination is necessary to confirm the diagnosis. Surgical treatment is recommended, and intraoperative frozen sections are essential for determining the extent of the operation.

High diagnostic yield of electromagnetic navigation bronchoscopy performed under cone beam CT guidance: results of a randomized Belgian monocentric study

BACKGROUND

With the increasing use of low dose CT scans, numerous pulmonary nodules are detected. As majority of them are benign, development of efficient non-surgical diagnostic intervention is mandatory. Electromagnetic navigation bronchoscopy (ENB) has been developed to reach difficult to access lesions. The aim of the present study was to compare the diagnostic yield of ENB procedures performed in a classical endoscopy suite or in a hybrid room equipped by a cone beam CT (CBCT).

METHODS

A monocentric randomized study was performed in the Erasme Hospital between January 2020 and December 2021. Lung nodules of maximum 30 mm of diameter were eligible. In both arms (endoscopy or CBCT suites), ENB, fluoroscopic guidance and a radial endobronchial ultrasound were used to reach the lesion. Then six trans-bronchial biopsies (TBB) and one trans-bronchial lung cryobiopsy (TBLC) were performed. Primary outcomes were the diagnostic yield and diagnostic accuracy of the procedure.

RESULTS

Forty-nine patients were randomized (24 in the endoscopy and 25 in the CBCT arms). The lesion size was 15,9 ± 4,6 mm and 16,6 ± 6,0 mm respectively (mean ± SD, p = NS). The diagnostic yield of ENB performed under CBCT guidance was 80% compared to 42% when performed in the endoscopy suite under standard fluoroscopic guidance (p < 0,05). Similarly, the diagnostic accuracy in the CBCT group was 87% compared to 54% for the endoscopy group (p < 0,05). Duration of the procedure in the CBCT and endoscopy arms was 80 ± 23 and 61 ± 13 min respectively (mean ± SD, p < 0,01). Performing TBLC in addition to TBB increased the diagnostic yield by 14% (17 and 12,5% in CBCT and endoscopy suites respectively, p = NS).

CONCLUSION

This study highlighted the additional value to perform ENB procedure under CBCT guidance for small size (less than 2 cm of diameter) pulmonary nodules.

TRIAL REGISTRATION

Clinical trial registration number: NCT05257382.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

PURPOSE

Persistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.

MATERIAL AND METHODS

We retrospectively selected 60 NSNs with an axial diameter of 6-30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.

RESULTS

In multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.

CONCLUSION

According to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Benign and malignant pulmonary parenchymal findings on chest CT among adult survivors of childhood and young adult cancer with a history of chest radiotherapy

PURPOSE

Childhood and young adult cancer survivors exposed to chest radiotherapy are at increased risk of lung cancer. In other high-risk populations, lung cancer screening has been recommended. Data is lacking on prevalence of benign and malignant pulmonary parenchymal abnormalities in this population.

METHODS

We conducted a retrospective review of pulmonary parenchymal abnormalities in chest CTs performed more than 5 years post-cancer diagnosis in survivors of childhood, adolescent, and young adult cancer. We included survivors exposed to radiotherapy involving the lung field and followed at a high-risk survivorship clinic between November 2005 and May 2016. Treatment exposures and clinical outcomes were abstracted from medical records. Risk factors for chest CT-detected pulmonary nodule were assessed.

RESULTS

Five hundred and ninety survivors were included in this analysis: median age at diagnosis, 17.1 years (range, 0.4-39.8); and median time since diagnosis, 22.3 years (range, 1-58.6). At least one chest CT more than 5 years post-diagnosis was performed in 338 survivors (57%). Among these, 193 (57.1%) survivors had at least one pulmonary nodule detected on a total of 1057 chest CTs, resulting in 305 CTs with 448 unique nodules. Follow-up was available for 435 of these nodules; 19 (4.3%) were malignant. Risk factors for first pulmonary nodule were older age at time of CT, CT performed more recently, and splenectomy.

CONCLUSIONS

Benign pulmonary nodules are very common among long-term survivors of childhood and young adult cancer.

IMPLICATIONS FOR CANCER SURVIVORS

High prevalence of benign pulmonary nodules in cancer survivors exposed to radiotherapy could inform future guidelines on lung cancer screening in this population.

The influence of different previous cancer histories on the diagnostic efficacy of Lung Imaging Reporting and Data System

Background

For lung cancer screening in patients with previous malignant tumors, Lung Imaging Reporting and Data System (Lung-RADS) and other lung cancer screening tools are controversial in terms of requirements for the previous cancer history. This study investigated the effect of the length and type of malignancy history on the diagnostic efficacy of Lung Imaging Reporting and Data System (Lung-RADS) 2022 in pulmonary nodules (PNs).

Methods

Chest computed tomography and clinical data of PNs in patients with a history of cancer who underwent surgical resection in The First Affiliated Hospital of Chongqing Medical University from January 1, 2018, to November 30, 2021, were retrospectively collected and evaluated based on Lung-RADS. All PNs were divided into 2 groups: the prior lung cancer (PLC) and the prior extrapulmonary cancer (PEPC) groups. Each group was divided into the ≥5 years and <5 years groups based on the duration of cancer history. The diagnostic agreement of Lung-RADS was evaluated based on the pathological diagnosis of nodules after operation. The diagnostic agreement rate (AR) of Lung-RADS and the composition ratios of different types between different groups were calculated and compared.

Results

A total of 451 patients with 565 PNs were included in this study. These patients were divided into the PLC group (<5 years: 135 cases, 175 PNs; ≥5 years: 9 cases, 12 PNs) and the PEPC group (<5 years: 219 cases, 278 PNs; ≥5 years: 88 cases, 100 PNs). The diagnostic AR of partial solid nodules (93.0%; 95% CI: 88.7-97.2%) and solid nodules (88.1%; 95% CI: 84.1-92.1%) was close (P=0.13), while both were higher than that of the pure ground-glass nodules (24.0%; 95% CI: 17.5-30.4%; all P values <0.001). Within 5 years, the composition ratio of PNs and the diagnostic AR (PLC: 58.9%, 95% CI: 51.5-66.2%; PEPC: 76.6%, 95% CI: 71.6-81.6%) between the PLC and PEPC groups were all different (all P values <0.001), and the others [composition ratio of PNs & the diagnostic AR: PLC (≥5 years) vs. PEPC (≥5 years); PLC (<5 years) vs. PLC (≥5 years); PEPC (<5 years) vs. PEPC (≥5 years)] were similar (all P values >0.05; range: 0.10-0.93).

Conclusions

The length of prior cancer history may affect the diagnostic agreement of Lung-RADS, especially for patients with prior lung cancer within 5 years.

Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China

BACKGROUND

PM2.5 is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the CT scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5 exposure and pulmonary nodules was limited. &#xD;Objective: To examine potential associations of exposures to PM2.5 and its major chemical constituents with the prevalence of pulmonary nodules.&#xD;Methods: 16865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5 and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5 and its components on the risk of pulmonary nodules, respectively. &#xD;Results: Each 1mg/m3 increase in PM2.5 (OR 1.011 (95%CI: 1.007-1.014)) was positively associated with pulmonary nodules. Among five PM2.5 components, in single-pollutant effect models, every 1 μg/m3 increase in OM, BC, and NO3- elevated the risk of pulmonary nodule prevalence by 1.040 (95%CI: 1.025-1.055), 1.314 (95%CI: 1.209-1.407) and 1.021 (95%CI: 1.007-1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5 components was 1.076 (95%CI: 1.023-1.133) fold. Notably, NO3- BC and OM contributed higher risks of pulmonary nodules than other PM2.5 components. And the NO3- particles were identified to have the highest contribution. The impacts of PM2.5 components on pulmonary nodules were consistent across gender and age.&#xD;Conclusion: These findings provide important evidence for the positive correlation between exposure to PM2.5 and pulmonary nodules in China and identify that NO3- particles have the highest contribution to the risk.&#xD.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Detection of solid and subsolid pulmonary nodules with lung MRI: performance of UTE, T1 gradient-echo, and single-shot T2 fast spin echo

BACKGROUND

Although MRI is a radiation-free imaging modality, it has historically been limited in lung imaging due to inherent technical restrictions. The aim of this study is to explore the performance of lung MRI in detecting solid and subsolid pulmonary nodules using T1 gradient-echo (GRE) (VIBE, Volumetric interpolated breath-hold examination), ultrashort time echo (UTE) and T2 Fast Spin Echo (HASTE, Half fourier Single-shot Turbo spin-Echo).

METHODS

Patients underwent a lung MRI in a 3 T scanner as part of a prospective research project. A baseline Chest CT was obtained as part of their standard of care. Nodules were identified and measured on the baseline CT and categorized according to their density (solid and subsolid) and size (> 4 mm/ ≤ 4 mm). Nodules seen on the baseline CT were classified as present or absent on the different MRI sequences by two thoracic radiologists independently. Interobserver agreement was determined using the simple Kappa coefficient. Paired differences were compared using nonparametric Mann-Whitney U tests. The McNemar test was used to evaluate paired differences in nodule detection between MRI sequences.

RESULTS

Thirty-six patients were prospectively enrolled. One hundred forty-nine nodules (100 solid/49 subsolid) with mean size 10.8 mm (SD = 9.4) were included in the analysis. There was substantial interobserver agreement (k = 0.7, p = 0.05). Detection for all nodules, solid and subsolid nodules was respectively; UTE: 71.8%/71.0%/73.5%; VIBE: 61.6%/65%/55.1%; HASTE 72.4%/72.2%/72.7%. Detection rate was higher for nodules > 4 mm in all groups: UTE 90.2%/93.4%/85.4%, VIBE 78.4%/88.5%/63.4%, HASTE 89.4%/93.8%/83.8%. Detection of lesions ≤4 mm was low for all sequences. UTE and HASTE performed significantly better than VIBE for detection of all nodules and subsolid nodules (diff = 18.4 and 17.6%, p = < 0.01 and p = 0.03, respectively). There was no significant difference between UTE and HASTE. There were no significant differences amongst MRI sequences for solid nodules.

CONCLUSIONS

Lung MRI shows adequate performance for the detection of solid and subsolid pulmonary nodules larger than 4 mm and can serve as a promising radiation-free alternative to CT.

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

BACKGROUND

Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs).

METHOD

150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed. GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images. Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass.

RESULT

Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference. The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group (P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752.

CONCLUSION

SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT. Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.

Computed tomography fluoroscopy-guided percutaneous biopsy of pulmonary nodules ≤ 10 mm: retrospective analysis of procedures performed during the COVID-19 pandemic

Objective

To evaluate the diagnostic performance of computed tomography (CT) fluoroscopy-guided percutaneous transthoracic needle biopsy (PTNB) in pulmonary nodules ≤ 10 mm during the coronavirus disease 2019 pandemic.

Materials and Methods

Between January 1, 2020 and April 30, 2022, a total of 359 CT fluoroscopy-guided PTNBs were performed at an interventional radiology center. Lung lesions measured between 2 mm and 108 mm. Of the 359 PTNBs, 27 (7.5%) were performed with an 18G core needle on nodules ≤ 10 mm in diameter.

Results

Among the 27 biopsies performed on nodules ≤ 10 mm, the lesions measured < 5 mm in four and 5-10 mm in 23. The sensitivity and overall diagnostic accuracy of PTNB were 100% and 92.3%, respectively. The mean dose of ionizing radiation during PTNB was 581.33 mGy*cm (range, 303-1,129 mGy*cm), and the mean biopsy procedure time was 6.6 min (range, 2-12 min). There were no major postprocedural complications.

Conclusion

CT fluoroscopy-guided PTNB appears to provide a high diagnostic yield with low complication rates.

Artificial intelligence: A critical review of applications for lung nodule and lung cancer

Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.

Computed Tomography-guided Techniques for Localizing Pulmonary Nodules by Localization Needle versus Methylene Blue

OBJECTIVE

The aim of this study was to evaluate the clinical value of computed tomography-guided localization needle (LN) and methylene blue (MB) for pulmonary nodule localization.

MATERIALS AND METHODS

Between November 2019 and January 2022, 547 patients underwent computed tomography-guided LN (n=171) or MB (n=376) localization and video-assisted thoracoscopic surgery (VATS) resection.

RESULTS

In total, 171 and 376 nodules were localized via LN and MB, respectively, with respective technical localization success rates of 92.98 % and 88.56 % (p = 0.111). The pulmonary nodule localization time was significantly shorter for the MB group than for the LN group (p < 0.05). However, the rates of total complication, chest pain, and cough were significantly lower in the LN group (c2 = 8.251/25.092/5.127, all p < 0.05). Furthermore, the LN group had a shorter VATS time than the MB group (p < 0.05). The VATS achieved a 100% operation success rate in both groups, and there was no significance between groups with respect to the types of surgery (p ＞ 0.05) or the blood loss (p ＞ 0.05). Moreover, logistic regression analysis showed that the localization technique was an independent risk factor for total complications and chest pain.

CONCLUSION

Both techniques can effectively localize pulmonary nodules before VATS. MB had a shorter localization time, but the LN had a shorter VATS time and a lower incidence of complications, especially chest pain.

[Hemophagocytic lymphohistiocytosis caused by hematogenous disseminated pulmonary tuberculosis: A case report]

Hemophagocytic lymphohistiocytosis (HLH) was a life-threatening syndrome due to the uncontrolled immune activation of cytotoxic T lymphocytes, natural killer (NK) cells, and macrophages. HLH is characterized by primary and secondary causes, the early diagnosis and treatment of patients are closely related to the prognosis and clinical outcome of patients. The clinical presentation is variable but mostly includes prolonged fever, splenomegaly, coagulopathy, hypertriglyceridemia, and hemophagocytosis, none of them is specific and particular for HLH. Tuberculosis (TB) infection is one of the causes of HLH. HLH caused by TB is very rare clinically, but it has a high mortality. For patients with fever of unknown origin, HLH-related clinical manifestations sometimes present before the final diagnosis of TB, and HLH is associated with the most significant mortality rate. This article is mainly about a 28-year-old patient with HLH who suffered from severe TB infection. The patient attended a hospital with a history of 2 months of prolonged fever, 10 days booger and subcutaneous hemorrhage in lower limbs. Before this, he was in good health and denied any history of tuberculosis exposure. Combined with relevant laboratory test results (such as splenomegaly, hemoglobin, platelet count, and hypertriglyceridemia) and clinical manifestations (e.g. fever), the patient was diagnosed with hemophagocytic lymphohistiocytosis, but the etiology of HLH remained to be determined. To confirm the etiology, the patient was asked about the relevant medical history (intermittent low back pain) and was performed chest CT scan, bone marrow biopsy, and fundus photography. Finally, he was diagnosed with hemophagocytic lymphohistiocytosis caused by hematogenous disseminated pulmonary tuberculosis. In response to this, intravenous methylprednisolone and anti-tuberculosis treatment (isoniazid, pyrazinamide, moxifloxacin, and amikacin) were administered to the patient. After more than a month of treatment, the patient recovered from HLH caused by severe TB infection. Therefore, this case suggests that we should be vigilant to the patient who admitted to the hospital with fever for unknown reasons, to diagnose HLH as early as possible and clarify its cause, then perform interventions and treatment, especially HLH secondary to tuberculosis. Also, cases of atypical TB and severe TB should be carefully monitored to achieve early diagnosis and early intervention.

Shape-Sensing Robotic-Assisted Bronchoscopy with Concurrent use of Radial Endobronchial Ultrasound and Cone Beam Computed Tomography in the Evaluation of Pulmonary Lesions

PURPOSE

Lung nodules are a common radiographic finding. Non-surgical biopsy is recommended in patients with moderate or high pretest probability for malignancy. Shape-sensing robotic-assisted bronchoscopy (ssRAB) combined with radial endobronchial ultrasound (r-EBUS) and cone beam computed tomography (CBCT) is a new approach to sample pulmonary lesions. Limited data are available regarding the diagnostic accuracy of combined ssRAB with r-EBUS and CBCT.

METHODS

We conducted a retrospective analysis of the first 200 biopsy procedures of 209 lung lesions using ssRAB, r-EBUS, and CBCT at UT Southwestern Medical Center in Dallas, Texas. Outcomes were based on pathology interpretations of samples taken during ssRAB, clinical and radiographic follow-up, and/or additional sampling.

RESULTS

The mean largest lesion dimension was 22.6 ± 13.3 mm with a median of 19 mm (range 7 to 73 mm). The prevalence of malignancy in our data was 64.1%. The diagnostic accuracy of ssRAB combined with advanced imaging was 91.4% (CI 86.7-94.8%). Sensitivity was 87.3% (CI 80.5-92.4%) with a specificity of 98.7% (CI 92.8-100%). The negative and positive predictive values were 81.3% and 99.2%. The rate of non-diagnostic sampling was 11% (23/209 samples). The only complication was pneumothorax in 1% (2/200 procedures), with 0.5% requiring a chest tube.

CONCLUSION

Our results of the combined use of ssRAB with r-EBUS and CBCT to sample pulmonary lesions suggest a high diagnostic accuracy for malignant lesions with reasonably high sensitivity and negative predictive values. The procedure is safe with a low rate of complications.

Local tumour residue after microwave ablation for lung cancer: a case report

Thermal ablation has become a novel method for the treatment of pulmonary nodules, but the short-time evaluation of the ablation effect is mainly based on computed tomography images. We report a case of local tumour residue after microwave ablation, which was confirmed by pathology after lobectomy. This case alerts us that thermal ablation should not be the preferred treatment for operable pulmonary nodules.

Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

Background

Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [¹⁸F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [¹⁸F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging.

Methods

Twenty four lung nodules from 20 patients were delineated. Radiomics features were derived on FB flow PET/CT and on the corresponding DDG reconstruction using the QuantImage v2 platform. Lin’s concordance factor (C_b) and the mean difference percentage (DIFF%) were calculated for each radiomics feature using the delineated nodules which were also classified by anatomical localisation and volume. Non-reproducible radiomics features were defined as having a bias correction factor C_b  < 0.8 and/or a mean difference percentage DIFF% > 10.

Results

In total 141 features were computed on each concordance analysis, 10 of which were non-reproducible on all pulmonary lesions. Those were first-order features from Laplacian of Gaussian (LoG)-filtered images (sigma = 1 mm): Energy, Kurtosis, Minimum, Range, Root Mean Squared, Skewness and Variance; Texture features from Gray Level Cooccurence Matrix (GLCM): Cluster Prominence and Difference Variance; First-order Standardised Uptake Value (SUV) feature: Kurtosis. Pulmonary lesions located in the superior lobes had only stable radiomics features, the ones from the lower parts had 25 non-reproducible radiomics features. Pulmonary lesions with a greater size (defined as long axis length > median) showed a higher reproducibility (9 non-reproducible features) than smaller ones (20 non-reproducible features).

Conclusion

Calculated on all pulmonary lesions, 131 out of 141 radiomics features can be used interchangeably between DDG and FB PET/CT acquisitions. Radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41824-022-00153-2.

[Research Progress in the Effect of Consolidation Tumor Ratio on the Diagnosis and Treatment of Early-stage Peripheral Lung Cancer]

Consolidation tumor ratio (CTR) is a hot issue in lung cancer imaging studies in recent years. It is defined as the proportion of the maximum consolidation diameter divided by the maximum tumor diameter in the lung window scanned by high resolution computed tomography (HRCT). Many studies have also confirmed that it can be used as an indicator to identify whether a lung tumor is benign or malignant at the early stage, the main basis on which to decide whether sublobectomy can be performed, and is an independent factor for the recurrence and prognosis of early-stage lung cancer. Especially after tumor size and CTR results of JCOG0804 and JCOG0802 trials in Japan were published, a breakthrough in the treatment method upends the conventional surgical approach, which benefits patients with early-stage lung cancer. But insufficient research data on CTR leads to the fact that an evaluation system to measure CTR is yet to be built. This paper discusses the research progress in CTR prediction of benign or malignancy of pulmonary nodules, how to choose a surgical approach, lymph node dissection, spread through air spaces (STAS) and other hot issues. It also investigates the possible indicators to predict efficacy based on CTR, summarizes and analyzes the development trend of surgical methods to treat early-stage peripheral lung cancer and challenges, to provide new ideas for clinical application.
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Localization of small peripheral pulmonary nodules for surgical resection: a new intraoperative technique in hybrid operating room

Objective

The purpose of this study was to introduce a new feasible and effective intraoperative localization technique for small peripheral pulmonary nodules in hybrid operating room.

Methods

Between June 2020 and June 2021, the intraoperative localization was performed in 27 patients for 35 small pulmonary nodules at our institution. The procedure was undergone under thoracoscopic observation. After making the VATS ports, a titanium clip was clipped at the visceral pleura as near the pulmonary nodule as possible to be a marker for the nodule. VATS resection was performed next.

Results

A total of 27 patients were included in this study, including 6 males and 21 females. The median age was 58 years (range 34–78 years). All surgeries were performed by two-port VATS. A total of 35 pulmonary nodules underwent intraoperative localization. The mean diameter of nodules was 10.6 ± 3.7 mm. The distance of nodules to visceral pleura was 8.3 ± 8.7 mm. The mean localization time was 23.3 ± 3.3 min. The median time of C-arm scanning was 3 (range 2–4) times. The median times for clipping were 2 (range 1–3) times. All the nodules were localized successfully and resected precisely. No VATS were converted to thoracotomy. There were no complications related to localization procedures.

Conclusions

This new intraoperative localization technique was feasible, safe and effective. And also the intraoperative procedure could avoid extra suffering for patients.

[Case-based learning in thoracic surgery : A comparison between online and face-to-face teaching]

BACKGROUND

In modern medical education, traditional face-to-face teaching is increasingly being supplemented by innovative teaching methods, such as case-based learning or e‑learning.

AIM OF THE WORK

Design and evaluation of a new course concept in thoracic surgery to motivate students and at the same time find ways to relieve the medical staff in their teaching activities.

MATERIAL AND METHODS

All students of the third clinical semester at the University of Regensburg completed a case-based course on the topic of pulmonary nodules in the summer semester 2016 and winter semester 2016/2017 as part of the practical course in thoracic surgery. This involved a moderated face-to-face seminar for one group of students and a purely online event for the other group. The knowledge gain and the subjective learning experience of the students were evaluated via questionnaires.

RESULTS

A total of 190 students took part in the courses, 88 in the face-to-face course and 102 in the online course. Although both groups recorded a significant increase in knowledge as a result of the course intervention, the students in the face-to-face course showed significantly higher subjective satisfaction compared to their peers in the online course.

CONCLUSION

Case-based learning was shown to be a promising concept in thoracic surgery education, although establishing online learning procedures proved to be more challenging than presence-based teaching.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers

BACKGROUND

Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.

METHODS

Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.

RESULTS

Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.

CONCLUSIONS

A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

Association between visual emphysema and lung nodules on low-dose CT scan in a Chinese Lung Cancer Screening Program (Nelcin-B3)

OBJECTIVES

This study aimed to evaluate the association between visual emphysema and the presence of lung nodules, and Lung-RADS category with low-dose CT (LDCT).

METHODS

Baseline LDCT scans of 1162 participants from a lung cancer screening study (Nelcin-B3) performed in a Chinese general population were included. The presence, subtypes, and severity of emphysema (at least trace) were visually assessed by one radiologist. The presence, size, and classification of non-calcified lung nodules (≥ 30 mm³) and Lung-RADS category were independently assessed by another two radiologists. Multivariable logistic regression and stratified analyses were performed to estimate the association between emphysema and lung nodules, Lung-RADS category, after adjusting for age, sex, BMI, smoking status, pack-years, and passive smoking.

RESULTS

Emphysema and lung nodules were observed in 674 (58.0%) and 424 (36.5%) participants, respectively. Participants with emphysema had a 71% increased risk of having lung nodules (adjusted odds ratios, aOR: 1.71, 95% CI: 1.26-2.31) and 70% increased risk of positive Lung-RADS category (aOR: 1.70, 95% CI: 1.09-2.66) than those without emphysema. Participants with paraseptal emphysema (n = 47, 4.0%) were at a higher risk for lung nodules than those with centrilobular emphysema (CLE) (aOR: 2.43, 95% CI: 1.32-4.50 and aOR: 1.60, 95% CI: 1.23-2.09, respectively). Only CLE was associated with positive Lung-RADS category (p = 0.02). CLE severity was related to a higher risk of lung nodules (ranges aOR: 1.44-2.61, overall p < 0.01).

CONCLUSION

In a Chinese general population, visual emphysema based on LDCT is independently related to the presence of lung nodules (≥ 30 mm³) and specifically CLE subtype is related to positive Lung-RADS category. The risk of lung nodules increases with CLE severity.

KEY POINTS

• Participants with emphysema had an increased risk of having lung nodules, especially smokers. • Participants with PSE were at a higher risk for lung nodules than those with CLE, but nodules in participants with CLE had a higher risk of positive Lung-RADS category. • The risk of lung nodules increases with CLE severity.

A diagnostic dilemma of a pulmonary nodule of a patient who suffered advanced ovarian cancer: A case report and a hypothesis

INTRODUCTION

This report presents a case of lung cancer which can be easily misdiagnosed as distant metastasis. Along with a literature review on the morphological, pathological and prognostic characteristics of lung cancer associated with cystic airspaces (LCCA), it would help to improve our understanding of the dynamic evolution of LCCA, to avoid its delayed diagnosis and treatment. We also propose here a hypothesis on the etiology of LCCA.

CASE PRESENTATION

A patient with advanced ovarian cancer who presented with elevated serum CA125 at time of admission and had undergone TAHBSO at first, and second operation of sigmoid colon resection was performed due to her locoregional recurrence of ovarian cancer. After her second operation, patient showed further increment of serum CA125 and CECT scan indicated an airspace-related pulmonary nodule in the right middle lobe of her lungs. It was suspected that distant metastases of ovarian cancer had reoccurred postoperatively.

CLINICAL DISCUSSION

After comparing the characteristic of metastatic ovarian cancer with LCCA, we diagnosed the pulmonary nodule as primary lung cancer. Surgery eventually confirmed the pulmonary nodule as second primary lung cancer associated with cystic airspaces.

CONCLUSION

The rare occurrence of LCCA should merit special attention from clinicians and radiologists so as to avoid missed or delayed diagnosis. We propose here a hypothesis that LCCA is related to spreading of tumour cells during surgical procedures in lung cancer surgery. Should our hypothesis be substantiated in further studies, this would affect the operation procedures for surgeons in the future.

[Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules]

BACKGROUND

Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules.

METHODS

Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed.

RESULTS

In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively.

CONCLUSIONS

Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.

[Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version)]

Low-dose computed tomography (CT) for lung cancer screening has been proven to reduce lung cancer deaths in the screening group compared with the control group. The increasing number of pulmonary nodules being detected by CT scans significantly increase the workload of the radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of pulmonary nodule discrimination and has been tested in preliminary studies for nodule management. As more and more artificial AI products are commercialized, the consensus statement has been organized in a collaborative effort by Thoracic Surgery Committee, Department of Simulated Medicine, Wu Jieping Medical Foundation to aid clinicians in the application of AI-assisted management for pulmonary nodules.
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Robotic Navigational Bronchoscopy Combined with Needle-Based Confocal Laser Endomicroscopy: Case Report of a Novel Approach to Diagnose Small Lung Nodules

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Advancements in navigational bronchoscopy have shown encouraging results but the diagnostic yield of small lung nodules by bronchoscopic techniques is still below that of transthoracic needle aspiration. The development of robotic bronchoscopy has demonstrated a significantly improved navigational success but the diagnostic yield is regularly limited by near-miss of the target nodule. Needle-based confocal laser endomicroscopy is a novel imaging technique that allows for the real-time visualization of individual cells and structures with microscopic resolution at the tip of the needle. We present the first reported case of confocal laser endomicroscopy guided robotic bronchoscopy for the real-time diagnosis of a small, partially cystic lung nodule.

Accuracy of Pulmonary Nodule Sampling Using Robotic Assisted Bronchoscopy with Shape Sensing, Fluoroscopy, and Radial Endobronchial Ultrasound (The ACCURACY Study)

BACKGROUND

Despite recent advances in guided bronchoscopy, the yield of bronchoscopic biopsy of a peripheral pulmonary nodule (PPN) remains highly variable.

OBJECTIVE

The aim of the study was to evaluate which features of robotic assisted bronchoscopy (RAB) contribute to a successful biopsy in a cadaver model.

METHODS

A preclinical, prospective, single-blinded trial using a ventilated human cadaveric model assessed the successful puncture of implanted pulmonary nodules using various localization techniques with RAB. The different approaches included positioning the robotic catheter at predefined distances from the target nodule (<10 mm, 10-20 mm, 20-30 mm), bronchoscopist correction of divergence between the software virtual map and bronchoscopic view if observed, and impact of fluoroscopy and radial endobronchial ultrasound (rEBUS). The primary endpoint was a central target hit (defined as an inner 2/3 target puncture) verified by cone-beam computed tomography.

RESULTS

Thirty-eight RAB procedures were performed to target 16 PPNs. Median nodule size was 16.2 mm. All targets were located in the outer 1/3 of the lung with a bronchus sign in 31.3%. Central target hit rates were improved when the robotic catheter tip was closer to the nodule (<10 mm 68%, 10-20 mm 66%, 20-30 mm 11%, p < 0.001). Multivariable analysis confirmed the strongest predictor of a central target hit was robotic catheter distance to nodule (OR 0.89 per increase in 1 mm, p < 0.001), independent of the presence of a bronchus sign, divergence or concentric rEBUS view.

CONCLUSIONS

Utilizing a RAB platform, closer proximity of the robotic catheter to the target nodule results in an increase in peripheral nodule biopsy success.

Detection of pulmonary nodules with scoutless fixed-dose ultra-low-dose CT: a prospective study

OBJECTIVES

To determine the accuracy of scoutless, fixed-dose ultra-low-dose (ULD) CT compared to standard-dose (SD) CT for pulmonary nodule detection and semi-automated nodule measurement, across different patient sizes.

METHODS

Sixty-three patients underwent ULD and SD CT. Two readers examined all studies visually and with computer-aided detection (CAD). Nodules detected on SD CT were included in the reference standard by consensus and stratified into 4 categories (nodule category, NODCAT) from the Dutch-Belgian Lung Cancer Screening trial (NELSON). Effects of NODCAT and patient size on nodule detection were determined. For each nodule, volume and diameter were compared between both scans.

RESULTS

The reference standard comprised 173 nodules. For both readers, detection rates on ULD versus SD CT were not significantly different for NODCAT 3 and 4 nodules > 50 mm³ (reader 1: 93% versus 89% (p = 0.257); reader 2: 96% versus 98% (p = 0.317)). For NODCAT 1 and 2 nodules < 50 mm³, detection rates on ULD versus SD CT dropped significantly (reader 1: 66% versus 80% (p = 0.023); reader 2: 77% versus 87% (p = 0.039)). Body mass index and chest circumference did not influence nodule detectability (p = 0.229 and p = 0.362, respectively). Calculated volumes and diameters were smaller on ULD CT (p < 0.0001), without altering NODCAT (84% agreement).

CONCLUSIONS

Scoutless ULD CT reliably detects solid lung nodules with a clinically relevant volume (> 50 mm³) in lung cancer screening, irrespective of patient size. Since detection rates were lower compared to SD CT for nodules < 50 mm³, its use for lung metastasis detection should be considered on a case-by-case basis.

KEY POINTS

• Detection rates of pulmonary nodules > 50 mm³are not significantly different between scoutless ULD and SD CT (i.e. volumes clinically relevant in lung cancer screening based on the NELSON trial), but were different for the detection of nodules < 50 mm³(i.e. volumes still potentially relevant in lung metastasis screening). • Calculated nodule volumes were on average 0.03 mL or 9% smaller on ULD CT, which is below the 20-25% interscan variability previously reported with software-based volumetry. • Even though a scoutless, fixed-dose ULD CT protocol was used (CTDI_vol0.15 mGy), pulmonary nodule detection was not influenced by patient size.

Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification

OBJECTIVE

To evaluate performance of AI as a standalone reader in ultra-low-dose CT lung cancer baseline screening, and compare it to that of experienced radiologists.

METHODS

283 participants who underwent a baseline ultra-LDCT scan in Moscow Lung Cancer Screening, between February 2017-2018, and had at least one solid lung nodule, were included. Volumetric nodule measurements were performed by five experienced blinded radiologists, and independently assessed using an AI lung cancer screening prototype (AVIEW LCS, v1.0.34, Coreline Soft, Co. ltd, Seoul, Korea) to automatically detect, measure, and classify solid nodules. Discrepancies were stratified into two groups: positive-misclassification (PM); nodule classified by the reader as a NELSON-plus /EUPS-indeterminate/positive nodule, which at the reference consensus read was < 100 mm3, and negative-misclassification (NM); nodule classified as a NELSON-plus /EUPS-negative nodule, which at consensus read was ≥ 100 mm3.

RESULTS

1149 nodules with a solid-component were detected, of which 878 were classified as solid nodules. For the largest solid nodule per participant (n = 283); 61 [21.6 %; 53 PM, 8 NM] discrepancies were reported for AI as a standalone reader, compared to 43 [15.1 %; 22 PM, 21 NM], 36 [12.7 %; 25 PM, 11 NM], 29 [10.2 %; 25 PM, 4 NM], 28 [9.9 %; 6 PM, 22 NM], and 50 [17.7 %; 15 PM, 35 NM] discrepancies for readers 1, 2, 3, 4, and 5 respectively.

CONCLUSION

Our results suggest that through the use of AI as an impartial reader in baseline lung cancer screening, negative-misclassification results could exceed that of four out of five experienced radiologists, and radiologists' workload could be drastically diminished by up to 86.7%.