British Thoracic Society guidelines for the investigation and management of pulmonary nodules

Differences in VA and Non-VA Pulmonary Nodules: All Evaluations Are not Created Equal

BACKGROUND

Indeterminate pulmonary nodules present a common challenge for clinicians who must recommend surveillance or intervention based on an assessed risk of malignancy.

PATIENTS AND METHODS

In this cohort study, patients presenting for indeterminate pulmonary nodule evaluation were enrolled at sites participating in the Colorado SPORE in Lung Cancer. They were followed prospectively and included for analysis if they had a definitive malignant diagnosis, benign diagnosis, or radiographic resolution or stability of their nodule for > 2 years.

RESULTS

Patients evaluated at the Veterans Affairs (VA) and non-VA sites were equally as likely to have a malignant diagnosis (48%). The VA cohort represented a higher-risk group than the non-VA cohort regarding smoking history and chronic obstructive pulmonary disease (COPD). There were more squamous cell carcinoma diagnoses among VA malignant nodules (25% vs. 10%) and a later stage at diagnosis among VA patients. Discrimination and calibration of risk calculators produced estimates that were wide-ranging and different when comparing between risk score calculators as well as between VA/non-VA cohorts. Application of current American College of Chest Physicians guidelines to our groups could have resulted in inappropriate resection of 12% of benign nodules.

CONCLUSION

Comparison of VA with non-VA patients shows important differences in underlying risk, histology of malignant nodules, and stage at diagnosis. This study highlights the challenge in applying risk calculators to a clinical setting, as the model discrimination and calibration were variable between calculators and between our higher-risk VA and lower-risk non-VA groups.

MICROABSTRACT

Risk stratification and management of indeterminate pulmonary nodules (IPNs) is a common clinical problem. In this prospective cohort study of 282 patients with IPNs from Veterans Affairs (VA) and non-VA sites, we found differences in patient and nodule characteristics, histology and diagnostic stage, and risk calculator performance. Our findings highlight challenges and shortcomings of current IPN management guidelines and tools.

ERS International Congress 2022: highlights from the Thoracic Oncology Assembly

Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.

Diagnosis of primary lung cancer and benign pulmonary nodules: a comparison of the breath test and 18F-FDG PET-CT

With the application of low-dose computed tomography in lung cancer screening, pulmonary nodules have become increasingly detected. Accurate discrimination between primary lung cancer and benign nodules poses a significant clinical challenge. This study aimed to investigate the viability of exhaled breath as a diagnostic tool for pulmonary nodules and compare the breath test with 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT). Exhaled breath was collected by Tedlar bags and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). A retrospective cohort (n = 100) and a prospective cohort (n = 63) of patients with pulmonary nodules were established. In the validation cohort, the breath test achieved an area under the receiver operating characteristic curve (AUC) of 0.872 (95% CI 0.760-0.983) and a combination of 16 volatile organic compounds achieved an AUC of 0.744 (95% CI 0.7586-0.901). For PET-CT, the SUVmax alone had an AUC of 0.608 (95% CI 0.433-0.784) while after combining with CT image features, 18F-FDG PET-CT had an AUC of 0.821 (95% CI 0.662-0.979). Overall, the study demonstrated the efficacy of a breath test utilizing HPPI-TOFMS for discriminating lung cancer from benign pulmonary nodules. Furthermore, the accuracy achieved by the exhaled breath test was comparable with 18F-FDG PET-CT.

Developing a pan-European technical standard for a comprehensive high-quality lung cancer computed tomography screening programme: an ERS technical standard

BACKGROUND

Screening for lung cancer with low radiation dose computed tomography (LDCT) has a strong evidence base. The European Council adopted a recommendation in November 2022 that lung cancer screening (LCS) be implemented using a stepwise approach. The imperative now is to ensure that implementation follows an evidence-based process that delivers clinical and cost-effectiveness. This European Respiratory Society (ERS) Task Force was formed to provide a technical standard for a high-quality LCS programme.

METHOD

A collaborative group was convened to include members of multiple European societies. Topics were identified during a scoping review and a systematic review of the literature was conducted. Full text was provided to members of the group for each topic. The final document was approved by all members and the ERS Scientific Advisory Committee.

RESULTS

Topics were identified representing key components of a screening programme. The actions on findings from the LDCT were not included as they are addressed by separate international guidelines (nodule management and clinical management of lung cancer) and by a linked ERS Task Force (incidental findings). Other than smoking cessation, other interventions that are not part of the core screening process were not included (e.g. pulmonary function measurement). 56 statements were produced and areas for further research identified.

CONCLUSIONS

This European collaborative group has produced a technical standard that is a timely contribution to implementation of LCS. It will serve as a standard that can be used, as recommended by the European Council, to ensure a high-quality and effective programme.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study

Objective

The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.

Methods

A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R²(determination coefficient).

Results

The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation.

Conclusion

The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

It has been proved that Raman spectral intensities could be used to diagnose lung cancer patients. However, the application of Raman spectroscopy in identifying the patients with pulmonary nodules was barely studied. In this study, we revealed that Raman spectra of serum samples from healthy participants and patients with benign and malignant pulmonary nodules were significantly different. A support vector machine (SVM) model was developed for the classification of Raman spectra with wave points, according to ANOVA test results. It got a good performance with a median area under the curve (AUC) of 0.89, when the SVM model was applied in discriminating benign from malignant individuals. Compared with three common clinical models, the SVM model showed a better discriminative ability and added more net benefits to participants, which were also excellent in the small-size nodules. Thus, the Raman spectroscopy could be a less-invasive and low-costly liquid biopsy.

Facilitating combined biopsy and percutaneous microwave ablation of pulmonary ground-glass opacities using lipiodol localisation

OBJECTIVES

Whether preoperative localisation is necessary and valuable for the microwave ablation (MWA) of small pulmonary lesions with ground-glass opacity (GGO) remains unclear. This study aimed to explore the role of the Chiba needle and lipiodol localisation techniques in facilitating MWA and biopsy.

METHODS

This retrospective before-after study included patients with GGOs who underwent conventional MWA and biopsy treatment in our hospital between January 2018 and December 2019 (group A) or who underwent the Chiba needle and lipiodol localisation treatment before MWA and biopsy between January 2020 and December 2020 (group B). The characteristics of each patient and GGO lesion were collected and analysed to evaluate the safety and effectiveness of the localisation technique.

RESULTS

A total of 122 patients with 152 GGOs and 131 patients with 156 GGOs underwent MWA and biopsy in groups A and B, respectively. The primary technique efficacy rate of MWA differed significantly between the two groups (A vs. B: 94.1% vs. 99.4%; p = 0.009). The positive biopsy rate in the two groups was determined by the difference (A vs. B: 93.4% vs. 98.1%; p = 0.042). The incidence of complications did not increase in group B.

CONCLUSIONS

Compared with the unmarked group, the Chiba needle and lipiodol localisation technique improved the positive rate of biopsy and the initial effective rate of MWA, without significantly increasing the complication rate.

KEY POINTS

• The localisation of the Chiba needle and lipiodol could improve the positive biopsy rate and the initial effective rate of MWA. • The localisation of the Chiba needle and lipiodol does not affect the subsequent MWA and biopsy and does not increase the incidence of pneumothorax and haemorrhage.

B-mode ultrasound and contrast-enhanced ultrasound for evaluation of pneumonia: A pictorial essay

Due to their often peripheral pleural-based location, pneumonias can be visualised by B-mode ultrasound. Therefore, sonography can be used as an alternative imaging modality to chest X-ray in suspected cases of pneumonia. Depending on the clinical background of the patient, and various underlying pathological mechanisms, a heterogeneous pattern of pneumonia is seen in both B-mode lung ultrasound and contrast-enhanced ultrasound. Here, we describe the spectrum of sonographic manifestations of pneumonic/inflammatory consolidation on B-mode lung ultrasound and contrast-enhanced ultrasound.

Defining the road map to a UK national lung cancer screening programme

Lung cancer screening with low-dose CT was recommended by the UK National Screening Committee (UKNSC) in September, 2022, on the basis of data from trials showing a reduction in lung cancer mortality. These trials provide sufficient evidence to show clinical efficacy, but further work is needed to prove deliverability in preparation for a national roll-out of the first major targeted screening programme. The UK has been world leading in addressing logistical issues with lung cancer screening through clinical trials, implementation pilots, and the National Health Service (NHS) England Targeted Lung Health Check Programme. In this Policy Review, we describe the consensus reached by a multiprofessional group of experts in lung cancer screening on the key requirements and priorities for effective implementation of a programme. We summarise the output from a round-table meeting of clinicians, behavioural scientists, stakeholder organisations, and representatives from NHS England, the UKNSC, and the four UK nations. This Policy Review will be an important tool in the ongoing expansion and evolution of an already successful programme, and provides a summary of UK expert opinion for consideration by those organising and delivering lung cancer screenings in other countries.

Growth Rates of Pulmonary Carcinoid Tumors and Hamartomas

BACKGROUND

Pulmonary nodule growth is often measured by volume doubling time (VDT), which may guide management. Most malignant nodules have a VDT of 20 to 400 days, with longer VDTs typically observed in indolent nodules. We assessed the utility of VDT in differentiating pulmonary carcinoids and hamartomas.

METHODS

A review was performed from January 2012 to October 2021 to identify patients with pathologic diagnoses and at least 2 chest computed tomography scans obtained 6 or more months apart. Visualization software was used to segment nodules and calculate diameter and volume. Volume doubling time was calculated for scans with 1-mm slices. For the remainder, estimated nodule volume doubling time (eVDT) was calculated using nodule diameter. Volume doubling times/eVDTs were placed into growth categories: less than 400 days; 400-600 days; and more than 600 days.

RESULTS

Sixty nodules were identified, 35 carcinoids and 25 hamartomas. Carcinoids were larger than hamartomas (median diameter, 13.5 vs 11.5 mm; P = 0.05). For carcinoid tumors, median VDT (n = 15) was 1485 days, and median eVDT (n = 32) was 1309 days; for hamartomas, median VDT (n = 8) was 2040 days and median eVDT (n = 25) was 2253 days. Carcinoid tumor eVDT was significantly shorter than hamartomas ( P = 0.03). By growth category, 1 of 25 hamartomas and 5 of 35 carcinoids had eVDT less than 400 days and 24 of 25 hamartomas and 27 of 35 carcinoids had eVDT more than 600 days. Of 4 carcinoid tumors with metastases, 2 had eVDT less than 400 days and 2 had eVDT more than 600 days.

CONCLUSIONS

Growth rate was not a reliable differentiator of pulmonary hamartomas and carcinoids. Slow growing carcinoids can metastasize. Radiologists should be cautious when discontinuing computed tomography follow-up based on growth rates alone.

Comparison of ventilation defects quantified by Technegas SPECT and hyperpolarized 129Xe MRI

Introduction: The ideal contrast agents for ventilation SPECT and MRI are Technegas and ¹²⁹Xe gas, respectively. Despite increasing interest in the clinical utility of ventilation imaging, these modalities have not been directly compared. Therefore, our objective was to compare the ventilation defect percent (VDP) assessed by Technegas SPECT and hyperpolarized ¹²⁹Xe MRI in patients scheduled to undergo lung cancer resection with and without pre-existing obstructive lung disease. Methods: Forty-one adults scheduled to undergo lung cancer resection performed same-day Technegas SPECT, hyperpolarized ¹²⁹Xe MRI, spirometry, and diffusing capacity of the lung for carbon monoxide (DL_CO). Ventilation abnormalities were quantified as the VDP using two different methods: adaptive thresholding (VDP_T) and k-means clustering (VDP_K). Correlation and agreement between VDP quantified by Technegas SPECT and ¹²⁹Xe MRI were determined by Spearman correlation and Bland-Altman analysis, respectively. Results: VDP measured by Technegas SPECT and ¹²⁹Xe MRI were correlated (VDP_T: r = 0.48, p = 0.001; VDP_K: r = 0.63, p < 0.0001). A 2.0% and 1.6% bias towards higher Technegas SPECT VDP was measured using the adaptive threshold method (VDP_T: 23.0% ± 14.0% vs. 21.0% ± 5.2%, p = 0.81) and k-means method (VDP_K: 9.4% ± 9.4% vs. 7.8% ± 10.0%, p = 0.02), respectively. For both modalities, higher VDP was correlated with lower FEV₁/FVC (SPECT VDP_T: r = -0.38, p = 0.01; MRI VDP_K: r = -0.46, p = 0.002) and DL_CO (SPECT VDP_T: r = -0.61, p < 0.0001; MRI VDP_K: r = -0.68, p < 0.0001). Subgroup analysis revealed that VDP measured by both modalities was significantly higher for participants with COPD (n = 13) than those with asthma (n = 6; SPECT VDP_T: p = 0.007, MRI VDP_K: p = 0.006) and those with no history of obstructive lung disease (n = 21; SPECT VDP_T: p = 0.0003, MRI VDP_K: p = 0.0003). Discussion: The burden of ventilation defects quantified by Technegas SPECT and ¹²⁹Xe MRI VDP was correlated and greater in participants with COPD when compared to those without. Our observations indicate that, despite substantial differences between the imaging modalities, quantitative assessment of ventilation defects by Technegas SPECT and ¹²⁹Xe MRI is comparable.

Performance of radiomics models derived from different CT reconstruction parameters for lung cancer risk prediction

BACKGROUND

This study analysed the performance of radiomics features extracted from computed tomography (CT) images with different reconstruction parameters in differentiating malignant and benign pulmonary nodules.

METHODS

We evaluated routine chest CT images acquired from 148 participants with pulmonary nodules, which were pathologically diagnosed during surgery in West China Hospital, including a 5 mm unenhanced lung window, a 5 mm unenhanced mediastinal window, a 5 mm contrast-enhanced mediastinal window and a 1 mm unenhanced lung window. The pulmonary nodules were segmented, and 1409 radiomics features were extracted for each window. Then, we created 15 cohorts consisting of single windows or multiple windows. Univariate correlation analysis and principal component analysis were performed to select the features, and logistic regression analysis was performed to establish models for each cohort. The area under the curve (AUC) was applied to compare model performance.

RESULTS

There were 75 benign and 73 malignant pulmonary nodules, with mean diameters of 18.63 and 19.86 mm, respectively. For the single-window setting, the AUCs of the radiomics model from the 5 mm unenhanced lung window, 5 mm unenhanced mediastinal window, 5 mm contrast-enhanced mediastinal window and 1 mm unenhanced lung window were 0.771, 0.808, 0.750, and 0.771 in the training set and 0.711, 0.709, 0.684, and 0.674 in the test set, respectively. Regarding the multiple-window setting, the radiomics model based on all four windows showed an AUC of 0.825 in the training set and 0.743 in the test set. Statistically, the 15 models demonstrated comparable performances (P > 0.05).

CONCLUSION

A single chest CT window was acceptable in predicting the malignancy of pulmonary nodules, and additional windows did not statistically improve the performance of the radiomics models. In addition, slice thickness and contrast enhancement did not affect the diagnostic performance.

Diagnostic yield and safety of navigation bronchoscopy: A systematic review and meta-analysis

BACKGROUND

Navigation bronchoscopy has seen rapid development in the past decade in terms of new navigation techniques and multi-modality approaches utilizing different techniques and tools. This systematic review analyses the diagnostic yield and safety of navigation bronchoscopy for the diagnosis of peripheral pulmonary nodules suspected of lung cancer.

METHODS

An extensive search was performed in Embase, Medline and Cochrane CENTRAL in May 2022. Eligible studies used cone-beam CT-guided navigation (CBCT), electromagnetic navigation (EMN), robotic navigation (RB) or virtual bronchoscopy (VB) as the primary navigation technique. Primary outcomes were diagnostic yield and adverse events. Quality of studies was assessed using QUADAS-2. Random effects meta-analysis was performed, with subgroup analyses for different navigation techniques, newer versus older techniques, nodule size, publication year, and strictness of diagnostic yield definition. Explorative analyses of subgroups reported by studies was performed for nodule size and bronchus sign.

RESULTS

A total of 95 studies (n = 10,381 patients; n = 10,682 nodules) were included. The majority (n = 63; 66.3%) had high risk of bias or applicability concerns in at least one QUADAS-2 domain. Summary diagnostic yield was 70.9% (95%-CI 68.4%-73.2%). Overall pneumothorax rate was 2.5%. Newer navigation techniques using advanced imaging and/or robotics(CBCT, RB, tomosynthesis guided EMN; n = 24 studies) had a statistically significant higher diagnostic yield compared to longer established techniques (EMN, VB; n = 82 studies): 77.5% (95%-CI 74.7%-80.1%) vs 68.8% (95%-CI 65.9%-71.6%) (p < 0.001).Explorative subgroup analyses showed that larger nodule size and bronchus sign presence were associated with a statistically significant higher diagnostic yield. Other subgroup analyses showed no significant differences.

CONCLUSION

Navigation bronchoscopy is a safe procedure, with the potential for high diagnostic yield, in particular using newer techniques such as RB, CBCT and tomosynthesis-guided EMN. Studies showed a large amount of heterogeneity, making comparisons difficult. Standardized definitions for outcomes with relevant clinical context will improve future comparability.

Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules

A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.

Lung dual energy CT: Impact of different technological solutions on quantitative analysis

PURPOSE

To evaluated the accuracy of spectral parameters quantification of four different CT scanners in dual energy examinations of the lung using a dedicated phantom.

METHOD

Measurements were made with different technologies of the same vendor: one dual source CT scanner (DSCT), one TwinBeam (i.e. split filter) and two sequential acquisition single source scanners (SSCT). Angular separation of Calcium and Iodine signals were calculated from scatter plots of low-kVp versus high-kVp HUs. Electron density (ρe), effective atomic number (Zeff) and Iodine concentration (Iconc) were measured using Syngo.via software. Accuracy (A) of ρe, Zeff and Iconc was evaluated as the absolute percentage difference (D%) between reference values and measured ones, while precision (P) was evaluated as the variability σ obtained by repeating the measurement with different acquisition/reconstruction settings.

RESULTS

Angular separation was significantly larger for DSCT (α = 9.7°) and for sequential SSCT (α = 9.9°) systems. TwinBeam was less performing in material separation (α = 5.0°). The lowest average A was observed for TwinBeam (A_ρe = [4.7 ± 1.0], A_Z = [9.1 ± 3.1], A_Iconc = [19.4 ± 4.4]), while the best average A was obtained for Flash (A_ρe = [1.8 ± 0.4], A_Z = [3.5 ± 0.7], A_Iconc = [7.3 ± 1.8]). TwinBeam presented inferior average P (P_ρe = [0.6 ± 0.1], P_Z = [1.1 ± 0.2], P_Iconc = [10.9 ± 4.9]), while other technologies demonstrate a comparable average.

CONCLUSIONS

Different technologies performed material separation and spectral parameter quantification with different degrees of accuracy and precision. DSCT performed better while TwinBeam demonstrated not excellent performance. Iodine concentration measurements exhibited high variability due to low Iodine absolute content in lung nodules, thus limiting its clinical usefulness in pulmonary applications.

The evolution of non-small cell lung cancer metastases in TRACERx

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse.

Artificial intelligence aided diagnosis of pulmonary nodules segmentation and feature extraction

AIM

To develop a high-accuracy low-dose computed tomography (LDCT) lung nodule diagnosis system by combining artificial intelligence (AI) technology with the Lung CT Screening Reporting and Data System (Lung-RADS), which can be used in the future AI-aided diagnosis of pulmonary nodules.

MATERIALS AND METHODS

The study comprised the following steps: (1) the best deep-learning segmentation method for pulmonary nodules was compared and selected objectively; (2) the Image Biomarker Standardization Initiative (IBSI) was used for feature extraction and to determine the best feature reduction method; and (3) a principal component analysis (PCA) and three machine learning methods were used to analyse the extracted features, and the best method was determined. The Lung Nodule Analysis 16 dataset was applied to train and test the established system in this study.

RESULTS

The competition performance metric (CPM) score of the nodule segmentation reached 0.83, the accuracy of nodule classification was 92%, the kappa coefficient with the ground truth was 0.68, and the overall diagnostic accuracy (calculated by the nodules) was 0.75.

CONCLUSION

This paper summarises a more efficient AI-assisted diagnosis process of pulmonary nodules, and has better performance compared with the previous literature. In addition, this method will be validated in a future external clinical study.

Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing

BACKGROUND

Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer.

METHODS

Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model.

RESULTS

We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules.

CONCLUSIONS

Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers.

IMPACT

Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.

Virtual fluoroscopic preprocedural planning using Ziostation2 for transbronchial biopsy: A prospective self-controlled study

BACKGROUND

Bronchoscopes cannot reach the periphery of the lung because the bronchi are tapered. Therefore, selectively advancing a device-e.g., an endobronchial ultrasonography (EBUS) probe-to the targets can be challenging. Virtual fluoroscopic preprocedural planning (VFPP) is a method in which the route to the target is superimposed on an X-ray fluoroscopy-like image reconstructed from CT images, facilitating the advancement of the EBUS probe to the target. The VFPP method was integrated into the Ziostation2 bronchoscopic navigation system (Ziosoft, Inc., Tokyo, Japan) in 2018. Here, we prospectively examined the feasibility of the VFPP method using Ziostation2 (Zio-VFPP).

METHODS

Thirty-six patients who had pulmonary lesions with long axes ≤30 mm and who underwent thin-slice CT with ≤0.625-mm thickness were enrolled. We initiated bronchoscopy using EBUS with a guide sheath (EBUS-GS) while referring to Ziostation2 bronchoscopic navigation. When the probe was not "within" a lesion, we attempted to correct its position based on Zio-VFPP. EBUS findings before and after Zio-VFPP were compared.

RESULTS

Zio-VFPP was performed in 24 patients, and EBUS findings improved in nine patients. Before Zio-VFPP, 18 patients were "outside," but after Zio-VFPP, the number decreased to ten. Statistically, this difference was significant (p = 0.0392). There were no cases in which EBUS findings worsened with Zio-VFPP.

CONCLUSION

Zio-VPFPP improves EBUS findings and significantly reduces "outside" cases. However, further investigation is necessary to verify its effectiveness.

Detection and size quantification of pulmonary nodules in ultralow-dose versus regular-dose CT: a comparative study in COPD patients

OBJECTIVE

To evaluate detectability and semi-automatic diameter and volume measurements of pulmonary nodules in ultralow-dose CT (ULDCT) vs regular-dose CT (RDCT).

METHODS

Fifty patients with chronic obstructive pulmonary disease (COPD) underwent RDCT on 64-multidetector CT (120 kV, filtered back projection), and ULDCT on third-generation dual source CT (100 kV with tin filter, advanced modeled iterative reconstruction). One radiologist evaluated the presence of nodules on both scans in random order, with discrepancies judged by two independent radiologists and consensus reading. Sensitivity of nodule detection on RDCT and ULDCT was compared to reader consensus. Systematic error in semi-automatically derived diameter and volume, and 95% limits of agreement (LoA) were evaluated. Nodule classification was compared by κ statistics.

RESULTS

ULDCT resulted in 83.1% (95% CI: 81.0-85.2) dose reduction compared to RDCT (p < 0.001). 45 nodules were present, with diameter range 4.0-25.3 mm and volume range 16.0-4483.0 mm³. Detection sensitivity was non-significant (p = 0.503) between RDCT 88.8% (95% CI: 76.0-96.3) and ULDCT 95.5% (95% CI: 84.9-99.5). No systematic bias in diameter measurements (median difference: -0.2 mm) or volumetry (median difference: -6 mm³) was found for ULDCT compared to RDCT. The 95% LoA for diameter and volume measurements were ±3.0 mm and ±33.5%, respectively. κ value for nodule classification was 0.852 for diameter measurements and 0.930 for volumetry.

CONCLUSION

ULDCT based on Sn100 kV enables comparable detectability of solid pulmonary nodules in COPD patients, at 83% reduced radiation dose compared to RDCT, without relevant difference in nodule measurement and size classification.

ADVANCES IN KNOWLEDGE

Pulmonary nodule detectability and measurements in ULDCT are comparable to RDCT.

The additional diagnostic value of virtual bronchoscopy navigation in patients with pulmonary nodules - The NAVIGATOR study

BACKGROUND

The number of solitary pulmonary nodules to be evaluated is expected to increase and therefore we need to improve diagnostic and therapeutic tools to approach these nodules. To prevent patients from futile invasive procedures and receiving treatment without histological confirmation of cancer, we evaluated the value of virtual bronchoscopy navigation to obtain a diagnosis of the solitary pulmonary nodule in a real-world clinical setting.

METHODS

In the NAVIGATOR single center, prospective, observational cohort study patients underwent a virtual bronchoscopy navigation procedure with or without guide sheet tunnelling to assess a solitary pulmonary nodule. Nodules were considered not accessible if a diagnosis could not be obtained by either by CT-guided transthoracic biopsy or conventional bronchoscopy.

RESULTS

Between February 2021 and January 2022 35 patients underwent the virtual bronchoscopy navigation procedure. The overall diagnostic yield was 77% and was dependent on size of the nodule and chosen path, with highest yield in lesions with an airway path. Adverse events were few and manageable.

CONCLUSION

Virtual bronchoscopy navigation with or without sheet tunnelling is a new technique with a good diagnostic yield, also in patients in whom previously performed procedures failed to establish a diagnosis and/or alternative procedures are considered not feasible based on expected yield and/or safety. Preventing futile or more invasive procedures like surgery or transthoracic punctures with a higher complication rate is beneficial for patients, and allowed treatment adaptation in two-third of the analyzed patient population.

Qualitative and Semiquantitative Parameters of 18F-FDG-PET/CT as Predictors of Malignancy in Patients with Solitary Pulmonary Nodule

This study aims to evaluate the reliability of qualitative and semiquantitative parameters of ¹⁸F-FDG PET-CT, and eventually a correlation between them, in predicting the risk of malignancy in patients with solitary pulmonary nodules (SPNs) before the diagnosis of lung cancer. A total of 146 patients were retrospectively studied according to their pre-test probability of malignancy (all patients were intermediate risk), based on radiological features and risk factors, and qualitative and semiquantitative parameters, such as SUVmax, SUVmean, TLG, and MTV, which were obtained from the FDG PET-CT scan of such patients before diagnosis. It has been observed that visual analysis correlates well with the risk of malignancy in patients with SPN; indeed, only 20% of SPNs in which FDG uptake was low or absent were found to be malignant at the cytopathological examination, while 45.45% of SPNs in which FDG uptake was moderate and 90.24% in which FDG uptake was intense were found to be malignant. The same trend was observed evaluating semiquantitative parameters, since increasing values of SUVmax, SUVmean, TLG, and MTV were observed in patients whose cytopathological examination of SPN showed the presence of lung cancer. In particular, in patients whose SPN was neoplastic, we observed a median (MAD) SUVmax of 7.89 (±2.24), median (MAD) SUVmean of 3.76 (±2.59), median (MAD) TLG of 16.36 (±15.87), and a median (MAD) MTV of 3.39 (±2.86). In contrast, in patients whose SPN was non-neoplastic, the SUVmax was 2.24 (±1.73), SUVmean 1.67 (±1.15), TLG 1.63 (±2.33), and MTV 1.20 (±1.20). Optimal cut-offs were drawn for semiquantitative parameters considered predictors of malignancy. Nodule size correlated significantly with FDG uptake intensity and with SUVmax. Finally, age and nodule size proved significant predictors of malignancy. In conclusion, considering the pre-test probability of malignancy, qualitative and semiquantitative parameters can be considered reliable tools in patients with SPN, since cut-offs for SUVmax, SUVmean, TLG, and MTV showed good sensitivity and specificity in predicting malignancy.

Lung cancer screening

Randomised controlled trials, including the National Lung Screening Trial (NLST) and the NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT compared with chest radiography or no screening. Although research has provided clarity on key issues of lung cancer screening, uncertainty remains about aspects that might be critical to optimise clinical effectiveness and cost-effectiveness. This Review brings together current evidence on lung cancer screening, including an overview of clinical trials, considerations regarding the identification of individuals who benefit from lung cancer screening, management of screen-detected findings, smoking cessation interventions, cost-effectiveness, the role of artificial intelligence and biomarkers, and current challenges, solutions, and opportunities surrounding the implementation of lung cancer screening programmes from an international perspective. Further research into risk models for patient selection, personalised screening intervals, novel biomarkers, integrated cardiovascular disease and chronic obstructive pulmonary disease assessments, smoking cessation interventions, and artificial intelligence for lung nodule detection and risk stratification are key opportunities to increase the efficiency of lung cancer screening and ensure equity of access.

Lung nodules: sorting the wheat from the chaff

Pulmonary nodules are a common finding on CT scans of the chest. In the United Kingdom, management should follow British Thoracic Society Guidelines, which were published in 2015. This review covers key aspects of nodule management also looks at new and emerging evidence since then.

Growing small solid nodules in lung cancer screening: safety and efficacy of a 200 mm3 minimum size threshold for multidisciplinary team referral

The optimal management of small but growing nodules remains unclear. The SUMMIT study nodule management algorithm uses a specific threshold volume of 200 mm3 before referral of growing solid nodules to the multidisciplinary team for further investigation is advised, with growing nodules below this threshold kept under observation within the screening programme. Malignancy risk of growing solid nodules of size >200 mm3 at initial 3-month interval scan was 58.3% at a per-nodule level, compared with 13.3% in growing nodules of size ≤200 mm3 (relative risk 4.4, 95% CI 2.17 to 8.83). The positive predictive value of a combination of nodule growth (defined as percentage volume change of ≥25%), and size >200 mm3 was 65.9% (29/44) at a cancer-per-nodule basis, or 60.5% (23/38) on a cancer-per-participant basis. False negative rate of the protocol was 1.9% (95% CI 0.33% to 9.94%). These findings support the use of a 200 mm3 minimum volume threshold for referral as effective at reducing unnecessary multidisciplinary team referrals for small growing nodules, while maintaining early-stage lung cancer diagnosis.

Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging

BACKGROUND

Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy.

RESEARCH QUESTION

What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup?

STUDY DESIGN AND METHODS

This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test.

RESULTS

Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only).

INTERPRETATION

An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier; No.: NCT02013063.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT

OBJECTIVE

To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up.

METHODS

A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators.

RESULTS

We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans.

CONCLUSION

A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

Apical fibrosis was the most common incidental pulmonary finding in a familial Mediterranean fever cohort

INTRODUCTION

Familial Mediterranean fever (FMF) is one of the common autoinflammatory diseases with multisystemic manifestation. Pleuritis is the only known pulmonary involvement of FMF; however, as far as we know, thoracic involvements in pleural, parenchymal, bronchial, and vascular structures have not been evaluated yet.

METHOD

We included 243 consecutive FMF patients who applied to our clinic within the last 5 years and were requested to have a thorax CT for any reason and 122 trauma patients without any comorbidity. An experienced radiologist evaluated the thorax CT images blindly according to the relevant guidelines. We then presented the common incidental pulmonary and mediastinal findings on the thorax CT. Additionally, we compared patients with and without lung involvement according to demographic and disease-related parameters.

RESULTS

In our study, 167 of 243 patients (68.7%) had at least one of the pulmonary findings on their thorax CT. The most common pulmonary findings were apical fibrosis in 96 (39.5%) patients, parenchymal fibrotic changes in 48 (19.8%) patients, and a solitary parenchymal nodule smaller than 4 mm in 33 (13.6%) patients. All demographic, genetic, and disease-related characteristics, including the frequency of spondyloarthropathy, were similar in patients with and without pulmonary findings.

CONCLUSIONS

We showed that the most common incidental pulmonary finding in our FMF cohort was apical fibrosis on thoracic CT. Our data did not show causality between FMF and apical fibrosis; therefore, more studies are needed to evaluate the frequency and clinical significance of apical fibrosis in FMF. Key Points • More than two-thirds of familial Mediterranean fever (FMF) patients in our study group who underwent a thoracic scan for any reason had pulmonary and mediastinal findings on thorax computed tomography (CT). • In our FMF cohort, the most common incidental pulmonary finding on their thorax CT was apical fibrosis. • All demographic and disease-related characteristics, including the frequency of spondyloarthritis, were similar between patients with and without pulmonary and mediastinal findings.

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Peripheral pulmonary lesions (PPLs) are frequent incidental findings in subjects when performing chest radiographs or chest computed tomography (CT) scans. When a PPL is identified, it is necessary to proceed with a risk stratification based on the patient profile and the characteristics found on chest CT. In order to proceed with a diagnostic procedure, the first-line examination is often a bronchoscopy with tissue sampling. Many guidance technologies have recently been developed to facilitate PPLs sampling. Through bronchoscopy, it is currently possible to ascertain the PPL's benign or malignant nature, delaying the therapy's second phase with radical, supportive, or palliative intent. In this review, we describe all the new tools available: from the innovation of bronchoscopic instrumentation (e.g., ultrathin bronchoscopy and robotic bronchoscopy) to the advances in navigation technology (e.g., radial-probe endobronchial ultrasound, virtual navigation, electromagnetic navigation, shape-sensing navigation, cone-beam computed tomography). In addition, we summarize all the PPLs ablation techniques currently under experimentation. Interventional pulmonology may be a discipline aiming at adopting increasingly innovative and disruptive technologies.

Natural progression of persistent pure ground-glass nodules 10 mm or smaller: long-term observation and risk factor assessment

PURPOSE

Semi-automatic segmentation was used to investigate the natural progression of pure ground-glass nodules (pGGNs) of 5-10 mm in long-term follow-up and to analyze independent risk factors for subsequent growth.

MATERIALS AND METHODS

A total of 154 pGGNs of 5-10 mm from 132 patients with 698 follow-up CT scans were retrospectively identified. Subsequently, enrolled pGGNs were semiautomatically segmented on initial and follow-up CT to obtain diameter, density and volume, thus calculating mass, volume doubling time (VDT), and mass doubling time (MDT). Kaplan‒Meier analysis and multivariate Cox proportional risk regression were performed to explore independent predictors of pGGN growth. We analyzed growth differences among different pathological results of pGGNs confirmed by surgery. The prognosis was analyzed using the total diameter or solid size of the nodules on the last preoperative CT.

RESULTS

Among the 85 (55.2%) pGGNs with growth, 5.9%, 51.8%, and 80.0% showed growth within 1, 3, and 5 years, respectively. The median VDT and MDT were 1206.4 (range 349.8-5134.4) days and 1161.3 (range 339.4-6630.4) days, respectively. The multivariate Cox risk regression analysis showed that mean CT attenuation (m-CTA) [hazard ratio (HR) = 2.098, p = 0.010] and roundness index (HR = 1.892, p = 0.021) were independent risk factors for pGGN growth. In total, 67.6% of surgically resected and growing pGGNs were invasive non-mucinous adenocarcinoma (IA), including 2 cases of endpoint events, showing a PSN with solid components of 5.6 mm and a solid nodule with a diameter of 19.9 mm.

CONCLUSIONS

pGGNs of 5-10 mm showed an indolent clinical course. Follow-up CT imaging of pGGNs in the latter half of the first two years should be a rational management strategy. Small pGGNs with a larger overall m-CTA and roundness index on baseline CT are more likely to grow.

Artificial intelligence in lung cancer: current applications and perspectives

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

The long-term course of subsolid nodules and predictors of interval growth on chest CT: a systematic review and meta-analysis

OBJECTIVES

To calculate the pooled incidence of interval growth after long-term follow-up and identify predictors of interval growth in subsolid nodules (SSNs) on chest CT.

METHODS

A search of MEDLINE (PubMed), Cochrane Library, Web of Science Core Collection, and Embase was performed on November 08, 2021, for relevant studies. Patient information, CT scanner, and SSN follow-up information were extracted from each included study. A random-effects model was applied along with subgroup and meta-regression analyses. Study quality was assessed by the Newcastle-Ottawa scale, and publication bias was assessed by Egger's test.

RESULTS

Of the 6802 retrieved articles, 16 articles were included and analyzed, providing a total of 2898 available SSNs. The pooled incidence of growth in the 2898 SSNs was 22% (95% confidence interval [CI], 15-29%). The pooled incidence of growth in the subgroup analysis of pure ground-glass nodules was 26% (95% CI: 12-39%). The incidence of SSN growth after 2 or more years of stability was only 5% (95% CI: 3-7%). An initially large SSN size was found to be the most frequent risk factor affecting the incidence of SSN growth and the time of growth.

CONCLUSIONS

The pooled incidence of SSN growth was as high as 22%, with a 26% incidence reported for pure ground-glass nodules. Although the incidence of growth was only 5% after 2 or more years of stability, long-term follow-up is needed in certain cases. Moreover, the initial size of the SSN was the most frequent risk factor for growth.

KEY POINTS

• Based on a meta-analysis of 2898 available subsolid nodules in the literature, the pooled incidence of growth was 22% for all subsolid nodules and 26% for pure ground-glass nodules. • After 2 or more years of stability on follow-up CT, the pooled incidence of subsolid nodule growth was only 5%. • Given the incidence of subsolid nodule growth, management of these lesions with long-term follow-up is preferred.

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.

The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management

The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.

The Feasibility of Using the "Artery Sign" for Pre-Procedural Planning in Navigational Bronchoscopy for Parenchymal Pulmonary Lesion Sampling

BACKGROUND

Electromagnetic navigation bronchoscopy (ENB) and robotic-assisted bronchoscopy (RAB) systems are used for pulmonary lesion sampling, and utilize a pre-procedural CT scan where an airway, or "bronchus sign", is used to map a pathway to the target lesion. However, up to 40% of pre-procedural CT's lack a "bronchus sign" partially due to surrounding emphysema or limitation in CT resolution. Recognizing that the branches of the pulmonary artery, lymphatics, and airways are often present together as the bronchovascular bundle, we postulate that a branch of the pulmonary artery ("artery sign") could be used for pathway mapping during navigation bronchoscopy when a "bronchus sign" is absent. Herein we describe the navigation success and safety of using the "artery sign" to create a pathway for pulmonary lesion sampling.

METHODS

We reviewed data on consecutive cases in which the "artery sign" was used for pre-procedural planning for conventional ENB (superDimension™, Medtronic) and RAB (Monarch™, Johnson & Johnson). Patients who underwent these procedures from July 2020 until July 2021 at the University of Minnesota Medical Center and from June 2018 until December 2019 at the University of Chicago Medical Center were included in this analysis (IRB #19-0011 for the University of Chicago and IRB #00013135 for the University of Minnesota). The primary outcome was navigation success, defined as successfully maneuvering the bronchoscope to the target lesion based on feedback from the navigation system. Secondary outcomes included navigation success based on radial EBUS imaging, pneumothorax, and bleeding rates.

RESULTS

A total of 30 patients were enrolled in this analysis. The median diameter of the lesions was 17 mm. The median distance of the lesion from the pleura was 5 mm. Eleven lesions were solid, 15 were pure ground glass, and 4 were mixed. All cases were planned successfully using the "artery sign" on either the superDimension™ ENB (n = 15) or the Monarch™ RAB (n = 15). Navigation to the target was successful for 29 lesions (96.7%) based on feedback from the navigation system (virtual target). Radial EBUS image was acquired in 27 cases (90%) [eccentric view in 13 (43.33%) and concentric view in 14 patients (46.66%)], while in 3 cases (10%) no r-EBUS view was obtained. Pneumothorax occurred in one case (3%). Significant airway bleeding was reported in one case (3%).

CONCLUSIONS

We describe the concept of using the "artery sign" as an alternative for planning EMN and RAB procedures when "bronchus sign" is absent. The navigation success based on virtual target or r-EBUS imaging is high and safety of sampling of such lesions compares favorably with prior reports. Prospective studies are needed to assess the impact of the "artery sign" on diagnostic yield.

Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy

BACKGROUND

Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included.

METHODS

A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn.

RESULTS

There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22.

CONCLUSIONS

We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.

A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules

BACKGROUND

Large lung nodules (≥15 mm) have the highest risk of malignancy, and may exhibit important differences in phenotypic or clinical characteristics to their smaller counterparts. Existing risk models do not stratify large nodules well. We aimed to develop and validate an integrated segmentation and classification pipeline, incorporating deep-learning and traditional radiomics, to classify large lung nodules according to cancer risk.

METHODS

502 patients from five U.K. centres were recruited to the large-nodule arm of the retrospective LIBRA study between July 2020 and April 2022. 838 CT scans were used for model development, split into training and test sets (70% and 30% respectively). An nnUNet model was trained to automate lung nodule segmentation. A radiomics signature was developed to classify nodules according to malignancy risk. Performance of the radiomics model, termed the large-nodule radiomics predictive vector (LN-RPV), was compared to three radiologists and the Brock and Herder scores.

FINDINGS

499 patients had technically evaluable scans (mean age 69 ± 11, 257 men, 242 women). In the test set of 252 scans, the nnUNet achieved a DICE score of 0.86, and the LN-RPV achieved an AUC of 0.83 (95% CI 0.77-0.88) for malignancy classification. Performance was higher than the median radiologist (AUC 0.75 [95% CI 0.70-0.81], DeLong p = 0.03). LN-RPV was robust to auto-segmentation (ICC 0.94). For baseline solid nodules in the test set (117 patients), LN-RPV had an AUC of 0.87 (95% CI 0.80-0.93) compared to 0.67 (95% CI 0.55-0.76, DeLong p = 0.002) for the Brock score and 0.83 (95% CI 0.75-0.90, DeLong p = 0.4) for the Herder score. In the international external test set (n = 151), LN-RPV maintained an AUC of 0.75 (95% CI 0.63-0.85). 18 out of 22 (82%) malignant nodules in the Herder 10-70% category in the test set were identified as high risk by the decision-support tool, and may have been referred for earlier intervention.

INTERPRETATION

The model accurately segments and classifies large lung nodules, and may improve upon existing clinical models.

FUNDING

This project represents independent research funded by: 1) Royal Marsden Partners Cancer Alliance, 2) the Royal Marsden Cancer Charity, 3) the National Institute for Health Research (NIHR) Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London, 4) the National Institute for Health Research (NIHR) Biomedical Research Centre at Imperial College London, 5) Cancer Research UK (C309/A31316).

The mediating role of decision-making conflict in the association between patient's participation satisfaction and distress during medical decision-making among Chinese patients with pulmonary nodules

OBJECTIVE

When diagnosed as having pulmonary nodules, patients may be mired in the conflict of medical decision-making and suffered from distress. The purpose of this study was to investigate the mediating role of decision-making conflict in the relationship between participation satisfaction in medical decision-making (PSMD) and distress among Chinese patients with incidental pulmonary nodules.

METHODS

A total of 163 outpatients with incidental pulmonary nodules detected in a tertiary hospital were recruited and investigated by Impact of Event Scale (IES), Decision Conflict Scale (DCS), participation satisfaction in medical decision-making Scale (PSMDS), and demographic questionnaire.

RESULTS

The mean IES score was 37.35 ± 16.65, representing a moderate level. PSMD was negatively associated with distress, while decision-making conflict was positively associated with distress. The final regression model contained three factors: having a first-degree relative diagnosed with lung cancer, worrying about getting lung cancer someday, and decision-making conflict. These three factors explained 49.4 % of the variance of distress. The total effect of PSMD on distress and indirect effect of SPMD on distress caused-by decision-making conflict were significant (P < 0.05). However, the direct effect of PSMD on distress was not significant.

CONCLUSIONS

Participation of patients in medical decision-making can lower their distress by reducing patient's decision-making conflict.

PRACTICE IMPLICATIONS

Interventions targeting at the decision-making conflict will help alleviate the distress level of patients with pulmonary nodules.

DATA AVAILABILITY

The data that support the findings of this study are available on request from the corresponding author.

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.

EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation

BACKGROUND

EarlyCDT Lung (Oncimmune Holdings plc, Nottingham, UK) is a blood test to assess malignancy risk in people with solid pulmonary nodules. It measures the presence of seven lung cancer-associated autoantibodies. Elevated levels of these autoantibodies may indicate malignant disease. The results of the test might be used to modify the risk of malignancy estimated by existing risk calculators, including the Brock and Herder models.

OBJECTIVES

The objectives were to determine the diagnostic accuracy, clinical effectiveness and cost-effectiveness of EarlyCDT Lung; and to develop a conceptual model and identify evidence requirements for a robust cost-effectiveness analysis.

DATA SOURCES

MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE), EMBASE, Cochrane Central Register of Controlled Trials, Science Citation Index, EconLit, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database ( NHS EED ) and the international Health Technology Assessment database were searched on 8 March 2021.

REVIEW METHODS

A systematic review was performed of evidence on EarlyCDT Lung, including diagnostic accuracy, clinical effectiveness and cost-effectiveness. Study quality was assessed with the quality assessment of diagnostic accuracy studies-2 tool. Evidence on other components of the pulmonary nodule diagnostic pathway (computerised tomography surveillance, Brock risk, Herder risk, positron emission tomography-computerised tomography and biopsy) was also reviewed. When feasible, bivariate meta-analyses of diagnostic accuracy were performed. Clinical outcomes were synthesised narratively. A simulation study investigated the clinical impact of using EarlyCDT Lung. Additional reviews of cost-effectiveness studies evaluated (1) other diagnostic strategies for lung cancer and (2) screening approaches for lung cancer. A conceptual model was developed.

RESULTS

A total of 47 clinical publications on EarlyCDT Lung were identified, but only five cohorts (695 patients) reported diagnostic accuracy data on patients with pulmonary nodules. All cohorts were small or at high risk of bias. EarlyCDT Lung on its own was found to have poor diagnostic accuracy, with a summary sensitivity of 20.2% (95% confidence interval 10.5% to 35.5%) and specificity of 92.2% (95% confidence interval 86.2% to 95.8%). This sensitivity was substantially lower than that estimated by the manufacturer (41.3%). No evidence on the clinical impact of EarlyCDT Lung was identified. The simulation study suggested that EarlyCDT Lung might potentially have some benefit when considering intermediate risk nodules (10-70% risk) after Herder risk analysis. Two cost-effectiveness studies on EarlyCDT Lung for pulmonary nodules were identified; none was considered suitable to inform the current decision problem. The conceptualisation process identified three core components for a future cost-effectiveness assessment of EarlyCDT Lung: (1) the features of the subpopulations and relevant heterogeneity, (2) the way EarlyCDT Lung test results affect subsequent clinical management decisions and (3) how changes in these decisions can affect outcomes. All reviewed studies linked earlier diagnosis to stage progression and stage shift to final outcomes, but evidence on these components was sparse.

LIMITATIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules was very limited, preventing meta-analyses and economic analyses.

CONCLUSIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules is insufficient to draw any firm conclusions as to its diagnostic accuracy or clinical or economic value.

FUTURE WORK

Prospective cohort studies, in which EarlyCDT Lung is used among patients with identified pulmonary nodules, are required to support a future assessment of the clinical and economic value of this test. Studies should investigate the diagnostic accuracy and clinical impact of EarlyCDT Lung in combination with Brock and Herder risk assessments. A well-designed cost-effectiveness study is also required, integrating emerging relevant evidence with the recommendations in this report.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42021242248.

FUNDING

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 49. See the NIHR Journals Library website for further project information.

Improved Complementary Pulmonary Nodule Segmentation Model Based on Multi-Feature Fusion

Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation.

CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule

It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011-2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features-cluster shade, homogeneity, and run-length variance-were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process.

Clinical usefulness of circulating tumor markers

Tumor markers are a heterogeneous group of substances released by cancer cells into bloodstream, but also expressed by healthy tissues. Thus, very small concentrations can be present in plasma and serum from healthy subjects. Cancer patients tend to show increased levels correlating with tumor bulk, but false positive results could be present in patients with benign conditions. The correct interpretation of TM results could be challenging and many factors should be considered, from pre-analytical conditions to patient concomitant diseases. In this line, the Clinical Chemistry and Laboratory Medicine journal has made important contributions though several publications promoting the adequate use of TM and therefore improving patient safety. TM measurement offers valuable information for cancer patient management in different clinical contexts, such as helping diagnosis, estimating prognosis, facilitating early detection of relapse and monitoring therapy response. Our review analyzes the clinical usefulness of tumor markers applied in most frequent epithelial tumors, based on recent evidence and guidelines.

Volume Doubling Time of Pulmonary Carcinoid Tumors Measured by Computed Tomography

INTRODUCTION

Pulmonary carcinoid tumor (PCT) is a rare neuroendocrine lung neoplasm comprising approximately 2% of lung cancer diagnoses. It is classified as either localized low-grade (typical) or intermediate-grade (atypical) subtypes. PCT is known clinically to be a slow-growing cancer, however few studies have established its true growth rate when followed over time by computed tomography (CT). Therefore, we sought to determine the volume doubling time for PCTs as visualized on CT imaging.

MATERIALS AND METHODS

We conducted a retrospective analysis of all PCTs treated at our institution between 2006 and 2020. Nodule dimensions were measured using a Picture Archiving and Communication System or retrieved from radiology reports. Volume doubling time was calculated using the Schwartz formula for PCTs followed by successive CT scans during radiographic surveillance. Consistent with Fleischner Society guidelines, tumors were considered to have demonstrated definitive growth by CT only when the interval change in tumor diameter was greater than or equal to 2 mm.

RESULTS

The median volume doubling time of 13 typical PCTs was 977 days, or 2.7 years. Five atypical PCTs were followed longitudinally, with a median doubling time of 327 days, or 0.9 years.

CONCLUSIONS

Typical pulmonary carcinoid features a remarkably slow growth rate as compared to more common lung cancers. Our analysis of atypical pulmonary carcinoid included too few cases to offer definitive conclusions. It is conceivable that clinicians following current nodule surveillance guidelines may mistake incidentally detected typical carcinoids for benign non-growing lesions when followed for less than 2 years in low-risk patients.

Pulmonary metastases from mucinous colorectal cancers and their appearance on CT: a case series

Mucinous colorectal adenocarcinoma represents a small proportion of all colorectal cancers, characterised by mucinous tumour components. While its pattern of metastatic spread differs from that of conventional colorectal adenocarcinoma, pulmonary metastases are commonly seen in both mucinous and non-mucinous types. The assessment of pulmonary nodules in the context of malignancy is a commonly encountered problem for the radiologist given the high prevalence of benign pulmonary lesions. Low density of a pulmonary nodule on CT evaluation is one of the recognised and well-documented features of benignity that is used in the radiological assessment of such nodules. We present three cases of patients with histologically proven mucinous colorectal adenocarcinoma with evidence of pulmonary metastases. In all cases, the metastases were of low density on CT and in one case were initially suspected to represent benign hamartomatous lesions. There has been little documented about the density of mucinous pulmonary metastases on CT. We suspect the low density seen in the metastases in each case is accounted for by their high internal mucinous components. The cases presented here demonstrate the importance of recognising that mucinous colorectal metastases can be of low density and therefore mimic benign pathology. This review may help the radiologist to consider shorter interval follow-up of such lesions in the context of known mucinous neoplasms, or to investigate for an extrathoracic mucinous carcinoma in the presence of multiple low-density pulmonary nodules.

Diagnostic value and safety of endobronchial ultrasonography with a guide sheath transbronchial biopsy for diagnosing peripheral pulmonary lesions in patients with interstitial lung disease

BACKGROUND

Radial endobronchial ultrasonography transbronchial biopsy with and without a guide sheath is a useful method for diagnosing peripheral pulmonary lesions (PPLs). However, the diagnostic yield and complications of radial endobronchial ultrasonography transbronchial biopsy for PPLs remains elusive in patients with interstitial lung disease (ILD).

METHODS

We retrospectively analysed 431 patients (69 with and 362 without ILD) who underwent radial endobronchial ultrasonography with a guide sheath transbronchial biopsy (EBUS-GS TBB) for PPLs from April 1, 2011, to March 31, 2020. We investigated the diagnostic yield and complications of the procedure for PPLs and compared them between patients with and without ILD. We also evaluated the factors contributing to successful diagnosis.

RESULTS

The diagnostic yield of radial endobronchial ultrasonography in patients with ILD was significantly lower than in those without ILD (62.3% vs. 75.4%, P=0.024). Multivariate analysis showed that the presence of ILD as background lung [odds ratio (OR) =0.517], probe position within the lesion (OR =4.654), and the presence of solid lesion (OR =1.946) significantly affected the diagnostic yield of PPLs. There was a significant difference in the rate of pneumothorax between the patients with ILD and those without ILD (4.3% vs. 0.6%, P=0.031).

CONCLUSIONS

The presence of ILD as the background lung significantly affected the diagnostic yield of PPLs with radial EBUS-GS TBB. Regarding the complications, pneumothorax occurred more frequently in patients with ILD than in those without ILD.

Prediction of single pulmonary nodule growth by CT radiomics and clinical features - a one-year follow-up study

Background

With the development of imaging technology, an increasing number of pulmonary nodules have been found. Some pulmonary nodules may gradually grow and develop into lung cancer, while others may remain stable for many years. Accurately predicting the growth of pulmonary nodules in advance is of great clinical significance for early treatment. The purpose of this study was to establish a predictive model using radiomics and to study its value in predicting the growth of pulmonary nodules.

Materials and methods

According to the inclusion and exclusion criteria, 228 pulmonary nodules in 228 subjects were included in the study. During the one-year follow-up, 69 nodules grew larger, and 159 nodules remained stable. All the nodules were randomly divided into the training group and validation group in a proportion of 7:3. For the training data set, the t test, Chi-square test and Fisher exact test were used to analyze the sex, age and nodule location of the growth group and stable group. Two radiologists independently delineated the ROIs of the nodules to extract the radiomics characteristics using Pyradiomics. After dimension reduction by the LASSO algorithm, logistic regression analysis was performed on age and ten selected radiological features, and a prediction model was established and tested in the validation group. SVM, RF, MLP and AdaBoost models were also established, and the prediction effect was evaluated by ROC analysis.

Results

There was a significant difference in age between the growth group and the stable group (P < 0.05), but there was no significant difference in sex or nodule location (P > 0.05). The interclass correlation coefficients between the two observers were > 0.75. After dimension reduction by the LASSO algorithm, ten radiomic features were selected, including two shape-based features, one gray-level-cooccurence-matrix (GLCM), one first-order feature, one gray-level-run-length-matrix (GLRLM), three gray-level-dependence-matrix (GLDM) and two gray-level-size-zone-matrix (GLSZM). The logistic regression model combining age and radiomics features achieved an AUC of 0.87 and an accuracy of 0.82 in the training group and an AUC of 0.82 and an accuracy of 0.84 in the verification group for the prediction of nodule growth. For nonlinear models, in the training group, the AUCs of the SVM, RF, MLP and boost models were 0.95, 1.0, 1.0 and 1.0, respectively. In the validation group, the AUCs of the SVM, RF, MLP and boost models were 0.81, 0.77, 0.81, and 0.71, respectively.

Conclusions

In this study, we established several machine learning models that can successfully predict the growth of pulmonary nodules within one year. The logistic regression model combining age and imaging parameters has the best accuracy and generalization. This model is very helpful for the early treatment of pulmonary nodules and has important clinical significance.

Advancements in navigational bronchoscopy for peripheral pulmonary lesions: A review with special focus on virtual bronchoscopic navigation

Lung cancer is often diagnosed at an advanced stage and is associated with significant morbidity and mortality. Low-dose computed tomography for lung cancer screening has increased the incidence of peripheral pulmonary lesions. Surveillance and early detection of these lesions at risk of developing cancer are critical for improving patient survival. Because these lesions are usually distal to the lobar and segmental bronchi, they are not directly visible with standard flexible bronchoscopes resulting in low diagnostic yield for small lesions <2 cm. The past 30 years have seen several paradigm shifts in diagnostic bronchoscopy. Recent technological advances in navigation bronchoscopy combined with other modalities have enabled sampling lesions beyond central airways. However, smaller peripheral lesions remain challenging for bronchoscopic biopsy. This review provides an overview of recent advances in interventional bronchoscopy in the screening, diagnosis, and treatment of peripheral pulmonary lesions, with a particular focus on virtual bronchoscopic navigation.

The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography

Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.