The pros and cons of lung cancer screening

Several trials have shown that low-dose computed tomography-based lung cancer screening (LCS) allows a substantial reduction in lung cancer-related mortality, carrying the potential for other clinical benefits. There are, however, some uncertainties to be clarified and several aspects to be implemented to optimize advantages and minimize the potential harms of LCS. This review summarizes current evidence on LCS, discussing some of the well-established and potential benefits, including lung cancer (LC)-related mortality reduction and opportunity for smoking cessation interventions, as well as the disadvantages of LCS, such as overdiagnosis and overtreatment. CLINICAL RELEVANCE STATEMENT: Different perspectives are provided on LCS based on the updated literature. KEY POINTS: Lung cancer is a leading cancer-related cause of death and screening should reduce associated mortality. This review summarizes current evidence related to LCS. Several aspects need to be implemented to optimize benefits and minimize potential drawbacks of LCS.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity

RATIONALE AND OBJECTIVES

Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred).

MATERIALS AND METHODS

In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure.

RESULTS

Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02).

CONCLUSION

In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Baseline characteristics from a 3-year longitudinal study to phenotype subjects with COPD: the FOOTPRINTS study

BACKGROUND

FOOTPRINTS® is a prospective, longitudinal, 3-year study assessing the association between biomarkers of inflammation/lung tissue destruction and chronic obstructive pulmonary disease (COPD) severity and progression in ex-smokers with mild-to-severe COPD. Here, we present baseline characteristics and select biomarkers of study subjects.

METHODS

The methodology of FOOTPRINTS® has been published previously. The study population included ex-smokers with a range of COPD severities (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages 1-3), ex-smokers with COPD and alpha-1-antitrypsin deficiency (A1ATD) and a control group of ex-smokers without airflow limitation (EwAL). At study entry, data were collected for: demographics, disease characteristics, history of comorbidities and COPD exacerbations, symptoms, lung function and volume, exercise capacity, soluble biomarkers, and quantitative and qualitative computed tomography. Baseline data are presented with descriptive statistical comparisons for soluble biomarkers in the individual GOLD and A1ATD groups versus EwAL.

RESULTS

In total, 463 subjects were enrolled. The per-protocol set comprised 456 subjects, mostly male (64.5%). The mean (standard deviation) age was 60.7 (6.9) years. At baseline, increasing pulmonary symptoms, worse lung function, increased residual volume, reduced diffusing capacity of the lung for carbon monoxide (DLco) and greater prevalence of centrilobular emphysema were observed with increasing disease severity amongst GOLD 1-3 subjects. Subjects with A1ATD (n = 19) had similar lung function parameters to GOLD 2-3 subjects, a high residual volume comparable to GOLD 3 subjects, and similar air trapping to GOLD 2 subjects. Compared with EwAL (n = 61), subjects with A1ATD had worse lung function, increased residual volume, reduced DLco, and a greater prevalence of confluent or advanced destructive emphysema. The soluble inflammatory biomarkers white blood cell count, fibrinogen, high-sensitivity C-reactive protein and plasma surfactant protein were higher in GOLD 1-3 groups than in the EwAL group. Interleukin-6 was expressed less often in EwAL subjects compared with subjects in the GOLD and A1ATD groups. Soluble receptor for advanced glycation end product was lowest in GOLD 3 subjects, indicative of more severe emphysema.

CONCLUSIONS

These findings provide context for upcoming results from FOOTPRINTS®, which aims to establish correlations between biomarkers and disease progression in a representative COPD population.

TRIAL REGISTRATION NUMBER

NCT02719184, study start date 13/04/2016.

Impact of COPD pulmonary structural remodeling on the prognosis of patients with advanced lung squamous cell carcinoma

Background

By observing the changes of lung imaging airway structure in patients with advanced lung squamous cell carcinoma（ALUSC）, the relationship between the different types of COPD pulmonary structural remodeling and the prognosis of patients with ALUSC was analyzed.

Methods

We reviewed the medical records of 278 patients with ALUSC. The degree of emphysema and the percentage of bronchial wall thickness（WT%） on chest HRCT were calculated by Synapse3D software, Lung structural remodeling can be divided into there types: airway remodeling dominated, emphysema dominated, and mixed types.

Results

Compared with the diagnosis, the Goddard score increased, the proportion of airway remodeling dominated type decreased and the proportion of mixed type increased during the progression of ALUSC. In Kaplan-Meier analysis, whether with or without COPD, the mPFS and mOS of patients with mixed type were the shortest, and the difference was statistically significant. Univariate and multivariate Cox proportional hazard regression analysis showed that mixed type was an independent risk factor for poor PFS and OS in patients with ALUSC.

Conclusion

Patients with ALUSC all have varying degrees of lung structural remodeling, and patients with mixed lung structural remodeling have the worst prognosis. In addition, the aggravation of emphysema during tumor progression is more pronounced than the thickening of the airway wall, and the changes of emphysema was more powerful in predicting the progression of ALUSC.Clinicians must pay more attention to the changes of COPD (especially emphysema) in the process of diagnosis and treatment of ALUSC.

Pulmonary emphysema and coronary artery calcifications at baseline LDCT and long-term mortality in smokers and former smokers of the ITALUNG screening trial

OBJECTIVES

Cardiovascular disease (CVD), lung cancer (LC), and respiratory diseases are main causes of death in smokers and former smokers undergoing low-dose computed tomography (LDCT) for LC screening. We assessed whether quantification of pulmonary emphysematous changes at baseline LDCT has a predictive value concerning long-term mortality.

METHODS

In this longitudinal study, we assessed pulmonary emphysematous changes with densitometry (volume corrected relative area below - 950 Hounsfield units) and coronary artery calcifications (CAC) with a 0-3 visual scale in baseline LDCT of 524 participants in the ITALUNG trial and analyzed their association with mortality after 13.6 years of follow-up using conventional statistics and a machine learning approach.

RESULTS

Pulmonary emphysematous changes were present in 32.3% of subjects and were mild (6% ≤ RA950 ≤ 9%) in 14.9% and moderate-severe (RA950 > 9%) in 17.4%. CAC were present in 67% of subjects (mild in 34.7%, moderate-severe in 32.2%). In the follow-up, 81 (15.4%) subjects died (20 of LC, 28 of other cancers, 15 of CVD, 4 of respiratory disease, and 14 of other conditions). After adjusting for age, sex, smoking history, and CAC, moderate-severe emphysema was significantly associated with overall (OR 2.22; 95CI 1.34-3.70) and CVD (OR 3.66; 95CI 1.21-11.04) mortality. Machine learning showed that RA950 was the best single feature predictive of overall and CVD mortality.

CONCLUSIONS

Moderate-severe pulmonary emphysematous changes are an independent predictor of long-term overall and CVD mortality in subjects participating in LC screening and should be incorporated in the post-test calculation of the individual mortality risk profile.

KEY POINTS

• Densitometry allows quantification of pulmonary emphysematous changes in low-dose CT examinations for lung cancer screening. • Emphysematous lung density changes are an independent predictor of long-term overall and cardio-vascular disease mortality in smokers and former smokers undergoing screening. • Emphysematous changes quantification should be included in the post-test calculation of the individual mortality risk profile.

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Development of a multi-task learning V-Net for pulmonary lobar segmentation on CT and application to diseased lungs

AIM

To develop a multi-task learning (MTL) V-Net for pulmonary lobar segmentation on computed tomography (CT) and application to diseased lungs.

MATERIALS AND METHODS

The described methodology utilises tracheobronchial tree information to enhance segmentation accuracy through the algorithm's spatial familiarity to define lobar extent more accurately. The method undertakes parallel segmentation of lobes and auxiliary tissues simultaneously by employing MTL in conjunction with V-Net-attention, a popular convolutional neural network in the imaging realm. Its performance was validated by an external dataset of patients with four distinct lung conditions: severe lung cancer, COVID-19 pneumonitis, collapsed lungs, and chronic obstructive pulmonary disease (COPD), even though the training data included none of these cases.

RESULTS

The following Dice scores were achieved on a per-segment basis: normal lungs 0.97, COPD 0.94, lung cancer 0.94, COVID-19 pneumonitis 0.94, and collapsed lung 0.92, all at p<0.05.

CONCLUSION

Despite severe abnormalities, the model provided good performance at segmenting lobes, demonstrating the benefit of tissue learning. The proposed model is poised for adoption in the clinical setting as a robust tool for radiologists and researchers to define the lobar distribution of lung diseases and aid in disease treatment planning.

A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD

RATIONALE AND OBJECTIVES

To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score.

RESULTS

38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set.

CONCLUSION

The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes previously identified on autopsy. Unsupervised learning of emphysema subtypes on computed tomography (CT) opens the way to new definitions of emphysema subtypes and eliminates the need of thorough manual labeling. However, CT-based emphysema subtypes have been limited to texture-based patterns without considering spatial location. In this work, we introduce a standardized spatial mapping of the lung for quantitative study of lung texture location and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs) that represent novel emphysema subtype candidates. Exploiting two cohorts of full-lung CT scans from the MESA COPD (n = 317) and EMCAP (n = 22) studies, we first show that our spatial mapping enables population-wide study of emphysema spatial location. We then evaluate the characteristics of the sLTPs discovered on MESA COPD, and show that they are reproducible, able to encode standard emphysema subtypes, and associated with physiological symptoms.

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen

Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have achieved performance comparable to humans in less time for several organ segmentation tasks, and some lesion detection and classification tasks. This article introduces the current published articles of AI applied to chest and abdominal radiology, including organ segmentation, lesion detection, classification, and predicting prognosis.

Pulmonary Emphysema Regional Distribution and Extent Assessed by Chest Computed Tomography Is Associated With Pulmonary Function Impairment in Patients With COPD

Objective: This study aimed to evaluate how emphysema extent and its regional distribution quantified by chest CT are associated with clinical and functional severity in patients with chronic obstructive pulmonary disease (COPD). Methods/Design: Patients with a post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) < 0.70, without any other obstructive airway disease, who presented radiological evidence of emphysema on visual CT inspection were retrospectively enrolled. A Quantitative Lung Imaging (QUALI) system automatically quantified the volume of pulmonary emphysema and adjusted this volume to the measured (EmphCTLV) or predicted total lung volume (TLV) (EmphPLV) and assessed its regional distribution based on an artificial neural network (ANN) trained for this purpose. Additionally, the percentage of lung volume occupied by low-attenuation areas (LAA) was computed by dividing the total volume of regions with attenuation lower or equal to -950 Hounsfield units (HU) by the predicted [LAA (%PLV)] or measured CT lung volume [LAA (%CTLV)]. The LAA was then compared with the QUALI emphysema estimations. The association between emphysema extension and its regional distribution with pulmonary function impairment was then assessed. Results: In this study, 86 patients fulfilled the inclusion criteria. Both EmphCTLV and EmphPLV were significantly lower than the LAA indices independently of emphysema severity. CT-derived TLV significantly increased with emphysema severity (from 6,143 ± 1,295 up to 7,659 ± 1,264 ml from mild to very severe emphysema, p < 0.005) and thus, both EmphCTLV and LAA significantly underestimated emphysema extent when compared with those values adjusted to the predicted lung volume. All CT-derived emphysema indices presented moderate to strong correlations with residual volume (RV) (with correlations ranging from 0.61 to 0.66), total lung capacity (TLC) (from 0.51 to 0.59), and FEV1 (~0.6) and diffusing capacity for carbon monoxide DLCO (~0.6). The values of FEV1 and DLCO were significantly lower, and RV (p < 0.001) and TLC (p < 0.001) were significantly higher with the increasing emphysema extent and when emphysematous areas homogeneously affected the lungs. Conclusions: Emphysema volume must be referred to the predicted and not to the measured lung volume when assessing the CT-derived emphysema extension. Pulmonary function impairment was greater in patients with higher emphysema volumes and with a more homogeneous emphysema distribution. Further studies are still necessary to assess the significance of CTpLV in the clinical and research fields.

Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy

Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.

Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper

Pulmonary MRI provides structural and quantitative functional images of the lungs without ionizing radiation, but it has had limited clinical use due to low signal intensity from the lung parenchyma. The lack of radiation makes pulmonary MRI an ideal modality for pediatric examinations, pregnant women, and patients requiring serial and longitudinal follow-up. Fortunately, recent MRI techniques, including ultrashort echo time and zero echo time, are expanding clinical opportunities for pulmonary MRI. With the use of multicoil parallel acquisitions and acceleration methods, these techniques make pulmonary MRI practical for evaluating lung parenchymal and pulmonary vascular diseases. The purpose of this Fleischner Society position paper is to familiarize radiologists and other interested clinicians with these advances in pulmonary MRI and to stratify the Society recommendations for the clinical use of pulmonary MRI into three categories: (a) suggested for current clinical use, (b) promising but requiring further validation or regulatory approval, and (c) appropriate for research investigations. This position paper also provides recommendations for vendors and infrastructure, identifies methods for hypothesis-driven research, and suggests opportunities for prospective, randomized multicenter trials to investigate and validate lung MRI methods.