ACR-STR practice parameter for the performance and reporting of lung cancer screening thoracic computed tomography (CT): 2014 (Resolution 4)

Deep Learning-enhanced Opportunistic Osteoporosis Screening in Ultralow-Voltage (80 kV) Chest CT: A Preliminary Study

RATIONALE AND OBJECTIVES

To explore the feasibility of deep learning (DL)-enhanced, fully automated bone mineral density (BMD) measurement using the ultralow-voltage 80 kV chest CT scans performed for lung cancer screening.

MATERIALS AND METHODS

This study involved 987 patients who underwent 80 kV chest and 120 kV lumbar CT from January to July 2024. Patients were collected from six CT scanners and divided into the training, validation, and test sets 1 and 2 (561: 177: 112: 137). Four convolutional neural networks (CNNs) were employed for automated segmentation (3D VB-Net and SCN), region of interest extraction (3D VB-Net), and BMD calculation (DenseNet and ResNet) of the target vertebrae (T12-L2). The BMD values of T12-L2 were obtained using 80 and 120 kV quantitative CT (QCT), the latter serving as the standard reference. Linear regression and Bland-Altman analyses were used to compare BMD values between 120 kV QCT and 80 kV CNNs, and between 120 kV QCT and 80 kV QCT. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of the 80 kV CNNs and 80 kV QCT for osteoporosis and low BMD from normal BMD.

RESULTS

Linear regression and Bland-ltman analyses revealed a stronger correlation (R2=0.991-0.998 and 0.990-0.991, P<0.001) and better agreement (mean error, -1.36 to 1.62 and 1.72 to 2.27 mg/cm3; 95% limits of agreement, -9.73 to 7.01 and -5.71 to 10.19mg/cm3) for BMD between 120 kV QCT and 80 kV CNNs than between 120 kV QCT and 80 kV QCT. The areas under the curve of the 80 kV CNNs and 80 kV QCT in detecting osteoporosis and low BMD were 0.997-1.000 and 0.997-0.998, and 0.998-1.000 and 0.997, respectively.

CONCLUSION

The DL method could achieve fully automated BMD calculation for opportunistic osteoporosis screening with high accuracy using ultralow-voltage 80 kV chest CT performed for lung cancer screening.

Effect of lung inflation states on chest CT image quality and pulmonary nodule detection with visualized respiratory Indicator

BACKGROUND

Parts of lung cancer screening guidelines describe the specific scanning protocol of low dose CT (LDCT), among which the requirement for respiratory state is full inspiration end-breath hold. The main focus of lung cancer screening is to evaluate and follow-up pulmonary nodule (PN), so the display and detection of PNs are important. To achieve full inspiration, strict breathing training is required for patients. In clinical scans, the lung inflation state of patient is not visualized and the possibility of incomplete inspiration exists. Thus, the image quality and nodule detection of chest CT in different lung inflation states need to be explored.

METHODS

Fifty-six participants (32 females, 24 males) were included in this prospective study. Each participant underwent non-contrast chest CT scanned three times continually with different lung inflation state, including deep inspiration end-breath hold, calm breath hold, and deep expiration end-breath hold. A respiratory indicator was used to monitor the state of lung inflation visually. Subjective and objective image quality and nodule detection among these lung inflation states were analyzed in this study.

RESULTS

The images of deep inspiration end-breath hold yielded the best, with superior subjective ratings and objective image quality, including the lowest image noise and the best signal-to-noise ratio. PN detection was most accurate in the inflation state of deep inspiration end-breath hold, particularly for nodules ≤ 5 mm, while fewer nodules detected in the inflation state of calm breath hold and deep expiration end-breath hold.

CONCLUSIONS

Lung inflation states significantly impact both image quality and PN detection in chest CT. Deep inspiration end-breath hold provided optimal image quality and nodule detection, while non-fully inflated states reduced diagnostic accuracy, especially for PNs≤5 mm. In clinical application, deep inspiration end-breath hold is recommended as the best inflation state of chest CT.

Derivation of Best-Practice Scan Speeds and Excess Scan Durations for CT Pulmonary Angiography: Analysis Using Registry Data for 166,769 Examinations Across 121 Sites

BACKGROUND. Radiology practices' potential use of a fixed scan speed results in some patients being scanned more slowly than is necessary for the clinical scenario. For CT pulmonary angiography (CTPA), use of fixed scan speeds can lead to prolonged breath-hold requirements and potentially lower image quality from motion artifact. OBJECTIVE. The purpose of this study was to develop best-practice scan speeds for CTPA examinations as well as to assess the extent of variation from these speeds and resulting excess scan durations in real-world clinical practice. METHODS. This retrospective study included 192,779 acquisitions from 166,769 CTPA examinations performed in adult patients (97,649 women and 68,925 men; median age, 60 years old) from January 1, 2016, to January 1, 2021. The examinations were performed at 121 sites using 277 physical scanners representing 28 scanner models from four vendors, and they were identified from an international CT dose registry. Acquisition characteristics were extracted from the registry and were used to calculate scan speeds and durations. Acquisitions were stratified into five equally sized radiation dose categories using CTDIvol values. For each combination of scanner model and dose category, the best-practice scan speed was calculated as the 95th-percentile actual speed, although the best-practice speed of a higher dose category was assigned if faster. Excess scan durations were calculated with respect to hypothetic durations if acquisitions used best-practice speeds. RESULTS. The acquisitions had speeds that, on average, were 30% slower than the best-practice scan speed. A total of 87% of acquisitions were slower than the best-practice speed; 62% were at least 20% slower than the best-practice speed. Acquisitions had a median scan duration of 4.8 seconds; use of best-practice scan speeds would have saved a median of 1.2 seconds. The 95th-percentile excess scan duration was 7.1 seconds, which was exceeded by at least one examination for 58% of sites. CONCLUSION. CTPA commonly uses speeds slower than the proposed best-practice speeds for a given scanner's capabilities, potentially leading to greater motion artifact and suggesting widespread opportunity for improvement. Simple protocol modifications can decrease scan duration while still allowing adequate radiation dose. CLINICAL IMPACT. The findings indicate widespread opportunity for CTPA protocol improvement through simple adjustments designed to shorten scan speeds at a maintained dose output that preserves image quality.

Hybrid iterative reconstruction in ultra-low-dose CT for accurate pulmonary nodule assessment: A Phantom study

This study evaluated hybrid iterative reconstruction in ultra-low-dose computed tomography (ULDCT) for solid pulmonary nodule detection. A 256-slice CT machine operating at 120 kVp imaged a chest phantom with 5 mm nodules. The imaging process involved adjusting low-dose computed tomography (LDCT) settings and conducting 3 ULDCT scans (A-C) with varied minimum and maximum mA settings (10/40 mA). Images were processed using iDose4 iterative reconstruction at levels 5 to 7. Measurements were taken for noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum (NPS), and detectability index (D') to assess image quality, noise texture, and detectability. Analysis of variance (ANOVA) was used to compare the protocols. Noise levels varied significantly across iDose4 iterative reconstruction levels, with the highest noise at 178 HU in iDose4 L5 (protocol C) and the lowest at 54.85 HU in level 7 (protocol A). ULDCT scans showed noise increases of 38.5%, 104.2%, and 118.7% for protocols A, B, and C, respectively, compared to LDCT. Protocol A (iDose4 level 7) significantly improved SNR and CNR (P < .001). The mean volume CT dose index was 2.4 mGy for LDCT and 2.0 mGy, 1.2 mGy, and 0.7 mGy for ULDCT protocols A, B, and C, respectively. Increasing iDose4 levels reduced noise magnitude in the NPS and improved the D'. ULDCT with iDose4 level 7 provides diagnostically acceptable image quality for solid pulmonary nodule assessment at significantly reduced radiation doses. This approach, supported by advanced metrics like NPS and D', demonstrates a potential pathway for safer, effective lung cancer screening in high-risk populations. Further clinical studies are needed to validate these findings in diverse patient populations.

OPPORTUNISTIC ASSESSMENT OF CARDIOVASCULAR RISK USING AI-DERIVED STRUCTURAL AORTIC AND CARDIAC PHENOTYPES FROM NON-CONTRAST CHEST COMPUTED TOMOGRAPHY

Background

Primary prevention of cardiovascular disease relies on accurate risk assessment using scores such as the Pooled Cohort Equations (PCE) and PREVENT. However, necessary input variables for these scores are often unavailable in the electronic health record (EHR), and information from routinely collected data (e.g., non-contrast chest CT) may further improve performance. Here, we test whether a risk prediction model based on structural features of the heart and aorta from chest CT has added value to existing clinical algorithms for predicting major adverse cardiovascular events (MACE).

Methods

We developed a LASSO model to predict fatal MACE over 12 years of follow-up using structural radiomics features describing cardiac chamber and aorta segmentations from 13,437 lung cancer screening chest CTs from the National Lung Screening Trial. We compared this radiomics model to the PCE and PREVENT scores in an external testing set of 4,303 individuals who had a chest CT at a Mass General Brigham site and had no history of diabetes, prior MACE, or statin treatment. Discrimination for incident MACE was assessed using the concordance index. We used a binary threshold to determine MACE rates in patients who were statin-eligible or ineligible by the PCE/PREVENT scores (≥7.5% risk) or the radiomics score (≥5.0% risk). Results were stratified by whether all variables were available to calculate the PCE or PREVENT scores.

Results

In the external testing set (n = 4,303; mean age 61.5 ± 9.3 years; 47.1% male), 8.0% had incident MACE over a median 5.1 years of follow-up. The radiomics risk score significantly improved discrimination beyond the PCE (c-index 0.653 vs. 0.567, p < 0.001) and performed similarly in individuals who were missing inputs. Those statin-eligible by both the radiomics and PCE scores had a 2.6-fold higher incidence of MACE than those eligible by the PCE score alone (29.5 [20.5, 39.1] vs. 11.2 [8.0, 14.4] events per 1,000 person-years among PCE-eligible individuals). In patients missing inputs, incident MACE rates were 1.8-fold higher in those statin-eligible by the radiomics score than those statin-ineligible (29.5 [21.9, 37.6] vs. 16.7 [14.3, 19.0] events per 1000 person-years). Similar results were found when comparing to the PREVENT score. Left ventricular volume and short axis length were most predictive of myocardial infarction, while left atrial sphericity and surface-to-volume ratio were most predictive of stroke.

Conclusions

Based on a single chest CT, a cardiac shape-based risk prediction model predicted cardiovascular events beyond clinical algorithms and demonstrated similar performance in patients who were missing inputs to standard cardiovascular risk calculators. Patients at high-risk by the radiomics score may benefit from intensified primary prevention (e.g., statin prescription).

Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring

In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.

Differential Radiomics-Based Signature Predicts Lung Cancer Risk Accounting for Imaging Parameters in NLST Cohort

OBJECTIVE

Lung cancer remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Hence, there is a need to develop effective predictive models for early detection. This study aims to investigate the impact of imaging parameters and delta radiomic features from temporal scans on lung cancer risk prediction.

METHODS

Using the National Lung Screening Trial (NLST) within a nested case-control study involving 462 positive screenings, radiomic features were extracted from temporal computed tomography (CT) scans and harmonized with ComBat method to adjust variations in slice thickness category (TC) and reconstruction kernel type (KT). Both harmonized and non-harmonized features from baseline (T0), delta features between T0 and a year later (T1), and combined T0 and delta features were utilized for the analysis. Feature reduction was done using LASSO, followed by five feature selection (FS) methods and nine machine learning (ML) models, evaluated with 5-fold cross-validation repeated 10 times. Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalances for lung cancer risk prediction.

RESULTS

Models using delta features outperformed baseline features, with SMOTE consistently boosting performance when using combination of baseline and delta features. TC-based harmonized features improved performance with SMOTE, but overall, harmonization did not significantly enhance the model performance. The highest test score of 0.76 was achieved in three scenarios: delta features with a Gradient Boosting (GB) model (TC-based harmonization and MultiSurf FS); and T0 + delta features, with both a Support Vector Classifier (SVC) model (KT-based harmonization and F-test FS), and an XGBoost (XGB) model (TC-based harmonization and Mutual Information (MI) FS), all using SMOTE.

CONCLUSIONS

This study underscores the significance of delta radiomic features and balanced datasets to improve lung cancer prediction. While our findings are based on a subsample of NLST data, they provide a valuable foundation for further exploration. Further research is needed to assess the impact of harmonization on imaging-derived models. Future investigations should explore advanced harmonization techniques and additional imaging parameters to develop robust radiomics-based biomarkers of lung cancer risk.

Beyond lung cancer screening, an opportunity for early detection of chronic obstructive pulmonary disease and cardiovascular diseases

BACKGROUND

Lung cancer screening programs concern smokers at risk for cardiovascular diseases (CVDs) and chronic obstructive pulmonary disease (COPD). The LUMASCAN (LUng Cancer Screening, MArkers and low-dose computed tomography SCANner) study aimed to evaluate the acceptability and feasibility of screening for these 3 diseases in a community population with centralized organization and to determine low-dose computed tomography (CT) markers associated with each disease.

METHODS

This cohort enrolled participants meeting National Comprehensive Cancer Network criteria (v1.2014) in an organized lung cancer-screening program including low-dose CT scans; spirometry; evaluations of coronary artery calcifications (CACs); and a smoking cessation plan at inclusion, 1, and 2 years; then telephone follow-up. Outcomes were the participation rate and the proportion of participants affected by lung cancer, obstructive lung disease, or CVD events. Logistic-regression models were used to identify radiological factors associated with each disease.

RESULTS

Between 2016 and 2019, a total of 302 participants were enrolled: 61% men; median age 58.8 years; 77% active smoker; 11% diabetes; 38% hypertension; and 27% taking lipid-lowering agents. Inclusion, 1-year, and 2-year participation rates were 99%, 81%, 79%, respectively. After a median follow-up of 5.81 years, screenings detected 12 (4%) lung cancer, 9 of 12 via low-dose CT (78% localized) and 3 of 12 during follow-up (all stage IV), 83 (27%) unknown obstructive lung disease, and 131 (43.4%) moderate to severe CACs warranting a cardiology consultation. Preexisting COPD and moderate to severe CACs were associated with major CVD events with odds ratios of 1.98 (95% confident interval [CI] = 1.00 to 3.88) and 3.27 (95% CI = 1.72 to 6.43), respectively.

CONCLUSION

The LUMASCAN study demonstrated the feasibility of combined screening for lung cancer, COPD, and CVD in a community population. Its centralized organization enabled high participation and coordination of healthcare practitioners.

Evaluation of an in-use chest CT protocol in lung cancer screening - A single institutional study

Background

Lung cancer is the most common cause of cancer-related death worldwide and therefore there has been a growing demand for low-dose computed tomography (LDCT) protocols.

Purpose

To investigate and evaluate the dose and image quality of patients undergoing lung cancer screening (LCS) using LDCT in Norway.

Materials and Methods

Retrospective dosimetry data, volumetric CT dose index (CTDIvol) and dose-length product (DLP), from 70 average-size and 70 large-size patients who underwent LDCT scan for LCS were included in the survey. Effective dose and size-specific dose were calculated for each examination and were compared with the American Association of Physicists in Medicine (AAPM) requirement. For a quantitative image quality analysis, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were determined for different regions in the chest with two iterative reconstruction techniques, iDose and Iterative Model Reconstruction. Differences in dose and image quality between average-size and large-size patients were evaluated by Independent sample t test, and Wilcoxon signed rank test within the same patient group.

Results

The independent sample t test revealed significant differences (p < .05) in dose values between average-size and large-size patients. Mean CTDIvol and DLP for average-size patients were 2.8 mGy and 115 mGy.cm, respectively, with appropriate increment for the large-size patients. Image quality (image noise, SNR, and CNR) did not significantly differ between patient groups when images were reconstructed with a model based iterative reconstruction algorithm.

Conclusion

The screening protocol assessed in this study resulted in CTDIvol values that were compliant with AAPM recommendation. No significant differences in objective image quality were found between patient groups.

Imaging of Solid Pulmonary Nodules

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Investigating the Small Pixel Effect in Ultra-High Resolution Photon-Counting CT of the Lung

OBJECTIVES

The aim of this study was to investigate potential benefits of ultra-high resolution (UHR) over standard resolution scan mode in ultra-low dose photon-counting detector CT (PCD-CT) of the lung.

MATERIALS AND METHODS

Six cadaveric specimens were examined with 5 dose settings using tin prefiltration, each in UHR (120 × 0.2 mm) and standard mode (144 × 0.4 mm), on a first-generation PCD-CT scanner. Image quality was evaluated quantitatively by noise comparisons in the trachea and both main bronchi. In addition, 16 readers (14 radiologists and 2 internal medicine physicians) independently completed a browser-based pairwise forced-choice comparison task for assessment of subjective image quality. The Kendall rank coefficient ( W ) was calculated to assess interrater agreement, and Pearson's correlation coefficient ( r ) was used to analyze the relationship between noise measurements and image quality rankings.

RESULTS

Across all dose levels, image noise in UHR mode was lower than in standard mode for scan protocols matched by CTDI vol ( P < 0.001). UHR examinations exhibited noise levels comparable to the next higher dose setting in standard mode ( P ≥ 0.275). Subjective ranking of protocols based on 5760 pairwise tests showed high interrater agreement ( W = 0.99; P ≤ 0.001) with UHR images being preferred by readers in the majority of comparisons. Irrespective of scan mode, a substantial indirect correlation was observed between image noise and subjective image quality ranking ( r = -0.97; P ≤ 0.001).

CONCLUSIONS

In PCD-CT of the lung, UHR scan mode reduces image noise considerably over standard resolution acquisition. Originating from the smaller detector element size in fan direction, the small pixel effect allows for superior image quality in ultra-low dose examinations with considerable potential for radiation dose reduction.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging

Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

PURPOSE

To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels.

PATIENTS AND METHODS

Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ).

RESULTS

The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively).

CONCLUSIONS

There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.

Current and Future Perspectives on Computed Tomography Screening for Lung Cancer: A Roadmap From 2023 to 2027 From the International Association for the Study of Lung Cancer

Low-dose computed tomography (LDCT) screening for lung cancer substantially reduces mortality from lung cancer, as revealed in randomized controlled trials and meta-analyses. This review is based on the ninth CT screening symposium of the International Association for the Study of Lung Cancer, which focuses on the major themes pertinent to the successful global implementation of LDCT screening and develops a strategy to further the implementation of lung cancer screening globally. These recommendations provide a 5-year roadmap to advance the implementation of LDCT screening globally, including the following: (1) establish universal screening program quality indicators; (2) establish evidence-based criteria to identify individuals who have never smoked but are at high-risk of developing lung cancer; (3) develop recommendations for incidentally detected lung nodule tracking and management protocols to complement programmatic lung cancer screening; (4) Integrate artificial intelligence and biomarkers to increase the prediction of malignancy in suspicious CT screen-detected lesions; and (5) standardize high-quality performance artificial intelligence protocols that lead to substantial reductions in costs, resource utilization and radiologist reporting time; (6) personalize CT screening intervals on the basis of an individual's lung cancer risk; (7) develop evidence to support clinical management and cost-effectiveness of other identified abnormalities on a lung cancer screening CT; (8) develop publicly accessible, easy-to-use geospatial tools to plan and monitor equitable access to screening services; and (9) establish a global shared education resource for lung cancer screening CT to ensure high-quality reading and reporting.

Variation in Reporting of Incidental Findings on Initial Lung Cancer Screening and Associations With Clinician Assessment

PURPOSE

The aim of this study was to quantify the distribution, frequency, and clinical significance of incidental findings (IFs) on initial lung cancer screening (LCS) and the association of report characteristics with subsequent assessment.

METHODS

Health records of patients undergoing initial LCS from 2015 to 2018 in the Minneapolis VA Health Care System were retrospectively reviewed for demographics, Lung CT Screening Reporting & Data System coding, IFs, and subsequent clinical assessment. IFs were considered potentially significant if they were likely to require any follow-up. High-risk significant IFs (SIFs) were potentially malignant. The primary outcome was the SIF being addressed. Outcomes were analyzed using a mixed-effects model.

RESULTS

Patients (n = 901) were primarily male (94.1%) smokers (62.1%) with a mean age of 65.2 years. IFs were extremely common (93.9%), with an average of 2.6 IFs per scan (n = 2,296). Seven hundred eighty-six IFs (34.2%) were deemed likely SIFs, of which 58 (7.4%) were high risk. Two hundred twenty-two (28.2%) were addressed by clinicians, of which 104 (13.2%) underwent testing. Reporting of SIFs varied among radiologists, with at least one SIF in the impression in 24% to 78% of low-dose CT studies with the S modifier, used to indicate the presence of a SIF, applied to 0% to 51% of reports. In the mutually adjusted model, radiologist recommendation (adjusted odds ratio [OR], 4.67; 95% confidence interval [CI], 2.23-9.76), high-risk finding (adjusted OR, 4.35; 95% CI, 1.81-10.45), and reporting in the impression (adjusted OR, 2.58; 95% CI, 1.28-5.18) were associated with increased odds of the SIF's being addressed.

CONCLUSIONS

Radiologists vary in their reporting of IFs on LCS. Further standardization of reporting of SIFs may improve this process, with the simultaneous goals of generating appropriate testing when needed and minimizing low-value care.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Objective

Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.

Approach

The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.

Main Results

DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom.

Significance

DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Lung Cancer Screening in Asia: An Expert Consensus Report

INTRODUCTION

The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West.

METHOD

A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population.

RESULTS

Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment.

CONCLUSIONS

Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia.

Body composition assessment with limited field-of-view computed tomography: A semantic image extension perspective

Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT). This poses limitations for opportunistic CT-based body composition (BC) assessment as key anatomical structures are missing. Traditionally, extending the FOV of CT is considered as a CT reconstruction problem using limited data. However, this approach relies on the projection domain data which might not be available in application. In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region. The training samples are simulated using CT slices with complete body in FOV, making the model development self-supervised. We evaluate the validity of the proposed method in automatic BC assessment using lung screening CT with limited FOV. The proposed method effectively restores the missing tissues and reduces BC assessment error introduced by FOV tissue truncation. In the BC assessment for large-scale lung screening CT datasets, this correction improves both the intra-subject consistency and the correlation with anthropometric approximations. The developed method is available at https://github.com/MASILab/S-EFOV.

"Will I change nodule management recommendations if I change my CAD system?"-impact of volumetric deviation between different CAD systems on lesion management

OBJECTIVES

To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study.

METHODS

In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems.

RESULTS

There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems.

CONCLUSIONS

Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist.

KEY POINTS

• The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.

A Novel Deep Learning Model Based on Multi-Scale and Multi-View for Detection of Pulmonary Nodules

Lung cancer manifests as pulmonary nodules in the early stage. Thus, the early and accurate detection of these nodules is crucial for improving the survival rate of patients. We propose a novel two-stage model for lung nodule detection. In the candidate nodule detection stage, a deep learning model based on 3D context information roughly segments the nodules detects the preprocessed image and obtain candidate nodules. In this model, 3D image blocks are input into the constructed model, and it learns the contextual information between the various slices in the 3D image block. The parameters of our model are equivalent to those of a 2D convolutional neural network (CNN), but the model could effectively learn the 3D context information of the nodules. In the false-positive reduction stage, we propose a multi-scale shared convolutional structure model. Our lung detection model has no significant increase in parameters and computation in both stages of multi-scale and multi-view detection. The proposed model was evaluated by using 888 computed tomography (CT) scans from the LIDC-IDRI dataset and achieved a competition performance metric (CPM) score of 0.957. The average detection sensitivity per scan was 0.971/1.0 FP. Furthermore, an average detection sensitivity of 0.933/1.0 FP per scan was achieved based on data from Shanghai Pulmonary Hospital. Our model exhibited a higher detection sensitivity, a lower false-positive rate, and better generalization than current lung nodule detection methods. The method has fewer parameters and less computational complexity, which provides more possibilities for the clinical application of this method.

Lung Cancer Screening Using Clinical Photon-Counting Detector Computed Tomography and Energy-Integrating-Detector Computed Tomography: A Prospective Patient Study

OBJECTIVE

To evaluate the diagnostic quality of photon-counting detector (PCD) computed tomography (CT) in patients undergoing lung cancer screening compared with conventional energy-integrating detector (EID) CT in a prospective multireader study.

MATERIALS

Patients undergoing lung cancer screening with conventional EID-CT were prospectively enrolled and scanned on a PCD-CT system using similar automatic exposure control settings and reconstruction kernels. Three thoracic radiologists blinded to CT system compared PCD-CT and EID-CT images and scored examinations using a 5-point Likert comparison score (-2 [left image is worse] to +2 [left image is better]) for artifacts, sharpness, image noise, diagnostic image quality, emphysema visualization, and lung nodule evaluation focusing on the border. Post hoc correction of Likert scores was performed such that they reflected PCD-CT performance in comparison to EID-CT. A nonreader radiologist measured objective image noise.

RESULTS

Thirty-three patients (mean, 66.9 ± 5.6 years; 11 female; body mass index; 30.1 ± 5.1 kg/m 2 ) were enrolled. Mean volume CT dose index for PCD-CT was lower (0.61 ± 0.21 vs 0.73 ± 0.22; P < 0.001). Pooled reader results showed significant differences between imaging modalities for all comparative rankings ( P < 0.001), with PCD-CT favored for sharpness, image noise, image quality, and emphysema visualization and lung nodule border, but not artifacts. Photon-counting detector CT had significantly lower image noise (74.4 ± 10.5 HU vs 80.1 ± 8.6 HU; P = 0.048).

CONCLUSIONS

Photon-counting detector CT with similar acquisition and reconstruction settings demonstrated improved image quality and less noise despite lower radiation dose, with improved ability to depict pulmonary emphysema and lung nodule borders compared with EID-CT at low-dose lung cancer CT screening.

Simultaneous assessment of heart and lungs with gated high-pitch ultra-low dose chest CT using artificial intelligence-based calcium scoring

Purpose

The combined testing for coronary artery and pulmonary diseases is of clinical interest as risk factors are shared. In this study, a novel ECG-gated tin-filtered ultra-low dose chest CT protocol (GCCT) for integrated heart and lung acquisition and the applicability of artificial intelligence (AI)-based coronary artery calcium scoring were assessed.

Methods

In a clinical registry of 10481 patients undergoing heart and lung CT, GCCT was applied in 44 patients on a dual-source CT. Coronary calcium scans (CCS) with 120 kVp, 100 kVp, and tin-filtered 100 kVp (Sn100) of controls, matched with regard to age, sex, and body-mass index, were retrieved from the registry (n_total=176, 66.5 (59.4-74.0) years, 52 men). Automatic tube current modulation was used in all scans. In 20 patients undergoing GCCT and Sn100 CCS, Agatston scores were measured both semi-automatically by experts and by AI, and classified into six groups (0, <10, <100, <400, <1000, ≥1000).

Results

Effective dose decreased significantly from 120 kVp CCS (0.50 (0.41-0.61) mSv) to 100 kVp CCS (0.34 (0.26-0.37) mSv) to Sn100 CCS (0.14 (0.11-0.17) mSv). GCCT showed higher values (0.28 (0.21-0.32) mSv) than Sn100 CCS but lower than 120 kVp and 100 kVp CCS (all p < 0.05) despite greater scan length. Agatston scores correlated strongly between GCCT and Sn100 CCS in semi-automatic and AI-based measurements (both ρ = 0.98, p < 0.001) resulting in high agreement in Agatston score classification (κ = 0.97, 95% CI 0.92-1.00; κ = 0.89, 95% CI 0.79-0.99). Regarding chest findings, further diagnostic steps were recommended in 28 patients.

Conclusions

GCCT allows for reliable coronary artery disease and lung cancer screening with ultra-low radiation exposure. GCCT-derived Agatston score shows excellent agreement with standard CCS, resulting in equivalent risk stratification.

Comparison of epicardial adipose tissue volume quantification between ECG-gated cardiac and non-ECG-gated chest computed tomography scans

BACKGROUND

This study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in non-ECG-gated chest computed tomography (CT) scans.

METHODS

EAT volume was semi-automatically quantified using a standard Hounsfield unit threshold (- 190, - 30) in three independent cohorts: (1) Cohort 1 (N = 49): paired 120 kVp ECG-gated cardiac non-contrast CT (NCCT) and 120 kVp non-ECG-gated chest NCCT; (2) Cohort 2 (N = 34): paired 120 kVp cardiac NCCT and 100 kVp non-ECG-gated chest NCCT; (3) Cohort 3 (N = 32): paired non-ECG-gated chest NCCT and chest contrast-enhanced CT (CECT) datasets (including arterial phase and venous phase). Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets.

RESULTS

In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, while chest NCCT-5 mm underestimated the EAT volume by 7.5%. In Cohort 2, 100 kVp chest NCCT-1.25 mm were 13.2% larger than 120 kVp cardiac NCCT EAT volumes. In Cohort 3, the chest arterial CECT and venous CECT dataset underestimated EAT volumes by ~ 28% and ~ 18%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were similarly associated with significant coronary atherosclerosis with cardiac CT counterparts.

CONCLUSION

The 120 kVp non-ECG-gated chest NCCT-1.25 mm images produced EAT volumes comparable to cardiac NCCT. Chest CT EAT volumes derived from consistent imaging settings are excellent alternatives to the cardiac NCCT to investigate their association with coronary artery disease.

SANet: A Slice-Aware Network for Pulmonary Nodule Detection

Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodules. However, it is hard even for experienced doctors to distinguish them from the massive CT slices. The currently existing nodule datasets are limited in both scale and category, which is insufficient and greatly restricts its applications. In this paper, we collect the largest and most diverse dataset named PN9 for pulmonary nodule detection by far. Specifically, it contains 8,798 CT scans and 40,439 annotated nodules from 9 common classes. We further propose a slice-aware network (SANet) for pulmonary nodule detection. A slice grouped non-local (SGNL) module is developed to capture long-range dependencies among any positions and any channels of one slice group in the feature map. And we introduce a 3D region proposal network to generate pulmonary nodule candidates with high sensitivity, while this detection stage usually comes with many false positives. Subsequently, a false positive reduction module (FPR) is proposed by using the multi-scale feature maps. To verify the performance of SANet and the significance of PN9, we perform extensive experiments compared with several state-of-the-art 2D CNN-based and 3D CNN-based detection methods. Promising evaluation results on PN9 prove the effectiveness of our proposed SANet. The dataset and source code is available at https://mmcheng.net/SANet/.

NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022

The NCCN Guidelines for Lung Cancer Screening recommend criteria for selecting individuals for screening and provide recommendations for evaluation and follow-up of lung nodules found during initial and subsequent screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

Lung-RADS Category 3 and 4 Nodules on Lung Cancer Screening in Clinical Practice

BACKGROUND. Lung-RADS category 3 and 4 nodules account for most screening-detected lung cancers and are considered actionable nodules with management implications. The cancer frequency among such nodules is estimated in the Lung-RADS recommendations and has been investigated primarily by means of retrospectively assigned Lung-RADS classifications. OBJECTIVE. The purpose of this study was to assess the frequency of cancer among lung nodules assigned Lung-RADS category 3 or 4 at lung cancer screening (LCS) in clinical practice and to evaluate factors that affect the cancer frequency within each category. METHODS. This retrospective study was based on review of clinical radiology reports of 9148 consecutive low-dose CT LCS examinations performed for 4798 patients between June 2014 and January 2021 as part of an established LCS program. Unique nodules assigned Lung-RADS category 3 or 4 (4A, 4B, or 4X) that were clinically categorized as benign or malignant in a multidisciplinary conference that considered histologic analysis and follow-up imaging were selected for further analysis. Benign diagnoses based on stability required at least 12 months of follow-up imaging. Indeterminate nodules were excluded. Cancer frequencies were evaluated. RESULTS. Of the 9148 LCS examinations, 857 (9.4%) were assigned Lung-RADS category 3, and 721 (7.9%) were assigned category 4. The final analysis included 1297 unique nodules in 1139 patients (598 men, 541 women; mean age, 66.0 ± 6.3 years). A total of 1108 of 1297 (85.4%) nodules were deemed benign, and 189 of 1297 (14.6%) were deemed malignant. The frequencies of malignancy of category 3, 4A, 4B, and 4X nodules were 3.9%, 15.5%, 36.3%, and 76.8%. A total of 45 of 46 (97.8%) endobronchial nodules (all category 4A) were deemed benign on the basis of resolution. Cancer frequency was 13.1% for solid, 24.4% for part-solid, and 13.5% for ground-glass nodules. CONCLUSION. In the application of Lung-RADS to LCS clinical practice, the frequency of Lung-RADS category 3 and 4 nodules and the cancer frequency in these categories were higher than the prevalence and cancer risk estimated for category 3 and 4 nodules in the Lung-RADS recommendations and those reported in earlier studies in which category assignments were retrospective. Nearly all endobronchial category 4A nodules were benign. CLINICAL IMPACT. Future Lung-RADS iterations should consider the findings of this study from real-world practice to improve the clinical utility of the system.

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

Prospective Multisite Cohort Study to Evaluate Shared Decision Making Utilization Among Individuals Screened for Lung Cancer

PURPOSE

The aim of this study was to determine the frequency, components of, and factors associated with shared decision-making (SDM) discussions according to electronic health record (EHR) documentation among individuals undergoing lung cancer screening (LCS).

METHODS

A prospective observational cohort study was conducted of individuals undergoing LCS between February 2015 and June 2020 at four LCS centers. The primary outcome was EHR-documented SDM, defined using Medicare-designated components. A multivariable logistic regression model was used to examine predictors of EHR-documented SDM. A secondary outcome was agreement of individual's self-report of SDM and EHR-documented SDM, evaluated using Cohen's κ statistic.

RESULTS

Among screened individuals, 41.9% (243 of 580) had EHR-documented SDM, and 71.1% (295 of 415) had self-reported SDM. Decision aids were used in 55.6% of EHR-documented SDM encounters (135 of 243), and 21.8% of documented SDM encounters (53 of 243) included all Medicare-designated components. SDM was documented more frequently in individuals with body mass index ≥ 25 versus <25 kg/m² (adjusted odds ratio [aOR], 1.63; 95% confidence interval [CI], 1.05-2.52) and in currently versus formerly smoking individuals (aOR, 1.53; 95% CI, 1.02-2.32). Nonpulmonary referring clinicians were less likely to document SDM than pulmonary clinicians (internal medicine: aOR, 0.32; 95% CI, 0.18-0.53; family medicine: aOR, 0.08; 95% CI, 0.04-0.14; other specialties: aOR, 0.08; 95% CI, 0.03-0.21). In a subset of 415 individuals, there was little agreement between individual self-report of SDM and EHR-documented SDM (κ = 0.184), with variation in agreement on the basis of referring clinician specialty.

CONCLUSIONS

Although EHR-documented SDM occurred in fewer than half of individuals undergoing LCS, self-reported SDM rates were higher, suggesting that SDM may be underdocumented in the EHR. In addition, EHR-documented SDM was more likely in individuals with higher body mass index and those referred for LCS by pulmonary clinicians. These findings indicate areas for improvement in the implementation and documentation of SDM.

Impact of pixel value truncation on image quality of low dose chest CT

PURPOSE

In some noisy low-dose CT lung cancer screening images, we noticed that the CT density values of air were increased and the visibility of emphysema was distinctly decreased. By examining histograms of these images, we found that the CT density values were truncated at -1,024 HU. The purpose of this study was to investigate the effect of pixel value truncation on the visibility of emphysema using mathematical models.

METHODS AND MATERIALS

Assuming CT noise follows a normal distribution, we derived the relationship between the mean CT density value and the standard deviation (SD) when the pixel values below -1,024 HU are truncated and replaced by -1,024 HU. To validate our mathematical model, 20 untruncated phantom CT images were truncated by simulation, and the mean CT density values and SD of air in the images were measured and compared with the theoretical values. In addition, the mean CT density values and SD of air were measured in 100 cases of real clinical images obtained by GE, Siemens, and Philips scanners, respectively, and the agreement with the theoretical values was examined. Next, the contrast-to-noise ratio (CNR) between air (-1,000 HU) and lung parenchyma (-850 HU) was derived from the mathematical model in the presence and absence of truncation as a measure of the visibility of emphysema. In addition, the radiation dose ratios required to obtain the same CNR in the case with and without truncation were also calculated.

RESULTS

The mathematical model revealed that when the pixel values are truncated, the mean CT density values are proportional to the noise magnitude when the magnitude exceeds a certain level. The mean CT density values and SD measured in the images with pixel values truncated by simulation and in the real clinical images acquired by GE and Philips scanners agreed well with the theoretical values from our mathematical model. In the Siemens images, the measured and theoretical values agreed well when a portion of the truncated values were replaced by random values instead of simply replacing by -1,024 HU. The CNR of air and lung parenchyma was lowered by truncating CT density values compared to that of no truncation. Furthermore, it was found that higher radiation dose was required to obtain the same CNR with truncation as without. As an example, when the noise SD was 60 HU, the radiation dose required for the GE and Philips truncation method was about 1.2 times higher than that without truncation, and that for the Siemens truncation method was about 1.4 times higher.

CONCLUSIONS

It was demonstrated mathematically that pixel value truncation causes a brightening of the mean CT density value and decreases the CNR of emphysema. Our results indicate that it is advisable to turn off truncation at -1,024 HU, especially when scanning at low and ultra-low radiation doses in the thorax. This article is protected by copyright. All rights reserved.

Detection and Early Referral of Patients With Interstitial Lung Abnormalities: An Expert Survey Initiative

BACKGROUND

Interstitial lung abnormalities (ILA) may represent undiagnosed early-stage or subclinical interstitial lung disease (ILD). ILA are often observed incidentally in patients who subsequently develop clinically overt ILD. There is limited information on consensus definitions for, and the appropriate evaluation of, ILA. Early recognition of patients with ILD remains challenging, yet critically important. Expert consensus could inform early recognition and referral.

RESEARCH QUESTION

Can consensus-based expert recommendations be identified to guide clinicians in the recognition, referral, and follow-up of patients with or at risk of developing early ILDs?

STUDY DESIGN AND METHODS

Pulmonologists and radiologists with expertise in ILD participated in two iterative rounds of surveys. The surveys aimed to establish consensus regarding ILA reporting, identification of patients with ILA, and identification of populations that might benefit from screening for ILD. Recommended referral criteria and follow-up processes were also addressed. Threshold for consensus was defined a priori as ≥ 75% agreement or disagreement.

RESULTS

Fifty-five experts were invited and 44 participated; consensus was reached on 39 of 85 questions. The following clinically important statements achieved consensus: honeycombing and traction bronchiectasis or bronchiolectasis indicate potentially progressive ILD; honeycombing detected during lung cancer screening should be reported as potentially significant (eg, with the Lung CT Screening Reporting and Data System "S-modifier" [Lung-RADS; which indicates clinically significant or potentially significant noncancer findings]), recommending referral to a pulmonologist in the radiology report; high-resolution CT imaging and full pulmonary function tests should be ordered if nondependent subpleural reticulation, traction bronchiectasis, honeycombing, centrilobular ground-glass nodules, or patchy ground-glass opacity are observed on CT imaging; patients with honeycombing or traction bronchiectasis should be referred to a pulmonologist irrespective of diffusion capacity values; and patients with systemic sclerosis should be screened with pulmonary function tests for early-stage ILD.

INTERPRETATION

Guidance was established for identifying clinically relevant ILA, subsequent referral, and follow-up. These results lay the foundation for developing practical guidance on managing patients with ILA.

An Image Quality-informed Framework for CT Characterization

Background Lack of standardization in CT protocol choice contributes to radiation dose variation. Purpose To create a framework to assess radiation doses within broad CT categories defined according to body region and clinical imaging indication and to cluster indications according to the dose required for sufficient image quality. Materials and Methods This was a retrospective study using Digital Imaging and Communications in Medicine metadata. CT examinations in adults from January 1, 2016 to December 31, 2019 from the University of California San Francisco International CT Dose Registry were grouped into 19 categories according to body region and required radiation dose levels. Five body regions had a single dose range (ie, extremities, neck, thoracolumbar spine, combined chest and abdomen, and combined thoracolumbar spine). Five additional regions were subdivided according to dose. Head, chest, cardiac, and abdomen each had low, routine, and high dose categories; combined head and neck had routine and high dose categories. For each category, the median and 75th percentile (ie, diagnostic reference level [DRL]) were determined for dose-length product, and the variation in dose within categories versus across categories was calculated and compared using an analysis of variance. Relative median and DRL (95% CI) doses comparing high dose versus low dose categories were calculated. Results Among 4.5 million examinations, the median and DRL doses varied approximately 10 times between categories compared with between indications within categories. For head, chest, abdomen, and cardiac (3 266 546 examinations [72%]), the relative median doses were higher in examinations assigned to the high dose categories than in examinations assigned to the low dose categories, suggesting the assignment of indications to the broad categories is valid (head, 3.4-fold higher [95% CI: 3.4, 3.5]; chest, 9.6 [95% CI: 9.3, 10.0]; abdomen, 2.4 [95% CI: 2.4, 2.5]; and cardiac, 18.1 [95% CI: 17.7, 18.6]). Results were similar for DRL doses (all P < .001). Conclusion Broad categories based on image quality requirements are a suitable framework for simplifying radiation dose assessment, according to expected variation between and within categories. © RSNA, 2021 See also the editorial by Mahesh in this issue.

Screening for Lung Cancer: CHEST Guideline and Expert Panel Report

BACKGROUND

Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part because of the results of the National Lung Screening Trial (NLST). Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, and increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not.

METHODS

Approved panelists reviewed previously developed key questions using the Population, Intervention, Comparator, Outcome format to address the benefit and harms of low-dose CT screening, and key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Meta-analyses were performed when enough evidence was available. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached.

RESULTS

The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in seven graded recommendations and nine ungraded consensus statements.

CONCLUSIONS

Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can impact this balance.

Establishing scanning protocols for a CT lung cancer screening trial in the UK

OBJECTIVES

To develop a CT scanning protocol for lung cancer screening which achieved low radiation dose and a high level of objectively assessed image quality.

METHODS

An anthropomorphic chest phantom and a commercially available lung screening image quality phantom were scanned on a series of scan protocols from a previous UK lung screening pilot and on an alternative protocol. The chest phantom scans were used to assess the CT dose metrics on community-based mobile CT scanners and comparisons were made with published recommended doses. Scans of the image quality phantom were objectively assessed against the RSNA Quantitative Imaging Biomarkers Alliance (QIBA) recommendations. Protocol adjustments were made to ensure that the recommended dose and image quality levels were both achieved.

RESULTS

The alternative scan protocol yielded doses up to 72% lower than on the previously used protocols with a CTDIvol of 0.6mGy for the 55 kg equivalent phantom and 1.3mGy with an additional 6 cm of tissue equivalent material in place. Scans on the existing protocols failed on two of the QIBA image quality metrics (edge enhancement and 3D resolution aspect ratio). Following adjustments to the reconstruction parameters of the resulting image quality met all six QIBA recommendations. Radiologist review of phantom images with this scan protocol deemed them suitable for a lung screening trial.

CONCLUSIONS

Scan protocols yielding low radiation doses and high levels of objectively assessed image quality which meet published criteria can be established through the use of specific anthropomorphic and image quality phantoms, and are deliverable in community-based lung cancer screening.

ADVANCES IN KNOWLEDGE

Development of a standard methodology for establishing CT lung screening scanning protocolsUse of QIBA recommendations as objective image quality metricsStandardised lung phantoms are essential tools for setting up lung screening protocols.

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning

Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To further improve the survival rate of lung cancer patients, computer-aided diagnosis (CAD) technology shows great potential. In this paper, we summarize existing CAD approaches applying deep learning to CT scan data for pre-processing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classification and retrieval. Selected papers are drawn from academic journals and conferences up to November 2020. We discuss the development of deep learning, describe several important aspects of lung nodule CAD systems and assess the performance of the selected studies on various datasets, which include LIDC-IDRI, LUNA16, LIDC, DSB2017, NLST, TianChi, and ELCAP. Overall, in the detection studies reviewed, the sensitivity of these techniques is found to range from 61.61% to 98.10%, and the value of the FPs per scan is between 0.125 and 32. In the selected classification studies, the accuracy ranges from 75.01% to 97.58%. The precision of the selected retrieval studies is between 71.43% and 87.29%. Based on performance, deep learning based CAD technologies for detection and classification of pulmonary nodules achieve satisfactory results. However, there are still many challenges and limitations remaining including over-fitting, lack of interpretability and insufficient annotated data. This review helps researchers and radiologists to better understand CAD technology for pulmonary nodule detection, segmentation, classification and retrieval. We summarize the performance of current techniques, consider the challenges, and propose directions for future high-impact research.

Screening for Lung Cancer: CHEST Guideline and Expert Panel Report - Executive Summary

BACKGROUND

Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part due to the results of the National Lung Screening Trial. Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, as well as increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not.

METHODS

Approved panelists reviewed previously developed key questions using the PICO (population, intervention, comparator, and outcome) format to address the benefit and harms of low-dose CT screening, as well as key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the GRADE approach. Meta-analyses were performed where appropriate. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and un-graded statements were drafted, voted on, and revised until consensus was reached.

RESULTS

The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in 7 graded recommendations and 9 ungraded consensus statements.

CONCLUSIONS

Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen detected findings, and the effectiveness of smoking cessation interventions, can impact this balance.

Radiation Dose Reduction in Kidney Stone CT: A Randomized, Facility-Based Intervention

OBJECTIVE

Kidney stones are common, tend to recur, and afflict a young population. Despite evidence and recommendations, adoption of reduced-radiation dose CT (RDCT) for kidney stone CT (KSCT) is slow. We sought to design and test an intervention to improve adoption of RDCT protocols for KSCT using a randomized facility-based intervention.

METHODS

Facilities contributing at least 40 KSCTs to the American College of Radiology dose index registry (DIR) during calendar year 2015 were randomized to intervention or control groups. The Dose Optimization for Stone Evaluation intervention included customized CME modules, personalized consultation, and protocol recommendations for RDCT. Dose length product (DLP) of all KSCTs was recorded at baseline (2015) and compared with 2017, 2018, and 2019. Change in mean DLP was compared between facilities that participated (intervened-on), facilities randomized to intervention that did not participate (intervened-off), and control facilities. Difference-in-difference between intervened-on and control facilities is reported before and after intervention.

RESULTS

Of 314 eligible facilities, 155 were randomized to intervention and 159 to control. There were 25 intervened-on facilities, 71 intervened-off facilities, and 96 control facilities. From 2015 to 2017, there was a drop of 110 mGy ∙ cm (a 16% reduction) in the mean DLP in the intervened-on group, which was significantly lower compared with the control group (P < .05). The proportion of RDCTs increased for each year in the intervened-on group relative to the other groups for all 3 years (P < .01).

DISCUSSION

The Dose Optimization for Stone Evaluation intervention resulted in a significant (P < .05) and persistent reduction in mean radiation doses for engaged facilities performing KSCTs.

Characteristics and Outcomes of Lung Cancers Detected on Low-Dose Lung Cancer Screening CT

BACKGROUND

Lung cancer screening (LCS) with low-dose CT (LDCT) was implemented in the United States following the National Lung Screening Trial (NLST). The real-world benefits of implementing LCS are yet to be determined with outcome-oriented data. The study objective is to investigate the characteristics and outcomes of screening-detected lung cancers.

METHODS

This single-institution retrospective study included LCS patients between June 2014 and December 2019. Patient demographics, number of screening rounds, imaging features, clinical workup, disease extent, histopathology, treatment, complications, and mortality outcomes of screening-detected lung cancers were extracted and compared with NLST data.

RESULTS

LCS LDCTs (7,480) were performed on 4,176 patients. The cancer detection rate was 3.8%, higher than reported by NLST (2.4%, P < 0.0001), and cancers were most often found in patients ≥65 years (62%), older than those in NLST (41%, P < 0.0001). The patients' ethnicity was similar to NLST, P = 0.87. Most LCS-detected cancers were early stage I tumors (71% vs. 54% in NLST, P < 0.0001). Two thirds of cancers were detected in the first round of screening (67.1%) and were multifocal lung cancers in 15%. As in NLST, the complication rate after invasive workup or surgery was low (24% vs. 28% in NLST, P = 0.32). Over a median follow-up of 3.3 years, the mortality rate was 0.45%, lower than NLST (1.33%, P < 0.0001).

CONCLUSIONS

LCS implementation achieved a higher cancer detection rate, detection of early-stage cancers, and more multifocal lung cancers compared with the NLST, with low complications and mortality.

IMPACT

The real-world implementation of LCS has been successful for detection of lung cancer with favorable outcomes.

Detectability of pancreatic lesions by low-dose unenhanced computed tomography using iterative reconstruction

OBJECTIVES

To investigate the detectability of pancreatic cystic lesions and main pancreatic duct dilation by low-dose unenhanced computed tomography (CT).

MATERIAL AND METHODS

This study included 2684 patients who underwent low-dose unenhanced CT using iterative reconstruction and magnetic resonance imaging (MRI) as a part of a health-screening program between February 1, 2019 and December 31, 2019. Patients diagnosed with pancreatic cystic lesions and/or dilatations of the main pancreatic duct on MRI were identified. Detection rates by low dose CT in terms of lesion size were tested for significance by Fisher's exact test.

RESULTS

Of the 2684 patients, 558 (20.8 %) had pancreatic cystic lesions and 22 (0.8 %) had main pancreatic duct dilatation on MRI. The low-dose CT detection rates among the pancreatic cystic lesions were as follows: 1-9-mm cysts, three (0.65 %) of 461; 10-19-mm cysts, 17 (21.25 %) of 80, and ≥20-mm cysts, eight (47.06 %) of 17. The detection rates were significantly higher in the 10-19-mm and the ≥20-mm cyst group than in the 1-9-mm cyst group (p <  0.001). The detection rates among the main pancreatic duct dilatations were as follows: 3-5-mm dilatations, two (11.76 %) of 17 and ≥6-mm dilatations, four (80 %) of five, which were significantly higher rates than that for the 3-5-mm dilatations (p =  0.009).

CONCLUSION

Small pancreatic cysts and slight main pancreatic duct dilatation were practically undetectable by low-dose unenhanced CT. The application of a low-dose CT protocol as a screening tool in the detection of pancreatic abnormalities is not recommended.

The radiologist's role in lung cancer screening

Lung cancer is still the deadliest cancer in men and women worldwide. This high mortality is related to diagnosis in advanced stages, when curative treatment is no longer an option. Large randomized controlled trials have shown that lung cancer screening (LCS) with low-dose computed tomography (CT) can detect lung cancers at earlier stages and reduce lung cancer-specific mortality. The recent publication of the significant reduction of cancer-related mortality by 26% in the Dutch-Belgian NELSON LCS trial has increased the likelihood that implementation of LCS in Europe will move forward. Radiologists are important stakeholders in numerous aspects of the LCS pathway. Their role goes beyond nodule detection and nodule management. Being part of a multidisciplinary team, radiologists are key players in numerous aspects of implementation of a high quality LCS program. In this non-systematic review we discuss the multifaceted role of radiologists in LCS.

Lung cancer screening in Europe: where are we in 2021?

This manuscript reviews the recent evidence obtained in lung cancer screening with low dose spiral CT-scan (LDSCT) and focuses on the issues associated with its implementation in Europe. After a review of the magnitude of the lung cancer toll in lives, disease and Euro's, the recently released data of the major lung cancer screening trials are reviewed and mirrored with the results of the US National Lung Screening Trial (NLST), comparing their strengths and weaknesses and areas of future research. The specific barriers and hurdles to be addressed for widely implementing this population screening in European countries are discussed, with special emphasis on the issues of inclusion of smokers, smoking cessation interventions, radiation injury and capacity planning. The pros and cons of including current smokers will be addressed together with the issue which is the better smoking cessation intervention. A medical physicist's view on radiation exposure and quality control will address concerns about radiation induced cancers. The downstream effects of a LDSCT screening program on the capacity of CT-scans, radiologists, thoracic surgeons and radiation oncologists will follow. An estimated roadmap for the future is sketched with the expected role of all key stakeholders. This roadmap reflects the opinion leader's reflections as expressed in a number of discussions with European health authorities, taking place as part of the recently released European Beating Cancer plan.

Comparison of artery-based methods for ordinal grading of coronary artery calcium on low-dose chest computed tomography

OBJECTIVES

To identify the optimal artery-based method for ordinal grading of coronary artery calcium (CAC) on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) among three methods.

METHODS

A total of 120 asymptomatic subjects who underwent both LDCT and ECG-gated calcium scoring CT on the same day were retrospectively enrolled. Three cardiothoracic radiologists independently assessed CAC severity on LDCT (1.25-mm and 2.5-mm slice thickness) and classified it into four categories (none, mild, moderate, or severe) using three artery-based ordinal scoring methods (extent-based scoring, Weston scoring, and length-based scoring). Inter- and intra-observer CAC severity agreements of each method were assessed by Fleiss kappa statistics. Agreements between each method and ECG-gated calcium scoring CT were assessed by weighted kappa statistics.

RESULTS

The inter-observer agreement was highest with length-based method for both 1.25-mm (Fleiss kappa 0.735 for extent-based method, 0.801 for Weston score, and 0.813 for length-based method) and 2.5-mm slice thickness evaluation (Fleiss kappa 0.755 for extent-based method, 0.776 for Weston score, and 0.833 for extent-based method). Agreement across the three grading methods for the same observer was poor to moderate on 1.25-mm (Fleiss kappa 0.379-0.441) and moderate on 2.5-mm thickness evaluation (Fleiss kappa 0.427-0.461). Agreement of CAC severity between each method and ECG-gated calcium scoring CT was highest with the length-based method for all three observers on both 1.25-mm (weighted kappa 0.773-0.786) and 2.5-mm (weighted kappa 0.794-0.825) LDCT images.

CONCLUSION

Among the three artery-based ordinal grading methods, the length-based method appears to be the most reliable for evaluating CAC on non-ECG-gated LDCT.

KEY POINTS

• The length-based method showed the highest inter-observer agreement and the highest agreement with the ECG-gated calcium scoring CT, compared with the extent-based method and the Weston score.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

BACKGROUND

Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT.

METHODS

A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis.

RESULTS

Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942).

CONCLUSION

We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Role of Key Guidelines in an Era of Precision Oncology: A Primer for the Radiologist

OBJECTIVE. The purpose of this article is to familiarize radiologists with the evidence-based imaging guidelines of major oncologic societies and organizations and to discuss approaches to effective implementation of the most recent guidelines in daily radiology practice. CONCLUSION. In an era of precision oncology, radiologists in practice and radiologists in training are key stakeholders in multidisciplinary care, and their awareness and understanding of society guidelines is critically important.

Adherence to annual lung cancer screening with low-dose CT scan in a diverse population

PURPOSE

Our aim was to develop a novel approach for lung cancer screening among a diverse population that integrates the Centers for Medicare and Medicaid Services (CMS) recommended components including shared decision making (SDM), low-dose CT (LDCT), reporting of results in a standardized format, smoking cessation, and arrangement of follow-up care.

METHODS

Between October of 2015 and March of 2018, we enrolled patients, gathered data on demographics, delivery of SDM, reporting of LDCT results using Lung-RADS, discussion of results, and smoking cessation counseling. We measured adherence to follow-up care, cancer diagnosis, cancer treatment, and smoking cessation at 2 years after initial LDCT.

RESULTS

We enrolled 505 patients who were 57% African American, 30% Caucasian, 13% Hispanic, < 1% Asian, and 61% were active smokers. All participants participated in SDM, 88.1% used a decision aid, and 96.1% proceeded with LDCT. Of 496 completing LDCT, all received a discussion about results and follow-up recommendations. Overall, 12.9% had Lung-RADS 3 or 4, and 3.2% were diagnosed with lung cancer resulting in a false-positive rate of 10.7%. All 48 patients with positive screens but no cancer diagnosis adhered to follow-up care at 1 year, but only 35.4% adhered to recommended follow-up care at 2 years. The annual follow-up for patients with negative lung cancer screening results (Lung-RADS 1 and 2) was only 23.7% after one year and 2.8% after 2 years. All active smokers received smoking cessation counseling, but only 11% quit smoking.

CONCLUSION

The findings show that an integrated lung cancer screening program can be safely implemented in a diverse population, but adherence to annual screening is poor.

Management and Outcomes of Suspected Infectious and Inflammatory Lung Abnormalities Identified on Lung Cancer Screening CT

Background: Incidental findings are frequently encountered during lung cancer screening (LCS). Limited data describe the prevalence of suspected acute infectious and inflammatory lung processes on LCS and how they should be managed. Objective: To determine the prevalence, radiologic reporting and management, and outcome of suspected infectious and inflammatory lung processes identified incidentally during LCS, and to propose a management algorithm. Methods: This retrospective study included 6314 low dose CT (LDCT) examinations performed between June 2014 and April 2019 in 3800 patients as part of an established LCS program. Radiology reports were reviewed, and patients with potentially infectious or inflammatory lung abnormalities were identified and analyzed for descriptors of imaging findings, Lung-RADS designation, recommendations, and clinical outcomes. Based on the descriptors, outcomes and a >2% threshold risk of malignancy, a follow-up algorithm was developed to decrease additional imaging without affecting cancer detection. Results: A total of 331/3800 (8.7%) patients (178 men, 153 women; mean age: 66 ± 7 years) undergoing LCS had lung findings that were attributed to infection or inflammation. These abnormalities were reported as potentially significant findings using the "S" modifier in 149/331 (45.0%) and as the "dominant nodule" determining the Lung-RADS category in 96/331 (29.0%). Abnormalities were multiple or multifocal in 260/331 (78.5%). Common descriptors were ground-glass (155/331; 46.8%), tree-in-bud (56/331; 16.9%), consolidation (41/331; 12.4%), and clustered (67/331; 20.2%) opacities. A follow-up chest CT outside of screening was performed within 12 months or less in 264/331 (79.8%) and within 6 months or less in 286/331 (56.2%). A total of 260/331 (78.5%) opacities resolved on follow-up imaging. Two malignancies (2/331; 0.60%) were associated with these abnormalities, and both had consolidations. Theoretical adoption of a proposed management algorithm for suspected infectious and inflammatory findings reduced unnecessary follow-up imaging by 82.6% without missing a single malignancy. Conclusions: Presumed acute infectious or inflammatory lung abnormalities are frequently encountered in the setting of LCS. These opacities are commonly multifocal and resolve on follow-up. Less than 1% are associated with malignancy. Clinical impact: Adoption of a conservative management algorithm can standardize recommendations and reduce unnecessary imaging without increasing the risk of missing a malignancy.

Artificial neural networks improve LDCT lung cancer screening: a comparative validation study

BACKGROUND

This study proposes a prediction model for the automatic assessment of lung cancer risk based on an artificial neural network (ANN) with a data-driven approach to the low-dose computed tomography (LDCT) standardized structure report.

METHODS

This comparative validation study analysed a prospective cohort from Chiayi Chang Gung Memorial Hospital, Taiwan. In total, 836 asymptomatic patients who had undergone LDCT scans between February 2017 and August 2018 were included, comprising 27 lung cancer cases and 809 controls. A derivation cohort of 602 participants (19 lung cancer cases and 583 controls) was collected to construct the ANN prediction model. A comparative validation of the ANN and Lung-RADS was conducted with a prospective cohort of 234 participants (8 lung cancer cases and 226 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models.

RESULTS

At the cut-off of category 3, the Lung-RADS had a sensitivity of 12.5%, specificity of 96.0%, positive predictive value of 10.0%, and negative predictive value of 96.9%. At its optimal cut-off value, the ANN had a sensitivity of 75.0%, specificity of 85.0%, positive predictive value of 15.0%, and negative predictive value of 99.0%. The area under the ROC curve was 0.764 for the Lung-RADS and 0.873 for the ANN (P = 0.01). The two most important predictors used by the ANN for predicting lung cancer were the documented sizes of partially solid nodules and ground-glass nodules.

CONCLUSIONS

Compared to the Lung-RADS, the ANN provided better sensitivity for the detection of lung cancer in an Asian population. In addition, the ANN provided a more refined discriminative ability than the Lung-RADS for lung cancer risk stratification with population-specific demographic characteristics. When lung nodules are detected and documented in a standardized structured report, ANNs may better provide important insights for lung cancer prediction than conventional rule-based criteria.

Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?

Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as "compatible pairs" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.