Low-dose CT Scans Research Articles

CT-based airway changes after smoking cessation in the general population.

Previous research has demonstrated improvements in CT-derived bronchial parameters in the first years after smoking cessation. This study investigates the association between longer smoking cessation duration and bronchial parameters in lung-healthy and lung-unhealthy ex-smokers from the general population. We conducted a cross-sectional analysis using low-dose CT scans of ex-smokers from the general population with at least 10 pack-years from the ImaLife study, a sub study within the Lifelines cohort. Participants ⩾45years who completed a lung-function test were recruited for low-dose CT imaging. We divided them into lung-healthy and lung-unhealthy based on spirometry, self-reported diagnosis and imaging signs of respiratory disease. Bronchial parameters Pi10, wall thickness, luminal area and wall area percent (WAP) were obtained using a previously validated method. Multivariable linear regression (MLR) was used to evaluate the independent associations between smoking cessation duration and bronchial parameters, adjusting for sex, age, height, weight, and pack-years. The study included 1,869 ex-smokers; 1,421 (76%) were classified as lung-healthy (58% men, mean age 64.2±9.8years, pack-years 16.5 [12.5-23.3], smoking cessation duration 20.0 [14.0-29.0] years) and 448 (24%) as unhealthy (56% men, mean age 66.1±10.5years, pack-years 18.2 [13.4-25.2], smoking cessation duration 20.0 [13.8-29.0] years). In the lung-unhealthy group, individuals with a longer duration of smoking cessation had a lower WAP compared to those with a shorter cessation duration (-0.528% per 10years, p=0.005). In contrast, in MLR no significant associations were observed for the lung-healthy group.. In individuals with respiratory conditions, longer smoking cessation duration is related to a decrease in wall area percent of the bronchial walls. The results suggest the potential for improvements in airway health when people quit smoking, warranting further investigation with longitudinal studies.

A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction.

The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations. A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel). Harmonized scans were evaluated for image similarity using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS), and for the reproducibility of radiomic and deep features using concordance correlation coefficient (CCC). CNNs consistently yielded higher image similarity metrics amongst others; for Sharp/10%, which exhibited the poorest visual similarity, PSNR increased from a mean ± CI of 17.763 ± 0.492 to 31.925 ± 0.571, SSIM from 0.219 ± 0.009 to 0.754 ± 0.017, and LPIPS decreased from 0.490 ± 0.005 to 0.275 ± 0.016. Texture-based radiomic features exhibited a greater degree of variability across conditions, i.e. a CCC of 0.500 ± 0.332, compared to intensity-based features (0.972 ± 0.045). GANs achieved the highest CCC (0.969 ± 0.009 for radiomic and 0.841 ± 0.070 for deep features) amongst others. Convolutional neural networks are suitable if downstream applications necessitate visual interpretation of images, whereas generative adversarial networks are better alternatives for generating reproducible quantitative image features needed for machine learning applications. Understanding the efficacy of harmonization in addressing multi-parameter variability is crucial for optimizing diagnostic accuracy and a critical step toward building generalizable models suitable for clinical use.

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR. A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses. A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001). DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT. Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

Comparison of the CT-based micromotion analysis method versus marker-based RSA in measuring femoral head translation and evaluation of its intra- and interobserver reliability: a prospective agreement diagnostic study on 27 patients up to 1 year.

Computed tomography radiostereometric analysis (CT-RSA) assesses implant micromovements using low-dose CT scans. We aimed to investigate whether CT-RSA is comparable to marker-based radiostereometric analysis (RSA) measuring early femoral head migration in cemented stems. We hypothesized that CT-RSA is comparable to marker-based RSA in evaluating femoral head subsidence. We prospectively included 31 patients undergoing cemented total hip arthroplasty (THA), of which 27 were eligible for the analysis. Femoral head migration at 1 year was measured with marker-based RSA and CT-RSA. Comparison was performed using paired analysis and Bland-Altman plots, and the intra- and interobserver reliability of CT-RSA was assessed Results:The median (interquartile range [IQR]) translation on the Y-axis measured with marker-based RSA was -0.86 mm (-1.10 to -0.37) and -0.83 mm (-1.11 to -0.48) for CT-RSA (i.e. subsidence), with a median difference of -0.03 mm (95% confidence interval [CI] -0.08 to 0.18). The minimal important difference in translation was set to 0.2 mm. This value was excluded from the CI of the differences. No statistical difference was found between marker-based RSA and CT-RSA regarding assessment of subsidence of the femoral head. The Bland-Altman plots showed good agreement between the 2 methods in measuring subsidence of the femoral head. The intra- and interobserver reliability of the CT-RSA method was excellent with intraclass correlation coefficient (ICC) = 1 (0.99-1) and ICC = 0.99 (0.99-1), respectively. We showed that CT-RSA was comparable to marker-based RSA in measuring femoral head subsidence. Moreover, the intra- and interobserver reliability of the CT-RSA method was excellent, suggesting that the method is assessor independent.

Longitudinal Assessment of Pulmonary Involvement and Prognosis in Different Subtypes of COVID-19 Patients After One Year Using Low-Dose CT: A Prospective Observational Study.

Severe COVID-19 typically results in pulmonary sequelae. However, current research lacks clarity on the differences in these sequelae among various clinical subtypes. This study aimed to evaluate the changing lung imaging features and predictive factors in patients with COVID-19 pneumonia in northern China over a 12-month follow-up period after the relaxation of COVID-19 restrictions in 2022. Imaging and clinically relevant data from three groups (moderate, severe, and critical) of patients with varying severity were prospectively analyzed. Low-dose CT scans were conducted at 3, 6, and 12 months after discharge, with chest CT images evaluated at baseline and each follow-up using qualitative and quantitative analyses. Clinical symptoms and pulmonary function recovery at 12 months were documented. The correlation between lung function and CT results was analyzed. Univariate and multivariable logistic regression analyses were employed to examine factors influencing prognosis, while a post-hoc analysis model was utilized to investigate the relationships among different groups, time points, and chest CT findings. Among the 103 hospitalized patients with COVID-19 pneumonia, 64 completed the 12-month evaluation. The median age was 63.70 ± 12.15%, and 62.5% (40/64) were men. During the follow-up period, while 67.19% (43/64) showed abnormalities, including fibrotic changes in 9.38% (6/64). Multivariable logistic regression identified age ≥ 65 (OR: 8.66; 95% CI: 1.86, 40.34; P = 0.006), length of hospital stays (OR: 1.23; 95% CI: 1.03, 1.47; P = 0.022), and baseline consolidation volume as a percentage of the whole lung (OR: 56.95; 95% CI: 1.198, 2706.782; P = 0.04) as independent risk factors for persistent CT lung abnormalities at 1 year. After 1 year, 34.38% (22/64) of patients still had abnormal lung function, and 9.38% (6/64) had pulmonary fibrosis and restrictive ventilatory dysfunction. The relationship between lung function and CT findings is weak correlation. The mixed model analysis revealed significant differences between groups, particularly between the moderate and severe groups, and significant changes in CT values over time. One year after infection, more than one third of even moderate patients with mild symptoms had persistent pulmonary abnormalities. In our study, fibrotic changes were seen in severe and critically ill patients and remained stable 6 months after discharge from hospital. Imaging parameters can predict the prognosis. The larger the extent of baseline consolidation, the worse the prognosis of elderly patients.

Comparison of Marker-Based RSA and CT-RSA for Analyzing Micromotions After Distal Radius Osteotomy: A 1-Year Retrospective Study of 24 Patients.

Radiostereometric Analysis (RSA) is the most accurate method for determining early micromotions of orthopedic implants. Computed Tomography Radiostereometric Analysis (CT-RSA) is a method that can be used to determine implant and bone micromovements using low-dose CT scans. This study aimed to evaluate the reliability of the CT-RSA method in measuring the interfragmental mobility in patients who have undergone a correction osteotomy due to a malunited distal radius fracture. Twenty-four patients were included and operated with a radiolucent volar plate. Markers were embedded in the plate and bone. RSA and CT examinations were obtained postoperatively up to 1-year postoperative. Micromovements of the distal radius segment relative to the proximal were compared between the methods with paired analysis and Bland-Altman plots. The limits of clinical significance were: dorsal/volar tilt < 10°, radial shortening < 5 mm, radial inclination ≥ 15°, and radial shift < 5 mm. For the dorsal/volar tilt, the paired analysis between the two methods, showed a mean difference (95% CI) of -0.06° (-0.67 to 0.55), for radial compression-0.04 mm (-0.09 to 0.01), for radial inclination 0.21° (-0.06 to 0.48), and for radial shift -0.07 mm (-0.21 to 0.07). The paired analysis for micromotions showed that the thresholds of clinical significance are excluded from the difference's 95% CI. The Bland-Altman plots showed comparable results up to 1 year, considering clinically relevant thresholds. In conclusion, the CT-RSA method is comparable to that of marker-based RSA in measuring micromotions after wrist osteotomy, as the differences between the methods are not clinically significant.

Coronary calcium score and emphysema extent on different CT radiation dose protocols in lung cancer screening.

To assess the consistency of automated measurements of coronary artery calcification (CAC) burden and emphysema extent on computed tomography (CT) images acquired with different radiation dose protocols in a lung cancer screening (LCS) population. The patient cohort comprised 361 consecutive screenees who underwent a low-dose CT (LDCT) scan and an ultra-low-dose CT (ULDCT) scan at an incident screening round. Exclusion criteria for CAC measurements were software failure and previous history of CVD, including coronary stenting, whereas for emphysema assessment, software failure only. CT images were retrospectively analyzed by a fully automated AI software for CAC scoring, using three predefined Agatston score categories (0-99, 100-399, and ≥ 400), and emphysema quantification, using the percentage of low attenuation areas (%LAA). Demographic and clinical data were obtained from the written questionnaire completed by each participant at the first visit. Agreement for CAC and %LAA categories was measured by the k-Cohen Index with Fleiss-Cohen weights (Kw) and Intraclass Correlation Coefficient (ICC) with 95% Confidence Interval (CI). An overlap of CAC strata was observed in 275/327 (84%) volunteers, with an almost perfect agreement (Kw = 0.86, 95% CI 0.82-0.90; ICC = 0.86, 95% CI 0.79-0.90), while an overlap of %LAA strata was found in 204/356 (57%) volunteers, with a moderate agreement (Kw = 0.57, 95% CI 0.51-0.63; ICC = 0.57, 95% CI 0.21-0.75). Automated CAC quantification on ULDCT seems feasible, showing similar results to those obtained on LDCT, while the quantification of emphysema tended to be overestimated on ULDCT images. Question Evidence demonstrating that coronary artery calcification and emphysema can be automatedly quantified on ultra-low-dose chest CT is still awaited. Findings Coronary artery calcification and emphysema measurements were similar among different CT radiation dose protocols; their automated quantification is feasible on ultra-low-dose CT. Clinical relevance Ultra-low-dose CT-based LCS might offer an opportunity to improve the secondary prevention of cardiovascular and respiratory diseases through automated quantification of both CAC burden and emphysema extent.

GI cancer mortality in participants in low dose CT screening for lung cancer with a focus on pancreatic cancer

Lung cancer, the leading cause of cancer deaths globally, has better survival rates with early detection. Annual low-dose CT (LDCT) screenings are recommended for high-risk individuals due to age and smoking. These individuals are also at risk for other cancers. Our study explores gastrointestinal (GI) cancer mortality in lung cancer screening participants and the potential of LDCT screenings to detect pancreatic cancer. We utilized data from a prospective multi-institutional cohort study, the International Early Lung Cancer Action Project (I-ELCAP). We analyzed GI cancer deaths among participants in New York State (1992–2010), exploring demographics and GI cancer distribution. Radiologists retrospectively reviewed pancreatic cancer cases within 24 months post-LDCT, comparing findings with original reports. Among 10,150 participants, 189 died from GI cancers; mean age 75, mostly male smokers. Pancreatic cancer (41.8%) led, followed by esophageal (17.5%) and colon cancer (16.9%). Median time between baseline LDCT and death was 116 months (9.7 years). 82/189 (43.4%) participants died within 5 years of their last LDCT screening, with pancreatic cancer again prominent (45.1%). In 79 pancreatic cancer deaths, 17.7% occurred within 24 months post-LDCT. A re-review identified previously undetected pancreatic findings, with 4 out of 14 participants (28.6%) showing abnormalities. This underscores the potential of lung cancer screening programs to provide insights beyond lung health. This study of over 10,000 participants in a lung cancer screening program reveals that they are at risk for GI cancer deaths, particularly pancreatic cancer. Re-reviews of LDCT scans revealed previously undocumented pancreatic findings in a third of participants who died from pancreatic cancer, underscoring the need to identify, document, and follow up on these findings.

CT-assessed sarcopenia and immune-related adverse events in patients with lung cancer: A competing risk time-to-event analysis.

Immune checkpoint inhibitors (ICIs) can induce immune-related adverse events (irAEs). This study investigates the relationship between CT-assessed sarcopenia and irAEs in patients with lung cancer who are receiving ICIs. Patients were enrolled if they had lung cancer treated with ICIs at the University Medical Center Groningen (2015-2021) and had undergone low-dose CT scans that included the third lumbar vertebral level (L3). CT-assessed sarcopenia was defined based on reported L3 skeletal muscle mass index (L3SMI) thresholds. Patients were categorized into no, any-grade, and severe irAE groups. The association between CT-assessed sarcopenia and irAEs was assessed by competing risk time-to-event analysis, accounting for the risk of death. Sub-distribution hazard ratios (SDHR) were calculated using Fine-Gray regression models adjusted for relevant confounders. The association between CT-assessed sarcopenia and overall survival (OS) was evaluated through survival analyses. We included 363 patients; most were male (60.9%), had favorable Eastern Cooperative Oncology Group (ECOG) performance statuses (0-1; 90.1%), had stage IV disease (92.8%), and received ICI monotherapy (82.9%). Of these, 45.6% developed any-grade irAEs and 21% developed severe irAEs. Endocrine disorders were the most common mild irAEs (24.8%), while respiratory disorders were the most common severe irAEs (24.7%). CT-assessed sarcopenia was more prevalent in the no irAE group (87%) compared with the any-grade (77%) and severe (79%) irAE groups. Presence of CT-assessed sarcopenia was associated with a lower risk of developing any irAEs (SDHR=0.62 [95% CI: 0.41-0.92]). No significant association was found between CT-assessed sarcopenia and severe irAEs (fully adjusted model, SDHR=0.74 [95% CI: 0.39-1.4]), or between CT-assessed sarcopenia and OS. CT-assessed sarcopenia is associated with a reduced risk of any irAEs in patients with lung cancer receiving ICIs, possibly because higher muscle mass enhances the host response to immunological stimulation. Recognizing sarcopenia as a predictive factor for irAEs is relevant for personalizing treatments.

Prevalent findings on low-dose CT scan lung cancer screening: a French prospective pilot study.

Despite significant therapeutic advances, lung cancer remains the biggest killer among cancers. In France, there is no national screening program against lung cancer. Thus, in this perspective, the Foch Hospital decided to implement a pilot and clinical low-dose CT screening program to evaluate the efficiency of such screening. The purpose of this study was to describe the prevalent findings of this low-dose CT screening program. Participants were recruited in the screening program through general practitioners (GPs), pharmacists, and specialists from June 2023 to June 2024. The inclusion criteria included male or female participants aged 50 to 80 years, current smokers or former smokers who had quit less than 15 years prior, with a smoking history of over 20 pack-years. Chest CT scans were conducted at Foch Hospital using a low-dose CT protocol based on volume mode with a multi-slice scanner (≥60 slices) without contrast injection. In total, 477 participants were recruited in the CT scan screening, 235 (49%) were males with a median age of 60 years [56-67] and 35 smoke pack-years [29-44] and 242 females (51%) with a median age of 60 years [55-60] and 30 smoke pack-years [25-40]. Eight participants showed positive nodules on CT scan, as a 1.7% rate. 66.7% of diagnosed cancers were in early stages (0-I). It is feasible to implement structured lung cancer screening using low-dose CT in a real-world setting among the general population. This approach successfully identifies most early-stage cancers that could be treated curatively.

BreathVisionNet: A pulmonary-function-guided CNN-transformer hybrid model for expiratory CT image synthesis

Background and objectiveChronic obstructive pulmonary disease (COPD) has high heterogeneity in etiologies and clinical manifestations. Expiratory Computed tomography (CT) can effectively assess air trapping, aiding in disease diagnosis. However, due to concerns about radiation exposure and cost, expiratory CT is not routinely performed. Recent work on synthesizing expiratory CT has primarily focused on imaging features while neglecting patient-specific pulmonary function. MethodsTo address these issues, we developed a novel model named BreathVisionNet that incorporates pulmonary function data to guide the synthesis of expiratory CT from inspiratory CT. An architecture combining a convolutional neural network and transformer is introduced to leverage the irregular phenotypic distribution in COPD patients. The model can better understand the long-range and global contexts by incorporating global information into the encoder. The utilization of edge information and multi-view data further enhances the quality of the synthesized CT. Parametric response mapping (PRM) can be estimated by using synthesized expiratory CT and inspiratory CT to quantify COPD phenotypes of the normal, emphysema, and functional small airway disease (fSAD), including their percentages, spatial distributions, and voxel distribution maps. ResultsBreathVisionNet outperforms other generative models in terms of synthesized image quality. It achieves a mean absolute error, normalized mean square error, structural similarity index and peak signal-to-noise ratio of 78.207 HU, 0.643, 0.847 and 25.828 dB, respectively. Comparing the predicted and real PRM, the Dice coefficient can reach 0.732 (emphysema) and 0.560 (fSAD). The mean of differences between true and predicted fSAD percentage is 4.42 for the development dataset (low radiation dose CT scans), and 9.05 for an independent external validation dataset (routine dose), indicating that model has great generalizability. A classifier trained on voxel distribution maps can achieve an accuracy of 0.891 in predicting the presence of COPD. ConclusionsBreathVisionNet can accurately synthesize expiratory CT images from inspiratory CT and predict their voxel distribution. The estimated PRM can help to quantify COPD phenotypes of the normal, emphysema, and fSAD. This capability provides additional insights into COPD diversity while only inspiratory CT images are available.

The Effect of Low-Dose CT Protocols on Shoulder Model-Based Tracking accuracy Using Biplane Videoradiography.

Model-based tracking is being increasingly used to quantify shoulder kinematics and typically employs computed tomography (CT) to create the 3D bone volumes, which adds to the total radiation exposure. Lower-dose CT protocols may be possible given the contrast between bone and the surrounding soft tissues. The purpose of this study was to describe the dose-accuracy tradeoff between low-dose CT scans and the kinematic tracking accuracy of the humerus, scapula, and clavicle when tracked using an intensity-based registration algorithm. Three fresh-frozen cadavers consisting of the torso and bilateral shoulders were tested. The CT protocols investigated included a full-dose protocol and 4 experimental low-dose protocols that modulated x-raytube current and peak voltage. Bead-based tracking (i.e., radiostereometric analysis) served as the reference standard to which model-based tracking results were compared. Accuracy was described in terms of both segmental (humerus, scapula, and clavicle) and joint (glenohumeral, acromioclavicular) kinematics using root-mean-square (RMSE), bias, precision, and worst-case errors. The low-dose CT scans resulted in an average dose reduction of 70.6-92.8%. RMSEs tended to increase as CT dose decreased with average glenohumeral errors increasing from 0.5° and 0.6 mm to 0.6° and 0.6 mm between the highest and lowest-dose protocols, and average acromioclavicular errors increasing from 0.6° and 0.8 mm to 0.7° and 0.9 mm. However, the difference in joint kinematic errors between the highest and lowest-dose CT scanning protocols was generally small (≤0.3°, ≤ 0.1 mm). It is possible to substantially reduce the CT dose associated with shoulder motion analysis using biplane videoradiography without significantly impacting data fidelity.

The Effect of MiR320a on Lung Cancer.

Lung cancer has a high mortality rate among cancers in both women and men. Currently, lung cáncer diagnosis is made with clinical examination, low-dose CT scan and molecular-based methods and its treatment options include chemotherapy, surgery, radiotherapy or immunotherapy. However, the life expectancy of lung cancer is not very high, and still it is usually diagnosed very lately, which leads to poorer prognosis. MicroRNAs [miRNAs] are small noncoding RNAs that regulate many diverse activities in the cell that can affect tumorigenesis by regulating many cell functions related to cancer, such as cell cycle, metastasis, angiogenesis, metabolism, and apoptosis. Also, it can have a potential diagnostic, therapeutic, and prognostic value for lung cancer. MiR320a is a promising microRNA that may help us in the diagnosis, treatment and prognosis of lung cancer, but some aspects of its clinical application are still vague, especially its effect on heavy smokers, delivery mechanism, toxicity and lack of reliable critical value. In this paper, we examined its comprehensive molecular interactions that lead to its tumor suppressor effect, and we reviewed its clinical application until now.

The TELEhealth Shared decision-making COaching and navigation in Primary carE (TELESCOPE) intervention: a study protocol for delivering shared decision-making for lung cancer screening by patient navigators.

Lung cancer screening (LCS) can reduce lung cancer mortality but has potential harms for patients. A shared decision-making (SDM) conversation about LCS is required by the Centers for Medicare & Medicaid Services (CMS) for LCS reimbursement. To overcome barriers to SDM in primary care, this protocol describes a telehealth decision coaching and navigationintervention for LCS in primary care clinics delivered by patient navigators. The objective of the study is to evaluate the effectiveness of the intervention and its implementation potential, compared with an enhanced usual care (EUC) arm. Patients (n = 420) of primary care clinicians (n = 120) are being recruited to a cluster randomized controlled trial. Clinicians are randomly assigned to 1) TELESCOPE intervention: prior to an upcoming non-acute clinic visit, patients participate in a telehealth decision coaching and navigationsession about LCS delivered by trained patient navigators and nurse navigators place a low-dose CT scan (LDCT) order for each TELESCOPE patient wanting LCS, or 2) EUC: patients receive enhanced usual care from a clinician. Usual care is enhanced by providing clinicians in both arms with access to a Continuing Medical Education (CME) webinar about LCS and an LCS discussion guide. Patients complete surveys at baseline and 1-week after the scheduled clinic visit to assess quality of the SDM process. Re-navigation is attempted with TELESCOPE patients who have not completed the LDCT within 3months. One month before being due for an annual screening, TELESCOPE patients whose initial LCS showed low-risk findings are randomly assigned to receive a telehealth decision coaching booster session with a navigator or no booster. Electronic health records are abstracted at 6, 12 and 18months after the initial decision coaching session (TELESCOPE) or clinic visit (EUC) to assess initial and annual LCS uptake, imaging results, follow-up testing for abnormal findings, cancer diagnoses, treatment, and tobacco treatment referrals. This study will evaluate factors that facilitate or interfere with program implementation using mixed methods. We will assess whether a decision coaching and patient navigation intervention can feasibly and effectivelysupport high-quality SDM for LCS and guideline-concordant LCS uptake for patients in busy primary care practices serving diverse patient populations. This study was registered at ClinicalTrials.gov (NCT05491213) on August 4, 2022. Version 1, April 10, 2024.

Trabecular Attenuation of L1 in Adult Patients with Multiple Myeloma: An Observational Study on Low-Dose CT Images.

The aim of this study was to evaluate the impact of trabecular attenuation of the L1 vertebral body in low-dose CT in adult patients with multiple myeloma (MM), smoldering multiple myeloma (SMM), and monoclonal gammopathy of undetermined significance (MGUS). The study population consisted of 22 patients with MGUS and 51 consecutive patients with newly diagnosed MM (SMM, n = 21; symptomatic MM, n = 36). CT scans were conducted using a 128-slice CT scanner (Somatom go.Top, Siemens, Munich, Germany). Low-dose whole-body CT scans were performed at a single time point for each patient. Trabecular bone density values were obtained by defining regions of interest on non-contrast images at the level of L1 vertebra. A threshold of p = 0.05 was applied to determine statistical significance. The median Hounsfield unit (HU) value in patients with MGUS, SMM, and MM was 148 HU (range 81-190), 130 HU (range 93-193), and 92 HU (range 26-190), respectively, with a statistically significant difference between the groups (p = 0.0015). Patients with HU values ≤ 92 had lower progression-free survival with statistically significant differences compared to the group with HU values > 92 (p < 0.0499). This is the earliest evidence of the importance of evaluating L1 attenuation values in low-dose CT images in patients with MGUS, SMM, and MM. Further prospective studies could contribute to reinforcing these results and exploring the clinical applicability and generalization of L1 attenuation values in low-dose whole-body CT scans in routine clinical practice.

Lung cancer screening by low-dose CT scan in France: final results of the DEP KP80 study after three rounds

In prior randomised controlled trials, lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality and overall mortality. Despite these results, organised screening in France remains a challenge. This study assessed the feasibility and efficacy of lung cancer screening within a real-life context in a French administrative territory. DEP KP80 was a single-arm prospective study. Participants aged between 55 and 74 years, smokers or former smokers of ≥30 pack-years, were recruited. An annual LDCT scan was scheduled and three rounds were performed. Subjects were selected by general practitioners or pulmonologists, who checked the inclusion criteria and prescribed the CT scan. Between March 2016 and February 2020, 1254 participants were enrolled. Overall, 945 (75.4%) participants underwent baseline LDCT (T0), 376 (42.8%) completed the first round (T1) and 270 (31%) the second (T2) one. Forty-two lung cancers were diagnosed, 30 cancers (71.4%) were stage I or II and 34 cancers (80.9%) were treated surgically. In this study, the overall positive predictive value for a positive screening was 48% (95% CI 37-59) and the negative predictive value 100% (95% CI 100-100). This study demonstrated the feasibility and efficacy of lung cancer screening in a real-life context with most lung cancers diagnosed at an early stage and surgically removed. Our results also highlighted the importance of participation in each round, underlining the fact that optimising organisation is a major goal. Agence Régionale de Santé de Picardie, La Ligue contre le cancer, le Conseil Départemental de la Somme, and AstraZeneca.

Performance of Lung Nodule Computer-Aided Detection Systems on Standard-Dose and Low-Dose Pediatric CT Scans: An Intraindividual Comparison.

BACKGROUND. When lung nodule computer-aided detection (CAD) systems are applied for pediatric CT, performance may be degraded on low-dose scans due to increased image noise. OBJECTIVE. The purpose of this study was to conduct an intraindividual comparison of the performance for lung nodule detection of two CAD systems trained using adult data between low-dose and standard-dose pediatric chest CT scans. METHODS. This retrospective study included 73 patients (32 female participants, 41 male participants; mean age, 14.7 years; age range, 4-20 years) who underwent both clinical standard-dose and investigational low-dose chest CT examinations during the same encounter from November 30, 2018, to August 31, 2020, as part of an earlier prospective study. Fellowship-trained pediatric radiologists annotated lung nodules to serve as the reference standard. Both CT scans were processed using two publicly available lung nodule CAD systems previously trained using adult data: FlyerScan (github.com/rhardie1/FlyerScanCT) and Medical Open Network for Artificial Intelligence (MONAI; github.com/Project-MONAI/model-zoo/releases). The sensitivities of the two CAD systems for nodules measuring 3-30 mm (n = 247) were calculated when operating at a fixed frequency of two false-positives per scan. RESULTS. FlyerScan exhibited detection sensitivities of 76.9% (190/247; 95% CI, 73.3-80.8%) on standard-dose scans and 66.8% (165/247; 95% CI, 62.6-71.5%) on low-dose scans. MONAI exhibited detection sensitivities of 67.6% (167/247; 95% CI, 61.5-72.1%) on standard-dose scans and 62.3% (154/247; 95% CI, 56.1-66.5%) on low-dose scans. The number of detected nodules for standard-dose versus low-dose scans for 3-mm nodules was 33 versus 24 (FlyerScan) and 16 versus 13 (MONAI), 4-mm nodules was 46 versus 42 (FlyerScan) and 39 versus 30 (MONAI), 5-mm nodules was 38 versus 33 (FlyerScan) and 32 versus 31 (MONAI), and 6-mm nodules was 27 versus 20 (FlyerScan) and 24 versus 24 (MONAI). For nodules measuring 7 mm or larger, detection did not show a consistent pattern between standard-dose and low-dose scans for either system. CONCLUSION. Two lung nodule CAD systems showed decreased sensitivity on low-dose versus standard-dose pediatric CT scans obtained in the same patients. The reduced detection at low dose was overall more pronounced for nodules measuring less than 5 mm. CLINICAL IMPACT. Caution is needed when using low-dose CT protocols in combination with CAD systems to help detect small lung nodules in pediatric patients.

Automated orofacial virtual patient creation: A proof of concept

ObjectivesTo (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice. Materials and methodsA dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale. ResultsThe accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool. ConclusionThe AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication. Clinical significanceUsing AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

The Growth of Screening-Detected Pure Ground-Glass Nodules Following 10 Years of Stability

It remains uncertain how long pure ground-glass nodules (pGGNs) detected on low dose computed tomography (LDCT) should be followed. Further studies with longer follow-up periods are needed to determine the optimal follow-up duration for pGGNs. What is the percentage of enlarging nodules among pGGNs that have remained stable for 10 years? This was a retrospective cohort study originating from subjects with pGGNs detected on LDCT scans between 1997 and 2006, whose natural courses were reported in 2013. We re-analyzed all the follow-up data until July 2022. The study subjects were followed by our institutional guidelines until they were no longer a candidate for definitive treatment. The growth of the pGGNs was defined as an increase in the diameter of the entire nodule by 2 mm or more or the appearance of new solid portions within the nodules. A total of 89 patients with 135 pGGNs were followed for a median of 193 months. Of 135 pGGNs, 23 (17.0%) increased in size, and the median time to the first detection of a size change was 71 months. Of the 23 growing pGGNs, 122 were detected on the first LDCT, and 13 were newly detected on the follow-up CT scan. An increase in size was observed within 5 years in 8 nodules (34.8%), between 5 and 10 years in 12 nodules (52.2%) and after 10 years in 3 nodules (13.0%). Fifteen nodules were histologically confirmed as adenocarcinoma by surgery. Among the 76 pGGNs stable for 10 years, 3 (3.9%) increased in size. Among pGGNs that remained stable for 10 years, 3.9% eventually grew, indicating that some pGGNs can grow even after a long period of stability. We suggest that pGGNs may need to be followed for more than 10 years to confirm growth.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.