Screening for lung cancer: 2023 guideline update from the American Cancer Society.

Lung cancer is the leading cause of mortality and person-years of life lost from cancer among US men and women. Early detection has been shown to be associated with reduced lung cancer mortality. Our objective was to update the American Cancer Society (ACS) 2013 lung cancer screening (LCS) guideline for adults at high risk for lung cancer. The guideline is intended to provide guidance for screening to health care providers and their patients who are at high risk for lung cancer due to a history of smoking. The ACS Guideline Development Group (GDG) utilized a systematic review of the LCS literature commissioned for the US Preventive Services Task Force 2021 LCS recommendation update; a second systematic review of lung cancer risk associated with years since quitting smoking (YSQ); literature published since 2021; two Cancer Intervention and Surveillance Modeling Network-validated lung cancer models to assess the benefits and harms of screening; an epidemiologic and modeling analysis examining the effect of YSQ and aging on lung cancer risk; and an updated analysis of benefit-to-radiation-risk ratios from LCS and follow-up examinations. The GDG also examined disease burden data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program. Formulation of recommendations was based on the quality of the evidence and judgment (incorporating values and preferences) about the balance of benefits and harms. The GDG judged that the overall evidence was moderate and sufficient to support a strong recommendation for screening individuals who meet the eligibility criteria. LCS in men and women aged 50-80 years is associated with a reduction in lung cancer deaths across a range of study designs, and inferential evidence supports LCS for men and women older than 80 years who are in good health. The ACS recommends annual LCS with low-dose computed tomography for asymptomatic individuals aged 50-80 years who currently smoke or formerly smoked and have a ≥20 pack-year smoking history (strong recommendation, moderate quality of evidence). Before the decision is made to initiate LCS, individuals should engage in a shared decision-making discussion with a qualified health professional. For individuals who formerly smoked, the number of YSQ is not an eligibility criterion to begin or to stop screening. Individuals who currently smoke should receive counseling to quit and be connected to cessation resources. Individuals with comorbid conditions that substantially limit life expectancy should not be screened. These recommendations should be considered by health care providers and adults at high risk for lung cancer in discussions about LCS. If fully implemented, these recommendations have a high likelihood of significantly reducing death and suffering from lung cancer in the United States.

Cost-effectiveness of risk-based low-dose computed tomography screening for lung cancer in Switzerland.

Throughout Europe, computed tomography (CT) screening for lung cancer is in a phase of clinical implementation or reimbursement evaluation. To efficiently select individuals for screening, the use of lung cancer risk models has been suggested, but their incremental (cost-)effectiveness relative to eligibility based on pack-year criteria has not been thoroughly evaluated for a European setting. We evaluate the cost-effectiveness of pack-year and risk-based screening (PLCOm2012 model-based) strategies for Switzerland, which aided in informing the recommendations of the Swiss Cancer Screening Committee (CSC). We use the MISCAN (MIcrosimulation SCreening ANalysis)-Lung model to estimate benefits and harms of screening among individuals born 1940 to 1979 in Switzerland. We evaluate 1512 strategies, differing in the age ranges employed for screening, the screening interval and the strictness of the smoking requirements. We estimate risk-based strategies to be more cost-effective than pack-year-based screening strategies. The most efficient strategy compliant with CSC recommendations is biennial screening for ever-smokers aged 55 to 80 with a 1.6% PLCOm2012 risk. Relative to no screening this strategy is estimated to reduce lung cancer mortality by 11.0%, with estimated costs per Quality-Adjusted Life-Year (QALY) gained of €19 341, and a €1.990 billion 15-year budget impact. Biennial screening ages 55 to 80 for those with 20 pack-years shows a lower mortality reduction (10.5%) and higher cost per QALY gained (€20 869). Despite model uncertainties, our estimates suggest there may be cost-effective screening policies for Switzerland. Risk-based biennial screening ages 55 to 80 for those with ≥1.6% PLCOm2012 risk conforms to CSC recommendations and is estimated to be more efficient than pack-year-based alternatives.

Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey.

BACKGROUND: Regular Low-Dose Computed Tomography (LDCT) for lung cancer high-risk population has been proved to improve health outcomes and relieve disease burden efficiently for both individual and society. With geographical impedance becoming the major barrier preventing patients from getting timely healthcare service, this study incorporated health seeking behavior in estimating spatial accessibility of relative scarce LDCT resource in China, thus to provide real-world evidence for future government investment and policy making. METHODS: Taking Sichuan Province in southwest China as the study area, a cross-sectional survey was first carried out to collect actual practice and preferences for seeking LDCT services. Using Computed Tomography (CT) registration data reported by owner institutions representing LDCT services capacity, and grided town-level high-risk population as demand, the Nearest Neighbor Method was then utilized to calculate spatial accessibility of LDCT services. RESULTS: A total of 2,529 valid questionnaires were collected, with only 34.72% of the high-risk populations (746 individuals) followed the recommended annual screening. Participants preferred to travel to municipal-level and above institutions within 60 min for LDCT services. Currently, every thousand high-risk populations own 0.0845 CT scanners in Sichuan Province, with 96.95% able to access LDCT within 60 min and over half within 15 min. Urban areas generally showed better accessibility than rural areas, and the more developed eastern regions were better than the western regions with ethnic minority clusters. CONCLUSIONS: Spatial access to LDCT services is generally convenient in Sichuan Province, but disparity exists between different regions and population groups. Improving LDCT capacity in county-level hospitals as well as promoting health education and policy guidance to the public can optimize efficiency of existing CT resources. Implementing mobile CT services and improving rural public transportation may alleviate emerging disparities in accessing early lung cancer detection.

3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population.

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

The pros and cons of lung cancer screening.

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

Photon Counting Computed Tomography with the Radiation Dose of a Chest X-Ray: Feasibility and Diagnostic Yield.

INTRODUCTION: Photon counting (PC) detectors allow a reduction of the radiation dose in CT. Chest X-ray (CXR) is known to have a low sensitivity and specificity for detection of pneumonic infiltrates. The aims were to establish an ultra-low-dose CT (ULD-CT) protocol at a PC-CT with the radiation dose comparable to the dose of a CXR and to evaluate its clinical yield in patients with suspicion of pneumonia. METHODS: A ULD-CT protocol was established with the aim to meet the radiation dose of a CXR. In this retrospective study, all adult patients who received a ULD-CT of the chest with suspected pneumonia were included. Radiation exposure of ULD-CT and CXR was calculated. The clinical significance (new diagnosis, change of therapy, additional findings) and limitations were evaluated by a radiologist and a pulmonologist considering previous CXR and clinical data. RESULTS: Twenty-seven patients (70% male, mean age 68 years) were included. With our ULD-CT protocol, the radiation dose of a CXR could be reached (mean radiation exposure 0.11 mSv). With ULD-CT, the diagnosis changed in 11 patients (41%), there were relevant additional findings in 4 patients (15%), an infiltrate (particularly fungal infiltrate under immunosuppression) could be ruled out with certainty in 10 patients (37%), and the therapy changed in 10 patients (37%). Two patients required an additional CT with contrast medium to rule out a pulmonary embolism or pleural empyema. CONCLUSIONS: With ULD-CT, the radiation dose of a CXR could be reached while the clinical impact is higher with change in diagnosis in 41%.

Study rationale and design of the PEOPLHE trial.

PURPOSE: Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS: PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION: The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.

Increasing Lung Cancer Screening for High-Risk Smokers in a Frontier Population.

Northern Plains American Indians (AIs) have some of the highest smoking and lung cancer mortality rates in the USA. They are a high-risk population in which many are eligible for low-dose computed tomography (LDCT) screening, but such screening is rarely used. This study investigated methods to increase LDCT utilization through both a provider and community intervention to lower lung cancer mortality rates. This study used the Precaution Adoption Model for provider and community interventions implemented in four study regions in western South Dakota. The goal was to increase LDCT screening for eligible participants. Intake surveys and LDCT screenings were compared at baseline and 6 months following the education programs for both interventions. A total of 131 providers participated in the provider intervention. At the 6-month follow-up survey, 31 (63%) referred at least one patient for LDCT (p < 0.05). Forty (32.3%) community participants reported their provider recommended an LDCT and of those, 30(75%) reported getting an LDCT (p < 0.05). A total of 2829 patient surveys were completed at the imaging sites and most (88%, n = 962) cited provider recommendation as their reason for obtaining an LDCT. Almost half (46%; n = 131) of the referring providers attended a provider education workshop, and 73% of the providers worked at a clinic that hosted at least one community education session. Over the study period, LDCT utilization increased from 640 to 1706, a 90.9% increase. The provider intervention had the strongest impact on LDCT utilization. This study demonstrated increased LDCT utilization through the provider intervention but increases also were documented for the other intervention combinations. The community-based education program increased both community and provider awareness on the value of LDCTs to lower lung cancer mortality rates.

Special issue "The advance of solid tumor research in China": Participants with a family history of cancer have a higher participation rate in low-dose computed tomography for lung cancer screening.

We aimed to determine participation in low-dose computed tomography (LDCT) of individuals with a family history of common cancers in a population-based screening program to provide timely evidence in high-risk populations in China. The analysis was conducted using data from the Cancer Screening Program in Urban China (CanSPUC), which recruited 282 377 participants aged 40 to 74 years from eight cities in the Henan province. Using the CanSPUC risk score system, 55 428 participants were evaluated to have high risk for lung cancer and were recommended for LDCT. We calculated the overall and group-specific participation rates using family history of common cancers and compared differences in participation rates between different groups. Odds ratios (ORs) and 95% confidence intervals were derived by multivariable logistic regression. Of the 55 428 participants, 22 260 underwent LDCT (participation rate, 40.16%). Family history of lung, esophageal, stomach, liver and colorectal cancer was associated with increased participation in LDCT screening. The odds of participants with a family history of one, two, three and four or more cancer cases undergoing LDCT screening were 1.9, 2.7, 2.8 and 3.5 times, respectively, than those without a family history of cancer. Compared to those without a history of cancer, participation in LDCT gradually increased as the number of cancer cases in the family increased (P < .001). Our findings suggest that there is room for improvement in lung cancer screening given the relatively low participation rate. Lung cancer screening in populations with a family history of cancer may improve efficiency and cost-effectiveness; however, this requires further verification.

HUNCHEST-II contributes to a shift to earlier-stage lung cancer detection: final results of a nationwide screening program.

OBJECTIVES: The introduction of low-dose CT (LDCT) altered the landscape of lung cancer (LC) screening and contributed to the reduction of mortality rates worldwide. Here we report the final results of HUNCHEST-II, the largest population-based LDCT screening program in Hungary, including the screening and diagnostic outcomes, and the characteristics of the LC cases. METHODS: A total of 4215 high-risk individuals aged between 50 and 75 years with a smoking history of at least 25 pack-years were assigned to undergo LDCT screening. Screening outcomes were determined based on the volume, growth, and volume doubling time of pulmonary nodules or masses. The clinical stage distribution of screen-detected cancers was compared with two independent practice-based databases consisting of unscreened LC patients. RESULTS: The percentage of negative and indeterminate tests at baseline were 74.2% and 21.7%, respectively, whereas the prevalence of positive LDCT results was 4.1%. Overall, 76 LC patients were diagnosed throughout the screening rounds (1.8% of total participants), out of which 62 (1.5%) patients were already identified in the first screening round. The overall positive predictive value of a positive test was 58%. Most screen-detected malignancies were stage I LCs (60.7%), and only 16.4% of all cases could be classified as stage IV disease. The percentage of early-stage malignancies was significantly higher among HUNCHEST-II screen-detected individuals than among the LC patients in the National Koranyi Institute of Pulmonology's archive or the Hungarian Cancer Registry (p < 0.001). CONCLUSIONS: HUNCHEST-II demonstrates that LDCT screening for LC facilitates early diagnosis, thus arguing in favor of introducing systematic LC screening in Hungary. CLINICAL RELEVANCE STATEMENT: HUNCHEST-II is the so-far largest population-based low-dose CT screening program in Hungary. A positive test's overall positive predictive value was 58%, and most screen-detected malignancies were early-stage lesions. These results pave the way for expansive systematic screening in the region. KEY POINTS: • Conducted in 18 medical facilities, HUNCHEST-II is the so far largest population-based low-dose CT screening program in Hungary. • The vast majority of screen-detected malignancies were early-stage lung cancers, and the overall positive predictive value of a positive test was 58%. • HUNCHEST-II facilitates early diagnosis, thus arguing in favor of introducing systematic lung cancer screening in Hungary.

Proactive Recruitment Strategy for Patient Identification for Lung Cancer Screening.

PURPOSE: We assessed low-dose computed tomography (LDCT) screening for lung cancer using a proactive patient education/recruitment program. METHODS: We identified patients aged 55-80 years from a family medicine group. In the retrospective phase (March-August, 2019), patients were categorized as current/former/never smokers, and screening eligibility was determined. Patients who underwent LDCT in the past year, along with outcomes, were documented. In the prospective phase (2020), patients in the same cohort who did not undergo LDCT were proactively contacted by a nurse navigator to discuss eligibility and prescreening. Eligible and willing patients were referred to their primary care physician. RESULTS: In the retrospective phase, of 451 current/former smokers, 184 (40.8%) were eligible for LDCT, 104 (23.1%) were ineligible, and 163 (36.1%) had an incomplete smoking history. Of those eligible, 34 (18.5%) had LDCT ordered. In the prospective phase, 189 (41.9%) were eligible for LDCT (150 [79.4%] of whom had no prior LDCT or diagnostic CT), 106 (23.5%) were ineligible, and 156 (34.6%) had an incomplete smoking history. The nurse navigator identified an additional 56/451 (12.4%) patients as eligible after contacting patients with incomplete smoking history. In total, 206 patients (45.7%) were eligible, an increase of 37.3% compared with the retrospective phase (150). Of these, 122 (59.2%) verbally agreed to screening, 94 (45.6%) met with their physician, and 42 (20.4%) were prescribed LDCT. CONCLUSIONS: A proactive education/recruitment model increased eligible patients for LDCT by 37.3%. Proactive identification/education of patients desiring to pursue LDCT was 59.2%. It is essential to identify strategies that will increase and deliver LDCT screening among eligible and willing patients.

Plain language summary and patient perspective of the 2020 lung cancer screening recommendations by the US Preventive Services Task Force.

WHAT IS THIS SUMMARY ABOUT?: This summary describes the research carried out by the United States Preventive Services Task Force (USPSTF for short) during a review and update of their lung cancer screening recommendations made in 2013. The USPSTF reviewed the results of clinical studies that used a type of scan called low dose computed tomography (LDCT for short). They wanted to see how successful LDCT was at finding lung cancers in people ho hadn't shown any physical signs of lung cancer, but had a history of smoking and were over 50 years of age. WHAT WERE THE RESULTS?: The review found that performing yearly LDCT scans in people who are at high risk of developing lung cancer is beneficial, as it means that some patients will be diagnosed earlier than they would be without this type of screening. People considered to be at high risk of developing lung cancer include: Adults aged 50 to 80 years who have smoked a pack of 20 cigarettes per day for 20 years or two packs per day for 10 years; OR Adults aged 50 to 80 years who currently smoke or have stopped smoking within the last 15 years. WHAT DO THE RESULTS OF THE STUDY MEAN?: The information gained from reviewing the research enabled the USPSTF to update their lung cancer screening recommendations.

Exploration of the fetal skeleton by ultra-low-dose computed tomography: guidelines from the Fetal Imaging Task Force of the European Society of Paediatric Radiology.

Skeletal anomalies are rare, requiring a systematic ultrasound (US) examination of each skeletal part when there is suspicion of a skeletal dysplasia. Although US examination can provide good evaluation of the fetal bones and cartilage, ultra-low-dose three-dimensional (3-D) multi-detector computed tomography (CT) is a useful complementary tool that can significantly improve prenatal diagnostic accuracy in select cases. Given that ultra-low-dose fetal CT remains an irradiating technique, indications should result from a multidisciplinary consensus, acquisition protocols should be optimized and the reporting standardized. In this paper we discuss guidelines from the Fetal Imaging Task Force of the European Society of Paediatric Radiology for indications, protocols and reporting of ultra-low-dose fetal CT.

Diagnostic clues for the identification of pediatric foreign body aspirations and consideration of novel imaging techniques.

OBJECTIVE: To better understand the diagnosis of foreign body aspiration by elucidating key components of its clinical presentation. METHODS: This is a retrospective cohort study of pediatric patients with suspected foreign body aspiration. We collected information regarding demographics, history, symptoms, physical exam, imaging, and operative findings for rigid bronchoscopies. An evaluation of these findings for an association with foreign body aspiration and the overall diagnostic algorithm was performed. RESULTS: 518 pediatric patients presented with 75.2 % presenting within one day of the inciting event. Identified history findings included wheeze (OR: 5.83, p < 0.0001) and multiple encounters (OR: 5.46, p < 0.0001). Oxygen saturation was lower in patients with foreign body aspiration (97.3 %, p < 0.001). Identified physical exam findings included wheeze (OR: 7.38, p < 0.001) and asymmetric breath sounds (OR: 5.48, p < 0.0001). The sensitivity and specificity of history findings was 86.7 % and 23.1 %, physical exam was 60.8 % and 88.4 %, and chest radiographs was 45.3 % and 88.0 %. 25 CT scans were performed with a sensitivity and specificity of 100 % and 85.7 %. Combining two components of the diagnostic algorithm yielded a high sensitivity and moderate specificity; the best combination was the history and physical exam. 186 rigid bronchoscopies were performed with 65.6 % positive for foreign body aspiration. CONCLUSION: Accurate diagnosis of foreign body aspiration requires careful history taking and examination. Low-dose CT should be included in the diagnostic algorithm. The combination of any two components of the diagnostic algorithm is the most accurate for foreign body aspiration.

Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma.

PURPOSE: Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS: A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS: No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS: The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.

Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction.

Low-dose computed tomography (LDCT) has been widely used for various clinic applications to reduce the X-ray dose absorbed by patients. However, LDCT is usually degraded by severe noise over the image space. The image quality of LDCT has attracted aroused attentions of scholars. In this study, we propose the bilateral weighted relative total variation (BRTV) used for image restoration to simultaneously maintain edges and further reduce noise, then propose the BRTV-regularized projections onto convex sets (POCS-BRTV) model for LDCT reconstruction. Referring to the spacial closeness and the similarity of gray value between two pixels in a local rectangle, POCS-BRTV can adaptively extract sharp edges and minor details during the iterative reconstruction process. Evaluation indexes and visual effects are used to measure the performances among different algorithms. Experimental results indicate that the proposed POCS-BRTV model can achieve superior image quality than the compared algorithms in terms of the structure and texture preservation.

Projection domain processing for low-dose CT reconstruction based on subspace identification.

PURPOSE: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT. METHODS: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom. RESULTS: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04. CONCLUSION: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

Improved deep convolutional dictionary learning with no noise parameter for low-dose CT image processing.

BACKGROUND: Low-Dose computed tomography (LDCT) reduces radiation damage to patients, however, the reconstructed images contain severe noise, which affects doctors' diagnosis of the disease. The convolutional dictionary learning has the advantage of the shift-invariant property. The deep convolutional dictionary learning algorithm (DCDicL) combines deep learning and convolutional dictionary learning, which has great suppression effects on Gaussian noise. However, applying DCDicL to LDCT images cannot get satisfactory results. OBJECTIVE: To address this challenge, this study proposes and tests an improved deep convolutional dictionary learning algorithm for LDCT image processing and denoising. METHODS: First, we use a modified DCDicL algorithm to improve the input network and make it do not need to input noise intensity parameter. Second, we use DenseNet121 to replace the shallow convolutional network to learn the prior on the convolutional dictionary, which can obtain more accurate convolutional dictionary. Last, in the loss function, we add MSSIM to enhance the detail retention ability of the model. RESULTS: The experimental results on the Mayo dataset show that the proposed model obtained an average value of 35.2975 dB in PSNR, which is 0.2954 -1.0573 dB higher than the mainstream LDCT algorithm, indicating the excellent denoising performance. CONCLUSION: The study demonstrates that the proposed new algorithm can effectively improve the quality of LDCT images acquired in the clinical practice.

A novel protocol for abdominal low-dose CT scans adapted with a model-based iterative reconstruction method.

PURPOSE: This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. METHODS: Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. RESULTS: The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. CONCLUSIONS: With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.

Lung cancer screening use and implications of varying eligibility criteria by race and ethnicity: 2019 Behavioral Risk Factor Surveillance System data.

BACKGROUND: In 2021, the US Preventive Services Task Force (USPSTF) expanded the eligibility criteria for low-dose computed tomographic lung cancer screening (LCS) to reduce racial disparities that resulted from the 2013 USPSTF criteria. The annual LCS rate has risen slowly since the 2013 USPSTF screening recommendations. Using the 2019 Behavioral Risk Factor Surveillance System (BRFSS), this study 1) describes LCS use in 2019, 2) compares the percent eligible for LCS using the 2013 versus 2021 USPSTF criteria, and 3) determines the percent eligible using the more detailed PLCOm2012Race3L risk-prediction model. METHODS: The analysis included 41,544 individuals with a smoking history from states participating in the BRFSS LCS module who were ≥50 years old. RESULTS: Using the 2013 USPSTF criteria, 20.7% (95% confidence interval [CI], 19.0-22.4) of eligible individuals underwent LCS in 2019. The 2013 USPSTF criteria was compared to the 2021 USPSTF criteria, and the overall proportion eligible increased from 21.0% (95% CI, 20.2-21.8) to 34.7% (95 CI, 33.8-35.6). Applying the 2021 criteria, the proportion eligible by race was 35.8% (95% CI, 34.8-36.7) among Whites, 28.5% (95% CI, 25.2-31.9) among Blacks, and 18.0% (95% CI, 12.4-23.7) among Hispanics. Using the 1.0% 6-year threshold that is comparable to the 2021 USPSTF criteria, the PLCOm2012Race3L model selected more individuals overall and by race. CONCLUSIONS: Using data from 20 states and using multiple imputation, higher LCS rates have been reported compared to prior BRFSS data. The 2021 expanded criteria will result in a greater number of screen-eligible individuals. However, risk-based screening that uses additional risk factors may be more inclusive overall and across subgroups. LAY SUMMARY: In 2013, lung cancer screening (lung screening) was recommended for high risk individuals. The annual rate of lung screening has risen slowly, particularly among Black individuals. In part, this racial disparity resulted in expanded 2021 criteria. Survey data was used to: 1) describe the number of people screened in 2019, 2) compare the percent eligible for lung screening using the 2013 versus 2021 guidelines, and 3) determine the percent eligible using more detailed criteria. Lung screening rates increased in 2019, and the 2021 criteria will result in more individuals eligible for screening. Using additional criteria may identify more individuals eligible for lung screening.