Low-dose CT Scans Research Articles

Recent advances in lung cancer research: unravelling the future of treatment.

Lung cancer, a multifaceted disease, demands tailored therapeutic approaches due to its diverse subtypes and stages. This comprehensive review explores the intricate landscape of lung cancer research, delving into recent breakthroughs and their implications for diagnosis, therapy, and prevention. Genomic profiling and biomarker identification have ushered in the era of personalised medicine, enabling targeted therapies that minimise harm to healthy tissues while effectively combating cancer cells. The relationship between pulmonary tuberculosis and lung cancer is examined, shedding light on potential mechanisms linking these two conditions. Early detection methods, notably low-dose computed tomography scans, have significantly improved patient outcomes, emphasising the importance of timely interventions. There has been a growing interest in segmentectomy as a surgical intervention for early-stage lung cancer in recent years. Immunotherapy has emerged as a transformative approach, harnessing the body's immune system to recognise and eliminate cancer cells. Combining immunotherapy with traditional treatments, such as chemotherapy and targeted therapies, has shown enhanced efficacy, addressing the disease's heterogeneity and overcoming drug resistance. Precision medicine, guided by genomic profiling, has enabled the development of targeted therapies like tyrosine kinase inhibitors, offering personalised treatments tailored to individual patients. Challenges such as drug resistance and limited accessibility to advanced therapies persist, emphasising the need for collaborative efforts and innovative technologies like artificial intelligence. Despite challenges, ongoing interdisciplinary collaborations and technological advancements offer hope for a future where lung cancer is treatable and preventable, reducing the burden on patients and healthcare systems worldwide.

The prognostic value of visual and automatic coronary calcium -scoring from low dose CT-[15O]-water PET.

Firstly, to validate automatically and visually scored coronary artery calcium (CAC) on low dose CT (LDCT) scans with a dedicated calcium scoring CT (CSCT) scan. Secondly, to assess the added value of CAC scored from LDCT scans acquired during [15O]-water-PET myocardial perfusion imaging (MPI) on prediction of major adverse cardiac events (MACE). 572 consecutive patients with suspected coronary artery disease, who underwent [15O]-water-PET MPI with LDCT and a dedicated CSCT scan were included. In the reference CSCT scans, manual CAC scoring was performed, while LDCT scans were scored visually and automatically using deep learning approach. Subsequently, based on CAC score results from CSCT and LDCT scans, each patient's scan was assigned to one out of five cardiovascular risk groups (0; 1-100; 101-400; 401-1000; >1000) and the agreement in risk group classification between CSCT and LDCT scans was investigated. MACE was defined as a composite of all-cause death, nonfatal myocardial infarction, coronary revascularization, and unstable angina. The agreement in risk group classification between reference CSCT manual scoring and visual/automatic LDCT scoring from LDCT was 0.66 (95% CI: 0.62-0.70) and 0.58 (95% CI: 0.53-0.62), respectively. Based on visual and automatic CAC scoring from LDCT scans, patients with CAC>100 and CAC>400, respectively, were at increased risk of MACE, independently of ischemic information from the [15O]-water-PET scan. There is a moderate agreement in risk classification between visual and automatic CAC scoring from LDCT and reference CSCT scans. Visual and automatic CAC scoring from LDCT scans improve identification of patients at higher risk of MACE.

Abstract 7489: Peripheral blood liquid biopsy for the screening of lung cancer

Abstract Lung cancer is the second most common type of cancer in both men and women and the leading cause of cancer-related mortality. Unfortunately, most patients have advanced stages (stage III or IV) at the time of diagnosis and the only U.S. Preventive Services Task Force-recommended screening test, Low Dose CT scan (LDCT), has a high false-positive rate. Therefore, there is an urgent need for the early detection of lung cancer with better discrimination between individuals with lung cancer and those without. In this study, we demonstrate the capability of a non-enrichment-based liquid biopsy workflow, the High Definition Single Cell Assay (HDSCA) to be used as a complementary tool to the current screening methodology. The HDSCA workflow can detect circulating cells of epithelial, mesenchymal, endothelial, and hematological origin, as well as acellular events such as oncosomes. This allows the investigation of the broad spectrum of circulating rare events for the potential identification of biomarkers associated with lung cancer. We investigated 52 peripheral blood samples collected from the second annual screening round of Manchester’s 'Lung Health Check' pilot of a community-based lung cancer screening cohort. Of these, 7 were subsequently diagnosed with non-small cell lung cancer, 2 with small cell lung cancer, and 43 were deemed at high risk of developing lung cancer. We additionally investigated 60 peripheral blood samples from individuals with no known pathology to use as controls. Within the screening cohort, a number of single rare analytes were found to have statistically significant differences between the lung cancer, high-risk cohort, and normal blood donor samples, which can potentially be biomarkers used to differentiate the cohorts. We also built a multi-analyte patient-level classification model utilizing the rare cellular and acellular events that stratified lung cancer patients from the normal donor individuals and high-risk cohort with high accuracy. Our results demonstrate the potential of liquid biopsy as a complementary screening approach for lung cancer. Citation Format: Jiyoun Seo, Stephanie N. Shishido, Jeremy Mason, Carmen Ruiz Velasco, Andrew Chen, Dominic Rothwell, Phil Crosbie, James Hicks, Caroline Dive, Peter Kuhn. Peripheral blood liquid biopsy for the screening of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7489.

Abstract 3448: A novel liquid biopsy lung cancer detection method using a coverage and fragment end motif machine learning analysis

Abstract Lung cancer remains the leading cause of cancer death in the United States. One reason for this high mortality is that >40% of cases are diagnosed at a late stage, where the 5-year survival rate is low. Liquid biopsy molecular testing has transformed lung cancer care through the identification of driver mutations when tissue is inaccessible. It also has promise as a tool in early detection and diagnosis, thereby reducing mortality. The existing paradigm for lung cancer screening and diagnosis includes low dose CT (LDCT) scans and tissue biopsy. LDCT scans yield a visualization of lung abnormalities. However, many of these abnormalities are difficult to characterize and next steps are often unclear to physicians. One option is to continue to monitor the patient, which may risk a delay in cancer treatment. Up to 15% of all individuals who are put on a monitoring regime will ultimately be diagnosed with lung cancer. A second option is to biopsy the nodule, which risks unnecessary invasive procedures and expenses to patients. 42-62% of indeterminate nodules detected by LDCT are found to be benign after biopsy. Furthermore, 15% of transthoracic needle biopsies result in complications. Genece Health aims to decrease uncertainty in cancer diagnosis and improve patient outcomes through a minimally invasive liquid biopsy assay that detects the presence of lung cancer. The technology utilizes a proprietary algorithm that leverages machine learning models to detect lung cancer signals in low-pass whole genome sequencing (LP-WGS) of patient cfDNA isolated from plasma. To establish assay performance, 50 clinical lung cancer samples from a commercial biobank, and 100 presumed normal samples were collected in Streck cfDNA BCT Devices. Following cfDNA extraction from plasma, LP-WGS was performed. Sequencing data was then processed to generate fragment end-motif and size (FEMS) and fragment coverage data that was input into Genece Health’s proprietary machine learning classifier to generate a final cancer prediction score. In addition, a limit of detection was determined using three late-stage lung cancer samples, with tumor fractions estimated using whole exome sequencing and ichorCNA scores. Our results show >80% specificity, >90% sensitivity, and a limit of detection of <1% tumor fraction. Through this testing, we demonstrate the assay’s ability to detect lung cancer across various lung cancer stages, including early stage disease, and histologies. Genece has developed a highly sensitive and specific lung cancer detection liquid biopsy assay. We present this assay as a cost-effective approach to discriminate between benign and malignant nodules identified by LDCT. In addition, this assay has the potential to be applied across the cancer care continuum. For example, future work will involve characterizing the assay’s ability to be applied in early cancer screening. Citation Format: Bryan Leatham, Michael Salmans, Molly Smith, Mengchi Wang, Andrew Carson, Kristin Fathe, Byung In Lee. A novel liquid biopsy lung cancer detection method using a coverage and fragment end motif machine learning analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3448.

Abstract 1071: Circulation of rare events in the liquid biopsy for early detection of lung mass lesions

Abstract Lung cancer screening with low-dose CT scans (LDCT) has reduced mortality for patients with high-risk smoking histories. However, this method of screening remains imperfect. High rates of false positive scans expose patients to additional morbidity as they pursue subsequent diagnostic evaluation. Real-world application of screening LDCT remains low. Finally, current screening guidelines exclude those without smoking histories, a population which comprises up to a quarter of lung cancer patients. We sought to explore the utility of liquid biopsy (LBx) in lung cancer patients and its use in early cancer screening and diagnosis. Using the third generation High-Definition Single Cell Assay (HDSCA3.0) workflow, we analyzed 99 peripheral blood samples divided into three cohorts: Normal Donors (ND, n=50) who were healthy volunteers with no known pathology, Screening CT patients (CT, n=25) who underwent standard LDCT screening with resultant Lung-RADS 1-2, Biopsy patients (BX, n=24) who had abnormal CT scans requiring tissue biopsy. The ND group was used to characterize baseline epithelial, mesenchymal, endothelial, and immune cells, alongside acellular events (oncosomes). For the CT and BX patients, demographic information including age, gender, and average BMI was roughly equivalent, however average pack-years smoked differed owing to 10 (42%) patients in the BX group who had no prior smoking history. A total of 14 (58%) BX patients were diagnosed with primary lung cancer (BX+), of which 11(75%) were adenocarcinoma, 2 (19%) were squamous cell carcinoma and 1 (6%) was small cell. Across the cohorts we observed intra- and interpatient heterogeneity of rare events including multiple phenotypic rare cell populations and oncosomes. The comparison of the rare event enumerations between the ND, CT, BX+, and BX- cohorts revealed a greater incidence of total events, total rare cells and oncosomes, as well as specific cellular phenotypes in the CT and BX cohorts compared to the ND cohort. LBx analytes were detected at a significant elevation in the BX samples compared to the CT samples, but there was no significant difference between BX+ and BX- samples. The data supports the utility of the LBx in distinguishing patients with an alveolar mass lesion from those without, providing a mechanism for screening prior to LDCT to better inform clinical workup and avoid unnecessary radiation exposure and biopsy. Citation Format: Anya Shah, Karen Resnick, Jeremy Mason, Peter Kuhn, Jorge Nieva, Stephanie Shishido. Circulation of rare events in the liquid biopsy for early detection of lung mass lesions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1071.

Abstract 1068: Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC)

Abstract Background: Lung cancer is the leading cause of cancer death. More than half of the patients are diagnosed in the advanced stage for which the 5-year survival is below 10%. Lung cancer screening using low-dose CT scan reduced lung cancer mortality among heavy smokers. However, no standard screening method exists for non-smokers. Up to 35% and 90% of male and female lung cancer patients in Southeast Asia, respectively, do not smoke. Half of NSCLC patients in this region harbor EGFR mutations, which is common in women and non-smokers. Thus, we sought to explore novel biomarkers that help to identify lung cancer patients and those who have EGFR mutation. Metabolic reprogramming is one of the hallmarks of cancer. It leads to changes in metabolite levels in the blood. Hence, we conducted plasma metabolomic analysis to identify potential metabolite biomarkers in lung cancer patients. Methods: Plasma samples were collected from 69 lung cancer patients at Siriraj hospital and 10 healthy individuals during 2018-2022. Plasma metabolites from 29 treatment-naïve EGFR-mutated NSCLC patients, 30 treatment-naïve EGFR-wild-type NSCLC patients, 10 treatment-resistant EGFR T790M-mutated NSCLC patients, and 10 healthy individuals were profiled using GC-MS and untargeted LC-MS with Metlin database matching. EGFR mutation was detected from plasma ctDNA using real-time PCR. Negative plasma EGFR mutation results were confirmed with corresponding tumor tissues. Metabolomics data were processed by the metabox R package. Results: Among 114 candidate metabolites that passed the variable importance in projection (VIP) cutoff of 1.5, the top ten metabolites that differentiate NSCLC patients with EGFR mutation from those without were shown in Table 1. Many of them can also separate between NSCLC patients and healthy individuals. Table 1. ROC curve analysis Discriminant metabolites (Mutated EGFR vs Wild-type EGFR NSCLC) AUC Accuracy Sensitivity Specificity PG(a-13:0/i-12:0) 0.8000 0.7833 0.8333 0.7333 Nervonic ceramide 0.7736 0.6772 0.7259 0.6333 Valine 0.7678 0.7298 0.7259 0.7333 L-Histidinol 0.7644 0.7281 0.7222 0.7333 Leucine 0.7563 0.7632 0.7259 0.8000 Tryptophan 0.7425 0.6289 0.5593 0.7000 8-methylcaffeine 0.6793 0.6096 0.6222 0.6000 Formic acid 0.6759 0.6447 0.5889 0.7000 PC(20:0/24:0) 0.6506 0.6614 0.8259 0.5000 Sucrose 0.6471 0.6596 0.7593 0.5667 Discriminant metabolites (NSCLC patients vs Healthy individuals) AUC Accuracy Sensitivity Specificity Tryptophan 0.9986 0.9867 1.0000 0.9848 PC(20:0/24:0) 0.8609 0.9505 0.6111 1.0000 L-Histidinol 0.8572 0.9625 0.7778 0.9861 Formic acid 0.6696 0.9262 0.6667 0.9704 Conclusions: Plasma L-Histidinol, tryptophan, PC(20:0/24:0), and formic acid are potential metabolite biomarkers for NSCLC patients harboring EGFR mutation. Citation Format: Lucksamon Thamlikitkul, Siriphan Manocheewa, Suphitcha Limjiasahapong, Kwanjeera Wanichthanarak, Atikorn Panya, Naravat Poungvarin, Yotsawat Pomyen, Sakda Khoomrung. Plasma metabolite biomarkers for EGFR-mutated non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1068.

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.

Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. In total, 107 radiomics features were extracted from each subject's CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64-0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55-0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.

Impact of Structured Reporting For Lung Cancer Screening Low-Dose CT Scan Incidental Findings on Physician Management

Impact of Structured Reporting For Lung Cancer Screening Low-Dose CT Scan Incidental Findings on Physician Management

Can Cardiac Indices in Computed Tomography (CT) Scan Predict COVID-19 Clinical Outcome?

Background: Assessing the measurable chest computed tomography (CT) scan cardiac parameters can help us evaluate cardiovascular involvement and risk stratification in patients with coronavirus disease 2019 (COVID-19). Objectives: This study investigated the measurable chest cardiac CT indices and their association with predicting outcomes in patients with COVID-19. Patients and Methods: Patients with confirmed COVID-19 were included in the study from March up to June 2020 and underwent chest CT scans on admission. The following parameters were analyzed: The extent and pattern of lung involvement, cardiothoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), pericardial effusion, pleural effusion, and inferior vena cava (IVC) diameter through low-dose CT scan. The association between cardiac indices and patients and the extent of lung involvement outcome was evaluated by logistic regression, Cox regression, and linear regression, respectively. Results: A total of 176 COVID-19 patients (51.1% men and 48.9% women) were enrolled in the study. Of them, 55 patients (31.25%) died, and 121 patients were discharged (68.75%). The mean age of participants was 64.9 ± 15.09 years. CTR > 0.49 was more frequent among deceased patients (50.9% vs. 31.4%, P = 0.018) and was associated with non-significant increases in odds ratio (OR) and hazard ratio (HR) (OR = 1.75, P = 0.11; HR = 1.43, P = 0.19). Moreover, PA/A > 1 was more common in deaths (52.7% vs. 32.2%; P = 0.012) and significantly increased OR but not HR (OR = 1.99; P = 0.04; HR = 1.36; P = 0.26). The moderate and severe pleural effusion was noticeably associated with prolonged hospitalization (HR = 4.09, P = 0.01; HR = 3.37, P = 0.02, respectively). The IVC diameter and pericardial effusion were not significantly correlated with outcomes. Conclusion: Cardiac indices in chest CT of COVID-19 patients can be accounted for the prediction of patient outcomes in the clinic, such as PA/A > 1 ratio, which increases the likelihood of in-hospital deaths.

The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers.

Differences in body composition in patients with COPD may have important prognostic value and may provide opportunities for patient-specific management. We investigated the relation of thoracic fat and muscle with computed tomography (CT)-measured emphysema and bronchial wall thickening. Low-dose baseline chest CT scans from 1031 male lung cancer screening participants from one site were quantified for emphysema, bronchial wall thickening, subcutaneous fat, visceral fat and skeletal muscle. Body composition measurements were performed by segmenting the first slice above the aortic arch using Hounsfield unit thresholds with region growing and manual corrections. COPD presence and severity were evaluated with pre-bronchodilator spirometry testing. Participants had a median age of 61.5 years (58.6-65.6, 25th-75th percentile) and median number of 38.0 pack-years (28.0-49.5); 549 (53.2%) were current smokers. Overall, 396 (38.4%) had COPD (256 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1, 140 GOLD 2-3). Participants with COPD had less subcutaneous fat, visceral fat and skeletal muscle (p<0.001 for all). With increasing GOLD stages, subcutaneous (p=0.005) and visceral fat values (p=0.004) were higher, and skeletal muscle was lower (p=0.004). With increasing severity of CT-derived emphysema, subcutaneous fat, visceral fat and skeletal muscle values were lower (p<0.001 for all). With increasing CT-derived bronchial wall thickness, subcutaneous and visceral fat values were higher (p<0.001 for both), without difference in skeletal muscle. All statistical relationships remained when adjusted for age, pack-years and smoking status. COPD presence and emphysema severity are associated with smaller amounts of thoracic fat and muscle, whereas bronchial wall thickening is associated with fat accumulation.

High Lung Cancer Cure Rate From Annual Low-Dose CT Scan

High Lung Cancer Cure Rate From Annual Low-Dose CT Scan

Comparison between model-based RSA and an AI-based CT-RSA: an accuracy study of 30 patients.

Radiostereometry (RSA) is the current gold standard for evaluating early implant migration. CT-based migration analysis is a promising method, with fewer handling requirements compared with RSA and no need for implanted bone-markers. We aimed to evaluate agreement between a new artificial intelligence (AI)-based CT-RSA and model-based RSA (MBRSA) in measuring migration of cup and stem in total hip arthroplasty (THA). 30 patients with THA for primary osteoarthritis (OA) were included. RSA examinations were performed on the first postoperative day, and at 2 weeks, 3 months, 1, 2, and 5 years after surgery. A low-dose CT scan was done at 2 weeks and 5 years. The agreement between the migration results obtained from MBRSA and AI-based CT-RSA was assessed using Bland-Altman plots. Stem migration (y-translation) between 2 weeks and 5 years, for the primary outcome measure, was -0.18 (95% confidence interval [CI] -0.31 to -0.05) mm with MBRSA and -0.36 (CI -0.53 to -0.19) mm with AI-based CT-RSA. Corresponding proximal migration of the cup (y-translation) was 0.06 (CI 0.02-0.09) mm and 0.02 (CI -0.01 to 0.05) mm, respectively. The mean difference for all stem and cup comparisons was within the range of MBRSA precision. The AI-based CT-RSA showed no intra- or interobserver variability. We found good agreement between the AI-based CT-RSA and MBRSA in measuring postoperative implant migration. AI-based CT-RSA ensures user independence and delivers consistent results.

A new method to screen high-risk COPD populations: machine learning-based cascade classification models based on low-dose CT scan

A new method to screen high-risk COPD populations: machine learning-based cascade classification models based on low-dose CT scan

Assessment of the sacroiliac joint with computed tomography motion analysis: a diagnostic study of 12 patients.

Pain in the sacroiliac joint may be caused by abnormal joint motion. Diagnosis is mainly based on clinical tests. The aims of this study were to examine whether low-dose computed tomography with provocation of the hip could detect sacroiliac joint motion, and to study whether provocation of the hip results in greater sacroiliac joint motion in the ipsilateral than in the contralateral sacroiliac joint. 12 patients with sacroiliac joint pain were examined with low-dose computed tomography scans of the sacroiliac joint, one with the hips in the neutral position, and one each in provocation with the left or the right hip in a figure-of-4 position. Accuracy was tested by comparing internal rotation of the sacrum with internal rotation in the sacroiliac joint. Motion in the sacroiliac joint was assessed by comparing the position of each of the ilia with the reference, the sacrum. Data is shown as mean with 95% confidence interval (CI). We observed greater motion in the sacroiliac joint than internally in the sacrum, i.e., 0.57° (CI 0.43-0.71) vs. 0.20° (CI 0.11-0.28). The motion of the geometric center of the moving object for the sacroiliac joint was larger on the provoked side; mean difference 0.17 mm (CI 0.01-0.33), P = 0.04. Corresponding figures for rotation were mean difference 0.19° (CI 0.10-0.28), P < 0.001. Compared with the sacrum, the largest motion was seen at the anterior superior iliac spine; mean difference 0.38 mm (CI 0.10-0.66), P = 0.001. Provocation in the figure-of-4 position of the hip results in sacroiliac joint motion measurable with computed tomography motion analysis. Provocation of the hip induces larger motion on the ipsilateral than on the contralateral sacroiliac joint.

Opportunistic lung cancer screening with low-dose computed tomography in National Cancer Center of China: The first 14 years' experience.

In China, over 50% of lung cancer cases occur in nonsmokers. Thus, identifying high-risk individuals for targeted lung cancer screening is crucial. Beyond age and smoking, determining other risk factors for lung cancer in the Asian population has become a focal point of research. Using 30,000 participants in the prospectively enrolled cohort at China's National Cancer Center (NCC) over the past 14 years, we categorized participants by risk, with an emphasis on nonsmoking females. Between November 2005 and December 2019, 31,431 individuals voluntarily underwent low-dose computed tomography (LDCT) scans for lung cancer screening at the NCC. We recorded details like smoking history, exposure to hazards, and family history of malignant tumors. Using the 2019 NCCN criteria, participants were categorized into high-, moderate-, and low-risk groups. Additionally, we separated non-high-risk groups into female never smokers (aged over 40) exposed to second-hand smoke (SHS) and others. Any positive results from initial scans were monitored per the I-ELCAP protocol (2006), and suspected malignancies were addressed through collaborative decisions between patients and physicians. We analyzed and compared the detection rates of positive results, confirmed lung cancers, and cancer stages across risk, age, and gender groups. Out of 31,431 participants (55.9% male, 44.1% female), 3695 (11.8%) showed positive baseline LDCT scans with 197 (0.6%; 106 females, 91 males) confirmed as lung cancer cases pathologically. Malignancy rate by age was 0.1% among those aged under 40 years, 0.4% among those aged 40-49 years, 0.8% among those aged 50-59 years, and 1.2% among those aged 60 years and older. From the 25,763 participants (56.9% male, 43.1% female) who completed questionnaires, 1877 (7.3%) were categorized as high risk, 6500 (25.2%) as moderate risk, and 17,386 (67.5%) as low risk. Of the 23,886 in the non-high-risk category, 8041 (33.7%) were females over 40 years old exposed to SHS. The high-risk group showed the highest lung cancer detection rate at 1.4%. However, females exposed to SHS had a notably higher detection rate than the rest of the non-high-risk group (1.1% vs. 0.5%; p < 0.0001). In this cohort, 84.8% of the detected lung cancers were at an early stage. In our study, using LDCT for lung cancer screening proved significant for high-risk individuals. For non-high-risk populations, LDCT screening could be considered for nonsmoking women with exposure to SHS.

Evaluation of Conventional Cardiovascular Risk Factors and Ordinal Coronary Artery Calcium Scoring in a Lung Cancer Screening Cohort.

(1) Background: Lung cancer screening (LCS) consists of low-dose computed tomography (LDCT) results to reduce lung cancer-related mortality. The LCS program has a unique opportunity to impact CVD mortality by providing tools for CVD risk assessment and implementing preventative strategies. In this study, we estimated standardized CVD risk (SCORE) and assessed the prevalence of coronary artery calcium (CAC) in a Polish LCS cohort. (2) Methods: In this observational study, 494 LCS participants aged 50-79 years with a cigarette smoking history of at least 30 pack-years were included. Medical history, anthropometric measurements, blood pressure measurements, serum glucose, and cholesterol levels were assessed in one visit. CVD risk assessment using SCORE tables was performed. The results were compared to the general population (NATPOL 2011 study). On LDCT scans, CAC was classified using an Ordinal Score ranging from 0 to 12. (3) Results: The prevalence of classic cardiovascular risk factors was very high. Among study participants, 83.7% of men and 40.7% of women were classified with a very high CVD SCORE risk (>10%). CAC was reported in 190 (47%) participants. Calcification was categorized as severe (CAC ≥ 4) in 84 (21%) participants. (4) Conclusions: Due to the high cardiovascular risk, intensive preventive strategies are recommended for LCS participants.

A two-stage deep-learning framework for CT denoising based on a clinically structure-unaligned paired data set.

In low-dose computed tomography (LDCT) lung cancer screening, soft tissue is hardly appreciable due to high noise levels. While deep learning-based LDCT denoising methods have shown promise, they typically rely on structurally aligned synthesized paired data, which lack consideration of the clinical reality that there are no aligned LDCT and normal-dose CT (NDCT) images available. This study introduces an LDCT denoising method using clinically structure-unaligned but paired data sets (LDCT and NDCT scans from the same patients) to improve lesion detection during LDCT lung cancer screening. A cohort of 64 patients undergoing both LDCT and NDCT was randomly divided into training (n=46) and testing (n=18) sets. A two-stage training approach was adopted. First, Gaussian noise was added to NDCT data to create simulated LDCT data for generator training. Then, the model was trained on a clinically structure-unaligned paired data set using a Wasserstein generative adversarial network (WGAN) framework with the initial generator weights obtained during the first stage of training. An attention mechanism was also incorporated into the network. Validated on a clinical CT data set, our proposed method outperformed other available methods [CycleGAN, Pixel2Pixel, block-matching and three-dimensional filtering (BM3D)] in noise removal and detail retention tasks in terms of the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) metrics. Compared with the results produced by BM3D, our method yielded an average improvement of approximately 7% in terms of the three evaluation indicators. The probability density profile of the denoised CT output produced using our method best fit the reference NDCT scan. Additionally, our two-stage model outperformed the one-stage WGAN-based model in both objective and subjective evaluations, further demonstrating the higher effectiveness of our two-stage training approach. The proposed method performed the best in removing noise from LDCT scans and exhibited good detail retention, which could potentially enhance the lesion detection and characterization effects obtained for soft tissues in the scanning scope of LDCT lung cancer screening.

Low dose CT scan of chest in screening for lung cancer in Iraq

Low dose CT scan of chest in screening for lung cancer in Iraq

Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models: A Systematic Literature Review

Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models: A Systematic Literature Review

Pulmonary circulatory system characteristics are associated with future lung cancer risk.

Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs. This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting. We analyzed the PLuSS cohort of 3635 lung screening patients from 2002 to 2016. Circulatory structures were segmented and quantified from LDCT scans. The time from the baseline CT scan to lung cancer diagnosis, accounting for death, was used to evaluate the prognostic ability (i.e., hazard ratio (HR)) of these structures independently and with demographic factors. Five-fold cross-validation was used to evaluate prognostic scores. Intrapulmonary vein volume had the strongest association with future lung cancer (HR=0.63, p<0.001). The joint model of intrapulmonary vein volume, age, smoking status, and clinical emphysema provided the strongest prognostic ability (HR=2.20, AUC=0.74). The addition of circulatory structures improved risk stratification, identifying the top 10% with 28% risk of lung cancer within 15 years. PCS characteristics, particularly intrapulmonary vein volume, are important predictors of lung cancer development. These factors significantly improve prognostication based on demographic factors and noncirculatory patient characteristics, particularly in the long term. Approximately 10% of the population can be identified with risk several times greater than average.