Low-dose CT Scans Research Articles

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

Lung cancer is the most common cause of cancer-related death worldwide and therefore there has been a growing demand for low-dose computed tomography (LDCT) protocols. To investigate and evaluate the dose and image quality of patients undergoing lung cancer screening (LCS) using LDCT in Norway. Retrospective dosimetry data, volumetric CT dose index (CTDIvol) and dose-length product (DLP), from 70 average-size and 70 large-size patients who underwent LDCT scan for LCS were included in the survey. Effective dose and size-specific dose were calculated for each examination and were compared with the American Association of Physicists in Medicine (AAPM) requirement. For a quantitative image quality analysis, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were determined for different regions in the chest with two iterative reconstruction techniques, iDose and Iterative Model Reconstruction. Differences in dose and image quality between average-size and large-size patients were evaluated by Independent sample t test, and Wilcoxon signed rank test within the same patient group. The independent sample t test revealed significant differences (p < .05) in dose values between average-size and large-size patients. Mean CTDIvol and DLP for average-size patients were 2.8mGy and 115mGy.cm, respectively, with appropriate increment for the large-size patients. Image quality (image noise, SNR, and CNR) did not significantly differ between patient groups when images were reconstructed with a model based iterative reconstruction algorithm. The screening protocol assessed in this study resulted in CTDIvol values that were compliant with AAPM recommendation. No significant differences in objective image quality were found between patient groups.

HAP-FAST: a feasibility study incorporating qualitative, mechanistic and costing sub-studies alongside a randomised pilot trial comparing chest x-ray to low-dose CT scan and empirical antibiotics to antibiotics guided by the BIOFIRE® FILM ARRAY® pneumonia plus panel in adults with suspected non-ventilator-associated hospital-acquired pneumonia

IntroductionNon-ventilator-associated hospital-acquired pneumonia (nv-HAP) is the most common healthcare-associated infection (HCAI), is associated with high mortality and morbidity and places a major burden on healthcare systems. Diagnosis currently relies on...

Health Economic Evaluation of Lung Cancer Screening Using a Diagnostic Blood Test: The Early Detection of Cancer of the Lung Scotland (ECLS).

Diagnostic blood tests have the potential to identify lung cancer in people at high risk. We assessed the cost-effectiveness of a lung cancer screening intervention, using the EarlyCDT®-Lung Test (ECLS) with subsequent X-ray and low-dose chest CT scans (LDCT) for patients with a positive test result, compared to both usual care and LDCT screening for the target population. We conducted a model-based lifetime analysis from a UK NHS and personal social services perspective. We estimated incremental net monetary benefit (NMB) for the ECLS intervention compared to no screening and to LDCT screening. The incremental NMB of ECLS intervention compared to no screening was GBP 33,179 (95% CI: -GBP 81,396, GBP 147,180) and GBP 140,609 (95% CI: -GBP 36,255, GBP 316,612), respectively, for a cost-effectiveness threshold of GBP 20,000 and GBP 30,000 per quality-adjusted life year. The same figures compared with LDCT screening were GBP 162,095 (95% CI: GBP 52,698, GBP 271,735) and GBP 52,185 (95% CI: -GBP 115,152, GBP 219,711). The ECLS intervention is the most cost-effective screening alternative, with the highest probability of being cost-effective, when compared to no screening or LDCT screening. This result may change with modifications of the parameters, suggesting that the three alternatives considered in the main analysis are potentially cost-effective.

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model.

Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.

Impact of annual trend volume of low-dose computed tomography for lung cancer screening on overdiagnosis, overmanagement, and gender disparities

BackgroundWith the increasing prevalence of nonsmoking-related lung cancer in Asia, Asian countries have increasingly adopted low-dose computed tomography (LDCT) for lung cancer screening, particularly in private screening programs. This study examined how annual LDCT volume affects lung cancer stage distribution, overdiagnosis, and gender disparities using a hospital-based lung cancer database.MethodsThis study analyzed the annual utilized LDCT volume, clinical characteristics of lung cancer, stage shift distribution, and potential overdiagnosis. At the individual level, this study also investigated the relationship between stage 0 lung cancer (potential strict definition regarding overdiagnosis) and the clinical characteristics of lung cancer.ResultsThis study reviewed the annual trend of 4971 confirmed lung cancer cases from 2008 to 2021 and conducted a link analysis with an LDCT imaging examination database over these years. As the volume of lung cancer screenings has increased over the years, the number and proportion of stage 0 lung cancers have increased proportionally.Our study revealed that the incidence of stage 0 lung cancer increased with increasing LDCT scan volume, particularly during the peak growth period from 2017 to 2020. Conversely, stage 4 lung cancer cases remained consistent across different time intervals. Furthermore, the increase in the lung cancer screening volume had a more pronounced effect on the increase in stage 0 lung cancer cases among females than it had among males. The estimated potential for overdiagnosis brought about by the screening process, compared to non-participating individuals, ranged from an odds ratio of 7.617 to one of 17.114. Both strict and lenient definitions of overdiagnosis (evaluating cases of stage 0 lung cancer and stages 0 to 1 lung cancer) were employed.ConclusionsThese results provide population-level evidence of potential lung cancer overdiagnosis in the Taiwanese population due to the growing use of LDCT screening, particularly concerning the strict definition of stage 0 lung cancer. The impact was greater in the female population than in the male population, especially among females younger than 40 years.To improve lung cancer screening in Asian populations, creating risk-based prediction models for smokers and nonsmokers, along with gender-specific strategies, is vital for ensuring survival benefits and minimizing overdiagnosis.

Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening

ObjectiveTo evaluate the consistency between doctors and artificial intelligence (AI) software in analysing and diagnosing pulmonary nodules, and assess whether the characteristics of pulmonary nodules derived from the two methods are consistent for the interpretation of carcinomatous nodules.Materials and MethodsThis retrospective study analysed participants aged 40–74 in the local area from 2011 to 2013. Pulmonary nodules were examined radiologically using a low-dose chest CT scan, evaluated by an expert panel of doctors in radiology, oncology, and thoracic departments, as well as a computer-aided diagnostic(CAD) system based on the three-dimensional(3D) convolutional neural network (CNN) with DenseNet architecture(InferRead CT Lung, IRCL). Consistency tests were employed to assess the uniformity of the radiological characteristics of the pulmonary nodules. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy. Logistic regression analysis is utilized to determine whether the two methods yield the same predictive factors for cancerous nodules.ResultsA total of 570 subjects were included in this retrospective study. The AI software demonstrated high consistency with the panel's evaluation in determining the position and diameter of the pulmonary nodules (kappa = 0.883, concordance correlation coefficient (CCC) = 0.809, p = 0.000). The comparison of the solid nodules' attenuation characteristics also showed acceptable consistency (kappa = 0.503). In patients diagnosed with lung cancer, the area under the curve (AUC) for the panel and AI were 0.873 (95%CI: 0.829–0.909) and 0.921 (95%CI: 0.884–0.949), respectively. However, there was no significant difference (p = 0.0950). The maximum diameter, solid nodules, subsolid nodules were the crucial factors for interpreting carcinomatous nodules in the analysis of expert panel and IRCL pulmonary nodule characteristics.ConclusionAI software can assist doctors in diagnosing nodules and is consistent with doctors' evaluations and diagnosis of pulmonary nodules.

Deep learning-based low-dose CT simulator for non-linear reconstruction methods.

Computer algorithms that simulate lower-doses computed tomography (CT) images from clinical-dose images are widely available. However, most operate in the projection domain and assume access to the reconstruction method. Access to commercial reconstruction methods may often not be available in medical research, making image-domain noise simulation methods useful. However, the introduction of non-linear reconstruction methods, such as iterative and deep learning-based reconstruction, makes noise insertion in the image domain intractable, as it is not possible to determine the noise textures analytically. To develop a deep learning-based image-domain method to generate low-dose CT images from clinical-dose CT (CDCT) images for non-linear reconstruction methods. We propose a fully image domain-based method, utilizing a series of three convolutional neural networks (CNNs), which, respectively, denoise CDCT images, predict the standard deviation map of the low-dose image, and generate the noise power spectra (NPS) of local patches throughout the low-dose image. All three models have U-net-based architectures and are partly or fully three-dimensional. As a use case for this study and with no loss of generality, we use paired low-dose and clinical-dose brain CT scans. A dataset of paired scans was retrospectively obtained. All images were acquired with a wide-area detector clinical system and reconstructed using its standard clinical iterative algorithm. Each pair was registered using rigid registration to correct for motion between acquisitions. The data was randomly partitioned into training ( samples), validation ( samples), and test ( samples) sets. The performance of each of these three CNNs was validated separately. For the denoising CNN, the local standard deviation decrease, and bias were determined. For the standard deviation map CNN, the real and estimated standard deviations were compared locally. Finally, for the NPS CNN, the NPS of the synthetic and real low-dose noise were compared inside and outside the skull. Two proof-of-concept denoising studies were performed to determine if the performance of a CNN- or a gradient-based denoising filter on the synthetic low-dose data versus real data differed. The denoising network had a median decrease in noise in the cerebrospinal fluid by a factor of and introduced a median bias of HU. The network for standard deviation map estimation had a median error of HU. The noise power spectrum estimation network was able to capture the anisotropic and shift-variant nature of the noise structure by showing good agreement between the synthetic and real low-dose noise and their corresponding power spectra. The two proof of concept denoising studies showed only minimal difference in standard deviation improvement ratio between the synthetic and real low-dose CT images with the median difference between the two being 0.0 and +0.05 for the CNN- and gradient-based filter, respectively. The proposed method demonstrated good performance in generating synthetic low-dose brain CT scans without access to the projection data or to the reconstruction method. This method can generate multiple low-dose image realizations from one clinical-dose image, so it is useful for validation, optimization, and repeatability studies of image-processing algorithms.

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

Intraindividual Comparison of Dose Reduction and Coronary Calcium Scoring Accuracy Using Kilovolt-independent and Tin Filtration CT Protocols.

Purpose To investigate the ability of kilovolt-independent (hereafter, kV-independent) and tin filter spectral shaping to accurately quantify the coronary artery calcium score (CACS) and radiation dose reductions compared with the standard 120-kV CT protocol. Materials and Methods This prospective, blinded reader study included 201 participants (mean age, 60 years ± 9.8 [SD]; 119 female, 82 male) who underwent standard 120-kV CT and additional kV-independent and tin filter research CT scans from October 2020 to July 2021. Scans were reconstructed using a Qr36f kernel for standard scans and an Sa36f kernel for research scans simulating artificial 120-kV images. CACS, risk categorization, and radiation doses were compared by analyzing data with analysis of variance, Kruskal-Wallis test, Mann-Whitney test, Bland-Altman analysis, Pearson correlations, and κ analysis for agreement. Results There was no evidence of differences in CACS across standard 120-kV, kV-independent, and tin filter scans, with median CACS values of 1 (IQR, 0-48), 0.6 (IQR, 0-58), and 0 (IQR, 0-51), respectively (P = .85). Compared with standard 120-kV scans, kV-independent and tin filter scans showed excellent correlation in CACS values (r = 0.993 and r = 0.999, respectively), with high agreement in CACS risk categorization (κ = 0.95 and κ = 0.93, respectively). Standard 120-kV scans had a mean radiation dose of 2.09 mSv ± 0.84, while kV-independent and tin filter scans reduced it to 1.21 mSv ± 0.85 and 0.26 mSv ± 0.11, cutting doses by 42% and 87%, respectively (P < .001). Conclusion The kV-independent and tin filter research CT acquisition techniques showed excellent agreement and high accuracy in CACS estimation compared with standard 120-kV scans, with large reductions in radiation dose. Keywords: CT, Cardiac, Coronary Arteries, Radiation Safety, Coronary Artery Calcium Score, Radiation Dose Reduction, Low-Dose CT Scan, Tin Filter, kV-Independent Supplemental material is available for this article. © RSNA, 2024.

Development of a serum miRNA panel for detection of early stage non-small cell lung cancer.

3058 Background: Lung cancer remains the leading cause of cancer deaths, largely due to late-stage diagnosis. Early detection significantly improves survival rates, but current methods like chest X-rays and low-dose CT (LDCT) scans face limitations in sensitivity and specificity. MicroRNAs (miRNAs) are small non-coding RNAs whose dysregulation has been widely observed in different stages of cancer. Evidence suggests that circulating miRNAs could be promising biomarkers for early diagnosis of lung cancer. In this study, we developed and validated a 15-miRNA panel as a blood-based minimally invasive test for early detection of non-small cell lung cancer (NSCLC) using the proprietary multiplex quantitative real-time PCR (qPCR). Methods: 332 serum samples (182 Stage I&amp;II NSCLC, 150 healthy controls) were divided into training (n=266) and validation (n=66) cohorts. Differentially expressed serum miRNAs were screened with Miseq sequencing followed by qPCR validation. The multi-miR panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic curves were constructed to evaluate the diagnostic accuracy of the panel. A stem-loop quadruplex qPCR assay was invented for concurrent detection of 4 miRs in a single reaction. Results: 15 differentially expressed miRNAs were selected for logistic model construction based on their Ct values (Ct&lt;35) and high diagnostic accuracy of stage I&amp;II NSCLC (AUC&gt;0.7). The 15-miRNA panel model achieved 88.4% sensitivity, 81.7% specificity (AUC = 0.912) in the training cohort (Table), and was validated with 94.4% sensitivity, 73.3% specificity (AUC = 0.892) in the validation cohort. Quantification of 15 miRs and 1 internal control was performed in 4 reactions tubes via quadruplex qPCR assay. Conclusions: The serum 15-miR panel developed and validated in this retrospective study exhibited robust performance for detection of early stage NSCLC. The panel demonstrates potential diagnostic applications and clinical utility to be implemented together with LDCT in current lung cancer screening program as a blood biomarker to reduce false positive results. In addition, the proposed stem-loop quadruplex qPCR system offers an efficient and promising approach for miRNA profiling in future clinical applications. [Table: see text]

DistilIQA: Distilling Vision Transformers for no-reference perceptual CT image quality assessment

No-reference image quality assessment (IQA) is a critical step in medical image analysis, with the objective of predicting perceptual image quality without the need for a pristine reference image. The application of no-reference IQA to CT scans is valuable in providing an automated and objective approach to assessing scan quality, optimizing radiation dose, and improving overall healthcare efficiency. In this paper, we introduce DistilIQA, a novel distilled Vision Transformer network designed for no-reference CT image quality assessment. DistilIQA integrates convolutional operations and multi-head self-attention mechanisms by incorporating a powerful convolutional stem at the beginning of the traditional ViT network. Additionally, we present a two-step distillation methodology aimed at improving network performance and efficiency. In the initial step, a ”teacher ensemble network” is constructed by training five vision Transformer networks using a five-fold division schema. In the second step, a ”student network”, comprising of a single Vision Transformer, is trained using the original labeled dataset and the predictions generated by the teacher network as new labels. DistilIQA is evaluated in the task of quality score prediction from low-dose chest CT scans obtained from the LDCT and Projection data of the Cancer Imaging Archive, along with low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our results demonstrate DistilIQA‘s remarkable performance in both benchmarks, surpassing the capabilities of various CNNs and Transformer architectures. Moreover, our comprehensive experimental analysis demonstrates the effectiveness of incorporating convolutional operations within the ViT architecture and highlights the advantages of our distillation methodology.

Screening for lung cancer using thin-slice low-dose computed tomography in southwestern China: a population-based real-world study.

Lung cancer is one of the most common malignant tumors threatening human life and health. At present, low-dose computed tomography (LDCT) screening for the high-risk population to achieve early diagnosis and treatment of lung cancer has become the first choice recommended by many authoritative international medical organizations. To further optimize the lung cancer screening method, we conducted a real-world study of LDCT lung cancer screening in a large sample of a healthy physical examination population, comparing differences in lung nodules and lung cancer detection between thin and thick-slice LDCT scanning. A total of 29 296 subjects who underwent low-dose thick-slice CT scanning (5 mm thickness) from January 2015 to December 2015 and 28 058 subjects who underwent low-dose thin-slice CT scanning (1 mm thickness) from January 2018 to December 2018 in West China Hospital were included. The positive detection rate, detection rate of lung cancer, pathological stage of lung cancer, and mortality rate of lung cancer were analyzed and compared between the two groups. The positive rate of LDCT screening in the thin-slice scanning group was significantly higher than that in the thick-slice scanning group (20.1% vs. 14.4%, p < 0.001). In addition, the lung cancer detection rate in the thin-slice LDCT screening positive group was significantly higher than that in the thick-slice scanning group (78.0% vs. 52.9%, p < 0.001). The screening positive rate of low-dose thin-slice CT scanning is higher and more early-stage lung cancer (IA1 stage) can be detected in the screen-positive group.

Low-dose computed tomography scan and biopsy for assessing lung cancer prevalence and related factors in high-risk populations

BackgroundLung cancer is the second most diagnosed cancer and the leading cause of cancer death in 2020, representing approximately one in 10 (11.4 %) cancers diagnosed and one in 5 (18.0 %) deaths. There are currently very few studies evaluating the prevalence and related factors of lung cancer detected using low-dose CT scans. ObjectiveEvaluate the prevalence and related factors of lung cancer using low-dose CT scans in high-risk populations in Vietnam. Materials and methodsA cross-sectional analysis study of 169 high-risk patients was conducted to assess the lung cancer prevalence and related factors. Enrolled patients received a physical examination, low-dose computerized tomography scan, and biopsy if abnormalities were found through the CT scan. Univariable and Multivariable analysis through Odd Ratio (OR) to assess the related risk of lung cancer. ResultsA total of 169 high-risk patients with a mean age of 62.93 ± 9.31 (years), and the majority were male (91.7 %). Of which 4 cases (2.37 %) were recorded with lung cancer, 3 cases of adenocarcinoma, and 1 case of squamous cell carcinoma. A history of smoking and Chronic Obstructive Pulmonary Disease (COPD) were associated with an increased risk of abnormalities on lung CT scans. Multivariate regression analysis revealed that smoking over 30 pack-years and COPD significantly increased the risk of abnormalities on lung CT scans, p < 0.05.100 % of lung cancer-detected cases were male and smoking ≥30 pack – years. ConclusionThe prevalence of lung cancer in the Vietnamese high-risk population was relatively high. Relative factors such as smoking ≥30 pack - years, and COPD had increased risk of CTscan abnormalities.

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules.

Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images. In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules. We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included. The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23mm and 12mm, respectively. We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.

Lung cancer screening: new frontiers

Lung cancer is the leading cause of cancer-related deaths worldwide. In early, asymptomatic stages, curative treatment is possible, but the disease is often diagnosed too late. Lung cancer screening (LCS) using low-dose computed tomography (LDCT) helps to detect potentially malignant lesions in early stages and to reduce lung cancer mortality. The application of artificial intelligence (AI) algorithms enables amore precise analysis of LDCT scans. Ameta-analysis of eight LCS studies revealed astatistically significant 12% relative reduction in lung cancer mortality. Based on strong scientific evidence, arecommendation for astructured lung cancer screening program using LDCT for the high-risk population in Germany was issued. The holistic LCS program requires aclear definition of the high-risk population, individual risk assessment, qualified personnel for conducting and reading examinations, verification of all diagnostic and therapeutic steps, central documentation and quality assurance, as well as the integration of tobacco cessation programs.

Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Abstract PO5-05-13: Clinical utility of whole body low dose computed tomography for detecting bone metastasis in breast cancer patients: A cross sectional study

Abstract Clinical utility of Whole Body Low Dose CT scan for detecting bone metastasis in Breast cancer patients: A cross sectional study Background: Skeletal scintigraphy (Bone scan) is the imaging of choice to detect bone metastasis in locally advanced and metastatic breast cancer patients. Nuclear medicine facilities are however limited in their availability. As an alternative diagnostic modality to bone scan, we studied the diagnostic capability of Whole-Body Low Dose CT (WBLDCT) scan for detection of bone metastasis in these patients. Materials and Method: This cross-sectional study was conducted from November 2021 to May 2023 at a tertiary healthcare center in India with an aim to study the clinical applicability of whole-body low dose computed tomography scan for detecting bone metastasis in patients with breast cancer. Breast cancer patients with primary tumor measuring 5cm or more, pathologically proven axillary lymph node metastasis, Stage III/IV disease, symptoms attributable to metastasis and suspected disease recurrence were included. All patients underwent WBLDCT and Bone scan within a period of about 2 weeks. WBLDCT was done during the same time as conventional dose CT scan was being done as part of metastatic workup. WBLDCT acquisition protocol was set in CT scan machine by modulating the technical parameters like Care dose 4D-automated, Care kV semi, rotation time &amp; noise index. The image analysis was performed by Radiologist and Nuclear medicine experts in a blinded fashion. Data was recorded in MS Excel sheet. Sensitivity, specificity, positive and negative predicative value, and concordance rates were calculated using SPSS software ver 25.0. Agreement amongst WBLDCT and Bone scan was calculated using Inter-rater agreement. p value &amp;lt; 0.05 was taken as significant. Results: 110 patients were enrolled in this study. The mean age of participants was 48.5 years and three fourth had stage III disease at presentation. Estrogen receptor positive disease was seen in 43.64% participants while 30% were triple negative. Her2neu positive disease was seen in 27.27% patients. Bone scan couldn’t be done in 03 patients who deferred treatment (these had no bone metastasis on WBLDCT). Both bone scan and WBLDCT detected bone metastasis in 19 patients. Bone scan picked up additional metastasis in three patients which were missed on WBLDCT. WBLDCT detected bone metastasis additionally in three patients which were not detected on Bone scan. Multiple lesions were most seen in vertebrae in 38.5% and 28.6% on bone scan and WBLDCT respectively. The sensitivity and specificity of WBLDCT for detection of bone metastasis was 86.36% and 96.47% respectively. The concordance rate between Bone scan and WBLDCT scan was 94.39% with an Inter-rater Kappa(k) quotient for agreement of 0.828 (p&amp;lt; 0.0001). Conclusion: WBLDCT scan has comparable diagnostic capability to skeletal scintigraphy in detecting bone metastasis in breast cancer and may be a useful alternative specially in resource limited settings. Table 1: Sensitivity, specificity, positive predictive value and negative predictive value of WBLDCT for detection of metastasis Variables Values Sensitivity (95% CI) 86.36% Specificity (95% CI) 96.47% AUC (95% CI) 0.91 Positive Predictive Value (95% CI) 86.36% Negative Predictive Value (95% CI) 96.47% Diagnostic accuracy 94.39% Inter-rater agreement (kappa) 0.828 Citation Format: Lipton Mitra, Suhani Suhani, Ankur Goyal, Rakesh Kumar, Mohit Joshi, Hemanga Bhattacharjee, Haresh KP, Raju Sharma, Rajinder Parshad. Clinical utility of whole body low dose computed tomography for detecting bone metastasis in breast cancer patients: A cross sectional study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-05-13.

ALCOHOL DRINKING MAY MODIFY THE ASSOCIATION BETWEEN HDL-CHOLESTEROL AND CORONARY CALCIUM SCORE IN AN OLDER POPULATION

Objective: The associations between lipids and Coronary Calcium Score (CAC score) remain controversial. We aimed to investigate if high-density lipoprotein cholesterol (HDL-C) is correlated with Coronary Calcium Score among older adults. Design and method: A total of 819 older aged more than 55 years old (Men: 378 and Women: 441) without coronary sent from the Healthy Aging Longitudinal Study in Taiwan were evaluated the Coronary Calcium Score by Low-Dose CT Scan in 2023. The metabolic features were measured at baseline (2nd wave 2014∼2019). CAC score was calculated by the Agatston score formula by an FDA-approved automated AI program (AView, Coreline) and modified by an experienced cardiac CT technologist if necessary. Linear and logistic regression were used to investigate the association between HDL-cholesterol and CAC score. Results: The median [Q1 and Q3] of CAC score was 122.6 [23.2 and 392.7]. The proportion of all older adults was 20.15% (n=165) for CAC Score less than ten, 55.31% (n=453) for CAC score between 11 and 400, and 24.5% for CAC score more than 400. HDL-Cholesterol (age-sex adjusted R [adj-R]=-0.208, p&lt;.0001) and triglycerides (adj-R=0.122, p&lt;.0001) were significantly associated with log-CAC, but LDL-cholesterol (adj-R=-0.005, p=0.8915) and total cholesterol(adj-R=-0.054, p=0.1237) did not. All blood pressure measures, body mass index, and waist circumference were significantly positively associated with log-CAC. Older age, male gender, elevated systolic blood pressure, higher HBA1C, and lower HDL-C were independently associated with CAC. Furthermore, HDL-C was significantly associated with log-CAC Score in those with alcohol drinking habits (standardized beta [STB] = -0.207, p=0.003), but not in those without alcohol drinking habits (STB=-0.05, p=0.2717) (p-value for interaction=0.016). The odds ratios of low HDL-cholesterol (&lt;40 for men and &lt;50 mg/dL for women) at baseline for higher CAC (&lt;10, 11∼400, 400+) were 1.91 (95% confidence intervals: 1.10∼3.31) and 1.26 (0.85∼1.85) for those with and without alcohol drinking habits, respectively. Conclusions: Lower HDL-cholesterol was associated with higher future CAC scores among older adults in this retrospective design. Furthermore, this association could be altered by alcohol consumption.

Barriers to Completing Low Dose Computed Tomography Scan for Lung Cancer Screening

IntroductionAnnual low-dose computed tomography (LDCT) screening has been shown to reduce lung cancer mortality in high-risk individuals by detecting the disease at an earlier stage. This study aims to assess the barriers to completing LDCT in a cohort of patients who were determined eligible for lung cancer screening (LCS). MethodsWe performed a single institution, mixed methods, cross-sectional study of patients who had a LDCT ordered from July to December 2022. We then completed phone surveys with patients who did not complete LDCT to assess knowledge, attitude, and perceptions toward LCS. ResultsWe identified 380 patients who met inclusion criteria, including 331 (87%) who completed LDCT and 49 (13%) who did not. Patients who completed a LDCT and those who did not were similar regarding age, sex, race, primary language, household income, body mass index, median pack years, and quit time. Positive predictors of LDCT completion were: meeting USPSTF guidelines (97.9% vs 81.6%), being married (58.3% vs 44.9%), former versus current smokers (55% vs 41.7%), personal history of emphysema (60.4% vs 42.9%), and family history of lung cancer (13.9% vs 4.1%) (all P < .05). Of the patients who participated in the phone survey, only 7% of respondents thought they were high risk for developing lung cancer despite attending a shared decision-making visit and only 10% wanted to re-schedule their LDCT. ConclusionThere exist barriers to completing LDCT even after patients are identified as eligible and complete a shared decision-making visit secondary to knowledge barriers, misperceptions, and patient disinterest.

Thyroid abnormalities identified on CT screening for lung cancer

PurposeBecause incidental thyroid nodules (ITNs) are common extrapulmonary findings in low-dose computed tomography (LDCT) scans for lung cancer screening, we aimed to investigate the frequency of ITNs on LDCT scans separately on baseline and annual repeat scans, the frequency of malignancy among the ITNs, and any association with demographic, clinical, CT characteristics. MethodsRetrospective case series of all 2309 participants having baseline and annual repeat screening in an Early Lung and Cardiac Action Program (MS-ELCAP) LDCT lung screening program from January 2010 to December 2016 was performed. Frequency of ITNs in baseline and annual repeat rounds were determined. Multivariable regression analysis was performed to identify significant predictors. ResultsDominant ITNs were seen in 2.5 % of 2309 participants on baseline and in 0.15 % of participants among 4792 annual repeat LDCTs. The low incidence of new ITNs suggests slow growth as it would take approximately an average of 16.8 years for a new ITN to be detected on annual rounds of screening. Newly detected ITNs on annual repeat LDCT were all smaller than 15 mm. Regression analysis showed that the increasing of age, coronary artery calcifications score and breast density grade were significant predictors for females having an ITN. No significant predictors were found for ITNs in males. ConclusionITNs are detected at LDCT however, no malignancy was found. Certain predictors for ITNs in females have been identified including breast density, which may point towards a common causal pathway.