Low-dose CT Scans Research Articles

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

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

The Effect of MiR320a on Lung Cancer.

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

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

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

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

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

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

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

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

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

Automated orofacial virtual patient creation: A proof of concept

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

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

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

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

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

P2.04B.03 Low Cost Low Dose CT Scan for Lung Cancer Screening - Seven Year Experience at a Sub-urban Community Hospital

P2.04B.03 Low Cost Low Dose CT Scan for Lung Cancer Screening - Seven Year Experience at a Sub-urban Community Hospital

Perceptions and Interest in Lung Cancer Screening by Smoking Status: A Cross-Sectional Study of HINTS 6 (2022).

Lung cancer screening guidelines prioritize individuals with a history of smoking due to their higher risk of the disease. Our study examines the awareness and interest in low-dose computed tomography (LDCT) lung cancer screening among different smoking statuses using data from the National Cancer Institute's Health Information National Trends Survey (HINTS) 6 (2022). We analyzed data from HINTS 6, including 3915 participants on smoking status, LDCT screening, and telehealth use. Participants were categorized as current smokers, former smokers, and non-smokers. Current smokers had the highest likelihood of being recommended for LDCT screening (OR: 7.1, aOR: 10.4) compared with non-smokers. Former smokers also had increased odds of screening recommendations (OR: 3.1, aOR: 3.4) than non-smokers. Despite higher screening recommendations, current smokers exhibited significantly lower interest in cancer screening (interest rating score: 2.1) compared with non-smokers (interest rating score: 2.4) and former smokers (interest rating score: 2.5). Current smokers rated their telehealth care experiences more positively in terms of care quality compared with non-smokers. Our findings underscore a gap in cancer screening interest among current smokers despite their higher likelihood of being recommended for LDCT screening. The favorable perception of telehealth among current smokers provides an opportunity to enhance engagement and promote LDCT scan through telehealth care.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management.

Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.

Abstract A145: Race and sex disparities in lung cancer screening and evaluation of alternative identifiers in patients’ electronic medical record to better identify lung cancer screening candidates

Abstract Introduction: The US Preventive Services Task Force (USPSTF) recommends lung cancer screening with a low dose CT scan in patients who are 50 – 80 years old with at least a 20 pack-year smoking history and currently smoke or quit in the last 15 years. Yet, only 5.8 percent of eligible Americans were screened in 2022. We investigated demographic screening patterns and evaluated for alternative identifiers in patients’ electronic medical record (EMR) to identify lung cancer screening candidates at a large and diverse cancer center in Los Angeles. Methods: We examined 607 patients with non-small cell lung cancer (NSCLC) treated at the University of Southern California Norris Comprehensive Care Center or Los Angeles General Medical Center from 2009-2023. EMR data included age of diagnosis, sex, race/ethnicity, smoking history, and stage at diagnosis; for patients with unknown number of pack years (n=25) they counted as not qualified for screening. Identifiers investigated were International Classification of Disease, Tenth Revision (ICD-10) code for chronic obstructive pulmonary disease (COPD), prescription of inhaled steroids, and documented lung cancer history. Fisher’s exact test was used to compare proportion of qualified screeners among groups. Results: 35% (215/607) of patients would have qualified for lung cancer screening according to the USPSTF guidelines, including a greater proportion of males 52% (164/313) versus females 17% (51/293) (p&amp;lt;0.0001). In males, Non-Hispanic Whites (NHW) 59% (55/93) had a greater proportion that would qualify compared to Hispanics 41% (31/75) (p=0.0295). In females, NHW 34% (30/89) had the highest proportion of qualifiers while Asians 6% (6/104) and Hispanics 9% (6/66) had the lowest proportion of qualifiers (p&amp;lt;0.0001). 65% (215/332) of smokers qualified for screening; males had a higher percentage who qualified for screening versus females (164/234, 70% vs. 51/98, 52%, p=0.0024). By race, NHW male smokers had the highest proportion of qualifiers 77% (55/71) while Hispanic women had the lowest proportion of qualifiers 50% (6/12). 27% (89/332) of patients were found to have an ICD-10 code for COPD, including 18% (21/117) had an ICD-10 code for COPD who did not qualify. 19% (63/332) smokers received a prescription for inhaled steroids, including 15% (18/117) of them who didn’t qualify for screening. 12% had a family history of lung cancer (39/332), while 9% (10/117) of these patients didn’t qualify for screening. Conclusion: There were significant race and sex screening disparities among known NSCLC patients including under 10% of Asian and Hispanic females. There were about 10-15% known lung cancer patients who would not have qualified for screening found to have an ICD-10 code for COPD, an inhaled steroids prescription, or documented family history. These alternative identifiers may serve as identifiers for lung cancer screening candidates in the EMR to help address race and sex disparities in screening. Future directions should include simulation of these identifiers in larger patient datasets. Citation Format: Darin Poei, Aubree Mades, Iris Yao, Nick Ahn, Ajay Prasad, Jennifer Tsui, Jacob S Thomas, Jorge J Nieva, Robert Hsu. Race and sex disparities in lung cancer screening and evaluation of alternative identifiers in patients’ electronic medical record to better identify lung cancer screening candidates [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A145.

Abstract B046: Factors influencing patient decision-making to complete Low Dose CT screening for lung cancer

Abstract As part of a randomized, controlled trial (RCT) to increase lung cancer screening (LCS) with low-dose CT scan (LDCT) within the Choctaw Nation of Oklahoma’s tribal health system, we conducted a qualitative study to explore the barriers behind patient decisions to forego screening. Thirty eligible participants who had been in the RCT for at least one year were identified as LCS completers (n=7) or non-completers (n=23). A single interviewer completed all interviews using a semi-structured interview guide, designed to explore experiences, barriers/facilitators to LCS, factors influencing screening decisions, and suggested strategies for expanding the LCS program into the community. Interviews were recorded and transcribed verbatim. Data were analyzed using conventional content analysis. Most non-completers reported having no conversations about the availability of LCS with their primary care provider (PCP) or receiving any information about the LCS program. In contrast, the completers all reported PCP-initiated discussions about the LCS program, their risk factors, and an overview of the testing process. Key themes that emerged included fears, e.g., being unsure about what the test involves, afraid of being diagnosed with lung cancer and what might follow, and afraid of dying. Some voiced concerns, e.g., feeling guilty as active smokers and being stigmatized should they be diagnosed with lung cancer. Many participants posited that there was no reason for screening without symptoms. Lack of understanding, especially about cancer care, was identified as a contributing factor. Suggested strategies to increase the reach into the community included using existing infrastructure within Choctaw Nation, such as disseminating information through the tribal newsletter, inserts in prescription refills, and notifications via the patient portal. Community education programs at local community centers, particularly around senior citizen meals, were offered as strategies to change the perception that lung cancer is a death sentence. Hosting an informational booth at the annual tribal Labor Day festival was commonly suggested. In conclusion, multiple reasons were cited for forgoing LDCT screening. In this tribal setting, findings are similar to ones documented in other settings. This suggests that interventions to overcome these challenges may build off known evidence-based interventions, such as the recommendations for cancer prevention documented by the Community Preventive Services Task Force. Addressing fears through multiple educational approaches and adapting strategies from the American Lung Association to address stigma could lead to enhanced interventions. Finally, capitalizing on tribal resources (newsletter) and tribal events to increase LCS awareness may be promising strategies to improve screening in the local tribal setting. Citation Format: Kathleen Dwyer, Zsolt Nagykaldi, Mark Doescher, Dorothy A. Rhoades. Factors influencing patient decision-making to complete Low Dose CT screening for lung cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B046.

Abstract A125: A nationwide initiative to expand access to lung cancer screening: Results and impact of the Color/American Cancer Society Lung Screening Access Program

Abstract Lung cancer is a leading health challenge in the United States – it is the third most common cancer and disproportionately impacts people of color. Early detection of lung cancer is possible through routine screenings using low-dose lung CT scans. Despite the efficacy of screening, 80-85% of people eligible for screening are not up-to-date. The underutilization of regular screening has often been attributed to gaps in primary care and insufficient support following an abnormal result. In an effort to improve access, Color and the American Cancer Society (ACS), launched a nationwide initiative to make lung cancer screening readily accessible and free across all 50 states based on ACS guidelines. The program simplifies the screening process by managing all logistical steps from physician orders to finding and booking appointments at local clinics to assisting with next steps if abnormalities are detected.Since the program launched in November 2023, 74 people completed the online health risk assessment. On average participants were 54 years of age (range: 18-81 years), 64% (n=41) female, and 65% (n=48) White. 28% (n=19) respondents currently smoked, 50% (n=34) had a former history of smoking, and average smoking history was 23.0 pack years (range: 0.1 - 66 pack years). While the program primarily supports individuals who meet ACS lung screening guidelines, all participants can speak with a clinician about screening.Of those who completed the health risk assessment, 45.6% (n=31) met ACS eligibility criteria for lung screening. Eligible participants were on average 62 years of age (range: 51 - 77 years), 74% (n=23) were female, and 87% (n=27) were White. Eligible participants resided in 21 states, with Georgia and Ohio having the highest number of eligible individuals.To date, 9 people have completed a lung CT and 3 are in progress of scheduling. On average, appointments were scheduled within 25 days from the initial referral submission (range: 8-55 days) and all appointments were within 17 miles from the individual’s preferred location (average: 6.8 miles, range: 0.5-17 miles).3 participants had a change in their lung screening plan based on their result, with 2 participants having Lung-RADS 3 category nodules (follow-up in 6 months) and 1 having Lung-RADS 4A nodules (follow-up in 6 months). Other findings included an ascending aortic aneurysm (4.2 cm), a hiatal hernia, an indeterminate adrenal nodule, and mild coronary artery calcification, some of which required additional follow-up care with a PCP or cardiologist within 3-12 months. No participants were recommended to have immediate diagnostic testing for lung cancer based on their results. The Color/ACS Lung Screening Access Program demonstrates the impact that a virtual lung cancer screening program has on closing screening gaps. It underscores the critical role of supporting patients not only in undergoing cancer screening, but also in navigating the complexities of follow-up care, thereby enhancing the overall efficacy of lung cancer prevention and early detection strategies. Citation Format: Deanna Brockman, Jennifer Fung, Ashly Emig, Gabrielle Hands, Wendy McKennon, Jennifer Cho, Robert Smith, William Dahut, Alicia Zhou, Keegan Duchicela. A nationwide initiative to expand access to lung cancer screening: Results and impact of the Color/American Cancer Society Lung Screening Access Program [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A125.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Implementation of high-quality lung cancer screening.

More than a decade has passed since researchers in the Early Lung Cancer Action Project and the National Lung Screening Trial demonstrated the ability to save lives of high-risk individuals from lung cancer through regular screening by low dose computed tomography scan. The emergence of the most recent findings in the Dutch-Belgian lung-cancer screening trial (Nederlands-Leuvens Longkanker Screenings Onderzoek [NELSON]) further strengthens and expands on this evidence. These studies demonstrate the benefit of integrating lung cancer screening into clinical practice, yet lung cancer continues to lead cancer mortality rates in the United States. Fewer than 20% of screen eligible individuals are enrolled in lung cancer screening, leaving millions of qualified individuals without the standard of care and benefit they deserve. This article, part of the American Cancer Society National Lung Cancer Roundtable (ACS NLCRT) strategic plan, examines the impediments to successful adoption, dissemination, and implementation of lung cancer screening. Proposed solutions identified by the ACS NLCRT Implementation Strategies Task Group and work currently underway to address these challenges to improve uptake of lung cancer screening are discussed. PLAIN LANGUAGE SUMMARY: The evidence supporting the benefit of lung cancer screening in adults who previously or currently smoke has led to widespread endorsement and coverage by health plans. Lung cancer screening programs should be designed to promote high uptake rates of screening among eligible adults, and to deliver high-quality screening and follow-up care.

Quantitative aortic Na[18F]F positron emission tomography computed tomography as a tool to associate vascular calcification with major adverse cardiovascular events

Abstract Purpose Sodium[18F]fluoride (Na[18F]F) used in positron emission tomography (PET) binds to active calcification and correlates consistently with higher cardiovascular risk. This study aims to investigate the feasibility of aortic Na[18F]F-PET in hybrid combination with low-dose computed tomography (CT) as a risk model for major adverse cardiovascular events (MACE). Methods Patient data and Na[18F]F-PET/CT scans from January 2019 to February 2022 were retrospectively collected at the University Medical Center Groningen (UMCG), the Netherlands. MACE-outcome was a composite of time to first documented myocardial infarction, cerebral vascular accident (CVA), acute heart failure hospitalization, and aortic aneurysms. MACE dates were recorded from the day of the scan until follow-up in December 2023. The aorta was manually segmented in all low-dose CT scans. To minimize spill-over effects from the vertebrae, the vertebrae were automatically segmented using an open-source model, dilated with 10 mm, and subtracted from the aortic mask. The total aortic Na[18F]F corrected maximum standardized uptake value (cSUVmax) and total aortic Agatston score were automatically calculated using SEQUOIA. Kaplan–Meier and Cox regression survival analysis were performed, stratifying patients into high, medium, and low cSUVmax and Agatston categories. Cox regression models were adjusted for age. Results Out of 280 identified scans, 216 scans of unique patients were included. During a median follow-up of 3.9 years, 12 MACE occurred. Kaplan–Meier survival analysis demonstrated a significant difference in MACE-free survival among the high cSUVmax group compared to the medium and low groups (p = 0.03 and p &lt; 0.01, respectively). Similarly, patients with high Agatston scores had a significantly lower MACE-free survival probability compared to those with medium and low scores (both p &lt; 0.01). Conclusion This study highlights the potential clinical utility of Na[18F]F-PET/CT as an imaging tool to predict the risk of MACE. Clinical validation of this novel proof-of-concept method is needed to confirm these results and expand the clinical context.

Economic Evaluation of a Novel Lung Cancer Diagnostic in a Population of Patients with a Positive Low-Dose Computed Tomography Result

Background: Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures. Consequently, there is a critical need for advanced diagnostics after an initial low-dose computed tomography (LDCT) scan. Objectives: This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. Methods: A cost offset model was developed to evaluate the net expected savings associated with the use of CyPath® Lung relative to the current standard of care for individuals whose IPNs range from 6 to 29 mm. Perspectives from both Medicare and private payers in a US setting are included, with a 1-year time horizon. Cost calculations included procedure expenses, complication costs, and diagnostic assessment costs per patient. Primary outcomes of this analysis include cost savings per cohort and cost savings per patient. Results: Our analysis showed positive cost savings from a private payer’s perspective, with expected savings of $895 202 311 per cohort and $6460 per patient, across all patients. Scenario analysis resulted in cost savings of $890 829 889 per cohort, and $6429 per patient. Similarly, savings of $378 689 020 per cohort or $2733 per patient were yielded for Medicare payers, across all patients. In addition, scenario analysis accounting for false negative patients from a Medicare payer perspective yielded savings of $376 902 203 per cohort and $2720 per patient. Discussion: The results suggest substantial cost savings, primarily due to reductions in follow-up diagnostic assessments and procedures, and highlight the importance of accurate diagnostic tools in reducing unnecessary healthcare expenditures. Conclusion: CyPath® Lung utilization yields savings for private and Medicare payers relative to the current standard of care in a US setting for individuals with 6 to 20 mm IPNs.

Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm.

The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) measured from computed tomography (CT) images is an established imaging biomarker used to assess patients' nutritional status. With the increasing prevalence of low-dose CT scans in clinical practice, accurate and automated skeletal muscle segmentation at the L3 level in low-dose CT images has become an issue to address. This study proposed a lightweight algorithm for automated segmentation of skeletal muscles at the L3 level in low-dose CT images. This study included 57 patients with rectal cancer, with both low-dose plain and contrast-enhanced pelvic CT image series acquired using a radiotherapy CT scanner. A training set of 30 randomly selected patients was used to develop a lightweight segmentation algorithm, and the other 27 patients were used as the test set. A radiologist selected the most representative axial CT image at the L3 level for both the image series for all the patients, and three groups of observers manually annotated the skeletal muscles in the 54 CT images of the test set as the gold standard. The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). The running time of the proposed algorithm was recorded. An open source deep learning-based AutoMATICA algorithm was compared with the proposed algorithm. The inter-observer variations were also used as the reference. The DSC, precision, recall, HD95, ASD, and running time were 93.2 ± 1.9% (mean ± standard deviation), 96.7 ± 2.9%, 90.0 ± 2.9%, 4.8 ± 1.3 mm, 0.8 ± 0.2 mm, and 303 ± 43 ms (on CPU) for the proposed algorithm, and 94.1 ± 4.1%, 92.7 ± 5.5%, 95.7 ± 4.0%, 7.4 ± 5.7 mm, 0.9 ± 0.6 mm, and 448 ± 40 ms (on GPU) for AutoMATICA, respectively. The differences between the proposed algorithm and the inter-observer reference were 4.7%, 1.2%, 7.9%, 3.2 mm, and 0.6 mm, respectively, for the averaged DSC, precision, recall, HD95, and ASD. The proposed algorithm can be used to segment skeletal muscles at the L3 level in either the plain or enhanced low-dose CT images.

Developing a systems-focused tool for modeling lung cancer screening resource needs

BackgroundEarly detection through screening dramatically improves lung cancer survival rates, including among war Veterans, who are at heightened risk. The effectiveness of low dose computed tomography scans in lung cancer screening (LCS) prompted the Veteran’s Affairs Lung Precision Oncology Program (VA LPOP) to increase screening rates. We aimed to develop an adaptive population health tool to determine adequate resource allocation for the program, with a specific focus on primary care providers, nurse navigators, and radiologists.MethodsWe developed a tool using C + + that uses inputs that represents the process of the VA LCS program in Ann Arbor, Michigan to calculate FTEs of human resource needs to screen a given population. Further, we performed a sensitivity analysis to understand how resource needs are impacted by changes in population, screening eligibility, and time allocated for the nurse navigators’ tasks.ResultsUsing estimates from the VA LCS Program as demonstrative inputs, we determined that the greatest number of full-time equivalents required were for radiologists, followed by nurse navigators and then primary care providers, for a target population of 75,000. An increase in the population resulted in a linear increase of resource needs, with radiologists experiencing the greatest rate of increase, followed by nurse navigators and primary care providers. These resource requirements changed with primary care providers, nurse navigators and radiologists demonstrating the greatest increase when 1–20, 20–40 and > 40% of Veterans accepted to be screened respectively. Finally, when increasing the time allocated to check eligibility by the nurse navigator from zero to three minutes, there was a linear increase in the full-time equivalents required for the nurse navigator.ConclusionVariation of resource utilization demonstrated by our user facing tool emphasizes the importance of tailored strategies to accommodate specific population demographics and downstream work. We will continue to refine this tool by incorporating additional variability in system parameters, resource requirements following an abnormal test result, and resource distribution over time to reach steady state. While our tool is designed for a specific program in one center, it has wider applicability to other cancer screening programs.