Adjunctive Screening Research Articles

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes

ObjectiveIn diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups. Materials and methodsTo achieve the task of non-invasive detection of hyperuricemia in diabetic patients, given that blood uric acid metabolism is directly related to estimated glomerular filtration rate(eGFR), we first performed a regression task for eGFR value before the classification task for hyperuricemia and reintroduced the eGFR regression values into the baseline information. We trained 3 deep learning models: (1) metadata model adjusted for sex, age, body mass index, duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure; (2) image model based on fundus photographs; (3)hybrid model combining image and metadata model. Data from the Shanghai General Hospital Diabetes Management Center (ShDMC) were used to develop (6091 participants with diabetes) and internally validated (using 5-fold cross-validation) the models. External testing was performed on an independent dataset (UK Biobank dataset) consisting of 9327 participants with diabetes. ResultsFor the regression task of eGFR, in ShDMC dataset, the coefficient of determination (R2) was 0.684±0.07 (95 % CI) for image model, 0.501±0.04 for metadata model, and 0.727±0.002 for hybrid model. In external UK Biobank dataset, a coefficient of determination (R2) was 0.647±0.06 for image model, 0.627±0.03 for metadata model, and 0.697±0.07 for hybrid model. Our method was demonstrably superior to previous methods. For the classification of hyperuricemia, in ShDMC validation, the area, under the curve (AUC) was 0.86±0.013for image model, 0.86±0.013 for metadata model, and 0.92±0.026 for hybrid model. Estimates with UK biobank were 0.82±0.017 for image model, 0.79±0.024 for metadata model, and 0.89±0.032 for hybrid model. ConclusionThere is a potential deep learning algorithm using fundus photographs as a noninvasively screening adjunct for hyperuricemia among individuals with diabetes. Meanwhile, combining patient's metadata enables higher screening accuracy. After applying the visualization tool, it found that the deep learning network for the identification of hyperuricemia mainly focuses on the fundus optic disc region.

A Retrospective Study: The Analysis of False Positives from Oral Cancer Screening Adjuncts

A Retrospective Study: The Analysis of False Positives from Oral Cancer Screening Adjuncts

The significance of pulsatility indices in the middle cerebral, internal carotid, and common carotid arteries for small vessel disease in the brain: A retrospective cross-sectional study

Background and objectives. The pulsatility index (PI) indicates the resistance in distant blood vessels and the stiffness of major arteries outside the brain. For cerebral small vessel disease (CSVD), our aim is to enhance our comprehension of the Pulsatility Index (PI) in the common carotid artery (CCA), internal carotid artery (ICA), and middle cerebral artery (MCA). Materials and methods. This study is a cross-sectional retrospective research, which was conducted between January 2020 and June 2020 at the Dr. Cipto Mangunkusumo National Hospital, Indonesia. Patients were divided into groups with CSVD and without CSVD. Univariate and bivariate analyses, along with Spearman correlation and multivariate regression, were conducted to assess the mean difference. The overall score for CSVD was determined based on findings from brain MRI scans. Results. The median age of the 79 participants was 60 years (49-66). CSVD made up 54.4% of the total. The median (interquartile range) PI values for the CCA, ICA, and MCA in cases of CSVD were 1.48 (1.3-1.68), 1.12±0.3, and 1.11 (0.95-1.38), respectively. Despite this, no significant differences in PI were observed between the groups. In cases of total stroke, the PI of the CCA showed a moderate correlation with the PI of the MCA (B 0.4, p=0.001) and the PI of the ICA (B 0.34, p=0.005). However, no correlation was found between PI and either the burden of CSVD or the total score for small vessel disease. Conclusions. The pulsatility indices for the CCA, ICA, and MCA generally rise in cases of CSVD, indicating heightened resistance in distal blood vessels and increased stiffness in the large arteries outside the skull. This highlights the potential use of PI measurement as a promising adjunctive screening and evaluation tool in CSVD.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

Comparison of immunoglobulin high-throughput sequencing MRD in bone marrow and peripheral blood in pediatric B-ALL: A report from the Children's Oncology Group AALL1731.

10014 Background: Minimal (measurable) residual disease (MRD) at end of induction (EOI) therapy is a strong predictor of outcome in pediatric B-ALL. Currently, EOI MRD is assessed in bone marrow (BM). We hypothesized that the highly sensitive assay, high-throughput sequencing (HTS) of immunoglobulin loci, can effectively monitor MRD in peripheral blood (PB) and may provide a less invasive way to track therapy response. Methods: We conducted HTS MRD on paired EOI BM and PB samples from 808 NCI standard risk (SR) pediatric B-ALL patients enrolled on Children’s Oncology Group study AALL1731 (NCT03914625). We determined the correlation between BM and PB HTS MRD via Spearman’s rank correlations. We calculated the BM/PB MRD ratio and compared these by subgroup using Kruskal-Wallis tests. We defined subgroups by cytogenetics (cyto) ( ETV6::RUNX1, double trisomies of chromosome 4 and 10 (DT), Unfavorable (hypodiploidy, iAMP21, or KMT2A-rearranged), or Neutral (lacking ETV6::RUNX1, DT, or unfavorable)), and risk group (SR-average (AVG) and SR-High). Flow cytometry-defined EOI BM MRD was < 0.01% for all SR-AVG patients (N = 623) and ≥0.01% for selected SR-High patients (N = 185). Results: There was strong correlation between PB and BM HTS MRD with an overall correlation coefficient of 0.75 (P < 0.001). Correlation was similar by cytogenetics: ETV6::RUNX1, 0.69 (N = 63; P < 0.001), DT, 0.75 (N = 147; P < 0.001), Neutral, 0.74 (N = 580; P < 0.001), and Unfavorable, 0.66(N = 18; P = 0.003). For risk groups, correlation for SR-AVG was 0.67 (p < 0.001) and SR-High, 0.64 (p < 0.001). Of the 591 SR-AVG patients with detectable BM HTS MRD, PB HTS MRD was detectable in 474 (80.2%), undetectable in 94 (15.9%) and indeterminate (no leukemic cell detected and < 500,000 total cells in sample) in 23 (3.9%). Among 182 SR-High patients with detectable BM HTS MRD, 175 (96.2%) had detectable PB HTS MRD. Disease burden was higher in the BM than PB with a significantly higher BM/PB ratio in SR-High compared to SR-AVG patients (median 16.5 vs 2.6, P < 0.001). The median BM/PB ratio also varied by cytogenetics with those with Unfavorable cyto having the highest ratio (15.3 vs 6.3 in DT, 3.8 in ETV6::RUNX1, 3.1 in Neutral; P = 0.013). Conclusions: This is the largest analysis of paired B-ALL BM/PB HTS MRD to date. We show strong correlation between PB and BM across risk and cytogenetic groups. The ratio of BM/PB MRD varied and was highest among patients with Unfavorable cyto suggesting BM tropism. Importantly, PB MRD was detectable in nearly all patients with flow EOI BM MRD ≥0.01%, a threshold warranting therapy intensification. However, most patients with EOI BM flow MRD < 0.01% also had detectable PB HTS MRD. Thus, PB HTS MRD may provide a useful adjunct for screening and clinical management of B-ALL patients. Defining a PB HTS MRD threshold useful for risk stratification will require correlation with outcome.

Abstract PO1-07-12: Inverse modeling with surface temperature accurately detects the presence of breast cancer

Abstract Breast cancer affects over 250,000 women in the United States every year. Patient outcomes including overall survival significantly decline with large sized tumors and nodal involvement on initial presentation. The introduction of mammogram screening has been instrumental in the early detection of cancer. However, it has lower sensitivity in dense breast tissue and limited specificity which leads to unnecessary additional invasive testing. Several methods such as magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound imaging are used as adjunctive methods. However, there is still a need for cost-effective techniques without additional radiation exposure for breast cancer screening for use with mammography. We propose an adjunctive screening method using surface temperature measurements and inverse breast modeling for early detection of breath cancer. Methods: We enrolled patients who presented with mammogram detected breast tumors that were confirmed to be cancerous lesions by biopsy. We used prone position steady state infrared imaging of bilateral breasts to measure surface temperatures and multi-view MRI images to create patient specific 3D breast models. The inverse technique applies Levenberg-Marquard algorithms (LMA) and utilizes commercial software for thermal transport modeling. The inverse technique predicts a heat generation map normalized for baseline surface temperatures to localize the presence of an underlying tumor. Results: A total of 25 breast tumors (diameter range from 5 to 27 mm) from 24 patients were included in the analysis with a median patient age of 67 years. One patient had bilateral breast tumors. Mammogram data showed inclusion of mixed breast tissue composition including dense and extremely dense breast tissue. LMA accurately detected the tumor in all 25 patients with maximum absolute errors of 7 mm in location of the tumor in the breast and 1.78 mm in diameter. The accuracy of detection was not affected by tumor histology which included invasive ductal and lobular carcinomas, ductal and lobular carcinoma in-situ and atypical ductal hyperplasia. No heat sensitive tumors were detected in the 23 contralateral breasts which acted as internal negative controls. Conclusion: Infrared temperature profiles and inverse modeling successfully detected malignant breast tumors with no missed tumors or false positive results and could be used as adjunctive screening along with mammography, especially in patients with dense breast tissue. Citation Format: Nithya Sritharan, Carlos Gutierrez, Isaac Perez-Raya, Jose-Luis Gonzalez-Hernandez, Alyssa Owens, Donnette Dabydeen, Lori Medeiros, Satish Kandlikar, Pradyumna Phatak. Inverse modeling with surface temperature accurately detects the presence of breast cancer [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 PO1-07-12.

3D Topographical Scanning for the Detection of Osteoporosis.

Osteoporosis is associated with greater risk of fracture, which can lead to increased morbidity and mortality. DEXA scans are often inaccessible for patients, leaving many cases of osteoporosis undetected. A portable 3D topographical scan offers an easily accessible and inexpensive potential adjunct screening tool. We hypothesized that 3D scanning of arm and calf circumference and volume would correlate with DEXA T-scores. 96 female patients were enrolled. Patients were consented and completed a topographical scan of bilateral arms and lower legs with a mobile 3D scanner for arm and calf circumference and volume in clinic. Patient charts were then retrospectively reviewed for DEXA T-scores. Forearm DEXA T-score was positively correlated with arm circumference (r = 0.49, p<0.01), arm volume (r=0.62, p<0.01), and calf volume (r=0.47, p<0.01). Femoral neck DEXA T-score was positively correlated with calf circumference (r=0.36, p<0.01) and calf volume (r=0.36, p<0.01). Our results showed significant correlations between DEXA T-scores and topographical measurements from mobile device acquired 3D scans, although these were in the "moderate" range. Mobile device-based 3D scanning may hold promise as an adjunct screening tool for osteoporosis when DEXA scanning is not available or feasible for patients, although further studies are needed to elucidate the full potential of its clinical utility. At a minimum, identifying a patient as high risk may promote earlier diagnostic DEXA scanning.

Developing technologies and areas of interest in lung cancer screening adjuncts.

Lung cancer remains the leading cause of cancer mortality. Screening guidelines have been implemented in the past decade to aid in earlier detection of at-risk groups. Nevertheless, computed tomography (CT) scans, the principal screening modality in use today, are still low yield, with 3.6% of lung cancer confirmed amongst 39.1% of lesions detected over a 3-year period. They also carry relatively high false positive rates, between 9% and 27%, which can bear unnecessary financial and emotional costs to patients. As such, research efforts have been dedicated to the development of lung cancer screening adjuncts to improve detection reliability. We herein review several emerging technologies in this specific arena and their efficacy. These include plasma markers (microDNA, DNA methylation, and tumor-associated antibodies), breath/sputum biomarkers [volatile organic compounds (VOCs) and exhaled breath condensate (EBC)], proteomics, metabolomics, and machine learning, such as radiomics technology. We find that, across the board, they offer promising results in terms of non-invasive diagnostics, genetic sequencing for higher-risk individuals, and accessibility for a diverse cohort of patients. While these screening adjuncts are unlikely to completely replace the current standard of care at the moment, continued research into these technologies is crucial to improve and personalize the identification, treatment, and outcome of lung cancer patients in the near future.

Investigate how Frequently Dental Professionals Conduct Oral Cancer Screenings During Routine Dental Check-Ups and the Methods they Use

ABSTRACT Background: Oral cancer is a significant global health concern with substantial morbidity and mortality rates. Early detection during routine dental check-ups is crucial for improved patient outcomes. Dental professionals play a pivotal role in conducting oral cancer screenings, but the frequency and methods they employ remain understudied. Aim: This cross-sectional study aimed to investigate the frequency of oral cancer screenings during routine dental check-ups conducted by dental professionals and the methods they commonly use Materials and Methods: A survey was administered to 500 dental professionals, including dentists and dental hygienists, representing diverse demographics and practice settings. The survey collected data on the frequency of oral cancer screenings and the primary methods employed. Descriptive statistics and Chi-square tests were used for data analysis. Results: The study found that 85% of dental professionals conduct oral cancer screenings during routine dental check-ups, with 48% performing them annually and 33% at every visit. Visual inspection (97%) and palpation (83%) were the most commonly used methods, while adjunctive screening tools (45%) were less frequently incorporated Conclusion: Dental professionals are actively engaged in oral cancer screenings during routine dental check-ups, demonstrating a commitment to early detection. Visual inspection and palpation are commonly employed methods, but there is room for improvement in the adoption of adjunctive screening tools.

P300 An Expert Delphi Consensus: Early Identification of Patients at Risk of Crohn's Disease in Perianal Fistulas and Abscesses (PREFAB) and Identification and Management of Isolated Perianal Crohn's Disease (ipCD) - Part A, PREFAB

Abstract Background In ± 10% of all Crohn’s disease (CD) patients a perianal abscess (PAA) or fistula (PAF) is the manifesting symptom. The delay in diagnosis of CD, from fistula or abscess onset, is very long and associated with worse outcomes. The aim of this Delphi study was to reach consensus on a clinical decision tool to help select patients with raised suspicion of CD in PAA/PAF patients, to identify underlying CD earlier and reduce the diagnostic delay. Methods A panel of international experts in the field of proctology and/or Inflammatory Bowel Disease (IBD), consisting of surgeons, gastroenterologists and radiologists were invited to participate in the PREFAB part of this Delphi study. The first round was an electronic survey and the second round a virtual consensus meeting. In the first round, participants were asked to anonymously provide their opinion probing 1) the relevance and use of clinical characteristics (“Red Flags”) suggestive of underlying CD, 2) the use of faecal calprotectin (FCP) as an adjunct for screening for CD and 3) on the diagnostic work-up for IBD in PAA/PAF patients with raised clinical suspicion. In the second round, statements were paired/revised based on the feedback from the first round and presented in a final set of statements. Consensus was predefined as ≥70% agreement. Results A total of 30 experts participated in the first round and 25 in the second round (83.3%). Final consensus was reached for 29% of all statements in the first Delphi round and, after adjustments/merging, for all 11 statements (100%) in the second round. Ninety-seven percent of participants agreed that shortening of the delay in diagnosis by using a clinical decision tool could improve outcomes in patients with perianal disease as a first symptom. Consensus was reached on the red flags to be included in the clinical decision tool, on screening of all patients with any perianal fistula (regardless of the complexity, biological behaviour and co-existent perianal symptoms) to identify CD in an early (sub)clinical phase, on referral criteria and on the diagnostic workup and follow up of patients with raised suspicion. Conclusion A clinical decision tool for early identification of CD in patients with PAA/PAF as a first symptom could shorten delay in diagnosis and improve outcomes. Global consensus for this tool, including a practical and relevant algorithm for finding or excluding CD in patients with PAA/PAF as a manifesting sign, was reached within two rounds. This clinical decision tool will be validated in 2024 in a large, prospective multicenter study.

Abstract 13731: Right-Sided Precordial U Waves as a Screening Tool for Aortic Stenosis

Introduction: AS affects 3% of individuals ≥ age 65 and portends an increased risk of morbidity and mortality. Cardiac auscultation to categorize AS severity depends on provider skill. Recent studies have identified an association between AS and right precordial U waves (RPUW) using machine learning. Therefore, the ECG may serve as an adjunctive screening tool for AS. Hypothesis: Are RPUW correlated with AS severity? Methods: A retrospective cohort analysis was performed of 50 patients ages 65-89 with a diagnosis of HTN, echocardiogram on file, and RPUW (V1-V3) in consecutive leads with U wave amplitude ≥ 0.5 mm in at least one lead. Patients with prior aortic valve replacement, ventricularly paced rhythms, AF or AFL, LBBB, potassium (K) &lt; 3, and HR &lt; 50 were excluded. A control cohort of 50 age- and gender- matched patients with RPUW &lt; 0.5 mm was included. Echocardiograms were unblinded only once patients were selected by ECG criteria. Results: In total, 976 charts and 294 charts were reviewed to identify 50 eligible patients for the experimental and control arms respectively. Median time between ECG and lab collection (to ensure a K ≥ 3) was 0 days for both arms. Median time between ECG and echocardiogram was 1 day and 1.5 days respectively for experimental and control arms. U waves were significantly correlated with AS severity using a chi-square test (X2 = 16.77, df = 3, p &lt; .001). A multinomial logistic regression analysis showed no significant association between gender (p = .69) or systolic blood pressure (SBP) as a categorical variable (SBP &lt; 125, 126-145, &gt; 145) (p = .35) and AS severity, but increasing age group (ages 65-73, 74-81, or 82-89) was associated with greater odds of moderate or severe AS (p = .002), OR 3.43 (95% CI: 1.59, 7.40). Conclusions: RPUW ≥ 0.5 mm demonstrate a significant correlation with AS severity. A prospective study which collects first-time echocardiograms for patients with RPUW serves as a next step in determining the utility of ECGs for screening of AS.

Association between transabdominal uterine artery Doppler and small-for-gestational-age: a systematic review and meta-analysis

BackgroundThe association between uterine artery Doppler (UtA) measurements and small for gestational age (SGA) has not been quantitatively analyzed throughout the whole pregnancy. This systematic review and meta-analysis aims to comprehensively explore the association between UtA measurements and SGA in the first, second, and third trimesters.MethodsStudies were searched from Pubmed, Embase, Cochrane Library, and Web of Science. Weighted mean difference (WMD), odds ratio (OR), and relative risk (RR) with 95% confidence interval (CI) were used as the effect size. Heterogeneity of all effect sizes was tested and quantified using I2 statistics. Sensitivity analysis was conducted for all outcomes, and publication bias was evaluated using Begg’s test.ResultsA total of 41 studies were finally included in our meta-analysis. In the first trimester, mean PI was significantly higher in the SGA group than the non-SGA group (WMD: 0.31, 95%CI: 0.19–0.44). In the second trimester, odds of notch presence (OR: 2.54, 95%CI: 2.10–3.08), mean PI (WMD: 0.21, 95%CI: 0.12–0.30), and mean RI (WMD: 0.05, 95%CI: 0.05–0.06) were higher in the SGA group. Also, abnormal UtA measurements were associated with the increased odds of SGA (all P < 0.05). In the third trimester, PI z-score (WMD: 0.62, 95%CI: 0.33–0.91) and PI MoM (WMD: 0.08, 95%CI: 0.06–0.09) showed a significant increase in the SGA group. The odds of SGA were higher in the women with mean PI > 95% (OR: 6.03, 95%CI: 3.24–11.24).ConclusionsAbnormal UtA measurements were associated with high odds of SGA, suggesting that UtA might be an adjunctive screening method for SGA in the whole pregnancy.

Deep learning algorithms to detect diabetic kidney disease fromretinal photographs in multiethnic populations with diabetes.

To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations. We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional). In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid. There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.

Abstract 3029: Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting

Abstract Background: Annual screening breast MRIs are endorsed by current breast cancer screening guidelines as an adjunctive screening modality for high-risk patients (defined by more than 20% lifetime risk) in addition to a screening mammogram. Recent studies showed screening breast MRI remained underutilized despite the current recommendations. On the other hand, validated breast cancer risk models for identifying high-risk patients also remained under-recognized among primary care providers. This study aims to identify the potential barriers for primary care providers (PCPs) to using breast cancer risk models and referring high-risk patients to breast MRI for screening. Methods: An anonymous survey was distributed via email to the PCPs and OB/GYN providers in the network of Mount Auburn Hospital, a community teaching hospital affiliated with Harvard Medical School in Cambridge, Massachusetts. The survey included questions regarding breast cancer screening in average-risk and high-risk patients, providers’ understanding of breast cancer risk models, and current referral status of breast MRIs and high-risk/genetic clinics. Results: The survey was sent to 104 providers, and 43 responses were received. When asked about performing breast cancer risk assessments, 58% reported “Always (91-100%)” and 33% said “Often (71-90%)”. For factors to consider when performing risk assessments, most of the providers considered personal medical history (98%), family history (100%), genetic syndrome history (86%), and previous breast biopsy results (90%). When asked about breast cancer risk models, 58% of the providers had not heard of or used one. 33% of the providers have used the Gail model, and 26% have used the Tyrer-Cuzick model. 51% of the providers correctly identified high-risk as lifetime risk &amp;gt;20%. 56% correctly answered annual mammograms with MRI for breast cancer screening for high-risk patients. The majority of the providers (84%) would prefer to refer patients to the high-risk clinic for risk assessment and screening management. For barriers to referring to MRI, the insurance approval process (49%) was reported to be the largest barrier. 72% of the providers prefer not ordering it and defer to the high-risk/genetic clinic for ordering the test. Conclusions: Our survey has shown different barriers to breast cancer screening in high-risk patients in the primary care setting, including knowledge and logistic barriers. Further education for PCPs is needed for correctly identifying high-risk patients, utilizing risk models, and referring high-risk patients for breast MRI screening. Citation Format: Yu Jen Jan, Prudence B. Lam. Barriers to optimal breast cancer screening modalities for high-risk patients in the primary care setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3029.

Long-term outcome and troponin I concentrations in Great Danes screened for dilated cardiomyopathy: an observational retrospective epidemiological study

IntroductionDilated cardiomyopathy (DCM) is common in Great Danes (GDs) but screening for this condition can be challenging. We hypothesised that cardiac troponin-I (cTnI) concentration is elevated in GDs with DCM and/or ventricular arrhythmias (VAs), and is associated with reduced survival time in GDs. AnimalsOne hundred and twenty-four client-owned GDs assigned echocardiographically as normal (n = 53), equivocal (n = 37), preclinical DCM (n = 21), or clinical DCM (n = 13). Materials and methodsA retrospective epidemiological study. Echocardiographic diagnosis, VAs, and contemporaneous cTnI concentrations were recorded. Diagnostic accuracy and cTnI cut-offs were determined with receiver operating characteristic analyses. Effects of the cTnI concentration and disease status on survival and cause of death were explored. ResultsMedian cTnI was greater in clinical DCM (0.6 ng/mL [25th–75th percentiles: 0.41–1.71 ng/mL]) and GDs with VAs (0.5 ng/mL [0.27–0.80 ng/mL], P<0.001). Elevated cTnI detected these dogs with good accuracy (area under the curve: 0.78–0.85; cut-offs 0.199–0.34 ng/mL). Thirty-eight GDs (30.6%) suffered a cardiac death (CD); GDs suffering CD (0.25 ng/mL [0.21–0.53 ng/mL]) and specifically sudden cardiac death (SCD) (0.51 ng/mL [0.23–0.72 ng/mL]) had higher cTnI than GDs dying of other causes (0.20 ng/mL [0.14–0.35 ng/mL]; P<0.001). Elevated cTnI (>0.199 ng/mL) was associated with shorter long-term survival (1.25 years) and increased risk of SCD. Great Danes with VAs had shorter survival times (0.97 years). ConclusionsA cardiac troponin-I concentration is a useful adjunctive screening tool. Elevated cTnI is a negative prognostic indicator.

Abstract P3-04-13: Patient experience with automated SoftVue 3D whole breast tomographic ultrasound

Abstract Authors: Mary Yamashita, MD; Rachel Brem, MD; Lauren Baker, Ph.D; MD; Taylor Mahoney, PhD; Patrick Walker, PharmD, MPH; Rachel Treat, MA; Linda Hovanessian Larsen, MD. Title: Patient experience with automated SoftVue 3D whole breast tomographic ultrasound. Purpose: SoftVue (SV) is an automated, 3D whole breast ultrasound tomographic imaging device which is FDA PMA approved as adjunct to mammography for women with dense breasts. Screening with handheld US is labor intensive and with ABUS is associated with significant patient discomfort. A benefit of SV is that image acquisition is not operator dependent and does not require compression. Because acceptance by patients is crucial to implementation of US screening, we evaluated patients’ experience with SV, specifically, asymptomatic women with BI-RADS c or d density undergoing FFDM. Materials and Methods: As part of a prospective, 10-site study, 7,439 asymptomatic women with BI-RADS density category c or d were screened on the same day with FFDM and SV. Each patient’s experience was assessed for perceived pain, discomfort and anxiety, discretion and modesty, overall satisfaction, and whether they would recommend it to others. The responses were measured on a Likert scale with 5 choices from strongly agree to strongly disagree, then studied by Chi-Squared analysis. Results: The mean age was 53.9 ± 9.7 yo, mostly white women (87.7%). The median BMI was 24.4. Majority had no personal history of breast cancer (97.2%), but 24.8% had a previous biopsy and almost half (46.3%) had a family history of breast cancer. Almost all patients (99.6%) completed the survey. SV was perceived as significantly more comfortable than FFDM (83.7% vs 52.2%, p&amp;lt; 0.001), was painless (94.9% vs 53.1%, p&amp;lt; 0.001), and was associated with less anxiety during the procedure (95.1% vs 79.9, p&amp;lt; 0.001). Lastly, 99.3% felt the experience was private and discreet, and 95% would recommend the SV exam to other women. Conclusion: Pain, fear, anxiety, and modesty concerns are some of the barriers preventing widespread implementation of screening breast US. This data suggests that SV, an FDA PMA approved adjunctive screening exam for women with dense breast tissue, is painless, offers a private and discreet scan that limits anxiety, and is well accepted by patients. Clinical relevance statement: SV is a novel automated US tomographic screening technology that is comfortable, well-accepted, FDA PMA approved, and will likely result in improved implementation of screening breast ultrasound in women with dense breasts. Citation Format: Mary Yamashita, Rachel F. Brem, Lauren Baker, Taylor F. Mahoney, Patrick Walker, Rachel M. Treat, Linda Hovanessian Larsen. Patient experience with automated SoftVue 3D whole breast tomographic ultrasound [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-13.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.

This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT). A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation. The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05. The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening. • This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Abstract 11707: How Age Affects the Association of Breast Arterial Calcification With Clinical Outcomes and Mortality

Background Cardiovascular disease (CVD) remains the number one cause of death in women. Current screening tools in this population do not identify all women who are at risk. Screening for breast artery calcification (BAC) via routine mammography is emerging as a risk enhancing tool; however, how risk prediction varies by age is unknown. Methods This single-center, retrospective study included 14,544 women who had a digital mammogram from 2008-2016. BAC was quantified with the Bradley score derived from an automated algorithm developed by CureMetrix; presence of BAC was defined as a Bradley score ≥10. Mortality data were collected after index mammogram by review of medical records. Adjusted hazard rations (aHR) were calculated using Cox regression models with interaction terms to evaluate the influence of age on the association between presence of BAC and mortality, after adjusting for age, diabetes, medications, race, current smoking, systolic blood pressure, total and HDL cholesterol, plus prior CVD. Results In the overall study population 8% had diabetes, 30% hypertension, 30% hyperlipidemia and 5% were current smokers. BAC was detectable in 522 of 9,027 (6%) of women age 40-59 years, 657 of 2,865 (23%) 60-69 years, and 702 of 1,358 (52%) 70+. Over mean follow-up of 4.2 years, there were 328 deaths (15.1%) among women with BAC. When subjects were separated by age, presence of BAC was associated with mortality among younger women (40-59 years: aHR 1.50 [95% CI 1.06-2.12]; 60-69 years: aHR 1.59 [95% CI 1.19-2.12], Figure ). Among women ages 70+, BAC was not a significant predictor of mortality (aHR 1.11 [95% CI 0.86-1.43]). There was a significant negative interaction effect of age with BAC for prediction of mortality (p &lt; 0.001). Conclusions: BAC is predictive of mortality, especially in younger women. BAC may be a useful screening adjunct to traditional CVD risk factors, to help identify women at an early age who may benefit from more targeted preventive measures. Figure

20 (PB-020) Poster Spotlight - Contrast Enhanced Mammography in Breast Cancer Surveillance

Background: Women with a personal history of breast cancer or DCIS (PHBC) are at increased risk of either a local recurrence or a new primary breast cancer. Adjunctive screening ultrasound or MRI is often used to supplement mammography. Contrast enhanced mammography (CEM) is reported to have higher sensitivity than MG and ultrasound, and similar performance with better accessibility than MRI.

135 (PB-048) Poster - Finding patient-reported deterrents to adjunct Breast Cancer screening among patients with dense breast tissues. A cross sectional study in Pakistan

Background: Women with dense breast tissues are at a greater risk of developing cancer because of reduced sensitivity of mammography. In order to address this issue, there’s need to incorporate adjunct screening modalities for this particular population. We aim to identify patient’s characteristics such as age, socioeconomic status, personal risk of breast cancer etc associated with patient cited concerns about adjunct breast screening modalities.