Combined Breast and Lung Cancer Screening Among Dual-Eligible Women: A Descriptive Study.

INTRODUCTION: Lung cancer is consistently the leading cause of cancer death among women in the United States, yet lung cancer screening (LCS) rates remain low. By contrast, screening mammography rates are reliably high, suggesting that screening mammography can be a "teachable moment" to increase LCS uptake among dual-eligible women. MATERIALS AND METHODS: This is a prospective survey study conducted at two academic institutions. Patients undergoing screening mammography were evaluated for LCS eligibility and offered enrollment in a pilot dual-cancer screening program. A series of surveys was administered to characterize participants' knowledge, perceptions, and attitudes about LCS before and after undergoing dual screening. Data were descriptively summarized. RESULTS: Between August 2022 and July 2023, 54 LCS-eligible patients were enrolled. The study cohort was 100% female and predominantly White (81%), with a median age of 57 y and median of 36 pack-y of smoking. Survey results showed that 98% felt they were at risk for lung cancer, with most (80%) motivated by early detection of potential cancer. Regarding screening barriers, 58% of patients lacked knowledge about LCS eligibility and 47% reported concerns about screening cost. Prior to undergoing LCS, 87% of patients expressed interest in combined breast and lung screening. Encouragingly, after LCS, 84% were likely or very likely to undergo dual screening again and 93% found the shared decision-making visit helpful or very helpful. CONCLUSIONS: Pairing breast and LCS is a feasible, acceptable intervention that, along with increasing patient and provider education about LCS, can increase LCS uptake and reduce lung cancer mortality.

AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection.

Background An artificial intelligence (AI) algorithm has been developed for fully automated body composition assessment of lung cancer screening noncontrast low-dose CT of the chest (LDCT) scans, but the utility of these measurements in disease risk prediction models has not been assessed. Purpose To evaluate the added value of CT-based AI-derived body composition measurements in risk prediction of lung cancer incidence, lung cancer death, cardiovascular disease (CVD) death, and all-cause mortality in the National Lung Screening Trial (NLST). Materials and Methods In this secondary analysis of the NLST, body composition measurements, including area and attenuation attributes of skeletal muscle and subcutaneous adipose tissue, were derived from baseline LDCT examinations by using a previously developed AI algorithm. The added value of these measurements was assessed with sex- and cause-specific Cox proportional hazards models with and without the AI-derived body composition measurements for predicting lung cancer incidence, lung cancer death, CVD death, and all-cause mortality. Models were adjusted for confounding variables including age; body mass index; quantitative emphysema; coronary artery calcification; history of diabetes, heart disease, hypertension, and stroke; and other PLCOM2012 lung cancer risk factors. Goodness-of-fit improvements were assessed with the likelihood ratio test. Results Among 20 768 included participants (median age, 61 years [IQR, 57-65 years]; 12 317 men), 865 were diagnosed with lung cancer and 4180 died during follow-up. Including the AI-derived body composition measurements improved risk prediction for lung cancer death (male participants: χ2 = 23.09, P < .001; female participants: χ2 = 15.04, P = .002), CVD death (males: χ2 = 69.94, P < .001; females: χ2 = 16.60, P < .001), and all-cause mortality (males: χ2 = 248.13, P < .001; females: χ2 = 94.54, P < .001), but not for lung cancer incidence (male participants: χ2 = 2.53, P = .11; female participants: χ2 = 1.73, P = .19). Conclusion The body composition measurements automatically derived from baseline low-dose CT examinations added predictive value for lung cancer death, CVD death, and all-cause death, but not for lung cancer incidence in the NLST. Clinical trial registration no. NCT00047385 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Fintelmann in this issue.

Left atrial appendage dimension predicts elevated brain natriuretic peptide in nonvalvular atrial fibrillation.

INTRODUCTION: BNP elevation in patients with AF is observed in the absence of heart failure; however, prior mechanistic studies have not included direct left atrial pressure measurements. This study sought to understand how emptying function of the left atrial appendage (LAA) and LAA dimension contributes to brain-natriuretic peptide elevations (BNP) in atrial fibrillation (AF) accounting for left atrial pressure (LAP). METHODS: 132 patients referredfor left atrial appendage occlusion (LAAO) were prospectively enrolled in this study. BNP levels and LAP were measured just before LAAO. Statistical analysis considered BNP, rhythm at time of procedure, LAP, LAA morphology, LAA size (ostial diameter, depth, volume), LAA emptying velocity, CHADS2-VASc score, body mass index (BMI), left ventricular ejection fraction (LVEF), estimated glomerular filtration rate (eGFR), and obstructive sleep apnea (OSA) diagnosis as covariates. RESULTS: Bivariate statistical analysis demonstrated positive associations with age, LAA ostial diameter, depth, and volume, LAP, AF status at time of measurement, OSA, and CHADS2-VASc score. BNP was negatively associated with LVEF, eGFR, LAA emptying velocity and BMI. With multivariate logistic regression including LAP as covariate, significant relationships between BNP and AF/AFL(OR 1.99 [1.03, 3.85]), LAP (OR 1.13 [1.06, 1.20]), LAA diameter (OR 1.14 [1.03, 1.27]), LAA depth (OR 1.14 [1.07, 1.22]), and LAA emptying velocity (OR 0.97 [0.96,0.99]) were observed; however, no significant associations were seen with LAA morphology or CHADS2-VASc score. CONCLUSIONS: BNP elevations in AF are associated with LAA size and function, but not CHADS2-VASc score or appendage morphology after accounting for changes in LAP.

NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022.

The NCCN Guidelines for Lung Cancer Screening recommend criteria for selecting individuals for screening and provide recommendations for evaluation and follow-up of lung nodules found during initial and subsequent screening. These NCCN Guidelines Insights focus on recent updates to the NCCN Guidelines for Lung Cancer Screening.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment.

The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.

Small airway determinants of airflow limitation in chronic obstructive pulmonary disease.

BACKGROUND: Although a variety of pathological changes have been described in small airways of patients with COPD, the critical anatomic features determining airflow limitation remain incompletely characterised. METHODS: We examined lung tissue specimens from 18 non-smokers without chronic lung disease and 55 former smokers with COPD for pathological features of small airways that could contribute to airflow limitation. Morphometric evaluation was performed for epithelial and subepithelial tissue thickness, collagen and elastin content, luminal mucus and radial alveolar attachments. Immune/inflammatory cells were enumerated in airway walls. Quantitative emphysema scoring was performed on chest CT scans. RESULTS: Small airways from patients with COPD showed thickening of epithelial and subepithelial tissue, mucus plugging and reduced collagen density in the airway wall (in severe COPD). In patients with COPD, we also observed a striking loss of alveolar attachments, which are connective tissue septa that insert radially into the small airway adventitia. While each of these parameters correlated with reduced airflow (FEV1), multivariable regression analysis indicated that loss of alveolar attachments was the major determinant of airflow limitation related to small airways. Neutrophilic infiltration of airway walls and collagen degradation in airway adventitia correlated with loss of alveolar attachments. In addition, quantitative analysis of CT scans identified an association between the extent of emphysema and loss of alveolar attachments. CONCLUSION: In COPD, loss of radial alveolar attachments in small airways is the pathological feature most closely related to airflow limitation. Destruction of alveolar attachments may be mediated by neutrophilic inflammation.

Evidence-based smoking cessation treatment: a comparison by healthcare system.

BACKGROUND: A systems-level approach to smoking cessation treatment may optimize healthcare provider adherence to guidelines. Institutions such as the Veterans Health Administration (VHA) are unique in their systematic approach, but comparisons of provider behavior in different healthcare systems are limited. METHODS: We surveyed general medicine providers and specialists in a large academic health center (AHC) and its affiliated VHA in the Mid-South in 2017 to determine the cross-sectional association of healthcare system in which the provider practiced (exposure: AHC versus VHA) with self-reported provision of evidence-based smoking cessation treatment (delivery of counseling plus smoking cessation medication or referral) at least once in the past 12 months (composite outcome). Multivariable logistic regression with adjustment for specialty was performed in 2017-2019. RESULTS: Of 625 healthcare providers surveyed, 407 (65%) responded, and 366 (59%) were analyzed. Most respondents practiced at the AHC (273[75%] vs VHA 93[25%]) and were general internists (215[59%]); pulmonologists (39[11%]); hematologists/oncologists (69[19%]); and gynecologists (43[12%]). Most respondents (328[90%]) reported the primary outcome. The adjusted odds of evidence-based smoking cessation treatment were higher among VHA vs. AHC healthcare providers (aOR = 4.3; 95% CI 1.3-14.4; p = .02). Health systems differed by provision of individual treatment components, including smoking cessation medication use (98% VHA vs. 90% AHC, p = 0.02) and referral to smoking cessation services (91% VHA vs. 65% AHC p = 0.001). CONCLUSIONS: VHA healthcare providers were significantly more likely to provide evidence-based smoking cessation treatment compared to AHC healthcare providers. Healthcare systems' prioritization of and investment in smoking cessation treatment is critical to improving providers' adherence to guidelines.

Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification.

With the rapid development of image acquisition and storage, multiple images per class are commonly available for computer vision tasks (e.g., face recognition, object detection, medical imaging, etc.). Recently, the recurrent neural network (RNN) has been widely integrated with convolutional neural networks (CNN) to perform image classification on ordered (sequential) data. In this paper, by permutating multiple images as multiple dummy orders, we generalize the ordered "RNN+CNN" design (longitudinal) to a novel unordered fashion, called Multi-path x-D Recurrent Neural Network (MxDRNN) for image classification. To the best of our knowledge, few (if any) existing studies have deployed the RNN framework to unordered intra-class images to leverage classification performance. Specifically, multiple learning paths are introduced in the MxDRNN to extract discriminative features by permutating input dummy orders. Eight datasets from five different fields (MNIST, 3D-MNIST, CIFAR, VGGFace2, and lung screening computed tomography) are included to evaluate the performance of our method. The proposed MxDRNN improves the baseline performance by a large margin across the different application fields (e.g., accuracy from 46.40% to 76.54% in VGGFace2 test pose set, AUC from 0.7418 to 0.8162 in NLST lung dataset). Additionally, empirical experiments show the MxDRNN is more robust to category-irrelevant attributes (e.g., expression, pose in face images), which may introduce difficulties for image classification and algorithm generalizability. The code is publicly available.

Low Provider Knowledge Is Associated With Less Evidence-Based Lung Cancer Screening.

BACKGROUND: Despite widespread recommendation and supportive policies, screening with low-dose CT (LDCT) is incompletely implemented in the US healthcare system. Low provider knowledge of the lung cancer screening (LCS) guidelines represents a potential barrier to implementation. Therefore, we tested the hypothesis that low provider knowledge of guidelines is associated with less provider-reported screening with LDCT. PATIENTS AND METHODS: A cross-sectional survey was performed in a large academic medical center and affiliated Veterans Health Administration in the Mid-South United States that comprises hospital and community-based practices. Participants included general medicine providers and specialists who treat patients aged >50 years. The primary exposure was LCS guideline knowledge (US Preventive Services Task Force/Centers for Medicare & Medicaid Services). High knowledge was defined as identifying 3 major screening eligibility criteria (55 years as initial age of screening eligibility, smoking status as current or former smoker, and smoking history of ≥30 pack-years), and low knowledge was defined as not identifying these 3 criteria. The primary outcome was self-reported LDCT order/referral within the past year, and the secondary outcome was screening chest radiograph. Multivariable logistic regression evaluated the adjusted odds ratio (aOR) of screening by knowledge. RESULTS: Of 625 providers recruited, 407 (65%) responded, and 378 (60.5%) were analyzed. Overall, 233 providers (62%) demonstrated low LCS knowledge, and 224 (59%) reported ordering/referring for LDCT. The aOR of ordering/referring LDCT was less among providers with low knowledge (0.41; 95% CI, 0.24-0.71) than among those with high knowledge. More providers with low knowledge reported ordering screening chest radiographs (aOR, 2.7; 95% CI, 1.4-5.0) within the past year. CONCLUSIONS: Referring provider knowledge of LCS guidelines is low and directly proportional to the ordering rate for LDCT in an at-risk US population. Strategies to advance evidence-based LCS should incorporate provider education and system-level interventions to address gaps in provider knowledge.

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks.

BACKGROUND: The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel choice is important for quantitative CT-based assessment as kernel differences can lead to substantial shifts in measurements unrelated to underlying anatomical structures. PURPOSE: In this study, we investigate kernel harmonization in a multi-vendor low-dose CT lung cancer screening cohort and evaluate our approach's validity in quantitative CT-based assessments. METHODS: Using the National Lung Screening Trial, we identified CT scan pairs of the same sessions with one reconstructed from a soft tissue kernel and one from a hard kernel. In total, 1000 pairs of five different paired kernel types (200 each) were identified. We adopt the pix2pix architecture to train models for kernel conversion. Each model was trained on 100 pairs and evaluated on 100 withheld pairs. A total of 10 models were implemented. We evaluated the efficacy of kernel conversion based on image similarity metrics including root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) as well as the capability of the models to reduce measurement shifts in quantitative emphysema and body composition measurements. Additionally, we study the reproducibility of standard radiomic features for all kernel pairs before and after harmonization. RESULTS: Our approach effectively converts CT images from one kernel to another in all paired kernel types, as indicated by the reduction in RMSE (p < 0.05) and an increase in the PSNR (p < 0.05) and SSIM (p < 0.05) for both directions of conversion for all pair types. In addition, there is an increase in the agreement for percent emphysema, skeletal muscle area, and subcutaneous adipose tissue (SAT) area for both directions of conversion. Furthermore, radiomic features were reproducible when compared with the ground truth features. CONCLUSIONS: Kernel conversion using deep learning reduces measurement variation in percent emphysema, muscle area, and SAT area.

Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer.

BACKGROUND: Large community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models. OBJECTIVE: A robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale. METHODS: In this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center. RESULTS: Our approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs. CONCLUSION: This work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations.

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

ACR Appropriateness Criteria® Workup of Pleural Effusion or Pleural Disease.

Pleural effusions are categorized as transudative or exudative, with transudative effusions usually reflecting the sequala of a systemic etiology and exudative effusions usually resulting from a process localized to the pleura. Common causes of transudative pleural effusions include congestive heart failure, cirrhosis, and renal failure, whereas exudative effusions are typically due to infection, malignancy, or autoimmune disorders. This document summarizes appropriateness guidelines for imaging in four common clinical scenarios in patients with known or suspected pleural effusion or pleural disease. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation.

Purpose: Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication. Segmenting lung lobes is an essential step in this analysis, but leading lobe segmentation algorithms have not been validated for lung screening computed tomography (CT). Approach: In this work, we develop an automated approach to lobar emphysema quantification and study its association with lung cancer incidence. We combine self-supervised training with level set regularization and finetuning with radiologist annotations on three datasets to develop a lobe segmentation algorithm that is robust for lung screening CT. Using this algorithm, we extract quantitative CT measures for a cohort (n=1189) from the National Lung Screening Trial and analyze the multivariate association with lung cancer incidence. Results: Our lobe segmentation approach achieved an external validation Dice of 0.93, significantly outperforming a leading algorithm at 0.90 (p<0.01). The percentage of low attenuation volume in the right upper lobe was associated with increased lung cancer incidence (odds ratio: 1.97; 95% CI: [1.06, 3.66]) independent of PLCOm2012 risk factors and diagnosis of whole lung emphysema. Quantitative lobar emphysema improved the goodness-of-fit to lung cancer incidence (χ2=7.48, p=0.02). Conclusions: We are the first to develop and validate an automated lobe segmentation algorithm that is robust to smoking-related pathology. We discover a quantitative risk factor, lending further evidence that regional emphysema is independently associated with increased lung cancer incidence. The algorithm is provided at https://github.com/MASILab/EmphysemaSeg.

Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography.

Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent features computed from repeated images can capture those changes and help identify malignant lesions by their temporal behavior. However, longitudinal medical imaging presents the unique challenge of sparse, irregular time intervals in data acquisition. While self-attention has been shown to be a versatile and efficient learning mechanism for time series and natural images, its potential for interpreting temporal distance between sparse, irregularly sampled spatial features has not been explored. In this work, we propose two interpretations of a time-distance vision transformer (ViT) by using (1) vector embeddings of continuous time and (2) a temporal emphasis model to scale self-attention weights. The two algorithms are evaluated based on benign versus malignant lung cancer discrimination of synthetic pulmonary nodules and lung screening computed tomography studies from the National Lung Screening Trial (NLST). Experiments evaluating the time-distance ViTs on synthetic nodules show a fundamental improvement in classifying irregularly sampled longitudinal images when compared to standard ViTs. In cross-validation on screening chest CTs from the NLST, our methods (0.785 and 0.786 AUC respectively) significantly outperform a cross-sectional approach (0.734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0.779 AUC) on benign versus malignant classification. This work represents the first self-attention-based framework for classifying longitudinal medical images. Our code is available at https://github.com/tom1193/time-distance-transformer.

Stratification of Lung Cancer Risk with Thoracic Imaging Phenotypes.

In lung cancer screening, estimation of future lung cancer risk is usually guided by demographics and smoking status. The role of constitutional profiles of human body, a.k.a. body habitus, is increasingly understood to be important, but has not been integrated into risk models. Chest low dose computed tomography (LDCT) is the standard imaging study in lung cancer screening, with the capability to discriminate differences in body composition and organ arrangement in the thorax. We hypothesize that the primary phenotypes identified using lung screening chest LDCT can form a representation of body habitus and add predictive power for lung cancer risk stratification. In this pilot study, we evaluated the feasibility of body habitus image-based phenotyping on a large lung screening LDCT dataset. A thoracic imaging manifold was estimated based on an intensity-based pairwise (dis)similarity metric for pairs of spatial normalized chest LDCT images. We applied the hierarchical clustering method on this manifold to identify the primary phenotypes. Body habitus features of each identified phenotype were evaluated and associated with future lung cancer risk using time-to-event analysis. We evaluated the method on the baseline LDCT scans of 1,200 male subjects sampled from National Lung Screening Trial. Five primary phenotypes were identified, which were associated with highly distinguishable clinical and body habitus features. Time-to-event analysis against future lung cancer incidences showed two of the five identified phenotypes were associated with elevated future lung cancer risks (HR=1.61, 95% CI = [1.08, 2.38], p=0.019; HR=1.67, 95% CI = [0.98, 2.86], p=0.057). These results indicated that it is feasible to capture the body habitus by image-base phenotyping using lung screening LDCT and the learned body habitus representation can potentially add value for future lung cancer risk stratification.