Low-dose Computed Tomography Lung Research Articles

Cone beam CT augmented fluoroscopy allows safe and efficient diagnosis of a difficult lung nodule

BackgroundDetection of small peripheral lung nodules is constantly increasing with the development of low dose computed tomography lung cancer screening programs. A tissue diagnosis is often required to confirm malignity, with endobronchial biopsies being associated with a lower pneumothorax rate than percutaneous approaches. Endoscopic diagnosis of peripheral small size lung nodules is however often challenging using traditional bronchoscopy and endobronchial ultrasound alone. New virtual bronchoscopic navigation techniques such as electromagnetic navigational bronchoscopy (ENB) have developed to improve peripheral navigation, with diagnostic yield however remaining in the 30–50% range for small lesions. Recent studies have shown the benefits of combining Cone beam computed tomography (CBCT) with ENB to improve diagnostic yield to up to 83%. The use of ENB however remains limited by disposable cost, bronchus sign dependency and inaccuracies due to CT to body divergence.Case presentationThis case report highlights the feasibility and usefulness of CBCT-guided bronchoscopy for the sampling of lung nodules difficult to reach through traditional bronchoscopy because of nodule size and peripheral position. Procedure was scheduled in a mobile robotic hybrid operating room with patient under general anaesthesia. CBCT acquisition was performed to localize the target lesion and plan the best path to reach it into bronchial tree. A dedicated software was used to segment the lesion and the bronchial path which 3D outlines were automatically fused in real time on the fluoroscopic images to augment live guidance. Navigation to the lesion was guided with bronchoscopy and augmented fluoroscopy alone. Before the sampling, CBCT imaging was repeated to confirm the proper position of the instrument into the lesion. Four transbronchial needle aspirations (TBNA) were performed and the tissue analysis showed a primary lung adenocarcinoma.ConclusionsCBCT and augmented fluoroscopy technique is a safe and effective and has potential to improve early stage peripheral lesions endobronchial diagnostic yield without ENB. Additional studies are warranted to confirm its safety, efficacy and technical benefits, both for diagnosis of oncological and non-oncological disease and for endobronchial treatment of inoperable patients.

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined and large population studies have indicated that low-dose computed tomography (CT) screening of the chest can significantly reduce this death rate. Recently, the usefulness of Deep Learning (DL) models for lung cancer risk assessment has been demonstrated. However, in many cases model performances are evaluated on small/medium size test sets, thus not providing strong model generalization and stability guarantees which are necessary for clinical adoption. In this work, our goal is to contribute towards clinical adoption by investigating a deep learning framework on larger and heterogeneous datasets while also comparing to state-of-the-art models. Three low-dose CT lung cancer screening datasets were used: National Lung Screening Trial (NLST, n = 3410), Lahey Hospital and Medical Center (LHMC, n = 3154) data, Kaggle competition data (from both stages, n = 1397 + 505) and the University of Chicago data (UCM, a subset of NLST, annotated by radiologists, n = 132). At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image context around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk for the entire CT scan. We trained our algorithm on a part of the NLST dataset, and validated it on the other datasets. Special care was taken to ensure there was no patient overlap between the train and validation sets. The proposed deep learning model is shown to: (a) generalize well across all three data sets, achieving AUC between 86% to 94%, with our external test-set (LHMC) being at least twice as large compared to other works; (b) have better performance than the widely accepted PanCan Risk Model, achieving 6 and 9% better AUC score in our two test sets; (c) have improved performance compared to the state-of-the-art represented by the winners of the Kaggle Data Science Bowl 2017 competition on lung cancer screening; (d) have comparable performance to radiologists in estimating cancer risk at a patient level.

A quality improvement method for lung LDCT images.

Low dose computed tomography (LDCT) reduces radiation damage to patients. However, with the decrease of radiation dose, LDCT images of the lung often appear some serious problems such as poor contrast, noise and streak artifacts. To improve the quality of lung LDCT images, this study proposed and investigated a new denoising method based on classification training structure combined dictionary for lung LDCT images. First, top-hat transform and anisotropic diffusion with a shock filter (ADSF) algorithm are used to enhance the image contrast and image details. Second, an adaptive dictionary is trained and used for noise reduction. Third, more image details are extracted from the residual image by using the atom activity measurement. The final result is obtained by combining the dictionary denoising result with the extracted detail information. The proposed method is then validated by both simulated and clinical lung LDCT images. Four metrics including Contrast-to-Noise Ratio (CNR), Noise Suppression Index (NSI), Edge Preserving Index (EPI), and Blurring Index (BI) are computed to quantitatively evaluate image quality. The results showed that the CNR, NSI, EPI, and BI of our method reached 8.953, 0.9500, 0.7230 and 0.0170, respectively. Noise and streak artifacts can be removed from lung LDCT images while keeping and retaining more details. Comparing with the results of other methods tested using the same dataset, this study demonstrated that our new method significantly improved quality of the lung LDCT images.

INTEREST GROUP SESSION—CANCER AND AGING: CURRENT CHALLENGES IN CANCER SCREENING AMONG OLDER MINORITY POPULATIONS

Cancer is an important public issue around the world. Among types of cancer, lung and colorectal cancer are the most common in men while breast and cervical cancer are the most common in women. Detection of early stage cancer via screening can significantly reduce the mortality and prolong life. Although cancer prevention and control has been served as the national priority, individual’s utilization of cancer screening services is low due to limited knowledge of cancer screening and ineffective patient-provider commutation, especially in minority populations. In this symposium, we will examine three scenarios that highlight the challenges of cancer screenings in minority populations. First, we will share the results from a mixed method study that investigate the knowledge and attitudes towards Low Dose Computed Tomography lung cancer (LDCT) screening and assess the smoking cessation needs for African Americans who receive LDCT screening in an effort to reduce the health burden of lung cancer. The next study will discuss how the characteristics of older Chinese adults from the United States and Taiwan are associated cancer screening communication with physicians (i.e., whether doctor recommended screenings and whether communicated screenings with doctor). Lastly, we will share the results from a cross-sectional study that analyzed 10 years data of National Health Interview Survey to examine the difference in LDCT screening eligibility among Asian American (i.e., Chinese, Filipino, and other Asian) smokers. The discussant will summarize with an overview of the topic, and comment on the disparities of cancer screening in older minority populations.

Multidisciplinary approach to low-dose CT screening for lung cancer in a metropolitan community.

Lung cancer is the primary cause of cancer death in men and women in the USA, led by Kentucky. In 2015, the Centers for Medicare and Medicaid Services initiated annual lung cancer screening with a low-dose computed tomography (LDCT) scan. This observational cohort study evaluated the multidisciplinary approach to this screening in our metropolitan community. We present the prospective findings of patients who underwent a screening lung LDCT scan over a 2-year period at our institution in Kentucky. Patients who fulfilled the screening criteria were identified during an office visit with their primary care provider. Of the 4170 patients who underwent a screening lung LDCT scan, a total of 838 (20.9%) patients had nodules >4 mm. Of the 70 patients diagnosed with lung cancer, Stage 1 non-small cell lung cancer was most commonly detected [38 cases (54.3%)]. A follow-up lung LDCT scan (n = 897), pulmonary function test (n = 157), positron emission tomography scan (n = 12) and a lung biopsy (n = 53) were performed for certain individuals who had anomalies observed on the screening lung LDCT scan. A total of 42% of patients enrolled in group tobacco cessation classes quit smoking. This study provides a unique perspective of a lung LDCT scan screening program driven by primary care providers in a state plagued by cigarette smoking and lung cancer deaths and offers a valuable message into the prevention, high-risk screening and diagnosis of lung cancer.

Knowledge and attitudes towards low dose computed tomography lung cancer screening and smoking among African Americans-a mixed method study.

BackgroundThe purpose of this study is to investigate knowledge, attitudes, and smoking cessation needs for African Americans who receive low dose computed tomography (LDCT) in an effort to reduce the health burden of lung cancer.MethodsA mixed method study was conducted among African Americans who received LDCT. Data were gathered using a self-administered questionnaire and structured in-depth interview. Descriptive statistics were used to provide summary information on knowledge, attitude and smoking behaviors. Thematic analysis was used to analyze interview data. The sample size for both the quantitative and qualitative approach was fifteen.ResultsThe results showed that 73% of participants were male, the mean age was 61.8 (SD =4.6) years old, and 66.7% of participants had an income less than $20,000. Eighty percent had an education level of high school or below and 73.3% were overweight or obese. Smoking history was long (mean years =39 SD =14.9), but the number of cigarettes smoked per day was low (mean =9.2 SD =7.3), and 64% of the patients had a low nicotine dependence. Assessment of knowledge and attitudes towards LDCT revealed that participants had a moderate/lower knowledge score (mean =4.3 SD =2.6), and most had a positive attitude. All participants planned to quit smoking, with 73% planning to quit within the next 6 months. Similar findings were also observed in the qualitative analysis.ConclusionsAfrican Americans who receive LDCT lung cancer screening in this study have a moderate/lower knowledge score and positive attitude towards LDCT. Most were not heavy smokers and had a lower nicotine dependence. Understanding the factors associated with smoking cessation among at-risk African American smokers will help reduce disparities in lung cancer burden, and is important to improve health for medically underserved minority populations.

Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations. In this study, we investigated an important factor affecting the CT dose-the scan length, for this CT exam. A neural network model based on the "UNET" framework was established to segment the lung region in the CT scout images. It was trained initially with 247 chest X-ray images and then with 40 CT scout images. The mean Intersection over Union (IOU) and Dice coefficient were reported to be 0.954 and 0.976, respectively. Lung scan boundaries were determined from this segmentation and compared with the boundaries marked by an expert for 150 validation images, resulting an average 4.7% difference. Seven hundred seventy CT low-dose lung screening exams were retrospectively analyzed with the validated model. The average "desired" scan length was 252mm with a standard deviation of 28mm. The average "over-range" was 58.5mm or 24%. The upper boundary (superior) on average had an "over-range" of 17mm, and the lower boundary (inferior) on average had an "over-range" of 41mm. Further analysis of this data showed that the extent of "over-range" was independent of acquisition date, acquisition time, acquisition station, and patient age, but dependent on technologist and patient weight. We concluded that this machine learning method could effectively support quality control on the scan length for CT low-dose screening scans, enabling the eliminations of unnecessary patient dose.

Knowledge and attitudes towards low dose computed tomography lung cancer screening and smoking among African Americans—a mixed method study

Background: The purpose of this study is to investigate knowledge, attitudes, and smoking cessation needs for African Americans who receive low dose computed tomography (LDCT) in an effort to reduce the health burden of lung cancer. Methods: A mixed method study was conducted among African Americans who received LDCT. Data were gathered using a self-administered questionnaire and structured in-depth interview. Descriptive statistics were used to provide summary information on knowledge, attitude and smoking behaviors. Thematic analysis was used to analyze interview data. The sample size for both the quantitative and qualitative approach was fifteen. Results: The results showed that 73% of participants were male, the mean age was 61.8 (SD =4.6) years old, and 66.7% of participants had an income less than $20,000. Eighty percent had an education level of high school or below and 73.3% were overweight or obese. Smoking history was long (mean years =39 SD =14.9), but the number of cigarettes smoked per day was low (mean =9.2 SD =7.3), and 64% of the patients had a low nicotine dependence. Assessment of knowledge and attitudes towards LDCT revealed that participants had a moderate/lower knowledge score (mean =4.3 SD =2.6), and most had a positive attitude. All participants planned to quit smoking, with 73% planning to quit within the next 6 months. Similar findings were also observed in the qualitative analysis. Conclusions: African Americans who receive LDCT lung cancer screening in this study have a moderate/lower knowledge score and positive attitude towards LDCT. Most were not heavy smokers and had a lower nicotine dependence. Understanding the factors associated with smoking cessation among at-risk African American smokers will help reduce disparities in lung cancer burden, and is important to improve health for medically underserved minority populations.

Pulmonary Dual Hemodynamic Changes in Severe COPD Patients: A Quantitative Study Using Low-Dose CT Lung Perfusion Scan

Background: Computed tomography (CT) lung perfusion scan could be used to evaluate regional, morphologic, and functional changes in chronic obstructive pulmonary disease (COPD) noninvasively. However, the dual hemodynamic changes in severe COPD patients have not been studied quantitatively using CT lung perfusion scan. Objectives: To determine the dual hemodynamic changes quantitatively in patients with severe COPD by using low-dose CT lung perfusion scan. Patients and Methods: Fifteen patients with severe COPD (global initiative for chronic obstructive lung disease [GOLD] class IV) and 31 controls were enrolled. All participants received low-dose CT lung perfusion scan using a Toshiba 320-detector row dynamic volumetric CT. The perfusion parameters including pulmonary artery flow (PAF), bronchial artery flow (BAF), perfusion index [PI = PAF/(PAF + BAF)] and time to peak (TTP) of the time density curve were generated and compared between the impaired lung parenchyma in the COPD group and normal lung parenchyma in the control group. Results: The PAF and PI values in the COPD group were significantly lower than that in the control group (P < 0.001, P < 0.001), while the BAF value was significantly higher (P < 0.001). The TTP of impaired lung parenchyma was significantly prolonged compared to the normal lung parenchyma (P < 0.001). Conclusion: Patients with severe COPD have distinct changes in pulmonary dual hemodynamics. Low-dose lung perfusion scan using a 320-detector row dynamic volumetric CT could be used to evaluate the pulmonary dual hemodynamics.

Quantification of growth patterns of screen-detected lung cancers: The NELSON study

ObjectivesAlthough exponential growth is assumed for lung cancer, this has never been quantified in vivo. Aim of this study was to evaluate and quantify growth patterns of lung cancers detected in the Dutch-Belgian low-dose computed tomography (CT) lung cancer screening trial (NELSON), in order to elucidate the development and progression of early lung cancer. Materials and methodsSolid lung nodules found at ≥3 CT examinations before lung cancer diagnosis were included. Lung cancer volume (V) growth curves were fitted with a single exponential, expressed as V=V1 exp(t/τ), with t time from baseline (days), V1 estimated baseline volume (mm3), and τ estimated time constant. The R2 coefficient of determination was used to evaluate goodness of fit. Overall volume-doubling time for the individual lung cancer is given by τ*log(2). ResultsForty-seven lung cancers in 46 participants were included. Forty participants were male (87.0%); mean age was 61.7 years (standard deviation, 6.2 years). Median nodule size at baseline was 99.5mm3 (IQR: 46.8–261.8mm3). Nodules were followed for a median of 770 days (inter-quartile range: 383–1102 days) before lung cancer diagnosis. One cancer (2.1%) was diagnosed after six CT examinations, six cancers (12.8%) were diagnosed after five CTs, 14 (29.8%) after four CTs, and 26 cancers (55.3%) after three CTs. Lung cancer growth could be described by an exponential function with excellent goodness of fit (R2 0.98). Median overall volume-doubling time was 348 days (inter-quartile range: 222–492 days). ConclusionThis study based on CT lung cancer screening provides in vivo evidence that growth of cancerous small-to-intermediate sized lung nodules detected at low-dose CT lung cancer screening can be described by an exponential function such as volume-doubling time.

High-risk community and primary care providers knowledge about and barriers to low-dose computed topography lung cancer screening

IntroductionUntil recently, there has not been a valid and reliable screening test for lung cancer. As compared to chest X-ray, low-dose computed tomography (LDCT) lung cancer screening has demonstrated greater sensitivity resulting in lung cancer diagnosis at an earlier stage, thereby reducing lung cancer mortality among high-risk individuals by 20%. In the current study, we sought to examine knowledge and attitudes about LDCT screening for lung cancer among an ethnically and racially diverse sample of high risk (HR) community members and primary care providers (PCP). MethodsEligible individuals participated in a focus group using semi-structured interview guides. Focus groups were conducted with PCPs (by telephone) and HRs (in-person). Sessions were audio-taped and transcribed verbatim. The constant comparison method and content analysis were used to analyze results. ResultsThe majority of PCPs had limited knowledge of lung cancer CT screening. PCPs cited barriers to recommendation including, cost/insurance barriers and the potential for false positives. PCPs perceived the main benefit to be early detection of lung cancer. The majority of HRs had never heard of lung LDCT screening and had never had a healthcare provider recommend it to them. Perceived barriers included fear of results (bad news) and financial costs. The main perceived benefit was early detection. ConclusionLack of knowledge about LDCT was a key a barrier across both the PCP and HR. respondents. Understanding the barriers to lung screening across diverse community populations is necessary to improve screening rates and shared decision-making.

P1.03-042 Nodule Size is Poorly Represented by Nodule Diameter in Low-Dose CT Lung Cancer Screening

In lung cancer screening, at least one pulmonary nodule is found in over 50% of participants, of which 99% is benign. As lung cancer probability in low-dose computed tomography (CT) lung cancer screening usually is based on nodule size and growth rate, accurate nodule size determination is of major importance to decrease false positive screen results. Previous studies showed that nodule size measurements based on semi-automated volume are preferred over diameter measurements. Aim of this study was to determine the correlation between nodule diameter and nodule size of nodules found in low-dose CT lung cancer screening, and to directly compare it with semi-automated volume measurements. We investigated baseline data of 2,240 solid nodules of intermediate size (volume 50-500mm3) in 1,500 lung cancer screening participants. Nodule volume, x, y, and z diameter and minimum / maximum diameter in any direction were generated by semi-automated software (LungCARE, Siemens). Range in maximum axial and mean nodule diameter per nodule volume category (50-100mm3, 100-200mm3, 200-300mm3, 300-400mm3, 400-500mm3) was determined. Semi-automated nodule volume represented nodule size. Intra-nodule diameter variation was defined as maximum minus minimum nodule diameter. Median participant age was 59 years, 14.1% were women. Median nodule volume was 82.4 mm3 (interquartile range [IQR], 62.9–125.4 mm3). Median nodule diameter was 6.1 mm (IQR, 5.4–7.2 mm) for mean diameter, and 6.6 mm (IQR, 5.9–7.7 mm) for maximum axial diameter. Range in mean nodule diameter per volume category varied from 8.55 mm (3.0 – 11.5 mm) for nodules with volume of 50-100 mm3 to 6.1 mm (7.2 – 13.3 mm) for nodules with volume of 200-300 mm3; range in maximum axial diameter varied from 11.2 mm (7.3 – 18.5 mm) for nodules with volume of 200-300 mm3, to 7.0 mm (9.1 – 16.1 mm) for nodules with volume of 400-500 mm3. Intra-nodule diameters varied by a median of 2.8 mm (IQR, 2.2-3.7 mm). Intra-nodule diameter variation for smaller intermediate-sized nodules (50-200 mm3) was 2.8 mm (IQR 2.2-3.5 mm), and was smaller than intra-nodule diameter variation for larger intermediate-sized nodules (200-500 mm3; median 3.6 mm [IQR 2.5-5.1 mm], P<0.01). Nodule size is poorly represented by diameter, as a nodule has an infinite number of diameters, but only one volume. Therefore, use of nodule diameter measurements may lead to misclassification of lung cancer probability. Median intra-nodule diameter variation was found to be higher as the 1.5mm LungRADS cutoff for nodule growth.

MA03.06 Cost Effectiveness of Chest Scan Screeing for Lung Cancer in Abestos Occupational Exposure Subjects: A Model Based Study

The National Lung Screening trial (NLST) showed that screening with low-dose computed tomography (CT) compared with chest radiography reduced lung-cancer mortality. There is very few data's on subjects with occupational asbestos exposure. We examined the cost-effectiveness of CT lung cancer screening in a French cohort of asbestos post professional exposure subjects (APEXS cohort). We estimated mean lif-years, costs and incremental cost-effectiveness ration (ICER) for screening with low-dose CT compare to no screening in this population of asbestos exposed subjects. Estimations of life-years gained were based on the efficacy of NLST trial applied to APEXS cohort, adjusted to sex and age. Costs were limited to direct costs, from the payer perspective. We also performed sensitivity analysis based on several assumptions of screening program efficacy. Compared with no screening, screening with low-dose CT, over a period of 2 years, will cost, for 1000 subjects of APEXS cohort 312 645 €, will provide 9.4 additional life-years. The corresponding ICER was 33 102 € per life-gained. Sensitivity analysis showed that this result is sensitive to screening program efficacy (number, stage, and survival diagnosed by the program). ICER of low-dose CT lung cancer program in a cohort of asbestos post occupational exposure population appears as acceptable from the French health system.

Abstract 932: A plasma microRNAs test predicts prognosis and disease status at follow-up in screening-detected lung cancer patients

Abstract Background: We previously reported that a miRNA signature classifier (MSC) based on 24 plasma circulating microRNAs is a sensitive and early biomarker for lung cancer, able to increase the specificity of low dose computed tomography (LDCT) in screening trials (Boeri et al. PNAS 2011; Sozzi et al. JCO 2014). In the present study, we have investigated the capacity of plasma MSC to predict prognosis and monitor the disease status at follow-up in LDCT screening detected lung cancer patients. Methods: Between 2000 and 2010, 3411 heavy smokers were enrolled in a screening program, with annual (2225) or biennial (1186) LDCT. During the first five years of screening, a total of 77 lung cancer patients underwent plasma MSC analysis, with three levels of risk: High, Intermediate or Low. Median follow-up of the alive patients was 4.5 years (Inter Quartile Range, IQR = 6.5). Changes of MSC risk profile at follow-up was assessed in 33 patients, with longitudinal plasma samples (n = 96) collected before and after surgical resection. Results: Five-year survival was 78% for low- to intermediate risk MSC (median nc), and 35% for High risk MSC (median 2.9 yrs, p&amp;lt;0.01). The prognostic power of MSC persisted when the analysis was restricted to LDCT-detected cases after exclusion of interval cancers (5-years survival: 87% vs. 38% respectively, p&amp;lt;0.01). Within stage I, the 5-years survival was 100% in Low to Intermediate risk MSC and 77% in High risk MSC (p = 0.10). Reduction of MSC risk profile after surgery was observed in 73% of the 26 analyzable subjects who remained disease free, at median time of 18 months (IQR = 24). In all relapsing patients the MSC test returned to Intermediate or High risk at the time of second primary or metastatic progression. Conclusions: Plasma MSC predicted lung cancer outcome and tumor recurrence at follow-up, showing potential efficacy in LDCT screening and lung cancer management. Citation Format: Gabriella Sozzi, Stefano Sestini, Mattia Boeri, Carla Verri, Alfonso Marchiano', Carlotta Galeone, Nicola Sverzellati, Carlo La Vecchia, Ugo Pastorino. A plasma microRNAs test predicts prognosis and disease status at follow-up in screening-detected lung cancer patients. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 932. doi:10.1158/1538-7445.AM2015-932

Screening Medicare-Eligible People for Lung Cancer

The National Lung Screening Trial (NLST) showed that screening high-risk people with low-dose computed tomography (CT) vs. radiography lowered lung

Model-based detection of lung nodules in computed tomography exams 1: Thoracic computer-aided diagnosis

Rationale and objectives In this study, we developed a prototype model-based computer aided detection (CAD) system designed to automatically detect both solid and subsolid pulmonary nodules in computed tomography (CT) images. By using this CAD algorithm, along with the radiologist’s initial interpretation, we aim to improve the sensitivity of radiologic readings of CT lung exams. Materials and methods We have developed a model-based CAD algorithm through the use of precise mathematic models that capture scanner physics and anatomic information. Our model-based CAD algorithm uses multiple segmentation algorithms to extract noteworthy structures in the lungs and a Bayesian statistical model selection framework to determine the probability of various anatomical events throughout the lung. We tested this algorithm on 50 low-dose CT lung cancer screening cases in which ground truth was produced through readings by three expert chest radiologists. Results Using this model-based CAD algorithm on 50 low-dose CT cases, we measured potential sensitivity improvements of 7% and 5% in two radiologists with respect to all noncalcified nodules, solid and subsolid, greater than 5 mm in diameter. The third radiologist did not miss any nodules in the ground truth set. The CAD algorithm produced 8.3 false positives per case. Conclusion Our prototype CAD system demonstrates promising results as a tool to improve the quality of radiologic readings by increasing radiologist sensitivity. A significant advantage of this model-based approach is that it can be easily extended to support additional anatomic models as clinical understanding and scanning practices improve.