Maternal and Fetal Radiation-Induced Cancer Risk From Computed Tomography Pulmonary Angiography During Pregnancy: A Retrospective Cohort Study Across a Multihospital Integrated Health Care Network.

OBJECTIVE: Computed tomography pulmonary angiogram (CTPA) is important to evaluate suspected pulmonary embolism in pregnancy but has maternal/fetal radiation risks. The objective of this study was to estimate maternal and fetal radiation-induced cancer risk from CTPA during pregnancy. METHODS: Simulation modeling via the National Cancer Institute's Radiation Risk Assessment Tool was used to estimate excess cancer risks from 17 organ doses from CTPA during pregnancy, with doses determined by a radiation dose indexing monitoring system. Organ doses were obtained from a radiation dose indexing monitoring system. Maternal and fetal cancer risks per 100,000 were calculated for male and female fetuses and several maternal ages. RESULTS: The 534 CTPA examinations had top 3 maternal organ doses to the breast, lung, and stomach of 17.34, 15.53, and 9.43 mSv, respectively, with a mean uterine dose of 0.21 mSv. The total maternal excess risks of developing cancer per 100,000 were 181, 151, 121, 107, 94.5, 84, and 74.4, respectively, for a 20-, 25-, 30-, 35-, 40-, 45-, and 50-year-old woman undergoing CTPA, compared with baseline cancer risks of 41,408 for 20-year-old patients. The total fetal excess risks of developing cancer per 100,000 were 12.3 and 7.3 for female and male fetuses, respectively, when compared with baseline cancer risks of 41,227 and 48,291. DISCUSSION: Excess risk of developing cancer from CTPA was small relative to baseline cancer risk for pregnant patients and fetuses, decreased for pregnant patients with increasing maternal age, and was greater for female fetuses than male fetuses.

Patient and Fetal Radiation-Induced Malignancy Risk From Imaging For Evaluation of Pulmonary Embolism in Pregnancy.

BACKGROUND: Imaging for diagnosis of suspected pulmonary embolism in pregnancy presents radiation concerns for patient and fetus. OBJECTIVES: Estimate the risks of radiation-induced breast cancer and childhood leukemia from common imaging techniques for the evaluation of suspected pulmonary embolism in pregnancy. METHODS: Breast and uterine absorbed doses for various imaging techniques were input into the National Cancer Institute Radiation Risk Assessment Tool to calculate risk of breast cancer for the patient and childhood leukemia for the fetus. Absorbed doses were obtained by synthesizing data from a recent systematic review and the International Commission on Radiological Protection. Primary outcomes were the estimated excess incidences of breast cancer and childhood leukemia per 100,000 exposures. RESULTS: Baseline incidences of breast cancer for a 30-year-old woman and childhood leukemia for a male fetus were 13,341 and 939, respectively. Excess incidences of breast cancer were 0.003 and 0.275 for a single and two-view chest radiograph, respectively, 9.53 and 20.6 for low- and full-dose computed tomography pulmonary angiography (CTPA), respectively, 0.616 and 2.54 for low- and full-dose perfusion scan, respectively, and 0.732 and 2.66 for low- and full-dose ventilation perfusion scan, respectively. Excess incidences of childhood leukemia were 0.004 and 0.007 for a single and two-view chest radiograph, respectively, 0.069 and 0.490 for low- and full-dose CTPA, respectively, 0.359 and 1.47 for low- and full-dose perfusion scan, respectively, and 0.856 and 1.97 for low- and full-dose ventilation perfusion scan, respectively. CONCLUSION: Excess cancer risks for all techniques were small relative to baseline cancer risks, with CTPA techniques carrying slightly higher risk of breast cancer for the patient and ventilation perfusion techniques a higher risk of childhood leukemia.

Utilization of a Guideline-recommended Imaging Paradigm for Pregnant Patients With Suspicion of Pulmonary Embolism.

PURPOSE: A dose reduction imaging paradigm utilizing chest x-ray (CXR) to triage between computed tomography pulmonary angiography (CTPA) and lung scintigraphy (LS) was introduced in 2001 and adopted in 2012 by the American Thoracic Society/Society of Thoracic Radiology (ATS) guideline for the evaluation of pulmonary embolism in pregnancy. We aimed to assess the utilization of this imaging paradigm preadoption and postadoption by the ATS guideline, and identify factors associated with its utilization. MATERIALS AND METHODS: This retrospective cohort study evaluated consecutive pregnant patients who received CTPA or LS for the evaluation of pulmonary embolism in pregnancy at 2 tertiary hospitals between September 2008 and March 2017, excluding 2012 for guideline release washout. Utilization of the imaging paradigm was defined per patient by the use of CXR before advanced imaging, with CTPA performed following positive CXR and LS performed following negative CXR. Multivariate analyses were performed to assess factors associated with utilization of the imaging paradigm. P <0.05 is considered significant. RESULTS: Overall, 9.8% (63/643) of studies utilized the dose reduction imaging paradigm, 13.3% (34/256) before the guidelines, and 7.5% (29/387) after. Multivariable analysis showed that the dose reduction imaging paradigm utilization was higher for inpatients (odds ratio [OR]: 4.5) and outpatients (OR: 3.1) relative to the emergency department patients, and lower for second (OR: 0.3) and third (OR: 0.2) trimester patients, without significant differences by study priority, patient age, or patient race. CONCLUSIONS: Guideline-recommended dose reduction imaging paradigm utilization was low, and decreased after guideline publication. Utilization varied by patient setting and trimester, which are potential targets for interventions to improve guideline compliance.

The patient-friendly radiology report: history, evolution, challenges and opportunities.

The past several decades have witnessed dramatic developments and improvements in the field of radiology, including technologic innovations and new imaging modalities, picture archiving and communication systems, and the rise of artificial intelligence. At the same time, an evolution has been occurring in a fundamental component of radiology practice - the radiologist's report. Initially, the radiology report was a private written communication between the radiologist and the referring physician 1,2. Today, the report is an electronic document, displayed on web portals, and visible to both physicians and the patients themselves.3 A provision in the 21st Century Cures Act, signed into law on December 13, 2016, ensures that radiology reports in the electronic health record are visible to patients without delay 4. To meet modern patient expectations and legal requirements, the structure and purpose of the radiologist report is changing. This article will provide an overview of the history of radiology reporting and the law, discuss the role of the radiologist report within the context of patient and family centered care, review current strategies and investigations in patient-friendly reporting, and summarize radiology reporting challenges and opportunities for the future.

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19.

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19. This retrospective cohort study evaluated the IMPROVE-DD VTE RAM in adult patients with COVID-19 admitted to one of thirteen Northwell Health hospitals in the New York metropolitan area between March 1, 2020 and April 27, 2020. VTE was defined as new-onset symptomatic deep venous thrombosis or pulmonary embolism. To assess the predictive value of the RAM, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Of 9407 patients who met study criteria, 274 patients developed VTE with a prevalence of 2.91%. The VTE rate was 0.41% for IMPROVE-DD score 0-1 (low risk), 1.21% for score 2-3 (moderate risk), and 5.30% for score ≥ 4 (high risk). Approximately 45.7% of patients were classified as high VTE risk, 33.3% moderate risk, and 21.0% low risk. Discrimination of low versus moderate-high VTE risk demonstrated sensitivity 0.971, specificity 0.215, PPV 0.036, and NPV 0.996. ROC AUC was 0.703. In this external validation study, the IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized COVID-19 patients at low, moderate, and high VTE risk.

Sex-Based Differences in COVID-19 Outcomes.

Background: Smaller studies suggest lower morbidity and mortality associated with coronavirus disease 2019 (COVID-19) in women. Our aim is to assess the impact of female sex on outcomes in a large cohort of patients hospitalized with COVID-19. Materials and Methods: This is a retrospective observational cohort study of 10,630 adult patients hospitalized with a confirmed COVID-19 polymerase chain reaction between March 1, 2020 and April 27, 2020, with follow-up conducted through June 4, 2020. Logistic regression was used to examine the relationship between sex and the primary outcomes, including length of stay, admission to intensive care unit (ICU), need for mechanical ventilation, pressor requirement, and all-cause mortality as well as major adverse events and in-hospital COVID-19 treatments. Results: In the multivariable analysis, women had 27% lower odds of in-hospital mortality (odds ratio [OR] = 0.73, 95% confidence interval [CI] 0.66-0.81; p < 0.001), 24% lower odds of ICU admission (OR = 0.76, 95% CI 0.69-0.84; p < 0.001), 26% lower odds of mechanical ventilation (OR = 0.74, 95% CI 0.66-0.82; p < 0.001), and 25% lower odds of vasopressor requirement (OR = 0.75, 95% CI 0.67-0.84; p < 0.001). Women had 34% less odds of having acute cardiac injury (OR = 0.66, 95% CI 0.59-0.74; p < 0.001; n = 7,289), 16% less odds of acute kidney injury (OR = 0.84, 95% CI 0.76-0.92; p < 0.001; n = 9,840), and 27% less odds of venous thromboembolism (OR = 0.73, 95% CI 0.56-0.96; p < 0.02; c-statistic 0.85, n = 9,407). Conclusions: Female sex is associated with lower odds of in-hospital outcomes, major adverse events, and all-cause mortality. There may be protective mechanisms inherent to female sex, which explain differences in COVID-19 outcomes.

Postdischarge thromboembolic outcomes and mortality of hospitalized patients with COVID-19: the CORE-19 registry.

Thromboembolic events, including venous thromboembolism (VTE) and arterial thromboembolism (ATE), and mortality from subclinical thrombotic events occur frequently in coronavirus disease 2019 (COVID-19) inpatients. Whether the risk extends postdischarge has been controversial. Our prospective registry included consecutive patients with COVID-19 hospitalized within our multihospital system from 1 March to 31 May 2020. We captured demographics, comorbidities, laboratory parameters, medications, postdischarge thromboprophylaxis, and 90-day outcomes. Data from electronic health records, health informatics exchange, radiology database, and telephonic follow-up were merged. Primary outcome was a composite of adjudicated VTE, ATE, and all-cause mortality (ACM). Principal safety outcome was major bleeding (MB). Among 4906 patients (53.7% male), mean age was 61.7 years. Comorbidities included hypertension (38.6%), diabetes (25.1%), obesity (18.9%), and cancer history (13.1%). Postdischarge thromboprophylaxis was prescribed in 13.2%. VTE rate was 1.55%; ATE, 1.71%; ΑCM, 4.83%; and MB, 1.73%. Composite primary outcome rate was 7.13% and significantly associated with advanced age (odds ratio [OR], 3.66; 95% CI, 2.84-4.71), prior VTE (OR, 2.99; 95% CI, 2.00-4.47), intensive care unit (ICU) stay (OR, 2.22; 95% CI, 1.78-2.93), chronic kidney disease (CKD; OR, 2.10; 95% CI, 1.47-3.0), peripheral arterial disease (OR, 2.04; 95% CI, 1.10-3.80), carotid occlusive disease (OR, 2.02; 95% CI, 1.30-3.14), IMPROVE-DD VTE score ≥4 (OR, 1.51; 95% CI, 1.06-2.14), and coronary artery disease (OR, 1.50; 95% CI, 1.04-2.17). Postdischarge anticoagulation was significantly associated with reduction in primary outcome (OR, 0.54; 95% CI, 0.47-0.81). Postdischarge VTE, ATE, and ACM occurred frequently after COVID-19 hospitalization. Advanced age, cardiovascular risk factors, CKD, IMPROVE-DD VTE score ≥4, and ICU stay increased risk. Postdischarge anticoagulation reduced risk by 46%.

Incidence of Venous Thromboembolism and Mortality in Patients with Initial Presentation of COVID-19.

Venous thromboembolism (VTE) has emerged as an important issue in patients with COVID-19. The purpose of this study is to identify the incidence of VTE and mortality in COVID-19 patients initially presenting to a large health system. Our retrospective study included adult patients (excluding patients presenting with obstetric/gynecologic conditions) across a multihospital health system in the New York Metropolitan Region from March 1-April 27, 2020. VTE and mortality rates within 8 h of assessment were described. In 10,871 adults with COVID-19, 118 patients (1.09%) were diagnosed with symptomatic VTE (101 pulmonary embolism, 17 deep vein thrombosis events) and 28 patients (0.26%) died during initial assessment. Among these 146 patients, 64.4% were males, 56.8% were 60 years or older, 15.1% had a BMI > 35, and 11.6% were admitted to the intensive care unit. Comorbidities included hypertension (46.6%), diabetes (24.7%), hyperlipidemia (14.4%), chronic lung disease (12.3%), coronary artery disease (11.0%), and prior VTE (7.5%). Key medications included corticosteroids (22.6%), statins (21.2%), antiplatelets (20.6%), and anticoagulants (20.6%). Highest D-Dimer was greater than six times the upper limit of normal in 51.4%. Statin and antiplatelet use were associated with decreased VTE or mortality (each p < 0.01). In COVID-19 patients who initially presented to a large multihospital health system, the overall symptomatic VTE and mortality rate was over 1.0%. Statin and antiplatelet use were associated with decreased VTE or mortality. The potential benefits of antithrombotics in high risk COVID-19 patients during the pre-hospitalization period deserves study.

Validation of the IMPROVE-DD risk assessment model for venous thromboembolism among hospitalized patients with COVID-19.

BACKGROUND: Antithrombotic guidance statements for hospitalized patients with coronavirus disease 2019 (COVID-19) suggest a universal thromboprophylactic strategy with potential to escalate doses in high-risk patients. To date, no clear approach exists to discriminate patients at high risk for venous thromboembolism (VTE). OBJECTIVES: The objective of this study is to externally validate the IMPROVE-DD risk assessment model (RAM) for VTE in a large cohort of hospitalized patients with COVID-19 within a multihospital health system. METHODS: This retrospective cohort study evaluated the IMPROVE-DD RAM on adult inpatients with COVID-19 hospitalized between March 1, 2020, and April 27, 2020. Diagnosis of VTE was defined by new acute deep venous thrombosis or pulmonary embolism by Radiology Department imaging or point-of-care ultrasound. The receiver operating characteristic (ROC) curve was plotted and area under the curve (AUC) calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using standard methods. RESULTS: A total of 9407 patients were included, with a VTE prevalence of 2.9%. The VTE rate was 0.4% for IMPROVE-DD score 0-1 (low risk), 1.3% for score 2-3 (moderate risk), and 5.3% for score ≥ 4 (high risk). Approximately 45% of the total population scored high VTE risk, while 21% scored low VTE risk. IMPROVE-DD discrimination of low versus medium/high risk showed sensitivity of 0.971, specificity of 0.218, PPV of 0.036, and NPV of 0.996. ROC AUC was 0.702. CONCLUSIONS: The IMPROVE-DD VTE RAM demonstrated very good discrimination to identify hospitalized patients with COVID-19 as low, moderate, and high VTE risk in this large external validation study with potential to individualize thromboprophylactic strategies.

Abdominal pelvic CT findings compared between COVID-19 positive and COVID-19 negative patients in the emergency department setting.

PURPOSE: Manifestations of COVID-19 are primarily respiratory based, however, gastrointestinal symptoms are now recognized as an important component of the disease. The purpose of this study is to evaluate differences in abdominal pelvic CT findings in the emergency department by COVID-19 test result. METHODS: This retrospective study identified patients tested by PCR for COVID-19 infection who underwent abdominal pelvic CT scan in the ED across an academic health system from March 15 to April 15, 2020. Radiology reports were reviewed for the presence of ground glass opacity in the lungs and acute abdominal pathology. A subset of patients with acute abdominal pathology were identified with inflammatory pathology in organs with high ACE2 receptor expression including bowel, pancreas, urinary bladder, and kidney. CT findings for COVID positive versus negative patients were compared with Chi-square test. RESULTS: 597 patients tested by PCR for COVID-19 infection underwent abdominal pelvic CT scan, 44% were COVID-19 positive. COVID-19 positive patients demonstrated significantly more ground glass opacity at the lung bases, 65.1%, (222/341) versus 12.4% (33/266), p < 0.001), and significantly less acute abdominal findings, 23.8% (81/341) versus 45.5% (121/266), p ≤ 0.001). When abdominal pathology was present, COVID-19 positive patients had higher rate of inflammatory pathology 58% (47/81) versus 29.8% (36/121). CONCLUSIONS: In patients undergoing abdominopelvic CT from the ED, COVID-19 positive patients are more likely to have ground glass opacities at the lung bases and less likely to have acute abdominal pathology compared with COVID-19 negative patients. Further, COVID-19 positive patients are more likely to have inflammation of organs with high expression of ACE2 receptors than other types of acute abdominal pathology.

CT colonography's role in the COVID-19 pandemic: a safe(r), socially distanced total colon examination.

PURPOSE: To describe the favorable procedural profile of CT colonography (CTC) during the COVID-19 pandemic. CONCLUSION: Postponement of cancer screening due to COVID-19 has resulted in a backlog of individuals needing to undergo structural examination of the colon. The experience during the initial COVID-19 surge with urgent evaluation of the colon for transplant patients prior to transplant suggests that CTC can be done in a lower risk manner as compared to other structural examinations. The procedural profile of CTC is advantageous during this pandemic as maintaining social distancing and preserving healthcare supplies including PPE are of paramount importance. CTC is an important option to utilize in the screening armamentarium to allow effective screening of average risk asymptomatic individuals in the COVID-19 era.

3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.

Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. human faces) from multiple slice (or color) images, a task similar to the polyp differentiation, given a large learning database. This study explores the potential of CNN learning from multiple slice (or feature) images to differentiate malignant from benign polyps from a relatively small database with pathological ground truth, including 32 malignant and 31 benign polyps represented by volumetric computed tomographic (CT) images. The feature image in this investigation is the gray-level co-occurrence matrix (GLCM). For each volumetric polyp, there are 13 GLCMs, computed from each of the 13 directions through the polyp volume. For comparison purpose, the CNN learning is also applied to the multi-slice CT images of the volumetric polyps. The comparison study is further extended to include Random Forest (RF) classification of the Haralick texture features (derived from the GLCMs). From the relatively small database, this study achieved scores of 0.91/0.93 (two-fold/leave-one-out evaluations) AUC (area under curve of the receiver operating characteristics) by using the CNN on the GLCMs, while the RF reached 0.84/0.86 AUC on the Haralick features and the CNN rendered 0.79/0.80 AUC on the multiple-slice CT images. The presented CNN learning from the GLCMs can relieve the challenge associated with relatively small database, improve the classification performance over the CNN on the raw CT images and the RF on the Haralick features, and have the potential to perform the clinical task of differentiating malignant from benign polyps with pathological ground truth.

Multilayer feature selection method for polyp classification via computed tomographic colonography.

Polyp classification is a feature selection and clustering process. Picking the most effective features from multiple polyp descriptors without redundant information is a great challenge in this procedure. We propose a multilayer feature selection method to construct an optimized descriptor for polyp classification with a feature-grouping strategy in a hierarchical framework. First, the proposed method makes good use of image metrics, such as intensity, gradient, and curvature, to divide their corresponding polyp descriptors into several feature groups, which are the preliminary units of this method. Then each preliminary unit generates two ranked descriptors, i.e., their optimized variable groups (OVGs) and preliminary classification measurements. Next, a feature dividing-merging (FDM) algorithm is designed to perform feature merging operation hierarchically and iteratively. Unlike traditional feature selection methods, the proposed FDM algorithm includes two steps for feature dividing and feature merging. At each layer, feature dividing selects the OVG with the highest area under the receiver operating characteristic curve (AUC) as the baseline while other descriptors are treated as its complements. In the fusion step, the FDM merges some variables with gains into the baseline from the complementary descriptors iteratively on every layer until the final descriptor is obtained. This proposed model (including the forward step algorithm and the FDM algorithm) is a greedy method that guarantees clustering monotonicity of all OVGs from the bottom to the top layer. In our experiments, all the selected results from each layer are reported by both graphical illustration and data analysis. Performance of the proposed method is compared to five existing classification methods by a polyp database of 63 samples with pathological reports. The experimental results show that our proposed method outperforms other methods by 4% to 23% gains in terms of AUC scores.

Multi-scale characterizations of colon polyps via computed tomographic colonography.

Texture features have played an essential role in the field of medical imaging for computer-aided diagnosis. The gray-level co-occurrence matrix (GLCM)-based texture descriptor has emerged to become one of the most successful feature sets for these applications. This study aims to increase the potential of these features by introducing multi-scale analysis into the construction of GLCM texture descriptor. In this study, we first introduce a new parameter - stride, to explore the definition of GLCM. Then we propose three multi-scaling GLCM models according to its three parameters, (1) learning model by multiple displacements, (2) learning model by multiple strides (LMS), and (3) learning model by multiple angles. These models increase the texture information by introducing more texture patterns and mitigate direction sparsity and dense sampling problems presented in the traditional Haralick model. To further analyze the three parameters, we test the three models by performing classification on a dataset of 63 large polyp masses obtained from computed tomography colonoscopy consisting of 32 adenocarcinomas and 31 benign adenomas. Finally, the proposed methods are compared to several typical GLCM-texture descriptors and one deep learning model. LMS obtains the highest performance and enhances the prediction power to 0.9450 with standard deviation 0.0285 by area under the curve of receiver operating characteristics score which is a significant improvement.

Structured reporting and quality control in CT colonography.

Standardized recommended techniques for performing and reporting CT colonography (CTC) examinations were developed by a consensus of experts. Published reporting guidelines, known as the CT colonography reporting and data system supplemented by recently updated comprehensive recommendations were incorporated into the American College of Radiology (ACR) practice guidelines. The application of continuous quality improvement to the practice of CT was aided by the development of an ACR national data registry (NRDR) for CTC that addressed both process and outcome quality measures. These measures can be used to benchmark an institution's CTC practice as compared to all participants. This article will discuss the best practices for reporting CTC and describe the use of NRDR to foster quality CTC performance.

AnaFe: Visual Analytics of Image-derived Temporal Features-Focusing on the Spleen.

We present a novel visualization framework, AnaFe, targeted at observing changes in the spleen over time through multiple image-derived features. Accurate monitoring of progressive changes is crucial for diseases that result in enlargement of the organ. Our system is comprised of multiple linked views combining visualization of temporal 3D organ data, related measurements, and features. Thus it enables the observation of progression and allows for simultaneous comparison within and between the subjects. AnaFe offers insights into the overall distribution of robustly extracted and reproducible quantitative imaging features and their changes within the population, and also enables detailed analysis of individual cases. It performs similarity comparison of temporal series of one subject to all other series in both sick and healthy groups. We demonstrate our system through two use case scenarios on a population of 189 spleen datasets from 68 subjects with various conditions observed over time.

Texture Feature Extraction and Analysis for Polyp Differentiation via Computed Tomography Colonography.

Image textures in computed tomography colonography (CTC) have great potential for differentiating non-neoplastic from neoplastic polyps and thus can advance the current CTC detection-only paradigm to a new level with diagnostic capability. However, image textures are frequently compromised, particularly in low-dose CT imaging. Furthermore, texture feature extraction may vary, depending on the polyp spatial orientation variation, resulting in variable results. To address these issues, this study proposes an adaptive approach to extract and analyze the texture features for polyp differentiation. Firstly, derivative (e.g. gradient and curvature) operations are performed on the CT intensity image to amplify the textures with adequate noise control. Then Haralick co-occurrence matrix (CM) is used to calculate texture measures along each of the 13 directions (defined by the first and second order image voxel neighbors) through the polyp volume in the intensity, gradient and curvature images. Instead of taking the mean and range of each CM measure over the 13 directions as the so-called Haralick texture features, Karhunen-Loeve transform is performed to map the 13 directions into an orthogonal coordinate system so that the resulted texture features are less dependent on the polyp orientation variation. These simple ideas for amplifying textures and stabilizing spatial variation demonstrated a significant impact for the differentiating task by experiments using 384 polyp datasets, of which 52 are non-neoplastic polyps and the rest are neoplastic polyps. By the merit of area under the curve of receiver operating characteristic, the innovative ideas achieved differentiation capability of 0.8016, indicating the CTC diagnostic feasibility.

An adaptive paradigm for computer-aided detection of colonic polyps.

Most previous efforts in developing computer-aided detection (CADe) of colonic polyps apply similar measures or parameters to detect polyps regardless of their locations under an implicit assumption that all the polyps reside in a similar local environment, e.g. on a relatively flat colon wall. In reality, this implicit assumption is frequently invalid, because the haustral folds can have a very different local environment from that of the relatively flat colon wall. We conjecture that this assumption may be a major cause of missing the detection of polyps, especially small polyps (<10 mm linear size) located on the haustral folds. In this paper, we take the concept of adaptiveness and present an adaptive paradigm for CADe of colonic polyps. Firstly, we decompose the complicated colon structure into two simplified sub-structures, each of which has similar properties, of (1) relatively flat colon wall and (2) ridge-shaped haustral folds. Then we develop local environment descriptions to adaptively reflect each of these two simplified sub-structures. To show the impact of the adaptiveness of the local environment descriptions upon the polyp detection task, we focus on the local geometrical measures of the volume data for both the detection of initial polyp candidates (IPCs) and the reduction of false positives (FPs) in the IPC pool. The experimental outcome using the local geometrical measures is very impressive such that not only the previously-missed small polyps on the folds are detected, but also the previously miss-removed small polyps on the folds during FP reduction are retained. It is expected that this adaptive paradigm will have a great impact on detecting the small polyps, measuring their volumes and volume changes over time, and optimizing their management plan.

Increasing computer-aided detection specificity by projection features for CT colonography.

PURPOSE: A large number of false positives (FPs) generated by computer-aided detection (CAD) schemes is likely to distract radiologists' attention and decrease their interpretation efficiency. This study aims to develop projection-based features which characterize true and false positives to increase the specificity while maintaining high sensitivity in detecting colonic polyps. METHODS: In this study, two-dimensional projection images are obtained from each initial polyp candidate or volume of interest, and features are extracted from both the gray and color projection images to differentiate FPs from true positives. These projection features were tested to exclude different types of FPs, such as haustral folds, rectal tubes, and residue stool using a database of 325 patient studies (from two different institutions), which includes 556 scans at supine and/or prone positions with 347 polyps and masses sized from 5 to 60 mm. For comparison, several well-established features were used to generate a baseline reference. The experimental evaluation was conducted for large polyps (> or = 10 mm) and medium-sized polyps (5-9 mm) separately. RESULTS: For large polyps, the additional usage of the projection features reduces the FP rate from 5.31 to 1.92 per scan at the comparable by-polyp sensitivity level of 93.1%. For medium-sized polyps, the FP rate is reduced from 8.89 to 5.23 at the sensitivity level of 80.6%. The percentages of FP reduction are 63.9% and 41.2% for the large and medium-sized polyps, respectively, without sacrificing detection sensitivity. CONCLUSIONS: The results have demonstrated that the new projection features can effectively reduce the FPs and increase the detection specificity without sacrificing the sensitivity. CAD of colonic polyps is supposed to help radiologists to improve their performance in interpreting computed tomographic colonography images.

National and local trends in CT colonography reimbursement: past, present, and future.

Computed tomographic colonography (CTC) was first introduced in the mid-1990s as a minimally invasive technology for colorectal cancer screening. Given its potential to significantly change colorectal cancer screening practices in the United States, it has attracted widespread multidisciplinary interest among radiologists, gastroenterologists, colorectal surgeons, and primary care physicians. Because of its potential for widespread utilization and the potential associated costs, it has also attracted much scrutiny from payers. The authors discuss the coding and reimbursement history of CTC, outline strategies for obtaining local coverage for CTC, and attempt to outline some of the possible future influences on CTC reimbursement.