An investigation of tomosynthesis on the diagnostic efficacy of spot compression mammography.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.

Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model.

Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lung cancer risk prediction (LCRP) model, achieving a test AUC of 0.827 ± 0.028, which was comparable to the AUC of 0.862 ± 0.028 achieved by the same LCRP model classifying real follow-up nodule images (p = 0.071). The net reclassification index yielded consistent outcomes (NRI = 0.04; p = 0.62). Other baseline methods, including Lung-RADS and the Brock model, achieved significantly lower performance (p < 0.05). The results demonstrated that the GP-nodules predicted by our GP-WGAN model achieved comparable performance with the nodules in the real follow-up scans for lung cancer diagnosis, indicating the potential to detect lung cancer earlier when coupled with accelerated clinical management versus the current approach of waiting until the next screening exam.

Leveraging Serial Low-Dose CT Scans in Radiomics-based Reinforcement Learning to Improve Early Diagnosis of Lung Cancer at Baseline Screening.

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to develop a radiomics-based reinforcement learning (RRL) model for improving early diagnosis of lung cancer at baseline screening. Materials and Methods In this retrospective study, 1951 participants (female patients, 822; median age, 61 years [range, 55-74 years]) (male patients, 1129; median age, 62 years [range, 55-74 years]) were randomly selected from the National Lung Screening Trial between August 2002 and April 2004. An RRL model using serial LDCT scans (S-RRL) was trained and validated using data from 1404 participants (372 with lung cancer) containing 2525 available serial LDCT scans up to 3 years. A baseline RRL (B-RRL) model was trained with only LDCT scans acquired at baseline screening for comparison. The 547 held-out individuals (150 with lung cancer) were used as an independent test set for performance evaluation. The area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to assess the performances of the models in the classification of screen-detected nodules. Results Deployment to the held-out baseline scans showed that the S-RRL model achieved a significantly higher test AUC (0.88 [95% CI: 0.85, 0.91]) than both the Brock model (AUC, 0.84 [95% CI: 0.81, 0.88]; P = .02) and the B-RRL model (AUC, 0.86 [95% CI: 0.83, 0.90]; P = .02). Lung cancer risk stratification was significantly improved by the S-RRL model as compared with Lung CT Screening Reporting and Data System (NRI, 0.29; P < .001) and the Brock model (NRI, 0.12; P = .008). Conclusion The S-RRL model demonstrated the potential to improve early diagnosis and risk stratification for lung cancer at baseline screening as compared with the B-RRL model and clinical models. Keywords: Radiomics-based Reinforcement Learning, Lung Cancer Screening, Low-Dose CT, Machine Learning © RSNA, 2024 Supplemental material is available for this article.

Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study.

Background: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas. Methods: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination. Results: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1. Conclusion: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.

Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.

OBJECTIVE: Accurate segmentation of the lung nodule in computed tomography images is a critical component of a computer-assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H-DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities. MATERIALS AND METHODS: A dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H-DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U-shaped architecture of U-Net, we redesigned two new U-shaped deep learning (U-DL) models that were expanded to six levels of convolutional layers. One U-DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG-19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet-201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U-DL models, and we referred to them as the shallow and deep U-DL models. Finally, an ensemble layer was used to combine the two U-DL models into the H-DL model. We compared the effectiveness of the H-DL, the shallow U-DL and the deep U-DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M-Dice, min-Dice, M-JI, and min-JI. RESULTS: For the 164 test cases with 327 nodules, our H-DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M-Dice was 0.778 ± 0.102, and the average M-JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H-DL model (p < 0.05). The radiologists' average min-Dice (0.685 ± 0.139) and the average min-JI (0.537 ± 0.153) were significantly lower than those achieved by the H-DL model (p < 0.05). The results indicated that the H-DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min-Dice and min-JI. Moreover, the average Dice and average JI achieved by the H-DL model were significantly higher than those achieved by the individual shallow U-DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.161; p < 0.05) or the individual deep U-DL model alone (Dice of 0.739 ± 0.145, JI of 0.604 ± 0.163; p < 0.05). CONCLUSION: Our newly developed H-DL model outperformed the individual shallow or deep U-DL models. The H-DL method combining multilevel features learned by both the shallow and deep DCNNs could achieve segmentation accuracy comparable to radiologists' segmentation for nodules with wide ranges of image characteristics.

Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation.

This study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and test set of 89 and 52 images, respectively. The positive training samples were extracted at each annotated cell and augmented with affine translation. The negative training samples were selected from the non-cellular regions free of nuclei using a histogram-based semi-automatic method. A U-Net model was initially trained by minimizing a custom loss function. After the first stage of training, the trained U-Net model was applied to the images in the training set in an inference mode. The U-Net segmented objects with high quality were selected by a semi-automated method. Combining the newly selected high quality objects with the annotated nuclei and the previously generated negative samples, the U-Net model was retrained recursively until the stopping criteria were satisfied. For the 52 test images, the U-Net trained with and without using our recursive training method achieved a sensitivity of 90.3% and 85.3% for nuclei detection, respectively. For nuclei segmentation, the average Dice coefficient and average Jaccard index were 0.831±0.213 and 0.750±0.217, 0.780±0.270 and 0.697±0.264, for U-Net with and without recursive training, respectively. The improvement achieved by our proposed method was statistically significant (P < 0.05). In conclusion, our recursive training method effectively enlarged the set of annotated objects for training the deep learning model and further improved the detection and segmentation performance.

Case Report: Mucocele-Like Tumor of the Breast Associated With Ductal Carcinoma In Situ.

Mucocele-like tumor of the breast is histologically characterized as mucin-containing cysts with mucin leaking to the stroma. It could be associated with atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We report a case of mucocele-like tumor of the breast associated with DCIS confirmed by paraffin section. We review the literature and discuss the imaging features, pathology, and clinical management of the lesion. These lesions demonstrate characteristic imaging features, and we especially highlight the MR characteristics, as they have not been well documented. Performing a diagnostic fine-needle aspiration cytology (FNAC) of mucocele-like tumor carries a risk of tumor underestimation; therefore, excision for all mucocele-like tumors is suggested to be the best approach. However, some recent reports recommend close follow-up for patients with low-risk factors who have mucocele-like tumor without atypia on FNAC.

Radiographic Graft Surveillance in Lung Transplantation: Prognostic Role of Parametric Response Mapping.

Rationale: Chronic lung allograft dysfunction (CLAD) results in significant morbidity after lung transplantation. Potential CLAD occurs when lung function declines to 80-90% of baseline. Better noninvasive tools to prognosticate at potential CLAD are needed. Objectives: To determine whether parametric response mapping (PRM), a computed tomography (CT) voxel-wise methodology applied to high-resolution CT scans, can identify patients at risk of progression to CLAD or death. Methods: Radiographic features and PRM-based CT metrics quantifying functional small airway disease (PRMfSAD) and parenchymal disease (PRMPD) were studied at potential CLAD (n = 61). High PRMfSAD and high PRMPD were defined as ⩾30%. Restricted mean modeling was performed to compare CLAD-free survival among groups. Measurements and Main Results: PRM metrics identified the following three unique signatures: high PRMfSAD (11.5%), high PRMPD (41%), and neither (PRMNormal; 47.5%). Patients with high PRMfSAD or PRMPD had shorter CLAD-free median survival times (0.46 yr and 0.50 yr) compared with patients with predominantly PRMNormal (2.03 yr; P = 0.004 and P = 0.007 compared with PRMfSAD and PRMPD groups, respectively). In multivariate modeling adjusting for single- versus double-lung transplant, age at transplant, body mass index at potential CLAD, and time from transplant to CT scan, PRMfSAD ⩾30% or PRMPD ⩾30% continue to be statistically significant predictors of shorter CLAD-free survival. Air trapping by radiologist interpretation was common (66%), was similar across PRM groups, and was not predictive of CLAD-free survival. Ground-glass opacities by radiologist read occurred in 16% of cases and were associated with decreased CLAD-free survival (P < 0.001). Conclusions: PRM analysis offers valuable prognostic information at potential CLAD, identifying patients most at risk of developing CLAD or death.

Improved detection of air trapping on expiratory computed tomography using deep learning.

BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are highly variable, resulting in limited efficacy for monitoring disease progression. OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) model for quantifying and monitoring AT, and to compare it with other quantitative AT measures obtained from threshold-based techniques. MATERIALS AND METHODS: Paired volumetric whole lung inspiratory and expiratory CT scans were obtained at four time points (0, 3, 12 and 24 months) on 36 subjects with mild CF lung disease. A densely connected CNN (DN) was trained using AT segmentation maps generated from a personalized threshold-based method (PTM). Quantitative AT (QAT) values, presented as the relative volume of AT over the lungs, from the DN approach were compared to QAT values from the PTM method. Radiographic assessment, spirometric measures, and clinical scores were correlated to the DN QAT values using a linear mixed effects model. RESULTS: QAT values from the DN were found to increase from 8.65% ± 1.38% to 21.38% ± 1.82%, respectively, over a two-year period. Comparison of CNN model results to intensity-based measures demonstrated a systematic drop in the Dice coefficient over time (decreased from 0.86 ± 0.03 to 0.45 ± 0.04). The trends observed in DN QAT values were consistent with clinical scores for AT, bronchiectasis, and mucus plugging. In addition, the DN approach was found to be less susceptible to variations in expiratory deflation levels than the threshold-based approach. CONCLUSION: The CNN model effectively delineated AT on expiratory CT scans, which provides an automated and objective approach for assessing and monitoring AT in CF patients.

Abdominal CT in COVID-19 patients: incidence, indications, and findings.

PURPOSE: The purpose of this study was to evaluate the frequency, indications, and findings of abdominal CTs ordered in the initial evaluation of patients who had a positive COVID-19 test performed in our institution. METHODS: Retrospective chart review was performed on all patients who had a positive test for COVID-19 performed at a single quaternary care center from 1/20/2020 through 5/8/2020. In a subset of patients undergoing abdominal CT as part of the initial evaluation, the demographics, suspected COVID-19 status at the time of scan, presenting complaints, and abdominal CT findings were recorded. Cardiothoracic radiologists reviewed and scored the visualized lung bases for the likelihood of COVID-19. RESULTS: Only 43 (4.1%) of 1057 COVID-19 patients presented with abdominal complaints sufficient to warrant an abdominal CT. Of these 43 patients, the vast majority (39, 91%) were known or suspected to have COVID-19 at the time of the scan. Most (27/43, 63%) scans showed no acute abdominal abnormality, and those that were positive did not share a discernable pattern of abnormalities. Lung base abnormalities were common, and there was moderate inter-reviewer reliability. CONCLUSION: A minority of COVID-19 patients present with abdominal complaints sufficient to warrant a dedicated CT of the abdomen, and most of these studies will be negative or have abdominal findings not associated with COVID-19. Appropriate lung base findings are a more consistent indication of COVID-19 infection than abdominal findings.

Case Report: Successful Treatment of Pulmonary Talaromyces marneffei Infection with Posaconazole in a Renal Transplant Recipient.

Talaromyces marneffei (T. marneffei), formerly Penicillium marneffei, is a dimorphic fungus prevalent in Southeast Asia that can cause severe systemic infection, especially in immunocompromised patients. There are few reports about the use of posaconazole in T. marneffei infection. Here, we present a case of pulmonary T. marneffei infection in a renal transplant recipient. The patient responded rapidly to oral posaconazole administration but experienced serum creatinine fluctuation because of the interaction between posaconazole and immunosuppressants. Seven months after adjusting the dose of immunosuppressants, the patient recovered completely. Posaconazole is a potentially promising therapy for T. marneffei infection, but it should be administered under close monitoring.

Comparison of clinical and epidemiological characteristics of asymptomatic and symptomatic SARS-CoV-2 infection: A multi-center study in Sichuan Province, China.

OBJECTIVES: Asymptomatic infection of SARS-CoV-2 has become a concern worldwide. This study aims to compare the epidemiology and the clinical characteristics of SARS-CoV-2 infection in asymptomatic and symptomatic individuals. METHODS: A total of 511 confirmed SARS-CoV-2 infection cases, including 100 asymptomatic (by the time of the pathogenic tests) and 411 symptomatic individuals were consecutively enrolled from January 25 to February 20, 2020 from hospitals in 21 cities and 47 counties or districts in Sichuan Province. Epidemiological and clinical characteristics were compared. RESULTS: Compared to the symptomatic patients, the asymptomatic cases were younger (P < 0.001), had similar co-morbidity percentages (P = 0.609), and came from higher altitude areas with lower population mobility (P < 0.001) with better defined epidemiological history (P < 0.001). 27.4% of well-documented asymptomatic cases developed delayed symptoms after the pathogenic diagnosis. 60% of asymptomatic cases demonstrated findings of pneumonia on the initial chest CT, including well-recognized features of coronavirus disease-19. None of the asymptomatic individuals died. Two elderly individuals with initially asymptomatic infection developed severe symptoms during hospitalization. One case of possible virus transmission by a patient during the incubation period was highly suspected. CONCLUSIONS: The epidemiological and clinical findings highlight the significance of asymptomatic infection with SARS-CoV-2. Inspecting the epidemiological history would facilitate the identification of asymptomatic cases. Evidence supports the chest CT scans for confirmed asymptomatic cases to evaluate the extent of lung involvement.

Pathologic categorization of lung nodules: Radiomic descriptors of CT attenuation distribution patterns of solid and subsolid nodules in low-dose CT.

PURPOSE: Develop a quantitative image analysis method to characterize the heterogeneous patterns of nodule components for the classification of pathological categories of nodules. MATERIALS AND METHODS: With IRB approval and permission of the National Lung Screening Trial (NLST) project, 103 subjects with low dose CT (LDCT) were used in this study. We developed a radiomic quantitative CT attenuation distribution descriptor (qADD) to characterize the heterogeneous patterns of nodule components and a hybrid model (qADD+) that combined qADD with subject demographic data and radiologist-provided nodule descriptors to differentiate aggressive tumors from indolent tumors or benign nodules with pathological categorization as reference standard. The classification performances of qADD and qADD + were evaluated and compared to the Brock and the Mayo Clinic models by analysis of the area under the receiver operating characteristic curve (AUC). RESULTS: The radiomic features were consistently selected into qADDs to differentiate pathological invasive nodules from (1) preinvasive nodules, (2) benign nodules, and (3) the group of preinvasive and benign nodules, achieving test AUCs of 0.847 ±â€¯0.002, 0.842 ±â€¯0.002 and 0.810 ±â€¯0.001, respectively. The qADD + obtained test AUCs of 0.867 ±â€¯0.002, 0.888 ±â€¯0.001 and 0.852 ±â€¯0.001, respectively, which were higher than both the Brock and the Mayo Clinic models. CONCLUSION: The pathologic invasiveness of lung tumors could be categorized according to the CT attenuation distribution patterns of the nodule components manifested on LDCT images, and the majority of invasive lung cancers could be identified at baseline LDCT scans.

Variabilities in Reference Standard by Radiologists and Performance Assessment in Detection of Pulmonary Embolism in CT Pulmonary Angiography.

Annotating lesion locations by radiologists' manual marking is a key step to provide reference standard for the training and testing of a computer-aided detection system by supervised machine learning. Inter-reader variability is not uncommon in readings even by expert radiologists. This study evaluated the variability of the radiologist-identified pulmonary emboli (PEs) to demonstrate the importance of improving the reliability of the reference standard by a multi-step process for performance evaluation. In an initial reading of 40 CTPA PE cases, two experienced thoracic radiologists independently marked the PE locations. For markings from the two radiologists that did not agree, each radiologist re-read the cases independently to assess the discordant markings. Finally, for markings that still disagreed after the second reading, the two radiologists read together to reach a consensus. The variability of radiologists was evaluated by analyzing the agreement between two radiologists. For the 40 cases, 475 and 514 PEs were identified by radiologists R1 and R2 in the initial independent readings, respectively. For a total of 545 marks by the two radiologists, 81.5% (444/545) of the marks agreed but 101 marks in 36 cases differed. After consensus, 65 (64.4%) and 36 (35.6%) of the 101 marks were determined to be true PEs and false positives (FPs), respectively. Of these, 48 and 17 were false negatives (FNs) and 14 and 22 were FPs by R1 and R2, respectively. Our study demonstrated that there is substantial variability in reference standards provided by radiologists, which impacts the performance assessment of a lesion detection system. Combination of multiple radiologists' readings and consensus is needed to improve the reliability of a reference standard.

Hypersensitivity Pneumonitis: Radiologic Phenotypes Are Associated With Distinct Survival Time and Pulmonary Function Trajectory.

BACKGROUND: Hypersensitivity pneumonitis (HP) is an interstitial lung disease with a better prognosis, on average, than idiopathic pulmonary fibrosis (IPF). We compare survival time and pulmonary function trajectory in patients with HP and IPF by radiologic phenotype. METHODS: HP (n = 117) was diagnosed if surgical/transbronchial lung biopsy, BAL, and exposure history results suggested this diagnosis. IPF (n = 152) was clinically and histopathologically diagnosed. All participants had a baseline high-resolution CT (HRCT) scan and FVC % predicted. Three thoracic radiologists documented radiologic features. Survival time is from HRCT scan to death or lung transplant. Cox proportional hazards models identify variables associated with survival time. Linear mixed models compare post-HRCT scan FVC % predicted trajectories. RESULTS: Subjects were grouped by clinical diagnosis and three mutually exclusive radiologic phenotypes: honeycomb present, non-honeycomb fibrosis (traction bronchiectasis and reticulation) present, and nonfibrotic. Nonfibrotic HP had the longest event-free median survival (> 14.73 years) and improving FVC % predicted (1.92%; 95% CI, 0.49-3.35; P = .009). HP with non-honeycomb fibrosis had longer survival than IPF (> 7.95 vs 5.20 years), and both groups experienced a significant decline in FVC % predicted. Subjects with HP and IPF with honeycombing had poor survival (2.76 and 2.81 years, respectively) and significant decline in FVC % predicted. CONCLUSIONS: Three prognostically distinct, radiologically defined phenotypes are identified among patients with HP. The importance of pursuing a specific diagnosis (eg, HP vs IPF) among patients with non-honeycomb fibrosis is highlighted. When radiologic honeycombing is present, invasive diagnostic testing directed at determining the diagnosis may be of limited value given a uniformly poor prognosis.

Development and validation of a radiological diagnosis model for hypersensitivity pneumonitis.

High-resolution computed tomography (HRCT) may be useful for diagnosing hypersensitivity pneumonitis. Here, we develop and validate a radiological diagnosis model and model-based points score.Patients with interstitial lung disease seen at the University of Michigan Health System (derivation cohort) or enrolling in the Lung Tissue Research Consortium (validation cohort) were included. A thin-section, inspiratory HRCT scan was required. Thoracic radiologists documented radiological features.The derivation cohort comprised 356 subjects (33.9% hypersensitivity pneumonitis) and the validation cohort comprised 424 subjects (15.5% hypersensitivity pneumonitis). An age-, sex- and smoking status-adjusted logistic regression model identified extent of mosaic attenuation or air trapping greater than that of reticulation ("MA-AT>Reticulation"; OR 6.20, 95% CI 3.53-10.90; p<0.0001) and diffuse axial disease distribution (OR 2.33, 95% CI 1.31-4.16; p=0.004) as hypersensitivity pneumonitis predictors (area under the receiver operating characteristic curve 0.814). A model-based score >2 (1 point for axial distribution, 2 points for "MA-AT>Reticulation") has specificity 90% and positive predictive value (PPV) 74% in the derivation cohort and specificity 96% and PPV 44% in the validation cohort. Similar model performance is seen with population restriction to those reporting no exposure (score >2: specificity 91%).When radiological mosaic attenuation or air trapping are more extensive than reticulation and disease has diffuse axial distribution, hypersensitivity pneumonitis specificity is high and false diagnosis risk low (<10%), but PPV is diminished in a low-prevalence setting.

Possible UIP pattern on high-resolution computed tomography is associated with better survival than definite UIP in IPF patients.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing lung disease of unknown etiology. Inter-society consensus guidelines on IPF diagnosis and management outline radiologic patterns including definite usual interstitial pneumonia (UIP), possible UIP, and inconsistent with UIP. We evaluate these diagnostic categories as prognostic markers among patients with IPF. METHODS: Included subjects had biopsy-proven UIP, a multidisciplinary team diagnosis of IPF, and a baseline high-resolution computed tomography (HRCT). Thoracic radiologists assigned the radiologic pattern and documented the presence and extent of specific radiologic findings. The outcome of interest was lung transplant-free survival. RESULTS: IPF patients with a possible UIP pattern on HRCT had significantly longer Kaplan-Meier event-free survival compared to those with definite UIP pattern (5.21 and 3.57 years, respectively, p = 0.002). In a multivariable Cox proportional hazards model adjusted for baseline age, gender, %-predicted FVC, and %-predicted DLCO via the GAP Stage, extent of fibrosis (via the traction bronchiectasis score) and ever-smoker status, possible UIP pattern on HRCT (versus definite UIP) was associated with reduced hazard of death or lung transplant (HR = 0.42, CI 95% 0.23-0.78, p = 0.006). CONCLUSIONS: Radiologic diagnosis categories outlined by inter-society consensus guidelines is a widely-reported and potentially useful prognostic marker in IPF patients, with possible UIP pattern on HRCT associated with a favorable prognosis compared to definite UIP pattern, after adjusting for relevant covariates.

Intrathoracic Fat Measurements Using Multidetector Computed Tomography (MDCT): Feasibility and Reproducibility.

Intrathoracic fat volume, more specifically, epicardial fat volume, is an emerging imaging biomarker of adverse cardiovascular events. The purpose of this work is to show the feasibility and reproducibility of intrathoracic fat volume measurement applied to contrast-enhanced multidetector computed tomography images. A retrospective cohort study of 62 subjects free of cardiovascular disease (55% females, age = 49 ± 11 years) conducted from 2008 to 2011 formed the study group. Intrathoracic fat volume was defined as all fat voxels measuring -50 to -250 Hounsfield Unit within the intrathoracic cavity from the level of the pulmonary artery bifurcation to the heart apex. The intrathoracic fat was separated into epicardial and extrapericardial fat by tracing the pericardium. The measurements were obtained by 2 readers and compared for interrater reproducibility. The fat volume measurements for the study group were 141 ± 72 cm3 for intrathoracic fat, 58 ± 27 cm3 for epicardial fat, and 84 ± 50 cm3 for extrapericardial fat. There was no statistically significant difference in intrathoracic fat volume measurements between the 2 readers, with correlation coefficients of 0.88 (P = .55) for intrathoracic fat volume and -0.12 (P = .33) for epicardial fat volume. Voxel-based measurement of intrathoracic fat, including the separation into epicardial and extrapericardial fat, is feasible and highly reproducible from multidetector computed tomography scans.

Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography.

The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.

Coronary artery analysis: Computer-assisted selection of best-quality segments in multiple-phase coronary CT angiography.

PURPOSE: The authors are developing an automated method to identify the best-quality coronary arterial segment from multiple-phase coronary CT angiography (cCTA) acquisitions, which may be used by either interpreting physicians or computer-aided detection systems to optimally and efficiently utilize the diagnostic information available in multiple-phase cCTA for the detection of coronary artery disease. METHODS: After initialization with a manually identified seed point, each coronary artery tree is automatically extracted from multiple cCTA phases using our multiscale coronary artery response enhancement and 3D rolling balloon region growing vessel segmentation and tracking method. The coronary artery trees from multiple phases are then aligned by a global registration using an affine transformation with quadratic terms and nonlinear simplex optimization, followed by a local registration using a cubic B-spline method with fast localized optimization. The corresponding coronary arteries among the available phases are identified using a recursive coronary segment matching method. Each of the identified vessel segments is transformed by the curved planar reformation (CPR) method. Four features are extracted from each corresponding segment as quality indicators in the original computed tomography volume and the straightened CPR volume, and each quality indicator is used as a voting classifier for the arterial segment. A weighted voting ensemble (WVE) classifier is designed to combine the votes of the four voting classifiers for each corresponding segment. The segment with the highest WVE vote is then selected as the best-quality segment. In this study, the training and test sets consisted of 6 and 20 cCTA cases, respectively, each with 6 phases, containing a total of 156 cCTA volumes and 312 coronary artery trees. An observer preference study was also conducted with one expert cardiothoracic radiologist and four nonradiologist readers to visually rank vessel segment quality. The performance of our automated method was evaluated by comparing the automatically identified best-quality segments identified by the computer to those selected by the observers. RESULTS: For the 20 test cases, 254 groups of corresponding vessel segments were identified after multiple phase registration and recursive matching. The AI-BQ segments agreed with the radiologist's top 2 ranked segments in 78.3% of the 254 groups (Cohen's kappa 0.60), and with the 4 nonradiologist observers in 76.8%, 84.3%, 83.9%, and 85.8% of the 254 groups. In addition, 89.4% of the AI-BQ segments agreed with at least two observers' top 2 rankings, and 96.5% agreed with at least one observer's top 2 rankings. In comparison, agreement between the four observers' top ranked segment and the radiologist's top 2 ranked segments were 79.9%, 80.7%, 82.3%, and 76.8%, respectively, with kappa values ranging from 0.56 to 0.68. CONCLUSIONS: The performance of our automated method for selecting the best-quality coronary segments from a multiple-phase cCTA acquisition was comparable to the selection made by human observers. This study demonstrates the potential usefulness of the automated method in clinical practice, enabling interpreting physicians to fully utilize the best available information in cCTA for diagnosis of coronary disease, without requiring manual search through the multiple phases and minimizing the variability in image phase selection for evaluation of coronary artery segments across the diversity of human readers with variations in expertise.