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.

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.

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.

How to perform a critical appraisal of diagnostic tests: 7 steps.

The critically appraised topic (CAT) is a format in evidence-based practice for sharing information. A CAT is a standardized way of summarizing the most current research evidence focused on a pertinent clinical question. Its aim is to provide both a critique of the most up-to-date retrieved research and an indication of the clinical relevance of results. A clinical question is initially generated following a patient encounter, which leads to and directs a literature search to answer the clinical question. Studies obtained from the literature search are assigned a level of evidence. This allows the most valid and relevant articles to be selected and to be critically appraised. The results are summarized, and this information is translated into clinically useful procedures and processes.

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.

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.

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.

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.

Comparison of the effect of low- and iso-osmolar contrast agents on heart rate during chest CT angiography: results of a prospective randomized multicenter study.

PURPOSE: To prospectively compare the effect of intravenous injection of low-osmolar iopamidol with that of intravenous injection of iso-osmolar iodixanol on heart rate (HR) during nongated chest computed tomographic (CT) angiography. MATERIALS AND METHODS: This multicenter study was approved by local institutional review boards, and patients provided written informed consent. Patient enrollment and examination at centers in the United States complied with HIPAA regulations. One hundred and thirty patients (54 male; mean age, 52 years) clinically suspected of having pulmonary embolism were referred for pulmonary CT angiography and were randomly assigned to receive 80 mL of either iopamidol (370 mg of iodine per milliliter, n = 63) or iodixanol (320 mg of iodine per milliliter, n = 67) at a rate of 4 mL/sec. HR (measured in beats per minute) was monitored from 5 minutes before the start of injection to the end of imaging, and precontrast HR and maximum postcontrast HR were recorded. Student t and χ(2) tests were used for continuous and categorical variables, respectively. RESULTS: Precontrast HR in patients who received iopamidol (mean, 81 beats per minute ± 18 [standard deviation]) was similar to that in patients who received iodixanol (mean, 77 beats per minute ± 17) (P = .16). Mean postcontrast HR was 87 beats per minute ± 17 and 82 beats per minute ± 18 (P = .16) in the iopamidol and iodixanol groups, respectively. Mean increase from precontrast HR to postcontrast HR was 5 beats per minute ± 9 and 5 beats per minute ± 7 (P = .72) in the iopamidol and iodixanol groups, respectively. Thirty-five (56%) of the 63 patients who received iopamidol and 33 (49%) of the 67 patients who received iodixanol had an HR increase of fewer than 5 beats per minute, 15 (24%) and 18 (27%) patients, respectively, had an increase of 5-9 beats per minute, and four (6%) and three (4%) patients, respectively, had an increase of more than 20 beats per minute. These proportions were not significantly different between the groups (P = .51, χ(2) test). CONCLUSION: High-rate intravenous administration of 80 mL of iopamidol and iodixanol during pulmonary CT angiography slightly increased HR; there was no difference in HR between the contrast agent groups.

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.

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.

Clinical utility of multiplanar reformation in pulmonary CT angiography.

OBJECTIVE: The purpose of this study was to determine whether the view used, multiplanar or axial, for image interpretation at pulmonary CT angiography for suspected acute pulmonary embolism alters the diagnostic confidence, accuracy, and interpretation time of cardiothoracic radiology specialists and radiology residents. MATERIALS AND METHODS: Patients who underwent 50 consecutive pulmonary 64-MDCT angiographic examinations formed the study group (18 men, 32 women; mean age, 53 years; range, 19-93 years). Three blinded cardiothoracic faculty radiologists and three blinded radiology residents reviewed each case independently initially using only axial display mode and later using multiplanar reformation (MPR) in any x-, y-, or z-axis. The presence of pulmonary embolism in the main through subsegmental pulmonary arteries was scored on a 5-point scale; diagnostic confidence for the overall examination was scored on a 3-point scale; and interpretation time was recorded. A surrogate reference standard consisted of either faculty agreement or, in cases of disagreement, adjudication by another, senior faculty member. Statistical analysis included the Kendall coefficient (W), receiver operating characteristics curves, and a univariate repeated measures model. RESULTS: Interobserver agreement between specialists on the diagnosis of pulmonary embolism was good for axial viewing (W=0.72) and for MPR viewing (W=0.79). Interobserver agreement between residents was good for axial viewing (W=0.62) and for MPR viewing (W=0.70). Reader confidence improved among all readers with MPR viewing, but the difference did not reach statistical significance. Interpretation time with MPR was significantly longer for two of the three specialists and significantly shorter for two of the three residents. CONCLUSION: Use of MPR for viewing increased the reader agreement and interpretation time of cardiothoracic specialists but increased reader agreement between residents and might have decreased interpretation time. All readers had a trend toward increased confidence.

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.

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.

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.

Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.

The purpose of this work is to develop a computer-aided diagnosis (CAD) system to differentiate malignant and benign lung nodules on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a 3D active contour method. The initial contour was obtained as the boundary of a binary object generated by k-means clustering within the VOI and smoothed by morphological opening. A data set of 256 lung nodules (124 malignant and 132 benign) from 152 patients was used in this study. In addition to morphological and texture features, the authors designed new nodule surface features to characterize the lung nodule surface smoothness and shape irregularity. The effects of two demographic features, age and gender, as adjunct to the image features were also investigated. A linear discriminant analysis (LDA) classifier built with features from stepwise feature selection was trained using simplex optimization to select the most effective features. A two-loop leave-one-out resampling scheme was developed to reduce the optimistic bias in estimating the test performance of the CAD system. The area under the receiver operating characteristic curve, A(z), for the test cases improved significantly (p < 0.05) from 0.821 +/- 0.026 to 0.857 +/- 0.023 when the newly developed image features were included with the original morphological and texture features. A similar experiment performed on the data set restricted to primary cancers and benign nodules, excluding the metastatic cancers, also resulted in an improved test A(z), though the improvement did not reach statistical significance (p = 0.07). The two demographic features did not significantly affect the performance of the CAD system (p > 0.05) when they were added to the feature space containing the morphological, texture, and new gradient field and radius features. To investigate if a support vector machine (SVM) classifier can achieve improved performance over the LDA classifier, we compared the performance of the LDA and SVMs with various kernels and parameters. Principal component analysis was used to reduce the dimensionality of the feature space for both the LDA and the SVM classifiers. When the number of selected principal components was varied, the highest test A(z) among the SVMs of various kernels and parameters was slightly higher than that of the LDA in one-loop leave-one-case-out resampling. However, no SVM with fixed architecture consistently performed better than the LDA in the range of principal components selected. This study demonstrated that the authors' proposed segmentation and feature extraction techniques are promising for classifying lung nodules on CT images.

Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets.

The authors are developing a computer-aided detection system for pulmonary emboli (PE) in computed tomographic pulmonary angiography (CTPA) scans. The pulmonary vessel tree is extracted using a 3D expectation-maximization segmentation method based on the analysis of eigen-values of Hessian matrices at multiple scales. A parallel multiprescreening method is applied to the segmented vessels to identify volume of interests (VOIs) that contained suspicious PE. A linear discriminant analysis (LDA) classifier with feature selection is designed to reduce false positives (FPs). Features that characterize the contrast, gray level, and size of PE are extracted as input predictor variables to the LDA classifier. With the IRB approval, 59 CTPA PE cases were collected retrospectively from the patient files (UM cases). With access permission, 69 CTPA PE cases were randomly selected from the data set of the prospective investigation of pulmonary embolism diagnosis (PIOPED) II clinical trial. Extensive lung parenchymal or pleural diseases were present in 22/59 UM and 26/69 PIOPED cases. Experienced thoracic radiologists manually marked 595 and 800 PE as the reference standards in the UM and PIOPED data sets, respectively. PE occlusion of arteries ranged from 5% to 100%, with PE located from the main pulmonary artery to the subsegmental artery levels. Of the 595 PE identified in the UM cases, 245 and 350 PE were located in the subsegmental arteries and the more proximal arteries, respectively. The detection performance was assessed by free response ROC (FROC) analysis. The FROC analysis indicated that the PE detection system could achieve an overall sensitivity of 80% at 18.9 FPs/case for the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases was 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases. The detection performance depended on the arterial level where the PE was located and on the percentage of occlusion. The sensitivity was lower for PE in the subsegmental arteries than in more proximal arteries and was lower for PE with less than 20% occlusion. The results indicate that the PE detection system achieves high sensitivity for PE detection on independent CTPA scans for both the PIOPED and UM data sets and demonstrate the potential that the automated PE detection approach can be generalized to unknown cases.

Evaluation of mechanical heart valve size and function with ECG-gated 64-MDCT.

OBJECTIVE: The purpose of our study was to determine whether CT can accurately evaluate mechanical heart valve size and function. MATERIALS AND METHODS: Sixty-two patients with mechanical valves (37 single-disc, 27 bileaflet; 59 aortic, 5 mitral) were evaluated with ECG-gated 64-MDCT and transthoracic echocardiography; a subset of 10 patients underwent cinefluoroscopy. Two readers independently interpreted each study. RESULTS: The mean age of the patients was 46.4 +/- 14.4 years; 50 were men and 12 were women. There was excellent correlation, and differences between CT readers were absent to small in measuring the opening angle (r = 0.96, p < 0.001; 76.7 +/- 9.0 degrees vs 76.8 +/- 9.6 degrees , p = 0.73), annulus diameter (r = 0.96, p < 0.001; 25.9 +/- 3.3 vs 25.9 +/- 3.2 mm, p = 0.62), and geometric orifice area (r = 0.98, p < 0.001; 3.8 +/- 0.9 vs 3.6 +/- 0.8 cm(2), p < 0.001). There was strong correlation without difference in opening angle between CT and cinefluoroscopy (r = 0.77, p < 0.001; 79.2 degrees +/- 9.8 degrees vs 77.2 degrees +/- 15.5 degrees , p = 0.45). Compared with manufacturer specifications, CT reported opening angles that were smaller for single-disc valves (n = 36, 67.4 degrees +/- 5.7 degrees vs 75 degrees , p < 0.001) and similar for bileaflet valves (n = 42 for 21 valves, 83.8 degrees +/- 3.9 degrees vs 85 degrees , p = 0.05), valves, with small underestimation with CT versus specifications in annulus diameter (n = 41; r = 0.75, p < 0.001; 26.4 +/- 3.0 vs 27.5 +/- 3.3 mm, p = 0.003), and geometric orifice area (n = 35; r = 0.90, p < 0.001; 3.7 +/- 0.7 vs 3.8 +/- 0.8 cm(2), p = 0.04). Each disc closed fully on CT; none had more than mild regurgitation on echocardiography. CONCLUSION: CT can measure the size and function of mechanical valves with high interobserver agreement and results similar to specifications. The opening angle with CT strongly correlates with cinefluoroscopy. CT is promising for the assessment of mechanical valves.