A pilot study of volumetric and density tumor analysis of ACC patients treated with vorinostat in a phase II clinical trial

Rationale and objectives

Adenoid cystic carcinoma (ACC) is a rare salivary gland cancer. The vast majority of clinical trials evaluating systemic therapy efficacy in solid tumors use the Response Evaluation Criteria in Solid Tumors (RECIST) to measure response that is limited to 2 dimensional only evaluations, not taking volume or density into account. The indolent behavior ACC represents a challenge toward an appropriate evaluation of therapy response. Objectives: 1) To describe and contrast volumetric and density changes at each time-point, including changes noted from baseline to best response, to currently used 2 dimensional-only criteria (RECIST) and 2) To report the coefficient of variation in volume measurement among three reviewers on a subset of ACC patients.

Materials and methods

We retrospectively assessed a cohort of 18 prospectively treated patients with ACC in a phase 2 trial with vorinostat using a volumetric (viable tumor volume, VTV) and density criteria. Three independent and blinded observers segmented target lesions across a sample of randomly selected computed tomography (CT) exams to examine inter-observer variation.

Results

We found that the average coefficient of variation among observers for all target lesions was 16.1%, with lung lesions displaying a smaller variation at 14.0% (p-value >0.17). We describe examples of decrease in volume and density in several lesions despite stable disease by RECIST.

Conclusion

This pilot study demonstrates that two-dimensional criteria such as RECIST may not be the best criteria to assess response to therapy, especially with evolving tools within picture archiving and communication system (PACS) that can assess volumetric size, density and texture, however, this should be prospectively studied.

Annotations of Lung Abnormalities in the Shenzhen Chest X-ray Dataset for Computer-Aided Screening of Pulmonary Diseases

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People’s Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs.

Dataset:https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.

GIMAP6 regulates autophagy, immune competence, and inflammation in mice and humans

Inborn errors of immunity (IEIs) unveil regulatory pathways of human immunity. We describe a new IEI caused by mutations in the GTPase of the immune-associated protein 6 (GIMAP6) gene in patients with infections, lymphoproliferation, autoimmunity, and multiorgan vasculitis. Patients and Gimap6^−/− mice show defects in autophagy, redox regulation, and polyunsaturated fatty acid (PUFA)–containing lipids. We find that GIMAP6 complexes with GABARAPL2 and GIMAP7 to regulate GTPase activity. Also, GIMAP6 is induced by IFN-γ and plays a critical role in antibacterial immunity. Finally, we observed that Gimap6^−/− mice died prematurely from microangiopathic glomerulosclerosis most likely due to GIMAP6 deficiency in kidney endothelial cells.

Gamification in Radiology Training Module Developed During the Society for Imaging Informatics in Medicine Annual Meeting Hackathon

The purpose of this manuscript is to report our experience in the 2021 SIIM Virtual Hackathon, where we developed a proof-of-concept of a radiology training module with elements of gamification. In the 50 h allotted in the hackathon, we proposed an idea, connected with colleagues from five different countries, and completed an operational proof-of-concept, which was demonstrated live at the hackathon showcase, competing with eight other teams. Our prototype involved participants annotating publicly available chest radiographs of patients with tuberculosis. We showed how we could give experience points to trainees based on annotation precision compared to ground truth radiologists' annotation, ranked in a live leaderboard. We believe that gamification elements could provide an engaging solution for radiology education. Our project was awarded first place out of eight participating hackathon teams.

Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers

Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e., the posterior-anterior (PA), and the anterior-posterior (AP) views for analysis and decision-making. Lateral CXRs which are heretofore not studied help detect clinically suspected pulmonary TB, particularly in children. Further, Vision Transformers (ViTs) with built-in self-attention mechanisms have recently emerged as a viable alternative to the traditional CNNs. Although ViTs demonstrated notable performance in several medical image analysis tasks, potential limitations exist in terms of performance and computational efficiency, between the CNN and ViT models, necessitating a comprehensive analysis to select appropriate models for the problem under study. This study aims to detect TB-consistent findings in lateral CXRs by constructing an ensemble of the CNN and ViT models. Several models are trained on lateral CXR data extracted from two large public collections to transfer modality-specific knowledge and fine-tune them for detecting findings consistent with TB. We observed that the weighted averaging ensemble of the predictions of CNN and ViT models using the optimal weights computed with the Sequential Least-Squares Quadratic Programming method delivered significantly superior performance (MCC: 0.8136, 95% confidence intervals (CI): 0.7394, 0.8878, p < 0.05) compared to the individual models and other ensembles. We also interpreted the decisions of CNN and ViT models using class-selective relevance maps and attention maps, respectively, and combined them to highlight the discriminative image regions contributing to the final output. We observed that (i) the model accuracy is not related to disease region of interest (ROI) localization and (ii) the bitwise-AND of the heatmaps of the top-2-performing models delivered significantly superior ROI localization performance in terms of mean average precision [mAP@(0.1 0.6) = 0.1820, 95% CI: 0.0771,0.2869, p < 0.05], compared to other individual models and ensembles. The code is available at https://github.com/sivaramakrishnan-rajaraman/Ensemble-of-CNN-and-ViT-for-TB-detection-in-lateral-CXR.

Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios

Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious annotation processes, and the limited availability of radiologists pose challenges to building such datasets. This review details model training strategies in scenarios with limited data, insufficiently labeled data, and/or limited expert resources. This review discusses strategies to enlarge the data sample, decrease the time burden of manual supervised labeling, adjust the neural network architecture to improve model performance, apply semisupervised approaches, and leverage efficiencies from pretrained models. Keywords: Computer-aided Detection/Diagnosis, Transfer Learning, Limited Annotated Data, Augmentation, Synthetic Data, Semisupervised Learning, Federated Learning, Few-Shot Learning, Class Imbalance.

Collaborative Writing Projects: Set Yourself up for Success

ABSTRACT

This article will review critical components for the successful completion of a multi-institution, multiauthor collaborative paper. Best practices for the creation and publication of a collaborative paper will be addressed.

Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Pulmonary Manifestations of GATA2 Deficiency

BACKGROUND

GATA2 deficiency is a genetic disorder of hematopoiesis, lymphatics, and immunity caused by autosomal dominant or sporadic mutations in GATA2. The disease has a broad phenotype encompassing immunodeficiency, myelodysplasia, leukemia, and vascular or lymphatic dysfunction as well as prominent pulmonary manifestations.

RESEARCH QUESTION

What are the pulmonary manifestations of GATA2 deficiency?

STUDY DESIGN AND METHODS

A retrospective review was conducted of clinical medical records, diagnostic imaging, pulmonary pathologic specimens, and tests of pulmonary function.

RESULTS

Of 124 patients (95 probands and 29 ascertained), the lung was affected in 56%. In addition to chronic infections, pulmonary alveolar proteinosis (11 probands) and pulmonary arterial hypertension (nine probands) were present. Thoracic CT imaging found small nodules in 54% (54 probands and 12 relatives), reticular infiltrates in 40% (45 probands and four relatives), paraseptal emphysema in 25% (30 probands and one relative), ground-glass opacities in 35% (41 probands and two relatives), consolidation in 21% (23 probands and two relatives), and a typical crazy-paving pattern in 7% (eight probands and no relatives). Nontuberculous mycobacteria were the most frequent organisms associated with chronic infection. Allogeneic hematopoietic stem cell transplantation successfully reversed myelodysplasia and immune deficiency and also improved pulmonary hypertension and pulmonary alveolar proteinosis in most patients.

INTERPRETATION

GATA2 deficiency has prominent pulmonary manifestations. These clinical observations confirm the essential role of hematopoietic cells in many aspects of pulmonary function, including infections, alveolar proteinosis, and pulmonary hypertension, many of which precede the formal diagnosis, and many of which respond to stem cell transplantation.

CT Evaluation of Lymph Nodes That Merge or Split during the Course of a Clinical Trial: Limitations of RECIST 1.1

Purpose To compare Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 with volumetric measurement in the setting of target lymph nodes that split into two or more nodes or merge into one conglomerate node. Materials and Methods In this retrospective study, target lymph nodes were evaluated on CT scans from 166 patients with different types of cancer; 158 of the scans came from The Cancer Imaging Archive. Each target node was measured using RECIST 1.1 criteria before and after merging or splitting, followed by volumetric segmentation. To compare RECIST 1.1 with volume, a single-dimension hypothetical diameter (HD) was determined from the nodal volume. The nodes were divided into three groups: (a) one-target merged (one target node merged with other nodes); (b) two-target merged (two neighboring target nodes merged); and (c) split node (a conglomerate node cleaved into smaller fragments). Bland-Altman analysis and t test were applied to compare RECIST 1.1 with HD. On the basis of the RECIST 1.1 concept, we compared response category changes between RECIST 1.1 and HD. Results The data set consisted of 30 merged nodes (19 one-target merged and 11 two-target merged) and 20 split nodes (mean age for all 50 included patients, 50 years ± 7 [standard deviation]; 38 men). RECIST 1.1, volumetric, and HD measurements indicated an increase in size in all one-target merged nodes. While volume and HD indicated an increase in size for nodes in the two-target merged group, RECIST 1.1 showed a decrease in size in all two-target merged nodes. Although volume and HD demonstrated a decrease in size of all split nodes, RECIST 1.1 indicated an increase in size in 60% (12 of 20) of the nodes. Discrepancy of the response categories between RECIST 1.1 and HD was observed in 5% (one of 19) in one-target merged, 82% (nine of 11) in two-target merged, and 55% (11 of 20) in split nodes. Conclusion RECIST 1.1 does not optimally reflect size changes when lymph nodes merge or split. Keywords: CT, Lymphatic, Tumor Response Supplemental material is available for this article. © RSNA, 2021.

Improved Semantic Segmentation of Tuberculosis-Consistent Findings in Chest X-rays Using Augmented Training of Modality-Specific U-Net Models with Weak Localizations

Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those pretrained on stock photography images. This character helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localization, postprocessed into an ROI mask, from a DL classifier trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections, including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution and cross-institutional collections (p < 0.05). We believe that this is the first study to i) use CXR modality-specific U-Nets for semantic segmentation of TB-consistent ROIs and ii) evaluate the segmentation performance while augmenting the training data with weak TB-consistent localizations.

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.

Cabozantinib in patients with platinum-refractory metastatic urothelial carcinoma: an open-label, single-centre, phase 2 trial

BACKGROUND

Cabozantinib is a multikinase inhibitor of MET, VEGFR, AXL, and RET, which also has an effect on the tumour immune microenvironment by decreasing regulatory T cells and myeloid-derived suppressor cells. In this study, we examined the activity of cabozantinib in patients with metastatic platinum-refractory urothelial carcinoma.

METHODS

This study was an open-label, single-arm, three-cohort phase 2 trial done at the National Cancer Institute (Bethesda, MD, USA). Eligible patients were 18 years or older, had histologically confirmed urothelial carcinoma or rare genitourinary tract histologies, Karnofsky performance scale index of 60% or higher, and documented disease progression after at least one previous line of platinum-based chemotherapy (platinum-refractory). Cohort one included patients with metastatic urothelial carcinoma with measurable disease as defined by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Two additional cohorts that enrolled in parallel (patients with bone-only urothelial carcinoma metastases and patients with rare histologies of the genitourinary tract) were exploratory. Patients received cabozantinib 60 mg orally once daily in 28-day cycles until disease progression or unacceptable toxicity. The primary endpoint was investigator-assessed objective response rate by RECIST in cohort one. Response was assessed in all patients who met the eligibility criteria and who received at least 8 weeks of therapy. All patients who received at least one dose of cabozantinib were included in the safety analysis. This completed study is registered with ClinicalTrials.gov, NCT01688999.

FINDINGS

Between Sept 28, 2012, and Oct, 20, 2015, 68 patients were enrolled on the study (49 in cohort one, six in cohort two, and 13 in cohort three). All patients received at least one dose of cabozantinib. The median follow-up was 61·2 months (IQR 53·8-70·0) for the 57 patients evaluable for response. In the 42 evaluable patients in cohort one, there was one complete response and seven partial responses (objective response rate 19%, 95% CI 9-34). The most common grade 3-4 adverse events were fatigue (six [9%] patients), hypertension (five [7%]), proteinuria (four [6%]), and hypophosphataemia (four [6%]). There were no treatment-related deaths.

INTERPRETATION

Cabozantinib has single-agent clinical activity in patients with heavily pretreated, platinum-refractory metastatic urothelial carcinoma with measurable disease and bone metastases and is generally well tolerated. Cabozantinib has innate and adaptive immunomodulatory properties providing a rationale for combining cabozantinib with immunotherapeutic strategies.

FUNDING

National Cancer Institute Intramural Program and the Cancer Therapy Evaluation Program.

Defective glycosylation and multisystem abnormalities characterize the primary immunodeficiency XMEN disease

X-linked immunodeficiency with magnesium defect, EBV infection, and neoplasia (XMEN) disease are caused by deficiency of the magnesium transporter 1 (MAGT1) gene. We studied 23 patients with XMEN, 8 of whom were EBV naive. We observed lymphadenopathy (LAD), cytopenias, liver disease, cavum septum pellucidum (CSP), and increased CD4-CD8-B220-TCRαβ+ T cells (αβDNTs), in addition to the previously described features of an inverted CD4/CD8 ratio, CD4+ T lymphocytopenia, increased B cells, dysgammaglobulinemia, and decreased expression of the natural killer group 2, member D (NKG2D) receptor. EBV-associated B cell malignancies occurred frequently in EBV-infected patients. We studied patients with XMEN and patients with autoimmune lymphoproliferative syndrome (ALPS) by deep immunophenotyping (32 immune markers) using time-of-flight mass cytometry (CyTOF). Our analysis revealed that the abundance of 2 populations of naive B cells (CD20+CD27-CD22+IgM+HLA-DR+CXCR5+CXCR4++CD10+CD38+ and CD20+CD27-CD22+IgM+HLA-DR+CXCR5+CXCR4+CD10-CD38-) could differentially classify XMEN, ALPS, and healthy individuals. We also performed glycoproteomics analysis on T lymphocytes and show that XMEN disease is a congenital disorder of glycosylation that affects a restricted subset of glycoproteins. Transfection of MAGT1 mRNA enabled us to rescue proteins with defective glycosylation. Together, these data provide new clinical and pathophysiological foundations with important ramifications for the diagnosis and treatment of XMEN disease.

INVESTIGATION OF THE INFLUENCE OF THYROID LOCATION ON IODINE-131 S VALUES

The use of iodine-131 S values based on reference computational phantoms with fixed thyroid model may lead to significant dosimetric errors in patients who may have different thyroid location from the reference phantoms. In the present study, we investigated individual thyroid location variation by examining the computed tomography image sets of 40 adult male and female patients. Subsequently, the thyroid location of the adult male and female mesh-type reference phantoms of the International Commission on Radiological Protection (ICRP) was adjusted to match each the highest, mean and the lowest locations of the thyroid observed in this dataset. The thyroid-adjusted phantoms were implemented into the Geant4 Monte Carlo code to calculate thyroid location-dependent iodine-131 S values (rT ← thyroid) for a total of 30 target regions. The maximum variation among the observed thyroid locations was 39 mm and 36 mm for male and female patients, respectively. The mean thyroid locations of both male and female patients showed a good agreement with the ICRP reference phantoms. The thyroid location-dependent Iodine-131 S values were significantly different from the reference phantoms for most target regions by up to a factor of 3. The use of thyroid location-dependent S values in dose reconstructions should help quantify the dosimetric uncertainty in epidemiologic investigations of patients receiving iodine-131 therapy for hyperthyroidism and thyroid cancer.

Lymphocyte-driven regional immunopathology in pneumonitis caused by impaired central immune tolerance

Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED), a monogenic disorder caused by AIRE mutations, presents with several autoimmune diseases. Among these, endocrine organ failure is widely recognized, but the prevalence, immunopathogenesis, and treatment of non-endocrine manifestations such as pneumonitis remain poorly characterized. We enrolled 50 patients with APECED in a prospective observational study and comprehensively examined their clinical and radiographic findings, performed pulmonary function tests, and analyzed immunological characteristics in blood, bronchoalveolar lavage fluid, and endobronchial and lung biopsies. Pneumonitis was found in >40% of our patients, presented early in life, was misdiagnosed despite chronic respiratory symptoms and accompanying radiographic and pulmonary function abnormalities, and caused hypoxemic respiratory failure and death. Autoantibodies against BPIFB1 and KCNRG and the homozygous c.967_979del13 AIRE mutation are associated with pneumonitis development. APECED pneumonitis features compartmentalized immunopathology, with accumulation of activated neutrophils in the airways and lymphocytic infiltration in intraepithelial, submucosal, peribronchiolar, and interstitial areas. Beyond APECED, we extend these observations to lung disease seen in other conditions with secondary AIRE deficiency (thymoma and RAG deficiency). Aire-deficient mice had similar compartmentalized cellular immune responses in the airways and lung tissue, which was ameliorated by deficiency of T and B lymphocytes. Accordingly, T and B lymphocyte-directed immunomodulation controlled symptoms and radiographic abnormalities and improved pulmonary function in patients with APECED pneumonitis. Collectively, our findings unveil lung autoimmunity as a common, early, and unrecognized manifestation of APECED and provide insights into the immunopathogenesis and treatment of pulmonary autoimmunity associated with impaired central immune tolerance.

Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays

We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays. This disease is caused by the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, also known as the novel Coronavirus (2019-nCoV). A custom convolutional neural network and a selection of ImageNet pretrained models are trained and evaluated at patient-level on publicly available CXR collections to learn modality-specific feature representations. The learned knowledge is transferred and fine-tuned to improve performance and generalization in the related task of classifying CXRs as normal, showing bacterial pneumonia, or COVID-19-viral abnormalities. The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.

Automatic Mapping of CT Scan Locations on Computational Human Phantoms for Organ Dose Estimation

To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.

Preparing Medical Imaging Data for Machine Learning

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.

Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence

RATIONALE AND OBJECTIVES

Our primary aim was to improve radiology reports by increasing concordance of target lesion measurements with oncology records using radiology preprocessors (RP). Faster notification of incidental actionable findings to referring clinicians and clinical radiologist exam interpretation time savings with RPs quantifying tumor burden were also assessed.

MATERIALS AND METHODS

In this prospective quality improvement initiative, RPs annotated lesions before radiologist interpretation of CT exams. Clinical radiologists then hyperlinked approved measurements into interactive reports during interpretations. RPs evaluated concordance with our tumor measurement radiologist, the determinant of tumor burden. Actionable finding detection and notification times were also deduced. Clinical radiologist interpretation times were calculated from established average CT chest, abdomen, and pelvis interpretation times.

RESULTS

RPs assessed 1287 body CT exams with 812 follow-up CT chest, abdomen, and pelvis studies; 95 (11.7%) of which had 241 verified target lesions. There was improved concordance (67.8% vs. 22.5%) of target lesion measurements. RPs detected 93.1% incidental actionable findings with faster clinician notification by a median time of 1 hour (range: 15 minutes-16 hours). Radiologist exam interpretation times decreased by 37%.

CONCLUSIONS

This workflow resulted in three-fold improved target lesion measurement concordance with oncology records, earlier detection and faster notification of incidental actionable findings to referring clinicians, and decreased exam interpretation times for clinical radiologists. These findings demonstrate potential roles for automation (such as AI) to improve report value, worklist prioritization, and patient care.

Cumulative Radiation Exposures from CT Screening and Surveillance Strategies for von Hippel-Lindau-associated Solid Pancreatic Tumors

Purpose To assess the potential ionizing radiation exposure from CT scans for both screening and surveillance of patients with von Hippel-Lindau (VHL) syndrome. Materials and Methods For this retrospective study, abdomen-pelvic (AP) and chest-abdomen-pelvic (CAP) CT scans were performed with either a three-phase (n = 1242) or a dual-energy virtual noncontrast protocol (VNC; n = 149) in 747 patients with VHL syndrome in the National Institutes of Health Clinical Center between 2009 and 2015 (mean age, 47.6 years ± 14.6 [standard deviation]; age range, 12-83 years; 320 women [42.8%]). CT scanning parameters for patients with pancreatic neuroendocrine tumors (PNETs; 124 patients and 381 scans) were compared between a tumor diameter-based surveillance protocol and a VHL genotype and tumor diameter-based algorithm (a tailored algorithm) developed by three VHL clinicians. Organ and lifetime radiation doses were estimated by two radiologists and five radiation scientists. Cumulative radiation doses were compared between the PNET surveillance algorithms by analyses of variance, and a two-tailed P value less than .05 indicated statistical significance. Results Median cumulative colon doses for annual CAP and AP CT scans from age 15 to 40 years ranged from 0.34 Gy (5th-95th percentiles, 0.18-0.75; dual-energy VNC CT) to 0.89 Gy (5th-95th percentiles, 0.42-1.0; three-phase CT). For the current PNET surveillance protocol, the cumulative effective radiation dose from age 40 to 65 years was 682 mSv (tumors < 1.2 cm) and 2125 mSv (tumors > 3 cm). The tailored algorithm could halve these doses for patients with initial tumor diameter less than 1.2 cm (P < .001). Conclusion CT screening of patients with von Hippel-Lindau syndrome can lead to substantial radiation exposures, even with dual-energy virtual noncontrast CT. A genome and tumor diameter-based algorithm for pancreatic neuroendocrine tumor surveillance may potentially reduce lifetime radiation exposure. © RSNA, 2018 Online supplemental material is available for this article.

Detecting drug-resistant tuberculosis in chest radiographs

Purpose

Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis.

Methods

A main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods.

Results

For discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient.

Conclusion

Our results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays.

Computed Tomography Window Blending: Feasibility in Thoracic Trauma

RATIONALE AND OBJECTIVES

This study aims to demonstrate the feasibility of processing computed tomography (CT) images with a custom window blending algorithm that combines soft-tissue, bone, and lung window settings into a single image; to compare the time for interpretation of chest CT for thoracic trauma with window blending and conventional window settings; and to assess diagnostic performance of both techniques.

MATERIALS AND METHODS

Adobe Photoshop was scripted to process axial DICOM images from retrospective contrast-enhanced chest CTs performed for trauma with a window-blending algorithm. Two emergency radiologists independently interpreted the axial images from 103 chest CTs with both blended and conventional windows. Interpretation time and diagnostic performance were compared with Wilcoxon signed-rank test and McNemar test, respectively. Agreement with Nexus CT Chest injury severity was assessed with the weighted kappa statistic.

RESULTS

A total of 13,295 images were processed without error. Interpretation was faster with window blending, resulting in a 20.3% time saving (P < .001), with no difference in diagnostic performance, within the power of the study to detect a difference in sensitivity of 5% as determined by post hoc power analysis. The sensitivity of the window-blended cases was 82.7%, compared to 81.6% for conventional windows. The specificity of the window-blended cases was 93.1%, compared to 90.5% for conventional windows. All injuries of major clinical significance (per Nexus CT Chest criteria) were correctly identified in all reading sessions, and all negative cases were correctly classified. All readers demonstrated near-perfect agreement with injury severity classification with both window settings.

CONCLUSIONS

In this pilot study utilizing retrospective data, window blending allows faster preliminary interpretation of axial chest CT performed for trauma, with no significant difference in diagnostic performance compared to conventional window settings. Future studies would be required to assess the utility of window blending in clinical practice.

Multimedia-enhanced Radiology Reports: Concept, Components, and Challenges

Multimedia-enhanced radiology report (MERR) development is defined and described from an informatics perspective, in which the MERR is seen as a superior information-communicating entity. Recent technical advances, such as the hyperlinking of report text directly to annotated images, improve MERR information content and accessibility compared with text-only reports. The MERR is analyzed by its components, which include hypertext, tables, graphs, embedded images, and their interconnections. The authors highlight the advantages of each component for improving the radiologist's communication of report content information and the user's ability to extract information. Requirements for MERR implementation (eg, integration of picture archiving and communication systems, radiology information systems, and electronic medical record systems) and the authors' initial experiences and challenges in MERR implementation at the National Institutes of Health are reviewed. The transition to MERRs has provided advantages over use of traditional text-only radiology reports because of the capacity to include hyperlinked report text that directs clinicians to image annotations, images, tables, and graphs. A framework is provided for thinking about the MERR from the user's perspective. Additional applications of emerging technologies (eg, artificial intelligence and machine learning) are described in the crafting of what the authors believe is the radiology report of the future. ^©RSNA, 2018.

JAK1/2 inhibition with baricitinib in the treatment of autoinflammatory interferonopathies

BACKGROUND

Monogenic IFN-mediated autoinflammatory diseases present in infancy with systemic inflammation, an IFN response gene signature, inflammatory organ damage, and high mortality. We used the JAK inhibitor baricitinib, with IFN-blocking activity in vitro, to ameliorate disease.

METHODS

Between October 2011 and February 2017, 10 patients with CANDLE (chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperatures), 4 patients with SAVI (stimulator of IFN genes-associated [STING-associated] vasculopathy with onset in infancy), and 4 patients with other interferonopathies were enrolled in an expanded access program. The patients underwent dose escalation, and the benefit was assessed by reductions in daily disease symptoms and corticosteroid requirement. Quality of life, organ inflammation, changes in IFN-induced biomarkers, and safety were longitudinally assessed.

RESULTS

Eighteen patients were treated for a mean duration of 3.0 years (1.5-4.9 years). The median daily symptom score decreased from 1.3 (interquartile range [IQR], 0.93-1.78) to 0.25 (IQR, 0.1-0.63) (P < 0.0001). In 14 patients receiving corticosteroids at baseline, daily prednisone doses decreased from 0.44 mg/kg/day (IQR, 0.31-1.09) to 0.11 mg/kg/day (IQR, 0.02-0.24) (P < 0.01), and 5 of 10 patients with CANDLE achieved lasting clinical remission. The patients' quality of life and height and bone mineral density Z-scores significantly improved, and their IFN biomarkers decreased. Three patients, two of whom had genetically undefined conditions, discontinued treatment because of lack of efficacy, and one CANDLE patient discontinued treatment because of BK viremia and azotemia. The most common adverse events were upper respiratory infections, gastroenteritis, and BK viruria and viremia.

CONCLUSION

Upon baricitinib treatment, clinical manifestations and inflammatory and IFN biomarkers improved in patients with the monogenic interferonopathies CANDLE, SAVI, and other interferonopathies. Monitoring safety and efficacy is important in benefit-risk assessment.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01724580 and NCT02974595.

FUNDING

This research was supported by the Intramural Research Program of the NIH, NIAID, and NIAMS. Baricitinib was provided by Eli Lilly and Company, which is the sponsor of the expanded access program for this drug.

JAK1/2 inhibition with baricitinib in the treatment of autoinflammatory interferonopathies

BACKGROUND. Monogenic IFN–mediated autoinflammatory diseases present in infancy with systemic inflammation, an IFN response gene signature, inflammatory organ damage, and high mortality. We used the JAK inhibitor baricitinib, with IFN-blocking activity in vitro, to ameliorate disease.

METHODS. Between October 2011 and February 2017, 10 patients with CANDLE (chronic atypical neutrophilic dermatosis with lipodystrophy and elevated temperatures), 4 patients with SAVI (stimulator of IFN genes–associated [STING-associated] vasculopathy with onset in infancy), and 4 patients with other interferonopathies were enrolled in an expanded access program. The patients underwent dose escalation, and the benefit was assessed by reductions in daily disease symptoms and corticosteroid requirement. Quality of life, organ inflammation, changes in IFN-induced biomarkers, and safety were longitudinally assessed.

RESULTS. Eighteen patients were treated for a mean duration of 3.0 years (1.5–4.9 years). The median daily symptom score decreased from 1.3 (interquartile range [IQR], 0.93–1.78) to 0.25 (IQR, 0.1–0.63) (P < 0.0001). In 14 patients receiving corticosteroids at baseline, daily prednisone doses decreased from 0.44 mg/kg/day (IQR, 0.31–1.09) to 0.11 mg/kg/day (IQR, 0.02–0.24) (P < 0.01), and 5 of 10 patients with CANDLE achieved lasting clinical remission. The patients’ quality of life and height and bone mineral density Z-scores significantly improved, and their IFN biomarkers decreased. Three patients, two of whom had genetically undefined conditions, discontinued treatment because of lack of efficacy, and one CANDLE patient discontinued treatment because of BK viremia and azotemia. The most common adverse events were upper respiratory infections, gastroenteritis, and BK viruria and viremia.

CONCLUSION. Upon baricitinib treatment, clinical manifestations and inflammatory and IFN biomarkers improved in patients with the monogenic interferonopathies CANDLE, SAVI, and other interferonopathies. Monitoring safety and efficacy is important in benefit-risk assessment.

TRIAL REGISTRATION. ClinicalTrials.gov NCT01724580 and NCT02974595.

FUNDING. This research was supported by the Intramural Research Program of the NIH, NIAID, and NIAMS. Baricitinib was provided by Eli Lilly and Company, which is the sponsor of the expanded access program for this drug.

Bilateral Ureteroenteric Strictures: A Case of the "Reverse 7"

Anastomotic stricture is a well-known complication of the urinary diversion that accompanies radical cystectomy. Management options range from endoscopic procedures to open surgeries, with a subset of the latter employing bowel as the interposing segment. In this report, we describe a rare patient, who successfully underwent a "Reverse 7" procedure, bypassing strictures at both anastomotic junctions between ureters and neobladder.

Lens Dose Reduction by Patient Posture Modification During Neck CT

OBJECTIVE

Radiation exposure of the lens during neck CT may increase a patient's risk of developing cataracts. Radiologists at the National Institutes of Health worked with technicians to modify the neck CT scanning procedure to include a reduction in the scanning range, a reduction in the tube potential (kilovoltage), and a change in neck positioning using a head tilt. We objectively quantified the organ dose changes after this procedure modification using a computer simulation.

MATERIALS AND METHODS

We retrospectively analyzed CT images of 40 patients (20 men and 20 women) scanned before and after the procedure change. Radiation dose to the lens delivered before and after the procedure change was calculated using an in-house CT dose calculator combined with computational human phantoms deformed to match head tilt angles. We also calculated the doses to other radiosensitive organs including the brain, pituitary gland, eye globes, and salivary glands before and after the procedure change.

RESULTS

Our dose calculations showed that modifying the neck position, shortening the scanning range, and reducing the tube potential reduced the dose to the lens by 89% (p < 0.0001). The median brain, pituitary gland, globes, and salivary gland doses also decreased by 59%, 52%, 66%, and 29%, respectively. We found that overranging significantly affects the lens dose.

CONCLUSION

Combining head tilt and scanning range reduction is an easy and effective method that significantly reduces radiation dose to the lens and other radiosensitive head and neck organs.

ENABLE (Exportable Notation and Bookmark List Engine): an Interface to Manage Tumor Measurement Data from PACS to Cancer Databases

Oncologists evaluate therapeutic response in cancer trials based on tumor quantification following selected "target" lesions over time. At our cancer center, a majority of oncologists use Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 quantifying tumor progression based on lesion measurements on imaging. Currently, our oncologists handwrite tumor measurements, followed by multiple manual data transfers; however, our Picture Archiving Communication System (PACS) (Carestream Health, Rochester, NY) has the ability to export tumor measurements, making it possible to manage tumor metadata digitally. We developed an interface, "Exportable Notation and Bookmark List Engine" (ENABLE), which produces prepopulated RECIST v1.1 worksheets and compiles cohort data and data models from PACS measurement data, thus eliminating handwriting and manual data transcription. We compared RECIST v1.1 data from eight patients (16 computed tomography exams) enrolled in an IRB-approved therapeutic trial with ENABLE outputs: 10 data fields with a total of 194 data points. All data in ENABLE's output matched with the existing data. Seven staff were taught how to use the interface with a 5-min explanatory instructional video. All were able to use ENABLE successfully without additional guidance. We additionally assessed 42 metastatic genitourinary cancer patients with available RECIST data within PACS to produce a best response waterfall plot. ENABLE manages tumor measurements and associated metadata exported from PACS, producing forms and data models compatible with cancer databases, obviating handwriting and the manual re-entry of data. Automation should reduce transcription errors and improve efficiency and the auditing process.

Resources Required for Semi-Automatic Volumetric Measurements in Metastatic Chordoma: Is Potentially Improved Tumor Burden Assessment Worth the Time Burden?

The Response Evaluation Criteria in Solid Tumors (RECIST) is the current standard for assessing therapy response in patients with malignant solid tumors; however, volumetric assessments are thought to be more representative of actual tumor size and hence superior in predicting patient outcomes. We segmented all primary and metastatic lesions in 21 chordoma patients for comparison to RECIST. Primary tumors were segmented on MR and validated by a neuroradiologist. Metastatic lesions were segmented on CT and validated by a general radiologist. We estimated times for a research assistant to segment all primary and metastatic chordoma lesions using semi-automated volumetric segmentation tools available within our PACS (v12.0, Carestream, Rochester, NY), as well as time required for radiologists to validate the segmentations. We also report success rates of semi-automatic segmentation in metastatic lesions on CT and time required to export data. Furthermore, we discuss the feasibility of volumetric segmentation workflow in research and clinical settings. The research assistant spent approximately 65 h segmenting 435 lesions in 21 patients. This resulted in 1349 total segmentations (average 2.89 min per lesion) and over 13,000 data points. Combined time for the neuroradiologist and general radiologist to validate segmentations was 45.7 min per patient. Exportation time for all patients totaled only 6 h, providing time-saving opportunities for data managers and oncologists. Perhaps cost-neutral resource reallocation can help acquire volumes paralleling our example workflow. Our results will provide researchers with benchmark resources required for volumetric assessments within PACS and help prepare institutions for future volumetric assessment criteria.

Opportunities to Reduce CT Radiation Exposure, Experience Over 5 Years at the NIH Clinical Center

Our current study was undertaken in order to compare CT exposures during various dose-reduction initiatives at the National Institutes of Health Clinical center, to show trends in exposure reduction over a 5-y period, and to provide benchmarks that other facilities may use. Using an in-house extraction tool (Radiation Exposure Extraction Engine), we derived CT exposure data from Digital Imaging and Communications in Medicine (DICOM) headers over 5 y. We present parameters used and compare most common exams between 2010 and 2015. During a period of exposure-reduction initiatives, data of 79 396 exams from nine CT scanners on 87 scan protocols were analyzed. Adult chest exposures were reduced 53% and chest, abdomen and pelvis exams were reduced 43%  (p < 0.001). Only extremity exams did not show significantly reduced exposure. Collecting data over several years allowed us to confirm and compare several initiatives. We demonstrated significant exposure reductions during continued reduction efforts on common exams. Our results may provide benchmarks for similar centers.

Feasibility of Dose-reduced Chest CT with Photon-counting Detectors: Initial Results in Humans

Purpose To investigate whether photon-counting detector (PCD) technology can improve dose-reduced chest computed tomography (CT) image quality compared with that attained with conventional energy-integrating detector (EID) technology in vivo. Materials and Methods This was a HIPAA-compliant institutional review board-approved study, with informed consent from patients. Dose-reduced spiral unenhanced lung EID and PCD CT examinations were performed in 30 asymptomatic volunteers in accordance with manufacturer-recommended guidelines for CT lung cancer screening (120-kVp tube voltage, 20-mAs reference tube current-time product for both detectors). Quantitative analysis of images included measurement of mean attenuation, noise power spectrum (NPS), and lung nodule contrast-to-noise ratio (CNR). Images were qualitatively analyzed by three radiologists blinded to detector type. Reproducibility was assessed with the intraclass correlation coefficient (ICC). McNemar, paired t, and Wilcoxon signed-rank tests were used to compare image quality. Results Thirty study subjects were evaluated (mean age, 55.0 years ± 8.7 [standard deviation]; 14 men). Of these patients, 10 had a normal body mass index (BMI) (BMI range, 18.5-24.9 kg/m²; group 1), 10 were overweight (BMI range, 25.0-29.9 kg/m²; group 2), and 10 were obese (BMI ≥30.0 kg/m², group 3). PCD diagnostic quality was higher than EID diagnostic quality (P = .016, P = .016, and P = .013 for readers 1, 2, and 3, respectively), with significantly better NPS and image quality scores for lung, soft tissue, and bone and with fewer beam-hardening artifacts (all P < .001). Image noise was significantly lower for PCD images in all BMI groups (P < .001 for groups 1 and 3, P < .01 for group 2), with higher CNR for lung nodule detection (12.1 ± 1.7 vs 10.0 ± 1.8, P < .001). Inter- and intrareader reproducibility were good (all ICC > 0.800). Conclusion Initial human experience with dose-reduced PCD chest CT demonstrated lower image noise compared with conventional EID CT, with better diagnostic quality and lung nodule CNR. ^© RSNA, 2017 Online supplemental material is available for this article.

CD55 Deficiency, Early-Onset Protein-Losing Enteropathy, and Thrombosis

BACKGROUND

Studies of monogenic gastrointestinal diseases have revealed molecular pathways critical to gut homeostasis and enabled the development of targeted therapies.

METHODS

We studied 11 patients with abdominal pain and diarrhea caused by early-onset protein-losing enteropathy with primary intestinal lymphangiectasia, edema due to hypoproteinemia, malabsorption, and less frequently, bowel inflammation, recurrent infections, and angiopathic thromboembolic disease; the disorder followed an autosomal recessive pattern of inheritance. Whole-exome sequencing was performed to identify gene variants. We evaluated the function of CD55 in patients' cells, which we confirmed by means of exogenous induction of expression of CD55.

RESULTS

We identified homozygous loss-of-function mutations in the gene encoding CD55 (decay-accelerating factor), which lead to loss of protein expression. Patients' T lymphocytes showed increased complement activation causing surface deposition of complement and the generation of soluble C5a. Costimulatory function and cytokine modulation by CD55 were defective. Genetic reconstitution of CD55 or treatment with a complement-inhibitory therapeutic antibody reversed abnormal complement activation.

CONCLUSIONS

CD55 deficiency with hyperactivation of complement, angiopathic thrombosis, and protein-losing enteropathy (the CHAPLE syndrome) is caused by abnormal complement activation due to biallelic loss-of-function mutations in CD55. (Funded by the National Institute of Allergy and Infectious Diseases and others.).

Advances in medical imaging for the diagnosis and management of common genitourinary cancers

Medical imaging of the 3 most common genitourinary (GU) cancers-prostate adenocarcinoma, renal cell carcinoma, and urothelial carcinoma of the bladder-has evolved significantly during the last decades. The most commonly used imaging modalities for the diagnosis, staging, and follow-up of GU cancers are computed tomography, magnetic resonance imaging (MRI), and positron emission tomography (PET). Multiplanar multidetector computed tomography and multiparametric MRI with diffusion-weighted imaging are the main imaging modalities for renal cell carcinoma and urothelial carcinoma, and although multiparametric MRI is rapidly becoming the main imaging tool in the evaluation of prostate adenocarcinoma, biopsy is still required for diagnosis. Functional and molecular imaging using 18-fluorodeoxyglucose-PET and sodium fluoride-PET are essential for the diagnosis, and especially follow-up, of metastatic GU tumors. This review provides an overview of the latest advances in the imaging of these 3 major GU cancers.

Quantitative Image Quality Comparison of Reduced- and Standard-Dose Dual-Energy Multiphase Chest, Abdomen, and Pelvis CT

We present a new image quality assessment method for determining whether reducing radiation dose impairs the image quality of computed tomography (CT) in qualitative and quantitative clinical analyses tasks. In this Institutional Review Board-exempt study, we conducted a review of 50 patients (male, 22; female, 28) who underwent reduced-dose CT scanning on the first follow-up after standard-dose multiphase CT scanning. Scans were for surveillance of von Hippel–Lindau disease (N = 26) and renal cell carcinoma (N = 10). We investigated density, morphometric, and structural differences between scans both at tissue (fat, bone) and organ levels (liver, heart, spleen, lung). To quantify structural variations caused by image quality differences, we propose using the following metrics: dice similarity coefficient, structural similarity index, Hausdorff distance, gradient magnitude similarity deviation, and weighted spectral distance. Pearson correlation coefficient and Welch 2-sample t test were used for quantitative comparisons of organ morphometry and to compare density distribution of tissue, respectively. For qualitative evaluation, 2-sided Kendall Tau test was used to assess agreement among readers. Both qualitative and quantitative evaluations were designed to examine significance of image differences for clinical tasks. Qualitative judgment served as an overall assessment, whereas detailed quantifications on structural consistency, intensity homogeneity, and texture similarity revealed more accurate and global difference estimations. Qualitative and quantitative results indicated no significant image quality degradation. Our study concludes that low(er)-dose CT scans can be routinely used because of no significant loss in quantitative image information compared with standard-dose CT scans.

Clinical Applications of a CT Window Blending Algorithm: RADIO (Relative Attenuation-Dependent Image Overlay)

A methodology is described using Adobe Photoshop and Adobe Extendscript to process DICOM images with a Relative Attenuation-Dependent Image Overlay (RADIO) algorithm to visualize the full dynamic range of CT in one view, without requiring a change in window and level settings. The potential clinical uses for such an algorithm are described in a pictorial overview, including applications in emergency radiology, oncologic imaging, and nuclear medicine and molecular imaging.

Low-dose lung cancer screening with photon-counting CT: a feasibility study

To evaluate the feasibility of using a whole-body photon-counting detector (PCD) CT scanner for low-dose lung cancer screening compared to a conventional energy integrating detector (EID) system. Radiation dose-matched EID and PCD scans of the COPDGene 2 phantom were acquired at different radiation dose levels (CTDI_vol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp). EID and PCD images were compared for quantitative Hounsfield unit (HU) accuracy, noise levels, and contrast-to-noise ratios (CNR) for detection of ground-glass nodules (GGN) and emphysema. The PCD HU accuracy was better than EID for water at all scan parameters. PCD HU stability for lung, GGN and emphysema regions were superior to EID and PCD attenuation values were more reproducible than EID for all scan parameters (all P  <  0.01), while HUs for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P  <  0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2  ±  0.3 HU versus 15.8  ±  0.2 HU, P  =  0.03; 16.1  ±  0.3 HU versus 18.0  ±  0.4 HU, P  =  0.003; and 16.1  ±  0.3 HU versus 17.9  ±  0.3 HU, P  =  0.001, respectively), resulting in superior CNR for evaluation of GGNs and emphysema at 100 and 80 kVp. PCD provided better HU stability for lung, ground-glass, and emphysema-equivalent foams at lower radiation dose settings with better reproducibility than EID. Additionally, PCD showed up to 10% less noise, and 11% higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.

Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST

BACKGROUND

The Response Evaluation Criteria in Solid Tumors (RECIST) are the current standard for evaluating disease progression or therapy response in patients with solid tumors. RECIST 1.1 calls for axial, longest-diameter (or perpendicular short axis of lymph nodes) measurements of a maximum of five tumors, which limits clinicians' ability to adequately measure disease burden, especially in patients with irregularly shaped tumors. This is especially problematic in chordoma, a disease for which RECIST does not always adequately capture disease burden because chordoma tumors are typically irregularly shaped and slow-growing. Furthermore, primary chordoma tumors tend to be adjacent to vital structures in the skull or sacrum that, when compressed, lead to significant clinical consequences.

METHODS

Volumetric segmentation is a newer technology that allows tumor burden to be measured in three dimensions on either MR or CT. Here, we compared the ability of RECIST measurements and tumor volumes to predict clinical outcomes in a cohort of 21 chordoma patients receiving immunotherapy.

RESULTS

There was a significant difference in radiologic time to progression Kaplan-Meier curves between clinical outcome groups using volumetric segmentation (P = 0.012) but not RECIST (P = 0.38). In several cases, changes in volume were earlier and more sensitive reflections of clinical status.

CONCLUSION

RECIST is a useful evaluation method when obvious changes are occurring in patients with chordoma. However, in many cases, RECIST does not detect small changes, and volumetric assessment was capable of detecting changes and predicting clinical outcome earlier than RECIST. Although this study was small and retrospective, we believe our results warrant further research in this area.

High pitch third generation dual-source CT: Coronary and cardiac visualization on routine chest CT

BACKGROUND

Chest CT scans are frequently performed in radiology departments but have not previously contained detailed depiction of cardiac structures.

OBJECTIVES

To evaluate myocardial and coronary visualization on high-pitch non-gated CT of the chest using 3rd generation dual-source computed tomography (CT).

METHODS

Cardiac anatomy of patients who had 3rd generation, non-gated high pitch contrast enhanced chest CT and who also had prior conventional (low pitch) chest CT as part of a chest abdomen pelvis exam was evaluated. Cardiac image features were scored by reviewers blinded to diagnosis and pitch. Paired analysis was performed.

RESULTS

3862 coronary segments and 2220 cardiac structures were evaluated by two readers in 222 CT scans. Most patients (97.2%) had chest CT for oncologic evaluation. The median pitch was 2.34 (IQR 2.05, 2.65) in high pitch and 0.8 (IQR 0.8, 0.8) in low pitch scans (p < 0.001). High pitch CT showed higher image visualization scores for all cardiovascular structures compared with conventional pitch scans (p < 0.0001). Coronary arteries were visualized in 9 coronary segments per exam in high pitch scans versus 2 segments for conventional pitch (p < 0.0001). Radiation exposure was lower in the high pitch group compared with the conventional pitch group (median CTDIvol 10.83 vs. 12.36 mGy and DLP 790 vs. 827 mGycm respectively, p < 0.01 for both) with comparable image noise (p = 0.43).

CONCLUSION

Myocardial structure and coronary arteries are frequently visualized on non-gated 3rd generation chest CT. These results raise the question of whether the heart and coronary arteries should be routinely interpreted on routine chest CT that is otherwise obtained for non-cardiac indications.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.

Abdominal Imaging with Contrast-enhanced Photon-counting CT: First Human Experience

PURPOSE

To evaluate the performance of a prototype photon-counting detector (PCD) computed tomography (CT) system for abdominal CT in humans and to compare the results with a conventional energy-integrating detector (EID).

MATERIALS AND METHODS

The study was HIPAA-compliant and institutional review board-approved with informed consent. Fifteen asymptomatic volunteers (seven men; mean age, 58.2 years ± 9.8 [standard deviation]) were prospectively enrolled between September 2 and November 13, 2015. Radiation dose-matched delayed contrast agent-enhanced spiral and axial abdominal EID and PCD scans were acquired. Spiral images were scored for image quality (Wilcoxon signed-rank test) in five regions of interest by three radiologists blinded to the detector system, and the axial scans were used to assess Hounsfield unit accuracy in seven regions of interest (paired t test). Intraclass correlation coefficient (ICC) was used to assess reproducibility. PCD images were also used to calculate iodine concentration maps. Spatial resolution, noise-power spectrum, and Hounsfield unit accuracy of the systems were estimated by using a CT phantom.

RESULTS

In both systems, scores were similar for image quality (median score, 4; P = .19), noise (median score, 3; P = .30), and artifact (median score, 1; P = .17), with good interrater agreement (image quality, noise, and artifact ICC: 0.84, 0.88, and 0.74, respectively). Hounsfield unit values, spatial resolution, and noise-power spectrum were also similar with the exception of mean Hounsfield unit value in the spinal canal, which was lower in the PCD than the EID images because of beam hardening (20 HU vs 36.5 HU; P < .001). Contrast-to-noise ratio of enhanced kidney tissue was improved with PCD iodine mapping compared with EID (5.2 ± 1.3 vs 4.0 ± 1.3; P < .001).

CONCLUSION

The performance of PCD showed no statistically significant difference compared with EID when the abdomen was evaluated in a conventional scan mode. PCD provides spectral information, which may be used for material decomposition.

Docetaxel Alone or in Combination With a Therapeutic Cancer Vaccine (PANVAC) in Patients With Metastatic Breast Cancer: A Randomized Clinical Trial

IMPORTANCE

Previous phase 1 and 2 trials of PANVAC, a poxviral-based cancer vaccine, have suggested clinical efficacy in some patients with breast, ovarian, and colorectal cancer and have shown evidence of immunologic activity. Preclinical data have shown that docetaxel can modify tumor phenotype, making tumor cells more amenable to T cell-mediated killing.

OBJECTIVE

The goal of this study was to determine if the treatment combination of docetaxel and PANVAC improves clinical outcomes in patients with metastatic breast cancer compared with docetaxel treatment alone.

DESIGN, SETTING, AND PARTICIPANTS

Between May 2006 and February 2012, this open-label, phase 2 randomized clinical trial enrolled 48 patients with metastatic breast cancer of all subtypes, without limitation on other lines of previous therapy, to receive treatment with either docetaxel with PANVAC (arm A) or docetaxel alone (arm B). Final clinical data were collected on September 16, 2013. All patients were treated at either the National Cancer Institute or the Department of Breast Medical Oncology, MD Anderson Cancer Center.

MAIN OUTCOMES AND MEASURES

The primary end point was progression-free survival (PFS), using a phase 2.5 statistical design, with the intent of identifying a trend toward benefit (defined as 1-sided P≤.10) to guide a larger trial design. Secondary end points included safety and immunologic correlative studies.

RESULTS

Forty-eight participants were enrolled: 25 were randomized to the combination treatment arm A, and 23 to arm B. No patient remained in the study at the time of the final analysis. Patient and tumor characteristics were well matched. Analysis of adverse events in both treatment arms demonstrated very little difference between the 2 groups. In the combination treatment arm (arm A), statistically significant increases were noted in the frequency of grades 1 and 2 edema (P=.02, likely related to greater median number of docetaxel cycles) and injection-site reactions (P<.001). In the final data analysis, median PFS was 7.9 months in arm A vs 3.9 months in arm B (hazard ratio, 0.65 [95% CI, 0.34-1.14]; P=.09).

CONCLUSIONS AND RELEVANCE

The results suggest that the combination of PANVAC with docetaxel in metastatic breast cancer may provide a clinical benefit. This study was hypothesis generating and provides both rationale and statistical assumptions for a larger definitive randomized study.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00179309.

Radiology preparedness in ebola virus disease: guidelines and challenges for disinfection of medical imaging equipment for the protection of staff and patients

The overlap of early Ebola virus disease (EVD) symptoms (eg, fever, headache, abdominal pain, diarrhea, emesis, and fatigue) with symptoms of other more common travel-related diseases (eg, malaria, typhoid fever, pneumonia, and meningococcemia) may result in delayed diagnosis of EVD before isolation of infected patients. Radiology departments should consider policies for and approaches to decontamination of expensive and potentially easily damaged radiology equipment. In addition, the protection of radiology personnel must be considered during the work-up phase of undiagnosed EVD patients presenting to emergency departments. The purpose of this article is to consider the effect of EVD on radiology departments and imaging equipment, with particular consideration of guidelines currently available from the Centers for Disease Control and Prevention that may be applicable to radiology.

Immune dysregulation in human subjects with heterozygous germline mutations in CTLA4

Cytotoxic T lymphocyte antigen-4 (CTLA-4) is an inhibitory receptor found on immune cells. The consequences of mutations in CTLA4 in humans are unknown. We identified germline heterozygous mutations in CTLA4 in subjects with severe immune dysregulation from four unrelated families. Whereas Ctla4 heterozygous mice have no obvious phenotype, human CTLA4 haploinsufficiency caused dysregulation of FoxP3(+) regulatory T (Treg) cells, hyperactivation of effector T cells, and lymphocytic infiltration of target organs. Patients also exhibited progressive loss of circulating B cells, associated with an increase of predominantly autoreactive CD21(lo) B cells and accumulation of B cells in nonlymphoid organs. Inherited human CTLA4 haploinsufficiency demonstrates a critical quantitative role for CTLA-4 in governing T and B lymphocyte homeostasis.

Dominant-activating germline mutations in the gene encoding the PI(3)K catalytic subunit p110δ result in T cell senescence and human immunodeficiency

The p110δ subunit of phosphatidylinositol-3-OH kinase (PI(3)K) is selectively expressed in leukocytes and is critical for lymphocyte biology. Here we report fourteen patients from seven families who were heterozygous for three different germline, gain-of-function mutations in PIK3CD (which encodes p110δ). These patients presented with sinopulmonary infections, lymphadenopathy, nodular lymphoid hyperplasia and viremia due to cytomegalovirus (CMV) and/or Epstein-Barr virus (EBV). Strikingly, they had a substantial deficiency in naive T cells but an over-representation of senescent effector T cells. In vitro, T cells from patients exhibited increased phosphorylation of the kinase Akt and hyperactivation of the metabolic checkpoint kinase mTOR, enhanced glucose uptake and terminal effector differentiation. Notably, treatment with rapamycin to inhibit mTOR activity in vivo partially restored the abundance of naive T cells, largely 'rescued' the in vitro T cell defects and improved the clinical course.

A cross-platform and distributive database system for cumulative tumor measurement

This paper discusses the development of a cross-platform, web accessible, vendor-independent database system capable of storing and comparing longitudinal tumor measurements for multiple tumors. This innovative system can create a comprehensive cumulative report that summarizes clinical findings and links to the original image studies, which will clinically enhance the workflow of oncologists. A case study on a pancreatic tumor data set with 524 tumor measurements and 134 patients is demonstrated.

Automated registration, segmentation, and measurement of metastatic melanoma tumors in serial CT scans

OBJECTIVES

Our goal was to evaluate a new software capability that integrates registration, segmentation and tumor measurement across serial exams within a picture archiving communication system (PACS) to expedite tumor measurement.

MATERIALS AND METHODS

Patients treated under institutional review board-approved protocols for metastatic melanoma were retrospectively reviewed. Of the 19 included patients, five were male, the median age was 43.2, and all received treatment using an adoptive cell therapy. Seventy-one lung, liver, and subcutaneous tumors were manually measured using RECIST (Response Evaluation Criteria In Solid Tumors) criteria before therapy (baseline computed tomography [CT]) and within 3 months after therapy (follow-up CT). We performed semiautomated registration, segmentation, and RECIST measurements at both time points within PACS (Carestream Health, Rochester, NY). We compared manual and software-generated RECIST measurements using Bland-Altman plots.

RESULTS

The median manually measured RECIST diameter for all baseline tumors was 2.1 (1.0-6.2) cm. The refined registration function identified 70/71 (98.6%) tumors on the follow-up CT. On the baseline CT, all 21 liver, 27/32 (84%) lung, and 10/18 (55%) subcutaneous tumors completed segmentation. On the follow-up CT, 19/21 (90%) liver, 21/27 (78%) lung, and 8/10 (80%) subcutaneous tumors completed segmentation. The Bland-Altman plot demonstrated a 95% confidence interval of ±0.7 cm when comparing the software-generated and manual RECIST measurements.

CONCLUSIONS

The PACS software performed semiautomated baseline tumor measurements and fully automated follow-up tumor measurements in a majority of lung, liver, and subcutaneous tumors. In our patients, semiautomated metastatic tumor measurement did not obviate the need for physician oversight due to disease and treatment-related factors.