Imaging of Pulmonary Hypertension in Adults: A Position Paper from the Fleischner Society.

Pulmonary hypertension (PH) is defined by a mean pulmonary artery pressure greater than 20 mm Hg and classified into five different groups sharing similar pathophysiologic mechanisms, hemodynamic characteristics, and therapeutic management. Radiologists play a key role in the multidisciplinary assessment and management of PH. A working group was formed from within the Fleischner Society based on expertise in the imaging and/or management of patients with PH, as well as experience with methodologies of systematic reviews. The working group identified key questions focusing on the utility of CT, MRI, and nuclear medicine in the evaluation of PH: (a) Is noninvasive imaging capable of identifying PH? (b) What is the role of imaging in establishing the cause of PH? (c) How does imaging determine the severity and complications of PH? (d) How should imaging be used to assess chronic thromboembolic PH before treatment? (e) Should imaging be performed after treatment of PH? This systematic review and position paper highlights the key role of imaging in the recognition, work-up, treatment planning, and follow-up of PH. This article is a simultaneous joint publication in Radiology and European Respiratory Journal. The articles are identical except for stylistic changes in keeping with each journal's style. Either version may be used in citing this article. © 2021 RSNA and the European Respiratory Society. Online supplemental material is available for this article.

Imaging of pulmonary hypertension in adults: a position paper from the Fleischner Society.

Pulmonary hypertension (PH) is defined by a mean pulmonary artery pressure greater than 20 mmHg and classified into five different groups sharing similar pathophysiologic mechanisms, haemodynamic characteristics, and therapeutic management. Radiologists play a key role in the multidisciplinary assessment and management of PH. A working group was formed from within the Fleischner Society based on expertise in the imaging and/or management of patients with PH, as well as experience with methodologies of systematic reviews. The working group identified key questions focusing on the utility of CT, MRI, and nuclear medicine in the evaluation of PH: a) Is noninvasive imaging capable of identifying PH? b) What is the role of imaging in establishing the cause of PH? c) How does imaging determine the severity and complications of PH? d) How should imaging be used to assess chronic thromboembolic PH before treatment? e) Should imaging be performed after treatment of PH? This systematic review and position paper highlights the key role of imaging in the recognition, work-up, treatment planning, and follow-up of PH.

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.

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.

Enhancing research in academic radiology departments: recommendations of the 2003 Consensus Conference.

Opportunities for funded radiologic research are greater than ever, and the amount of federal funding coming to academic radiology departments is increasing. Even so, many medical school-based radiology departments have little or no research funding. Accordingly, a consensus panel was convened to discuss ways to enhance research productivity and broaden the base of research strength in as many academic radiology departments as possible. The consensus panel included radiologists who have leadership roles in some of the best-funded research departments, radiologists who direct other funded research programs, and radiologists with related expertise. The goals of the consensus panel were to identify the attributes associated with successful research programs and to develop an action plan for radiology research based on these characteristics.

Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports.

PURPOSE: To evaluate translation of chest radiographic reports by using natural language processing and to compare the findings with those in the literature. MATERIALS AND METHODS: A natural language processor coded 10 years of narrative chest radiographic reports from an urban academic medical center. Coding for 150 reports was compared with manual coding. Frequencies and co-occurrences of 24 clinical conditions (diseases, abnormalities, and clinical states) were estimated. The ratio of right to left lung mass, association of pleural effusion with other conditions, and frequency of bullet and stab wounds were compared with independent observations. The sensitivity and specificity of the system's pneumothorax coding were compared with those of manual financial coding. RESULTS: The system coded 889,921 reports on 251,186 patients. On the basis of manual coding of 150 reports, the processor's sensitivity (0.81) and specificity (0.99) were comparable to those previously reported for natural language processing and for expert coders. The frequencies of the selected conditions ranged from 0.22 for pleural effusion to 0.0004 for tension pneumothorax. The database confirmed earlier observations that lung cancer occurs in a 3:2 right-to-left ratio. The association of pleural effusion with other conditions mirrored that in the literature. Bullet and stab wounds decreased during 10 years at a rate consistent with crime statistics. A review of pneumothorax cases showed that the database (sensitivity, 1.00; specificity, 0.996) was more accurate than financial discharge coding (sensitivity, 0.17; P =.002; specificity, 0.996; not significant). CONCLUSION: Internal and external validation in this study confirmed the accuracy of natural language processing for translating chest radiographic narrative reports into a large database of information.

Enhancing research in academic radiology departments: recommendations of the 2003 Consensus Conference.

Opportunities for funded radiologic research are greater than ever, and the amount of federal funding coming to academic radiology departments is increasing. Even so, many medical school-based radiology departments have little or no research funding. Accordingly, a consensus panel was convened to discuss ways to enhance research productivity and broaden the base of research strength in as many academic radiology departments as possible. The consensus panel included radiologists who have leadership roles in some of the best-funded research departments, radiologists who direct other funded research programs, and radiologists with related expertise. The goals of the consensus panel were to identify the attributes associated with successful research programs and to develop an action plan for radiology research based on these characteristics.

Enhancing research in academic radiology departments: recommendations of the 2003 Consensus Conference.

Opportunities for funded radiologic research are greater than ever, and the amount of federal funding coming to academic radiology departments is increasing. Even so, many medical school-based radiology departments have little or no research funding. Accordingly, a consensus panel was convened to discuss ways to enhance research productivity and broaden the base of research strength in as many academic radiology departments as possible. The consensus panel included radiologists who have leadership roles in some of the most well-funded research departments, radiologists who direct other funded research programs, and radiologists with related expertise. The goals of the consensus panel were to identify the attributes associated with successful research programs and to develop an action plan for radiology research on the basis of these characteristics.