Shades of Gray: Subsolid Nodule Considerations and Management
Subsolid nodules are common on chest CT and may be either benign or malignant. Their varied features, and broad differential diagnoses present management challenges. While subsolid nodules often represent lung adenocarcinomas, other possibilities are common, and influence management. Practice guidelines exist for subsolid nodule management for both incidentally and screening-detected nodules, incorporating patient and nodule characteristics. This review will highlight similarities and differences amongst these algorithms, with the intent of providing a resource for comparison, and aid in choosing management options.
Percutaneous Transthoracic Lung Biopsy: Optimizing Yield and Mitigating Risk
ABSTRACT/UNASSIGNED:Percutaneous computed tomography-guided transthoracic lung biopsy is an effective and minimally invasive procedure to achieve tissue diagnosis. Radiologists are key in appropriate referral for further workup, with percutaneous computed tomography-guided transthoracic lung biopsy performed by both thoracic and general interventionalists. Percutaneous computed tomography-guided transthoracic lung biopsy is increasingly performed for both diagnostic and research purposes, including molecular analysis. Multiple patient, lesion, and technique-related variables influence diagnostic accuracy and complication rates. A comprehensive understanding of these factors aids in procedure planning and may serve to maximize diagnostic yield while minimizing complications, even in the most challenging scenarios.
Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management
Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.
Influence of coronary dominance on coronary artery calcification burden
OBJECTIVE:To evaluate the influence of coronary artery dominance on observed coronary artery calcification burden in outpatients presenting for coronary computed tomography angiography (CCTA). METHODS:A 12-month retrospective review was performed of all CCTAs at a single institution. Coronary arterial dominance, Agatston score and presence or absence of cardiovascular risk factors including hypertension (HTN), hyperlipidemia (HLD), diabetes and smoking were recorded. Dominance groups were compared in terms of calcium score adjusted for covariates using analysis of covariance based on ranks. Only covariates observed to be significant independent predictors of the relevant outcome were included in each analysis. All statistical tests were conducted at the two-sided 5% significance level. RESULTS:1223 individuals, 618 women and 605 men were included, mean age 60Â years (24-93Â years). Right coronary dominance was observed in 91.7% (nÂ =Â 1109), left dominance in 8% (nÂ =Â 98), and codominance in 1.3% (nÂ =Â 16). The distribution of patients among Agatston score severity categories significantly differed between codominant and left (pÂ =Â 0.008), and codominant and right (pÂ =Â 0.022) groups, with higher prevalence of either zero or severe CAC in the codominant patients. There was no significant difference in Agatston score between dominance groups. In the subset of individuals with coronary artery calcification, Agatston score was significantly higher in codominant versus left dominant patients (mean Agatston score 595Â Â±Â 520 vs. mean 289Â Â±Â 607, respectively; pÂ =Â 0.049), with a trend towards higher scores in comparison to the right-dominant group (pÂ =Â 0.093). Significance was not maintained upon adjustment for covariates. CONCLUSIONS:While the distribution of Agatston score severity categories differed in codominant versus right- or left-dominant patients, there was no significant difference in Agatston score based on coronary dominance pattern in our cohort. Reporting and inclusion of codominant subsets in larger investigations may elucidate whether codominant anatomy is associated with differing risk.
Imaging Course of Lung Transplantation: From Patient Selection to Postoperative Complications
Lung transplant is increasingly performed for the treatment of end-stage lung disease. As the number of lung transplants and transplant centers continues to rise, radiologists will more frequently participate in the care of patients undergoing lung transplant, both before and after transplant. Potential donors and recipients undergo chest radiography and CT as part of their pretransplant assessment to evaluate for contraindications to transplant and to aid in surgical planning. After transplant, recipients undergo imaging during the postoperative hospitalization and also in the long-term outpatient setting. Radiologists encounter a wide variety of conditions leading to end-stage lung disease and a myriad of posttransplant complications, some of which are unique to lung transplantation. Familiarity with these pathologic conditions, including their imaging findings and their temporal relationship to the transplant, is crucial to accurate radiologic interpretation. Knowledge of the surgical techniques and expected postoperative appearance prevents confusing normal posttransplant imaging findings with complications. A basic understanding of the indications, contraindications, and surgical considerations of lung transplant aids in imaging interpretation and protocoling and also facilitates communication between radiologists and transplant physicians. Despite medical and surgical advances over the past several decades, lung transplant recipients currently have an average posttransplant life expectancy of only 6.7 years. As members of the transplant team, radiologists can help maximize patient survival and hopefully increase posttransplant life expectancy and quality of life in the coming decades. Â©RSNA, 2021 An invited commentary by Bierhals is available online. Online supplemental material is available for this article.
Bronchiolar Adenoma/Pulmonary Ciliated Muconodular Papillary Tumor
OBJECTIVES/OBJECTIVE:To describe the histologic features that are helpful in the diagnosis of the rare bronchiolar adenomas/ciliated muconodular papillary tumors (BAs/CMPTs) during intraoperative consultation. METHODS:Multi-institutional retrospective review of frozen sections of 18 BAs/CMPTs. RESULTS:In 14 of 18 cases, BA/CMPT was the primary reason for sublobar lung resection, and in 4 cases, BA/CMPT was an incidental finding intraoperatively for resections performed for carcinoma in other lobes. There were 11 proximal-type/classic BAs/CMPTs and 7 distal-type/nonclassic BAs/CMPTs. Only 3 (16.7%) of 18 were correctly diagnosed at the time of frozen section, all of which were proximal type/classic. The remainder were diagnosed as adenocarcinoma (n = 7); invasive mucinous adenocarcinoma (n = 1); non-small cell lung carcinoma (n = 1); cystic mucinous neoplasm, favor adenocarcinoma (either mucinous or colloid type) (n = 1); favor adenocarcinoma, cannot exclude CMPT (n = 1); atypical proliferation (n = 2); mucinous epithelial proliferation (n = 1); and mucous gland adenoma (n = 1). CONCLUSIONS:BA/CMPT can potentially be misdiagnosed as carcinoma during intraoperative consultation. On retrospective review of the frozen sections, the presence of the following may help to avoid misdiagnosis: a mixture of bland ciliated columnar cells, mucinous cells, and, most important, a basal cell layer, as well as a lack of necrosis, significant atypia, and mitoses.
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96â€‰hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.
Chest CT Angiography for Acute Aortic Pathologic Conditions: Pearls and Pitfalls
Chest CT angiography (CTA) is essential in the diagnosis of acute aortic syndromes. Chest CTA quality can be optimized with attention to technical parameters pertaining to noncontrast imaging, timing of contrast-enhanced imaging, contrast material volume, kilovolt potential, tube-current modulation, and decisions regarding electrocardiographic-gating and ultra-fast imaging, which may affect the accurate diagnosis of acute aortic syndromes. An understanding of methods to apply to address suboptimal image quality is useful, as the accurate identification of acute aortic syndromes is essential for appropriate patient management. Acute aortic syndromes have high morbidity and mortality, particularly when involving the ascending aorta, and include classic aortic dissection, penetrating atherosclerotic ulcer, and acute intramural hematoma. An understanding of the pathogenesis and distinguishing imaging features of acute aortic syndromes and aortic rupture and some less common manifestations is helpful when interpreting imaging examinations. Related entities, such as ulcerated plaque, ulcerlike projections, and intramural blood pools, and mimics, such as vasculitis and aortic thrombus, are important to recognize; knowledge of these is important to avoid interpretive pitfalls. In addition, an awareness of postsurgical aortic changes can be useful when interpreting CTA examinations when patient history is incomplete. The authors review technical considerations when performing CTA, discuss acute aortic syndromes, and highlight diagnostic challenges encountered when interpreting aortic CTA examinations. Â©RSNA, 2021.
Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features
OBJECTIVE:To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features. MATERIALS AND METHODS/METHODS:Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test. RESULTS:Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation. CONCLUSIONS:The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.
COVID-19 Deterioration Prediction via Self-Supervised Representation Learning and Multi-Image Prediction
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. Machine learning techniques using clinical data such as chest X-rays have been used to predict which patients are most at risk of deterioration. We consider the task of predicting two types of patient deterioration based on chest X-rays: adverse event deterioration (i.e., transfer to the intensive care unit, intubation, or mortality) and increased oxygen requirements beyond 6 L per day. Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images, but this is limited by the differences between the pretraining data and the target COVID-19 patient data. In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks. We present three results. The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0.742 for predicting an adverse event within 96 hours (compared to 0.703 with supervised pretraining) and an AUC of 0.765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0.749 with supervised pretraining). We then propose a new transformer-based architecture that can process sequences of multiple images for prediction and show that this model can achieve an improved AUC of 0.786 for predicting an adverse event at 96 hours and an AUC of 0.848 for predicting mortalities at 96 hours. A small pilot clinical study suggested that the prediction accuracy of our model is comparable to that of experienced radiologists analyzing the same information.