Predictors and characteristics of Rib fracture following SBRT for lung tumors.

BACKGROUND: The utilization of stereotactic body radiation therapy (SBRT) is increasing for primary and secondary lung neoplasms. Despite encouraging results, SBRT is associated with an increased risk of osteoradionecrosis-induced rib fracture. We aimed to (1) evaluate potential clinical, demographic, and procedure-related risk factors for rib fractures and (2) describe the radiographic features of post-SBRT rib fractures. METHODS: We retrospectively identified 106 patients who received SBRT between 2015 and 2018 for a primary or metastatic lung tumor with at least 12 months of follow up. Exclusion criteria were incomplete records, previous ipsilateral thoracic radiation, or relevant prior trauma. Computed tomography (CT) images were reviewed to identify and characterize rib fractures. Multivariate logistic regression modeling was employed to determine clinical, demographic, and procedural risk factors (e.g., age, sex, race, medical comorbidities, dosage, and tumor location). RESULTS: A total of 106 patients with 111 treated tumors met the inclusion criteria, 35 (32%) of whom developed at least one fractured rib (60 total fractured ribs). The highest number of fractured ribs per patient was five. Multivariate regression identified posterolateral tumor location as the only independent risk factor for rib fracture. On CT, fractures showed discontinuity between healing edges in 77% of affected patients. CONCLUSIONS: Nearly one third of patients receiving SBRT for lung tumors experienced rib fractures, 34% of whom experienced pain. Many patients developed multiple fractures. Post-SBRT fractures demonstrated a unique discontinuity between the healing edges of the rib, a distinct feature of post-SBRT rib fractures. The only independent predictor of rib fracture was tumor location along the posterolateral chest wall. Given its increasing frequency of use, describing the risk profile of SBRT is vital to ensure patient safety and adequately inform patient expectations.

Performance of a Severity Score on Admission Chest Radiography in Predicting Clinical Outcomes in Hospitalized Patients With Coronavirus Disease (COVID-19).

BACKGROUND. Chest radiographs (CXRs) are typically obtained early in patients admitted with coronavirus disease (COVID-19) and may help guide prognosis and initial management decisions. OBJECTIVE. The purpose of this study was to assess the performance of an admission CXR severity scoring system in predicting hospital outcomes in patients admitted with COVID-19. METHODS. This retrospective study included 240 patients (142 men, 98 women; median age, 65 [range, 50-80] years) admitted to the hospital from March 16 to April 13, 2020, with COVID-19 confirmed by real-time reverse-transcriptase polymerase chain reaction who underwent chest radiography within 24 hours of admission. Three attending chest radiologists and three radiology residents independently scored patients' admission CXRs using a 0- to 24-point composite scale (sum of scores that range from 0 to 3 for extent and severity of disease in upper and lower zones of left and right lungs). Interrater reliability of the score was assessed using the Kendall W coefficient. The mean score was obtained from the six readers' scores for further analyses. Demographic variables, clinical characteristics, and admission laboratory values were collected from electronic medical records. ROC analysis was performed to assess the association between CXR severity and mortality. Additional univariable and multivariable logistic regression models incorporating patient characteristics and laboratory values were tested for associations between CXR severity and clinical outcomes. RESULTS. Interrater reliability of CXR scores ranged from 0.687 to 0.737 for attending radiologists, from 0.653 to 0.762 for residents, and from 0.575 to 0.666 for all readers. A composite CXR score of 10 or higher on admission achieved 53.0% (35/66) sensitivity and 75.3% (131/174) specificity for predicting hospital mortality. Hospital mortality occurred in 44.9% (35/78) of patients with a high-risk admission CXR score (≥ 10) versus 19.1% (31/162) of patients with a low-risk CXR score (< 10) (p < .001). Admission composite CXR score was an independent predictor of death (odds ratio [OR], 1.17; 95% CI, 1.10-1.24; p < .001). composite CXR score was a univariable predictor of intubation (OR, 1.23; 95% CI, 1.12-1.34; p < .001) and continuous renal replacement therapy (CRRT) (OR, 1.15; 95% CI, 1.04-1.27; p = .007) but was not associated with these in multivariable models (p > .05). CONCLUSION. For patients admitted with COVID-19, an admission CXR severity score may help predict hospital mortality, intubation, and CRRT. CLINICAL IMPACT. CXR may assist risk assessment and clinical decision-making early in the course of COVID-19.

Imaging approach to COVID-19 associated pulmonary embolism.

The novel coronavirus disease-2019 (COVID-19) illness and deaths, caused by the severe acute respiratory syndrome coronavirus-2, continue to increase. Multiple reports highlight the thromboembolic complications, such as pulmonary embolism (PE), in COVID-19. Imaging plays an essential role in the diagnosis and management of COVID-19 patients with PE. There continues to be a rapid evolution of knowledge related to COVID-19 associated PE. This review summarises the current understanding of prevalence, pathophysiology, role of diagnostic imaging modalities, and management, including catheter-directed therapy for COVID-19 associated PE. It also describes infection control considerations for the radiology department while providing care for patients with COVID-19 associated PE.

Lung Cancer Screening Guidelines: How Readable Are Internet-Based Patient Education Resources?

OBJECTIVE: Following the findings of the National Lung Screening Trial, several national societies from multiple disciplines have endorsed the use of low-dose chest CT to screen for lung cancer. Online patient education materials are an important tool to disseminate information to the general public regarding the proven health benefits of lung cancer screening. This study aims to evaluate the reading level at which these materials related to lung cancer screening are written. MATERIALS AND METHODS: The four terms "pulmonary nodule," "radiation," "low-dose CT," and "lung cancer screening" were searched on Google, and the first 20 online resources for each term were downloaded, converted into plain text, and analyzed using 10 well-established readability scales. If the websites were not written specifically for patients, they were excluded. RESULTS: The 80 articles were written at a 12.6 ± 2.7 (mean ± SD) grade level, with grade levels ranging from 4.0 to 19.0. Of the 80 articles, 62.5% required a high school education to comprehend, and 22.6% required a college degree or higher (≥ 16th grade) to comprehend. Only 2.5% of the analyzed articles adhered to the recommendations of the National Institutes of Health and American Medical Association that patient education materials be written at a 3rd- to 7th-grade reading level. CONCLUSION: Commonly visited online lung cancer screening-related patient education materials are written at a level beyond the general patient population's ability to comprehend and may be contributing to a knowledge gap that is inhibiting patients from improving their health literacy.

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. The datasets were split into training (68.0%), validation (17.1%), and test (14.9%). Two different DCNNs, AlexNet and GoogLeNet, were used to classify the images as having manifestations of pulmonary TB or as healthy. Both untrained and pretrained networks on ImageNet were used, and augmentation with multiple preprocessing techniques. Ensembles were performed on the best-performing algorithms. For cases where the classifiers were in disagreement, an independent board-certified cardiothoracic radiologist blindly interpreted the images to evaluate a potential radiologist-augmented workflow. Receiver operating characteristic curves and areas under the curve (AUCs) were used to assess model performance by using the DeLong method for statistical comparison of receiver operating characteristic curves. Results The best-performing classifier had an AUC of 0.99, which was an ensemble of the AlexNet and GoogLeNet DCNNs. The AUCs of the pretrained models were greater than that of the untrained models (P < .001). Augmenting the dataset further increased accuracy (P values for AlexNet and GoogLeNet were .03 and .02, respectively). The DCNNs had disagreement in 13 of the 150 test cases, which were blindly reviewed by a cardiothoracic radiologist, who correctly interpreted all 13 cases (100%). This radiologist-augmented approach resulted in a sensitivity of 97.3% and specificity 100%. Conclusion Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99. A radiologist-augmented approach for cases where there was disagreement among the classifiers further improved accuracy. © RSNA, 2017.

Radiologic-pathologic discordance in biopsy-proven usual interstitial pneumonia.

The aim of this study was to compare the clinical, radiological and histological findings in a large population of subjects enrolled during a multicentre study of idiopathic pulmonary fibrosis, with a focus on discordance between imaging and histologic diagnoses of usual interstitial pneumonia (UIP).Two independent radiologists retrospectively reviewed 241 subjects who underwent high-resolution computed tomography (HRCT) and surgical lung biopsies. HRCT findings were classified as UIP, possible UIP and inconsistent with UIP. Histological findings were classified as definite, probable, possible and not UIP.Of the 241 cases, 102 (42.3%) had HRCT findings of UIP, 64 (26.6%) had possible UIP and 75 (31.1%) were inconsistent with UIP. Among those with UIP on HRCT, 99 (97.1%) had histologically definite or probable UIP (concordant group), and 71 (94.7%) of those with "inconsistent" HRCT features had histologically definite or probable UIP (discordant group). Discordant subjects were slightly younger and less likely to be smokers than concordant subjects, but no survival differences were identified.In this population of patients enrolled with a diagnosis of idiopathic pulmonary fibrosis, 94.7% of those with HRCT findings "inconsistent with UIP" demonstrated histological UIP. This suggests that the term "inconsistent with UIP" is misleading.

Imaging of breast cancer-related changes after nonsurgical therapy.

OBJECTIVE: The purpose of this article is to review both expected and unexpected thoracic CT manifestations of nonsurgical breast cancer treatment with multimodality imaging correlation. Specific topics include the spectrum of posttherapy changes attributed to chemotherapy and radiation therapy and the spread of breast cancer. CONCLUSION: Thoracic CT is an important tool commonly used for breast cancer staging and surveillance and for diagnostic indications such as shortness of breath and chest pain. Imaging findings can be related to progression of disease or to associated conditions, such as pulmonary embolism. The hallmarks of breast cancer spread in the thorax include pulmonary nodules, enlarged lymph nodes, pleural effusions, thickening or nodularity, and sclerotic or lytic skeletal lesions. Less common findings including pulmonary lymphangitic tumor spread and pericardial metastasis. The findings also may represent the sequelae of surgery, radiation therapy, or chemotherapy for breast cancer. Knowledge of various treatment methods and their expected and unexpected CT findings is important for recognizing treatment-related abnormalities to avoid confusion with breast cancer spread and thereby minimize the risk that unnecessary further diagnostic imaging will be performed.

Imaging of breast cancer-related changes after surgical therapy.

OBJECTIVE: The purpose of this article is to discuss the surgical treatment of breast cancer and its resultant thoracic CT and multimodality imaging manifestations. CONCLUSION: Many breast cancer patients undergo cross-sectional imaging at some point during or after treatment. Thoracic CT is an important modality performed for staging and surveillance. Thoracic CT examinations often show findings related to patients' surgical or adjuvant treatment. The postsurgical changes visible on thoracic CT may include those related to lumpectomy, mastectomy, breast reconstruction, and axillary surgery. Postsurgical complications may also be seen, including fluid collections, infection, fat necrosis, and lymphedema. Recognition and appropriate interpretation of the posttherapeutic spectrum of findings are important to avoid unnecessary diagnostic imaging and minimize patient anxiety.

Evaluation of characteristics, associations and clinical course of isolated spontaneous renal artery dissection.

BACKGROUND: Spontaneous renal artery dissection (SRAD) is a rare entity of unknown etiology. We aimed to study the clinical course and outcomes and compare the characteristics of patients with SRAD with those of the general population. METHODS: All cases of isolated renal artery dissection diagnosed at the University of Michigan Hospitals between January 2000 and July 2012 were identified by the ICD-9 code. Cases were matched by age, gender and race with individuals from the 2009-2010 National Health and Nutrition Examination Survey (NHANES). Characteristics and awareness of comorbid conditions were compared. Information about the clinical course after diagnosis was retrieved from the case group to ascertain their outcomes. RESULTS: Overall, 17 patients with SRAD with a mean age of 38.6 years (SD = 8.3) were identified. Eleven patients were male and 14 were white. The most common presenting symptom was excruciating sudden-onset flank pain ipsilateral to the site of dissection. Fibromuscular dysplasia, Ehlers-Danlos and polyarteritis nodosa were present in 4, 4 and 1 patients, respectively. After adjusting in a multivariable model, the case group was more likely to report history of hypertension, cancer and connective tissue disorders (P < 0.001), and less likely to have obesity (BMI ≥30 kg/m(2)) compared with the general population. Supportive medical treatment, endovascular intervention and surgery were required in 8, 5 and 4 cases, respectively. After discharge from the hospital, hypertension was adequately controlled in all the patients but one. CONCLUSION: SRAD may be part of a syndrome having multi-organ involvement. With appropriate medical or surgical management, long-term clinical outcome appears favorable.

Imaging of acute pulmonary embolism.

Acute pulmonary thromboembolism (PE) is a cardiovascular emergency associated with significant morbidity and a 5-35 % mortality for untreated pulmonary embolism. If promptly diagnosed and treated, the mortality rate can be significantly reduced. Diagnosis of acute PE continues to be a clinical challenge, with diagnostic imaging playing an important role. This review discusses the clinical challenges of diagnosing acute PE, presents an evidence-based review of the current tests and ever-evolving imaging technology, and highlights special considerations related to radiation dose, contrast media use, and pregnant patients.

Lung recovery with prolonged ECMO following fibrotic COVID-19 acute respiratory distress syndrome.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has been associated with acute respiratory distress syndrome (ARDS) and in some cases with pulmonary fibrosis. There is limited information regarding the long-term outcomes of patients who develop severe COVID-19 infection and subsequent pulmonary fibrosis. We present a patient with severe ARDS due to COVID-19 who required prolonged extra-corporeal oxygenation support and eventually recovered significant lung function. This case is unique because the patient survived one of the longest reported runs on extra-corporeal membrane oxygenation without requiring lung transplantation. Further, our patient developed severe parenchymal and airway distortion but ultimately resolved pulmonary fibrosis many months into the hospitalization. In addition to our detailed case discussion, we will provide a focused review on pulmonary fibrosis post COVID-19.

Mitral Annular Disjunction: Review of an Increasingly Recognized Mitral Valve Entity.

Mitral annular disjunction (MAD) refers to atrial displacement of the hinge point of the mitral valve annulus from the ventricular myocardium. MAD leads to paradoxical expansion of the annulus in systole and may often be associated with mitral valve prolapse (MVP), leaflet degeneration, myocardial and papillary muscle fibrosis, and, potentially, malignant cardiac arrhythmias. Patients with MAD and MVP may present similarly, and MAD is potentially the missing link in explaining why some patients with MVP experience adverse outcomes. Patients with a 5 mm or longer MAD distance have an elevated risk of malignant cardiac arrhythmia compared with those with a shorter MAD distance. Evaluation for MAD is an important component of cardiac imaging, especially in patients with MVP and unexplained cardiac arrhythmias. Cardiac MRI is an important diagnostic tool that aids in recognizing and quantifying MAD, MVP, and fibrosis in the papillary muscle and myocardium, which may predict and help improve outcomes following electrophysiology procedures and mitral valve surgery. This article reviews the history, pathophysiology, controversy, prevalence, clinical implications, and imaging considerations of MAD, focusing on cardiac MRI. Keywords: MR-Dynamic Contrast Enhanced, Cardiac, Mitral Valve, Mitral Annular Disjunction, Mitral Valve Prolapse, Floppy Mitral Valve, Cardiac MRI, Arrhythmia, Sudden Cardiac Death, Barlow Valve © RSNA, 2023.

Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A phantom study.

PURPOSE: Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in combination with various CT scan parameters on the semi-automated quantification using various lung nodules. METHODS: A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (ß) were reported to demonstrate effect size and directionality. RESULTS: The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (ß = 9.000, p < 0.001). APE had an inverse relationship with nodule diameter (mm; ß = -3.499, p < 0.001) and tube current (mA; ß = -0.006, p < 0.001). MAPE did not vary based on the ASIR-V level (range: 5.7%-13.1%). CONCLUSION: Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction.

Frequency of Statin Prescription Among Individuals with Coronary Artery Calcifications Detected Through Lung Cancer Screening.

Individuals eligible for lung cancer screening (LCS) are at risk for atherosclerotic cardiovascular disease (ASCVD) due to smoking history. Coronary artery calcifications (CAC), a common incidental finding on low-dose CT (LDCT) for LCS, is a predictor of cardiovascular events. Despite findings of high ASCVD risk and CAC, a substantial proportion of LCS patients are not prescribed primary preventive statin therapy for ASCVD. We assessed the frequency of statin prescription in LCS patients with moderate levels of CAC. Among 259 individuals with moderate CAC, 95% had ASCVD risk ≥ 7.5%. Despite this, 27% of patients were statin-free prior to LDCT and 21.2% remained statin-free after LDCT showing moderate CAC. Illustratively, while a substantial proportion of LCS patients are statin-eligible, many lack a statin prescription, even after findings of CAC burden. CAC reporting should be standardized, and interdisciplinary communication should be optimized to ensure that LCS patients are placed on appropriate preventive therapy.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

Validating deep learning inference during chest X-ray classification for COVID-19 screening.

The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

Racial disparities in COVID-19 associated pulmonary embolism: A multicenter cohort study.

BACKGROUND: Thromboembolism is a recognized component of severe coronavirus disease 2019 (COVID-19) disease. However, research into racial disparities in COVID-19-related pulmonary embolism is limited. MATERIALS AND METHODS: In this retrospective cohort study, we examined adults diagnosed with COVID-19 between January 20 and September 30, 2020, using a multicenter electronic health record dataset of over 73 million patients (TriNetX), mostly in the USA. The main study outcomes were development of pulmonary embolism or mortality within 30 days of COVID-19 diagnosis. Secondary outcome analysis included hospitalization, mechanical ventilation, and ICU admission within 30 days of diagnosis, as well as lab values within 0-1 days of diagnosis. Sociodemographic and clinical variables were used to create balanced cohorts via propensity matching. RESULTS: 346,953 patients were identified, with 56.0% non-Hispanic white and 14.7% non-Hispanic black; the mean age was 47.6 years. 3879 patients developed PE, with 2036 (1.30% of 157,049) white and 1088 (2.16% of 50,376) black patients. After propensity matching, black race was associated with higher mortality (risk ratio 1.890 [95% CI 1.727-2.067]) and PE (RR 1.537 [1.380-1.711]; p < 0.0001). Both races had higher mortality with COVID-associated PE than COVID or PE alone (RR 1.575-1.627 and 3.000-5.389 respectively; p < 0.0001). Black patients with COVID-19 and PE had a higher rate of mortality compared to white patients (RR 1.397 [1.059-1.844]; p = 0.0174). INTERPRETATION: Black race was associated with higher risk of pulmonary embolism and mortality after COVID-19. Additionally, black patients with COVID-19 and PE had a higher mortality compared to white patients.

Traumatic aortic injury: computerized tomographic findings at presentation and after conservative therapy.

OBJECTIVE: To determine the computerized tomographic (CT) findings in traumatic aortic injury (TAI) at presentation and after conservative management. METHODS: Institutional review board-approved retrospective review of trauma registry during a 6-year period identified class 1 or 2 trauma patients with TAI. The CT findings were correlated with patient outcome. RESULTS: Forty-eight of 3350 patients had TAI. Seven had TAI limited to the abdominal aorta. Twenty-nine of 48 had early (12) or delayed (17) aortic repair. Common abnormalities were pseudoaneurysms (69%) and intramural hematoma (IMH) (65%). Forty-one of 48 TAI were confirmed on endovascular imaging or surgery. Subsequent CT was available in those who had delayed repair (n = 9) or conservative management (10) and showed stable pseudoaneurysms with resolving IMH (n = 11), resolving IMH (n = 4), intimal flap (n = 2), aortic thrombus (n = 1), and dissection (n = 1). CONCLUSIONS: Traumatic aortic injury is rare. It commonly involves thoracic aorta with pseudoaneurysm and IMH. Significant TAI in stable patients remains stable on follow-up imaging. Minor TAI may resolve with conservative therapy.