Williampeacock2296

Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Kay in this issue.

To investigate the prognostic value of an integrative approach combining clinical variables and the Qanadli CT obstruction index (CTOI) in patients with nonmassive acute pulmonary embolism (PE).

This retrospective study included 705 consecutive patients (mean age, 63 years; range, 18-95 years) with proven PE. Clot burden was quantified using the CTOI, which reflects the ratio of fully or partially obstructed pulmonary arteries to normal arteries. Patients were subdivided into two groups according to the presence (group A) or absence (group B) of preexisting cardiopulmonary disease. Thirty-day and 3-month mortality was evaluated. CTOI thresholds of 20% and 40% were used to stratify patients regarding outcome (low, intermediate, and high risk). The predictive value of CTOI was assessed through logistic regression analysis.

Analysis included 690 patients (mean age, 63.3 years ± 18 [standard deviation]) with complete follow-up data 247 (36%) in group A and 443 (64%) in group B. The mean CTOI was 23% ± 19, 30-day mortality was 9.7%, and 3-month mortality was 11.6%. Three-month mortality was higher in group A than in group B (17.8% and 8.1%, respectively;

= .001). Within group B, CTOI predicted outcome and allowed stratification significantly higher mortality with CTOI greater than 40% (

< .001) and lower mortality with CTOI less than 20% (

= .05). CTOI did not predict outcome in group A. Age was an independent mortality risk factor (

≤ .04).

CTOI predicted outcome in this cohort of patients with PE and no cardiopulmonary disease, and it may provide a simple single-examination-based approach for risk stratification in this subset of patients.© RSNA, 2020See also the commentary by Kay and Abbara in this issue.

CTOI predicted outcome in this cohort of patients with PE and no cardiopulmonary disease, and it may provide a simple single-examination-based approach for risk stratification in this subset of patients.© RSNA, 2020See also the commentary by Kay and Abbara in this issue.Several studies investigated the appearance of intrapulmonary lymph nodes (IPLNs) at CT with pathologic correlation. IPLNs are benign lesions and do not require follow-up after initial detection. There are indications that IPLNs represent a considerable portion of incidentally found pulmonary nodules seen at high-resolution CT. The reliable and accurate identification of IPLNs as benign nodules may substantially reduce the number of unnecessary follow-up CT examinations. Typical CT features of IPLNs are a noncalcified solid nodule with sharp margins; a round, oval, or polygonal shape; distanced 15 mm or less from the pleura; and most being located below the level of the carina. The term perifissural nodule (PFN) was coined based on some of these characteristics. Standardization of those CT criteria are a prerequisite for accurate nodule classification. However, four different definitions of PFNs can currently be found in the literature. Furthermore, there is considerable variation in the reported interobserver agreement, malignancy rate, and prevalence of PFNs. The purpose of this review was to provide an overview of what is known about PFNs. In addition, knowledge gaps in defining PFNs will be discussed. A decision tree to guide clinicians in classifying nodules as PFNs is provided. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by White and Rubin in this issue.

To investigate the MRI characteristics, prevalence, and outcomes of hypertrophic cardiomyopathy (HCM) with restrictive phenotype.

A total of 2592 consecutive patients with HCM were evaluated to identify individuals who fulfilled the diagnostic criteria of restrictive phenotype. Thirty-four patients with HCM (mean age, 41 years ± 16 [standard deviation]; range, 21-62 years, 16 men) with restrictive phenotype were retrospectively identified. Alvelestat inhibitor Thirty-four patients with HCM with the same age and sex distributions were randomly selected as a control group. Kaplan-Meier survival curves were compared using log-rank statistics for survival analysis.

The anteroposterior diameters of the left and right atria were 55 mm ± 5 and 61 mm ± 9, respectively, which were larger than those of the control group (

< .001). The maximum wall thickness in the restrictive group was lower than that in the control group (16 mm ± 2 vs 19 mm ± 3,

< .001). No significant difference was found in late gadolinium enhancement fraction between the restricted phenotype and the control group (15% ± 8 vs 13% ± 7,

= .376). The 5-year event-free survival from any cause of death and cardiac transplantation was 81% in the restrictive group, compared with 94% in the control group (log-rank

= .018).

Restrictive phenotype is a rare subtype of HCM and is associated with severe clinical symptoms and poor prognosis. The MRI features of this phenotype include mild to moderate left ventricular hypertrophy, markedly enlarged atria, moderate myocardial fibrosis, and pericardial effusion.© RSNA, 2020.

Restrictive phenotype is a rare subtype of HCM and is associated with severe clinical symptoms and poor prognosis. The MRI features of this phenotype include mild to moderate left ventricular hypertrophy, markedly enlarged atria, moderate myocardial fibrosis, and pericardial effusion.© RSNA, 2020.