Bexotegrast in Patients with Idiopathic Pulmonary Fibrosis: The INTEGRIS-IPF Study.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) is a rare and progressive disease, which causes progressive cough, exertional dyspnea, impaired quality of life and death. OBJECTIVES: Bexotegrast (PLN 74809) is an oral, once-daily, investigational drug in development for the treatment of IPF. METHODS: This Phase 2a, multicenter, clinical trial, randomized participants with IPF to receive oral, once daily bexotegrast 40 mg, 80 mg, 160 mg, 320 mg, or placebo, with or without background IPF therapy (pirfenidone or nintedanib), in an approximately 3:1 ratio in each bexotegrast dose cohort, for at least 12 weeks. The primary endpoint was incidence of treatment-emergent adverse events (TEAEs). Exploratory efficacy endpoints included change from baseline in forced vital capacity (FVC); quantitative lung fibrosis (QLF) extent (%) and changes from baseline in fibrosis-related biomarkers. MEASUREMENTS AND MAIN RESULTS: Bexotegrast was well tolerated with similar rates of TEAEs in the pooled bexotegrast and placebo groups (62/89 [69.7%] and 21/31 [67.7%], respectively). Diarrhea was the most common TEAE; most participants with diarrhea also received nintedanib. Bexotegrast treated participants experienced a reduction in FVC decline over 12 weeks vs. placebo, with or without background therapy. A dose-dependent antifibrotic effect of bexotegrast was observed with QLF imaging and a decrease in fibrosis-associated biomarkers was observed with bexotegrast vs. placebo. CONCLUSIONS: Bexotegrast demonstrated a favorable safety and tolerability profile, up to 12 weeks for the doses studied. Exploratory analyses suggest an antifibrotic effect according to FVC, QLF imaging, and circulating levels of fibrosis biomarkers. Clinical trial registration available at www. CLINICALTRIALS: gov, ID: NCT04396756. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.

Despite progress in elucidation of disease mechanisms, identification of risk factors, biomarker discovery, and the approval of two medications to slow lung function decline in idiopathic pulmonary fibrosis and one medication to slow lung function decline in progressive pulmonary fibrosis, pulmonary fibrosis remains a disease with a high morbidity and mortality. In recognition of the need to catalyze ongoing advances and collaboration in the field of pulmonary fibrosis, the NHLBI, the Three Lakes Foundation, and the Pulmonary Fibrosis Foundation hosted the Pulmonary Fibrosis Stakeholder Summit on November 8-9, 2022. This workshop was held virtually and was organized into three topic areas: 1) novel models and research tools to better study pulmonary fibrosis and uncover new therapies, 2) early disease risk factors and methods to improve diagnosis, and 3) innovative approaches toward clinical trial design for pulmonary fibrosis. In this workshop report, we summarize the content of the presentations and discussions, enumerating research opportunities for advancing our understanding of the pathogenesis, treatment, and outcomes of pulmonary fibrosis.

Interstitial lung abnormality evaluated by an automated quantification system: prevalence and progression rate.

BACKGROUND: Despite the importance of recognizing interstitial lung abnormalities, screening methods using computer-based quantitative analysis are not well developed, and studies on the subject with an Asian population are rare. We aimed to identify the prevalence and progression rate of interstitial lung abnormality evaluated by an automated quantification system in the Korean population. METHODS: A total of 2,890 healthy participants in a health screening program (mean age: 49 years, men: 79.5%) with serial chest computed tomography images obtained at least 5 years apart were included. Quantitative lung fibrosis scores were measured on the chest images by an automated quantification system. Interstitial lung abnormalities were defined as a score ≥ 3, and progression as any score increased above baseline. RESULTS: Interstitial lung abnormalities were identified in 251 participants (8.6%), who were older and had a higher body mass index. The prevalence increased with age. Quantification of the follow-up images (median interval: 6.5 years) showed that 23.5% (59/251) of participants initially diagnosed with interstitial lung abnormality exhibited progression, and 11% had developed abnormalities (290/2639). Older age, higher body mass index, and higher erythrocyte sedimentation rate were independent risk factors for progression or development. The interstitial lung abnormality group had worse survival on follow-up (5-year mortality: 3.4% vs. 1.5%; P = 0.010). CONCLUSIONS: Interstitial lung abnormality could be identified in one-tenth of the participants, and a quarter of them showed progression. Older age, higher body mass index and higher erythrocyte sedimentation rate increased the risk of development or progression of interstitial lung abnormality.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

How AI Responds to Common Lung Cancer Questions: ChatGPT vs Google Bard.

Background The recent release of large language models (LLMs) for public use, such as ChatGPT and Google Bard, has opened up a multitude of potential benefits as well as challenges. Purpose To evaluate and compare the accuracy and consistency of responses generated by publicly available ChatGPT-3.5 and Google Bard to non-expert questions related to lung cancer prevention, screening, and terminology commonly used in radiology reports based on the recommendation of Lung Imaging Reporting and Data System (Lung-RADS) v2022 from American College of Radiology and Fleischner society. Materials and Methods Forty of the exact same questions were created and presented to ChatGPT-3.5 and Google Bard experimental version as well as Bing and Google search engines by three different authors of this paper. Each answer was reviewed by two radiologists for accuracy. Responses were scored as correct, partially correct, incorrect, or unanswered. Consistency was also evaluated among the answers. Here, consistency was defined as the agreement between the three answers provided by ChatGPT-3.5, Google Bard experimental version, Bing, and Google search engines regardless of whether the concept conveyed was correct or incorrect. The accuracy among different tools were evaluated using Stata. Results ChatGPT-3.5 answered 120 questions with 85 (70.8%) correct, 14 (11.7%) partially correct, and 21 (17.5%) incorrect. Google Bard did not answer 23 (19.1%) questions. Among the 97 questions answered by Google Bard, 62 (51.7%) were correct, 11 (9.2%) were partially correct, and 24 (20%) were incorrect. Bing answered 120 questions with 74 (61.7%) correct, 13 (10.8%) partially correct, and 33 (27.5%) incorrect. Google search engine answered 120 questions with 66 (55%) correct, 27 (22.5%) partially correct, and 27 (22.5%) incorrect. The ChatGPT-3.5 is more likely to provide correct or partially answer than Google Bard, approximately by 1.5 folds (OR = 1.55, P = 0.004). ChatGPT-3.5 and Google search engine were more likely to be consistent than Google Bard by approximately 7 and 29 folds (OR = 6.65, P = 0.002 for ChatGPT and OR = 28.83, P = 0.002 for Google search engine, respectively). Conclusion Although ChatGPT-3.5 had a higher accuracy in comparison with the other tools, neither ChatGPT nor Google Bard, Bing and Google search engines answered all questions correctly and with 100% consistency.

Quantitative interstitial lung disease scores in idiopathic inflammatory myopathies: longitudinal changes and clinical implications.

OBJECTIVES: To investigate computer-aided quantitative scores from high-resolution CT (HRCT) images and determine their longitudinal changes and clinical significance in patients with idiopathic inflammatory myopathies (IIMs)-related interstitial lung disease (IIMs-ILD). METHODS: The clinical data and HRCT images of 80 patients with IIMs who underwent serial HRCT scans at least twice were retrospectively analysed. Quantitative ILD (QILD) scores (%) were calculated as the sum of the extent of lung fibrosis, ground-glass opacity, and honeycombing. The individual time-estimated ΔQILD between two consecutive scans was derived using a linear approximation of yearly changes. RESULTS: The baseline median QILD (interquartile range) scores in the whole lung were 28.1% (19.1-43.8). The QILD was significantly correlated with forced vital capacity (r = -0.349, P = 0.002) and diffusing capacity for carbon monoxide (r = -0.381, P = 0.001). For ΔQILD between the first two scans, according to the visual ILD subtype, QILD aggravation was more frequent in patients with usual interstitial pneumonia (UIP) than non-UIP (80.0% vs 44.4%, P = 0.013). Multivariable logistic regression analyses identified UIP was significantly related to radiographic ILD progression (ΔQILD >2%, P = 0.015). Patients with higher baseline QILD scores (>28.1%) had a higher risk of lung transplantation or death (P = 0.015). In the analysis of three serial HRCT scans (n = 41), dynamic ΔQILD with four distinct patterns (improving, worsening, convex and concave) was observed. CONCLUSION: QILD changes in IIMs-ILD were dynamic, and baseline UIP patterns seemed to be related to a longitudinal progression in QILD. These may be potential imaging biomarkers for lung function, changes in ILD severity and prognosis in IIMs-ILD.

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Automated quantification system predicts survival in rheumatoid arthritis-associated interstitial lung disease.

OBJECTIVE: The prognosis of RA-associated interstitial lung disease (RA-ILD) is difficult to predict because of the variable clinical course. This study aimed to determine the prognostic value of an automated quantification system (AQS) in RA-ILD. METHODS: We retrospectively analysed the clinical data and high-resolution CT (HRCT) images of 144 patients with RA-ILD. Quantitative lung fibrosis (QLF, sum of reticulation and traction bronchiectasis) and ILD [QILD; sum of QLF, honeycombing (QHC), and ground-glass opacity (QGG)] scores were measured using the AQS. RESULTS: The mean age was 61.2 years, 43.8% of the patients were male, and the 5-year mortality rate was 30.5% (median follow-up, 52.2 months). Non-survivors showed older age, higher ESR and greater AQS scores than survivors. In multivariable Cox analysis, higher QLF, QHC and QILD scores were independent prognostic factors along with older age and higher ESR. In receiver-operating characteristic curve analysis, the QLF score showed better performance in predicting 5-year mortality than the QHC and QGG scores but was similar to the QILD score. Patients with high QLF scores (≥12% of total lung volume) showed higher 5-year mortality (50% vs 17.4%, P < 0.001) than those with low QLF scores and similar survival outcome to patients with idiopathic pulmonary fibrosis (IPF). Combining with clinical variables (age, ESR) further improved the performance of QLF score in predicting 5-year mortality. CONCLUSION: QLF scores might be useful for predicting prognosis in patients with RA-ILD. High QLF scores differentiate a poor prognostic phenotype similar to IPF.

Procedural outcomes associated with use of the AngioVac System for right heart thrombi: A safety report from RAPID registry data.

Background: Right heart thrombi can be a source of considerable morbidity and mortality, especially when associated with pulmonary embolism. Methods: To understand the safety and procedural efficacy associated with vacuum-assisted thrombectomy using the AngioVac System (AngioDynamics, Latham, NY, USA) to remove right heart thrombi, we conducted a subanalysis of the Registry of AngioVac Procedures in Detail (RAPID) multicenter registry representing 47 (20.1%) of 234 participants in the registry. Forty-two (89.4%) patients had thrombi located in the right atrium alone, three (6.4%) in the right ventricle alone, and two (4.3%) in both the right atrium and ventricle. Four (8.5%) patients had concomitant caval thrombi, three (6.4%) also had catheter-related thrombi, and one (2.1%) patient had both caval and catheter-related thrombi with their right heart thrombi. Results: Extracorporeal bypass time was less than 1 hour for 39 (83.0%) procedures. Seventy to 100% removal of thrombus was achieved in 59.6% of patients. Estimated blood loss was less than 250 cc for 43 procedures (91.6%). Mean hemoglobin decreased from 10.7 ± 2.2 g/dL preoperatively to 9.6 ± 1.6 g/dL postoperatively. Transfusions were administered for eight procedures (17.0%), with only one (2.1%) patient receiving more than 2 units of blood. Six patients (12.8%) experienced procedure-related adverse events, including three (6.4%) patients who experienced distal emboli and three (6.4%) patients who developed bleeding-related complications. All adverse events resolved prior to discharge. There was one death (2.1%) reported that was not procedure related. Conclusion: Vacuum-assisted thrombectomy can be performed safely in patients with right heart thrombi. ClinicalTrials.gov Identifier: NCT04414332.

Endovascular Removal of Thrombus and Right Heart Masses Using the AngioVac System: Results of 234 Patients from the Prospective, Multicenter Registry of AngioVac Procedures in Detail (RAPID).

PURPOSE: To assess device and procedural safety and technical success associated with the use of the AngioVac System to remove vascular thrombi and cardiac masses. MATERIALS AND METHODS: The Registry of AngioVac Procedures in Detail (RAPID) study prospectively collected data for 234 patients receiving treatment with AngioVac at 21 sites between March 2016 and August 2019: 84 (35.9%) with caval thromboemboli (CTEs), 113 (48.3%) with right heart masses (RHMs), 20 (8.5%) with catheter-related thrombi (CRTs), and 4 (1.7%) with pulmonary emboli (PEs). Thirteen patients had a combination of procedures during the same admission. RESULTS: Using the AngioVac system, 70%-100% thrombus or mass removal was achieved in 73.6% of patients with CTEs, 58.5% of patients with RHMs, 60% of patients with CRTs, and 57.1% of patients with PEs. Extracorporeal bypass time was < 1 hour for 176 (75.2%) procedures. Estimated blood loss was < 250 mL for 179 procedures (76.5%). Mean hemoglobin decreased from 10.4 g/dL ± 2.9 preoperatively to 9.4 g/dL ± 2.6 postoperatively. Transfusions were administered in 59 procedures (25.2%) with 47 transfusions (78.2%) being ≤ 2 U. There were 36 procedure-related complications, including 1 death. CONCLUSIONS: The RAPID registry data demonstrate that the AngioVac System can be safely and effectively used to remove vascular thrombi and cardiac masses across a broad range of patient populations. The limited use of the device to remove pulmonary emboli in the present series precludes recommending the use of the AngioVac device for this indication.

Prediction of idiopathic pulmonary fibrosis progression using early quantitative changes on CT imaging for a short term of clinical 18-24-month follow-ups.

OBJECTIVE: High-resolution computed tomography (HRCT) plays an indispensable role in the diagnosis of idiopathic pulmonary fibrosis (IPF). Due to unpredictability in progression and the short median survival of 2-5 years, it is critical to delineate the patients with rapid progression. The aim is to evaluate the predictability of IPF progression using the early quantitative changes. METHODS: Automated texture-based quantitative lung fibrosis (QLF) was calculated from the anonymized HRCT. Two datasets were collected retrospectively: (1) a pilot study of 35 subjects with three sequential scans (baseline and 6 and 12 months) to obtain a threshold, where visual assessments were stable at 6 months but worsened at 12 months; (2) 157 independent subjects to test the threshold. Landmark Cox regressions were used to compare the progression-free survival (PFS) defined by pulmonary function using the threshold from the early changes in QLF. C-indexes were reported as estimations of the concordance of prediction. RESULTS: A threshold of 4% QLF change at 6 months corresponded to the mean change that worsened on HRCT visually at 12 months from the pilot study. Using the threshold, significant differences were found in the independent dataset (hazard ratio (HZ) = 5.92, p = 0.001 by Cox model, C-index = 0.71 at the most severe lobe; and HZ = 3.22, p = 0.012, C-index = 0.68 in the whole lung). Median PFS was 11.9 months for subjects with ≥ 4% changes, whereas median PFS was greater than 18 months for subjects with < 4% changes at the most severe lobe. CONCLUSION: Early structural changes on HRCT using a quantitative score can predict progression in lung function. KEY POINTS: • Changes on HRCT using quantitative texture-based scores can play a pivotal role for providing information and an aid tool for timely management decision for patients with IPF. • Quantitative changes on HRCT of 4% or more, which matched 6-month prior changes with visual assessment of worsening, can play a pivotal role for providing prediction of clinical progression by 3-5 folds higher in the next incidence, compared with those of subjects with less than 4% changes. • Early structural changes of 4% or more in a paired HRCT scans derived by quantitative scores can predict the progression in lung function in 1-2 years in subjects with IPF, which is critical information for timely management decision for subjects with IPF where the median survival is 2 to 5 years.

Correction to: Toward clinically usable CAD for lung cancer screening with computed tomography.

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

T1-Mapping and extracellular volume estimates in pediatric subjects with Duchenne muscular dystrophy and healthy controls at 3T.

BACKGROUND: Cardiovascular disease is the leading cause of death in patients with Duchenne muscular dystrophy (DMD)-a fatal X-linked genetic disorder. Late gadolinium enhancement (LGE) imaging is the current gold standard for detecting myocardial tissue remodeling, but it is often a late finding. Current research aims to investigate cardiovascular magnetic resonance (CMR) biomarkers, including native (pre-contrast) T1 and extracellular volume (ECV) to evaluate the early on-set of microstructural remodeling and to grade disease severity. To date, native T1 measurements in DMD have been reported predominantly at 1.5T. This study uses 3T CMR: (1) to characterize global and regional myocardial pre-contrast T1 differences between healthy controls and LGE + and LGE- boys with DMD; and (2) to report global and regional myocardial post-contrast T1 values and myocardial ECV estimates in boys with DMD, and (3) to identify left ventricular (LV) T1-mapping biomarkers capable of distinguishing between healthy controls and boys with DMD and detecting LGE status in DMD. METHODS: Boys with DMD (N = 28, 13.2 ± 3.1 years) and healthy age-matched boys (N = 20, 13.4 ± 3.1 years) were prospectively enrolled and underwent a 3T CMR exam including standard functional imaging and T1 mapping using a modified Look-Locker inversion recovery (MOLLI) sequence. Pre-contrast T1 mapping was performed on all boys, but contrast was administered only to boys with DMD for post-contrast T1 and ECV mapping. Global and segmental myocardial regions of interest were contoured on mid LV T1 and ECV maps. ROI measurements were compared for pre-contrast myocardial T1 between boys with DMD and healthy controls, and for post-contrast myocardial T1 and ECV between LGE + and LGE- boys with DMD using a Wilcoxon rank-sum test. Results are reported as median and interquartile range (IQR). p-Values < 0.05 were considered significant. Receiver Operating Characteristic analysis was used to evaluate a binomial logistic classifier incorporating T1 mapping and LV function parameters in the tasks of distinguishing between healthy controls and boys with DMD, and detecting LGE status in DMD. The area under the curve is reported. RESULTS: Boys with DMD had significantly increased global native T1 [1332 (60) ms vs. 1289 (56) ms; p = 0.004] and increased within-slice standard deviation (SD) [100 (57) ms vs. 74 (27) ms; p = 0.001] compared to healthy controls. LGE- boys with DMD also demonstrated significantly increased lateral wall native T1 [1322 (68) ms vs. 1277 (58) ms; p = 0.001] compared to healthy controls. LGE + boys with DMD had decreased global myocardial post-contrast T1 [565 (113) ms vs 635 (126) ms; p = 0.04] and increased global myocardial ECV [32 (8) % vs. 28 (4) %; p = 0.02] compared to LGE- boys. In all classification tasks, T1-mapping biomarkers outperformed a conventional biomarker, LV ejection fraction. ECV was the best performing biomarker in the task of predicting LGE status (AUC = 0.95). CONCLUSIONS: Boys with DMD exhibit elevated native T1 compared to healthy, sex- and age-matched controls, even in the absence of LGE. Post-contrast T1 and ECV estimates from 3T CMR are also reported here for pediatric patients with DMD for the first time and can distinguish between LGE + from LGE- boys. In all classification tasks, T1-mapping biomarkers outperform a conventional biomarker, LVEF.

Reliability and minimal clinically important differences of forced vital capacity: Results from the Scleroderma Lung Studies (SLS-I and SLS-II).

OBJECTIVES: To assess the reliability and the minimal clinically important differences (MCID) for FVC% predicted in the Scleroderma Lung Study I and II. METHODS: Using data from SLS I and II (N=300), we evaluated the test-retest reliability for FVC% predicted (FVC%; screening vs. baseline) using intra-class correlation (ICC). MCID estimates at 12 months were calculated in the pooled cohort (SLS-I and II) using 2 anchors: Transition Dyspnea Index (≥change of 1.5 units for improvement and worsening, respectively) and the SF-36 Health Transition question: "Compared to one year ago, how would you rate your health in general now?", where "somewhat better" or "somewhat worse" were defined as the MCID estimates. We next assessed the association of MCID estimates for improvement and worsening of FVC% with patient reported outcomes (PROs) and computer-assisted quantitation of extent of fibrosis (QLF) and of total ILD (QILD) on HRCT. RESULTS: Reliability of FVC%, assessed at a mean of 34 days, was 0.93 for the pooled cohort. The MCID estimates for the pooled cohort at 12 months for FVC% improvement ranged from 3.0 % to 5.3% and for worsening from -3.0% to -3.3%. FVC% improvement by ≥MCID was associated with either statistically significant or numerical improvements in some PROs, QILD, and QLF, while FVC% worsening ≥MCID was associated with statistically significant or numerical worsening of PROs, QILD, and QLF. CONCLUSION: FVC% has acceptable test-retest reliability, and we have provided the MCID estimates for FVC% in SSc-ILD based changes at 12 months from baseline in two clinical trials. Clinical trial registration available at www.clinicaltrials.gov, IDs NCT00004563 and NCT00883129.

Free-breathing quantification of hepatic fat in healthy children and children with nonalcoholic fatty liver disease using a multi-echo 3-D stack-of-radial MRI technique.

BACKGROUND: In adults, noninvasive chemical shift encoded Cartesian magnetic resonance imaging (MRI) and single-voxel magnetic resonance (MR) spectroscopy (SVS) accurately quantify hepatic steatosis but require breath-holding. In children, especially young and sick children, breath-holding is often limited or not feasible. Sedation can facilitate breath-holding but is highly undesirable. For these reasons, there is a need to develop free-breathing MRI technology that accurately quantifies steatosis in all children. OBJECTIVE: This study aimed to compare non-sedated free-breathing multi-echo 3-D stack-of-radial (radial) MRI versus standard breath-holding MRI and SVS techniques in a group of children for fat quantification with respect to image quality, accuracy and repeatability. MATERIALS AND METHODS: Healthy children (n=10, median age [±interquartile range]: 10.9 [±3.3] years) and overweight children with nonalcoholic fatty liver disease (NAFLD) (n=9, median age: 15.2 [±3.2] years) were imaged at 3 Tesla using free-breathing radial MRI, breath-holding Cartesian MRI and breath-holding SVS. Acquisitions were performed twice to assess repeatability (within-subject mean difference, MDwithin). Images and hepatic proton-density fat fraction (PDFF) maps were scored for image quality. Free-breathing and breath-holding PDFF were compared using linear regression (correlation coefficient, r and concordance correlation coefficient, ρc) and Bland-Altman analysis (mean difference). P<0.05 was considered significant. RESULTS: In patients with NAFLD, free-breathing radial MRI demonstrated significantly less motion artifacts compared to breath-holding Cartesian (P<0.05). Free-breathing radial PDFF demonstrated a linear relationship (P<0.001) versus breath-holding SVS PDFF and breath-holding Cartesian PDFF with r=0.996 and ρc=0.994, and r=0.997 and ρc=0.995, respectively. The mean difference in PDFF between free-breathing radial MRI, breath-holding Cartesian MRI and breath-holding SVS was <0.7%. Repeated free-breathing radial MRI had MDwithin=0.25% for PDFF. CONCLUSION: In this pediatric study, non-sedated free-breathing radial MRI provided accurate and repeatable hepatic PDFF measurements and improved image quality, compared to standard breath-holding MR techniques.

Accelerated ferumoxytol-enhanced 4D multiphase, steady-state imaging with contrast enhancement (MUSIC) cardiovascular MRI: validation in pediatric congenital heart disease.

The purpose of this work was to validate a parallel imaging (PI) and compressed sensing (CS) combined reconstruction method for a recently proposed 4D non-breath-held, multiphase, steady-state imaging technique (MUSIC) cardiovascular MRI in a cohort of pediatric congenital heart disease patients. We implemented a graphics processing unit accelerated CS-PI combined reconstruction method and applied it in 13 pediatric patients who underwent cardiovascular MRI after ferumoxytol administration. Conventional breath-held contrast-enhanced magnetic resonance angiography (CE-MRA) was first performed during the first pass of ferumoxytol injection, followed by the original MUSIC and the proposed CS-PI MUSIC during the steady-state distribution phase of ferumoxytol. Qualities of acquired images were then evaluated using a four-point scale. Left ventricular volumes and ejection fractions calculated from the original MUSIC and the CS-PI MUSIC were also compared with conventional multi-slice 2D cardiac cine MRI. The proposed CS-PI MUSIC reduced the imaging time of the MUSIC acquisition to 4.6 ± 0.4 min from 8.9 ± 1.2 min. Computationally intensive image reconstruction was completed within 5 min without interruption of sequential clinical scans. The proposed method (mean 3.3-4.0) provided image quality comparable to that of the original MUSIC (3.2-4.0) (all P ≥ 0.42), and better than conventional breath-held first-pass CE-MRA (1.1-3.3) for 13 anatomical structures (all P ≤ 0.0014) with good inter-observer agreement (κ > 0.46). The calculated ventricular volumes and ejection fractions from both original MUSIC (r > 0.90) and CS-PI MUSIC (r > 0.85) correlated well with 2D cine imaging. In conclusion, PI and CS were successfully incorporated into the 4D MUSIC acquisition to further reduce scan time by approximately 50% while maintaining highly comparable image quality in a clinically practical reconstruction time.

Percutaneous Cryoablation for the Treatment of Recurrent Malignant Pleural Mesothelioma: Safety, Early-Term Efficacy, and Predictors of Local Recurrence.

PURPOSE: To determine safety and early-term efficacy of CT-guided cryoablation for treatment of recurrent mesothelioma and assess risk factors for local recurrence. MATERIALS AND METHODS: During the period 2008-2012, 24 patients underwent 110 cryoablations for recurrent mesothelioma tumors in 89 sessions. Median patient age was 69 years (range, 48-82 y). Median tumor size was 30 mm (range, 9-113 mm). Complications were graded using Common Terminology Criteria for Adverse Events version 4.0 (CTCAE v4.0). Recurrence was diagnosed on CT or positron emission tomography/CT by increasing size, nodular enhancement, or hypermetabolic activity and analyzed using the Kaplan-Meier method. Cox proportional hazards model was used to determine covariates associated with local tumor recurrence. RESULTS: Median duration of follow-up was 14.5 months. Complications occurred in 8 of 110 cryoablations (7.3%). All but 1 complication were graded CTCAE v4.0 1 or 2. No procedure-related deaths occurred. Freedom from local recurrence was observed in 100% of cases at 30 days, 92.5% at 6 months, 90.8% at 1 year, 87.3% at 2 years, and 73.7% at 3 years. Tumor recurrence was diagnosed 4.5-24.5 months after cryoablation (mean 5.7 months). Risk of tumor recurrence was associated with a smaller ablative margin from the edge of tumor to iceball ablation margin (multivariate hazard ratio 0.68, CI 0.48-0.95, P = .024). CONCLUSIONS: CT-guided cryoablation is safe for local control of recurrent mesothelioma, with a low rate of complications and promising early-term efficacy. A smaller ablative margin may predispose to tumor recurrence.

Toward clinically usable CAD for lung cancer screening with computed tomography.

OBJECTIVES: The purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice. METHODS: A new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set. RESULTS: The test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90. CONCLUSIONS: The new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality. KEY POINTS: • CAD requirements can be based on lung cancer screening trial results. • CAD systems can be evaluated using publically available annotated CT image databases. • A new CAD system was developed with a low false positive rate. • The CAD system has reliable measurement tools needed for clinical use.

MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.

Purpose: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. Approach: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. Results: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. Conclusions: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.