Purpose

To investigate the utility of a free-breathing ultrashort echo time (UTE) sequence for the evaluation of small pulmonary nodules in oncology patients by using a hybrid positron emission tomography (PET)/magnetic resonance (MR) imaging system and to compare the nodule detection rate between UTE and a conventional three-dimensional gradient-recalled-echo (GRE) technique.

Materials and methods

In this HIPAA-compliant, institutional review board-approved prospective study, 82 pulmonary nodules were identified in eight patients with extrathoracic malignancies. Patients underwent free-breathing UTE and dual-echo three-dimensional GRE imaging of the lungs in a hybrid PET/MR imaging unit immediately after clinical PET/computed tomography (CT). CT was considered the reference standard for nodule detection. Two reviewers identified nodules and obtained measurements on MR images. The McNemar test was used to evaluate differences in nodule detection rate between MR techniques, and interrater agreement was assessed by using Bland-Altman plots.

Results

Mean nodule diameter ± standard deviation was 6.2 mm ± 2.7 (range, 3-17 mm). The detection rate was higher for UTE imaging than for dual-echo GRE imaging for nodules of at least 4 mm (82% vs 34%, respectively; P < .001), with the largest difference in detection noted in the 4-8-mm nodule group (79% vs 21%, P < .001). UTE imaging displayed a higher detection rate than dual-echo GRE imaging for nodules without fluorodeoxyglucose avidity (68% vs 22%, respectively; P < .001). Interrater reliability of nodule detection with MR imaging was high (κ = 0.90 for UTE imaging and κ = 0.92 for dual-echo GRE imaging).

Conclusion

A free-breathing UTE sequence has high sensitivity for the detection of small pulmonary nodules (4-8 mm) and outperformed a three-dimensional dual-echo GRE technique for the detection of small, non-fluorodeoxyglucose-avid nodules.

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.