Purpose

To determine the variability of lesion size measurements in computed tomography data sets of patients imaged under a "no change" ("coffee break") condition and to determine the impact of two reading paradigms on measurement variability.

Method and materials

Using data sets from 32 non-small cell lung cancer patients scanned twice within 15 minutes ("no change"), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. Readers performed measurements using several sizing methods, including one-dimensional (1D) longest in-slice dimension and 3D semi-automated segmented volume. Change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. For each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method.

Results

The mean percent difference (±SD) when pooled across both readers and lesions for 1D and 3D measurements extracted from contours was 2.8 ± 22.2% and 23.4 ± 105.0%, respectively, for the independent reads. For the locked, sequential reads, the mean percent differences (±SD) reduced to 2.52 ± 14.2% and 7.4 ± 44.2% for the 1D and 3D measurements, respectively.

Conclusion

Even under a "no change" condition between scans, there is variation in lesion size measurements due to repeat scans and variations in reader, lesion, and measurement method. This variation is reduced when using a locked, sequential reading paradigm compared to an independent reading paradigm.

Rationale and objectives

To estimate and statistically compare the bias and variance of radiologists measuring the size of spherical and complex synthetic nodules.

Materials and methods

This study did not require the institutional review board approval. Six radiologists estimated the size of 10 synthetic nodules embedded within an anthropomorphic thorax phantom from computed tomography scans at 0.8- and 5-mm slice thicknesses. The readers measured the nodule size using unidimensional (1D) longest in-slice dimension, bidimensional (2D) area from longest in-slice and longest perpendicular dimension, and three-dimensional (3D) semiautomated volume. Intercomparisons of bias (difference between average and true size) and variance among methods were performed after converting the 2D and 3D estimates to a compatible 1D scale.

Results

The relative biases of radiologists with the 3D tool were -1.8%, -0.4%, -0.7%, -0.4%, and -1.6% for 10-mm spherical, 20-mm spherical, 20-mm elliptical, 10-mm lobulated, and 10-mm spiculated nodules compared to 1.4%, -0.1%, -26.5%, -7.8%, and -39.8% for 1D. The three-dimensional measurements were significantly less biased than 1D for elliptical, lobulated, and spiculated nodules. The relative standard deviations for 3D were 7.5%, 3.9%, 3.6%, 9.7%, and 8.3% compared to 5.7%, 2.6%, 20.3%, 5.3%, and 16.4% for 1D. Unidimensional sizing was significantly less variable than 3D for the lobulated nodule and significantly more variable for the ellipsoid and spiculated nodules. Three-dimensional bias and variability were smaller for thin 0.8-mm slice data compared to thick 5.0-mm data.

Conclusions

The study shows that radiologist-controlled 3D volumetric lesion sizing can not only achieve smaller bias but also achieve similar or smaller variability compared to 1D sizing, especially for complex lesion shapes.

As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies.

Context

The conventional view that obesity is beneficial for bone strength has recently been challenged by studies that link obesity, particularly visceral obesity, to low bone mass and fractures. It is controversial whether effects of obesity on bone are mediated by increased bone resorption or decreased bone formation.

Objective

The objective of the study was to evaluate bone microarchitecture and remodeling in healthy premenopausal women of varying weights.

Design

We measured bone density and trunk fat by dual-energy x-ray absorptiometry in 40 women and by computed tomography in a subset. Bone microarchitecture, stiffness, remodeling, and marrow fat were assessed in labeled transiliac bone biopsies.

Results

Body mass index (BMI) ranged from 20.1 to 39.2 kg/m(2). Dual-energy x-ray absorptiometry-trunk fat was directly associated with BMI (r = 0.78, P < .001) and visceral fat by computed tomography (r = 0.79, P < .001). Compared with women in the lowest tertile of trunk fat, those in the highest tertile had inferior bone quality: lower trabecular bone volume (20.4 ± 5.8 vs 29.1 ± 6.1%; P = .001) and stiffness (433 ± 264 vs 782 ± 349 MPa; P = .01) and higher cortical porosity (8.8 ± 3.5 vs 6.3 ± 2.4%; P = .049). Bone formation rate (0.004 ± 0.002 vs 0.011 ± 0.008 mm(2)/mm · year; P = .006) was 64% lower in the highest tertile. Trunk fat was inversely associated with trabecular bone volume (r = -0.50; P < .01) and bone formation rate (r = -0.50; P < .001). The relationship between trunk fat and bone volume remained significant after controlling for age and BMI.

Conclusions

At the tissue level, premenopausal women with more central adiposity had inferior bone quality and stiffness and markedly lower bone formation. Given the rising levels of obesity, these observations require further investigation.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.