Variance stabilization is an important step in statistical assessment of quantitative imaging biomarkers (QIBs) to meet the equal variances requirements across different subgroups for many statistical tests. The objective of this study is to compare the commonly used Log transformation to the Box-Cox transformation for variance stabilization in the context of the assessment of a computed tomography (CT) lung nodule volume estimation QIB. Our investigation included the following: (1) We developed a model characterizing repeated measurements typically observed in CT lung nodule volume estimation. Given the model, we derived the parameter of the Box-Cox transformation that stabilizes the variance of the volume measurements across lung nodules. (2) We validated our approach using simulation data and examined factors that impact the performance of the transformations by comparing it to the standard Log transformation. The coefficient of variation for the standard deviation (CVstd) was used as the metric for quantifying the performance of transformations, with smaller CVstd indicating better variance stabilization. Results showed for both transformations, CVstd decreased with larger number of repeated measurements. For all simulated datasets, the Box-Cox transformation yielded smaller CVstd than the Log transformation. This suggests the Box-Cox transformation has better performance in variance stabilization for the estimation of lung nodule volume from CT data and can be a practical alternative for improved variance stabilization in the assessment of some types of QIBs. We are generating a guideline for determining when the Box-Cox might be a viable option to the Log transformation within a QIB assessment framework.
The widely used multireader multicase ROC study design for comparing imaging modalities is the fully crossed (FC) design: every reader reads every case of both modalities. We investigate paired split-plot (PSP) designs that may allow for reduced cost and increased flexibility compared with the FC design. In the PSP design, case images from two modalities are read by the same readers, thereby the readings are paired across modalities. However, within each modality, not every reader reads every case. Instead, both the readers and the cases are partitioned into a fixed number of groups and each group of readers reads its own group of cases—a split-plot design. Using a U-statistic based variance analysis for AUC (i.e., area under the ROC curve), we show analytically that precision can be gained by the PSP design as compared with the FC design with the same number of readers and readings. Equivalently, we show that the PSP design can achieve the same statistical power as the FC design with a reduced number of readings. The trade-off for the increased precision in the PSP design is the cost of collecting a larger number of truth-verified patient cases than the FC design. This means that one can trade-off between different sources of cost and choose a least burdensome design. We provide a validation study to show the iMRMC software can be reliably used for analyzing data from both FC and PSP designs. Finally, we demonstrate the advantages of the PSP design with a reader study comparing full-field digital mammography with screen-film mammography.
The widely used multi-reader multi-case ROC study design for comparing imaging modalities is the fullycrossed (FC) design: every reader reads every case of both modalities. In this work, we investigate the paired split-plot (PSP) designs that allow for reduced cost and increased flexibility compared to the FC design. In the PSP design, patient images from two modalities are read by the same readers, thereby the readings are paired across modalities. However, within each modality, not every reader reads every case. Instead, both the readers and the cases are partitioned into a number of groups and each group of readers read their own group of cases - a split-plot design. Using the U-statistic based variance analysis for AUC (i.e., area under the ROC curve), we show analytically that, with a fixed number of readings per reader, substantial statistical efficiency can be gained by the PSP design as compared to the FC design. Equivalently, we show that the PSP design can achieve the same statistical power as the FC design with substantially reduced number of readings. However, the efficiency/power gain of the PSP design comes with the increased cost of collecting a larger number of truthverified patient cases than the FC design. This means that one can trade off between different sources of cost and choose a least burdensome design. We demonstrate the advantages of the PSP design with a real-world reader study for the comparison of full field digital mammography with screen-film mammography.
The purpose of this study is evaluating registration accuracy of evaluation environment of Digital and Analog Pathology (eeDAP). eeDAP was developed to help conduct studies in which pathologists view and evaluate the same fields of view (FOVs), cells, or features in a glass slide on a microscope and in a whole slide image (WSI) on a digital display by registering the two domains. Registration happens at the beginning of a study (global registration) and during a study (localregistration). The global registration is interactive and defines the correspondence between the WSI and stage coordinates. The local registration ensures the pathologist evaluates the correct FOVs, cells, and features. All registrations are based on image-based normalized cross correlation.This study evaluates the registration accuracy achieved throughout a study. To measure the registration accuracy, we used an eyepiece ruler reticle to measure the shift distance between the center of the eyepiece and a target feature expected in the center. Two readers independently registered 60 FOVs from 6 glass slides, which covered different tissue types, stains, and magnifications. The results show thatwhen the camera image is in focus, the registration was within 5micrometers in more than 95% of the FOVs. The tissue type, stain, magnification, or readerdid not appear to impact local registration accuracy. The registration error was mainly dependent on the microscope being in focus, the scan quality, and the FOVcontent (unique high-contrast structures are better than content that is homogeneous or low contrast).
In previous work we developed a method for predicting the minimum detectable change (MDC) in nodule volume based on volumetric CT measurements. MDC was defined as the minimum increase/decrease in a nodule volume distinguishable from the baseline measurement at a specified level of detection performance, assessed using the area under the ROC curve (AUC). In this work we derived volume estimates of a set of synthetic nodules and calculated the detection performance for distinguishing them from baseline measurements. Eight spherical objects of 100HU radio density ranging in diameter from 5.0mm to 5.75mm and 8.0mm to 8.75mm with 0.25mm increments were placed in an anthropomorphic phantom with either no background (high-contrast task) or gelatin background (low-contrast task). The baseline was defined as 5.0mm for the first set of nodules and 8.0mm for the second set. The phantom was scanned using varying exposures, and reconstructed with slice thickness of 0.75, 1.5, and 3.0mm and two reconstruction kernels (standard and smooth). Volume measurements were derived using a previously developed matched- filter approach. Results showed that nodule size, slice thickness, and nodule-to-background contrast affected detectable change in nodule volume when using our volume estimator and the acquisition settings from our study. We also compared our experimental results to the values estimated by our previously-developed MDC prediction method. We found that experimental data for the 8mm baseline nodules matched very well with our predicted values of MDC. These results support considering the use of this metric when standardizing imaging protocols for lung nodule size change assessment.