Rachael Y. Roberts

Dual energy subtraction and temporal subtraction chest radiography.

Digital radiography and display systems have revolutionized radiologic practice in recent years and have enabled clinical application of advanced image processing techniques. These include dual energy subtraction and temporal subtraction, both of which can improve diagnostic accuracy for abnormal findings in chest radiographs, especially for subtle lesions such as early lung cancer or focal pneumonia. Dual energy radiography exploits the differential attenuation of low-energy x-ray photons by calcium to produce separate images on the bones and soft tissues, which provides improved detection and characterization of both calcified and noncalcified lung lesions. Dual energy subtraction radiography is currently available from 2 of the major vendors and is in clinical use at many institutions in the United States. Temporal subtraction is a complementary technique that enhances interval change, by using a previous radiograph as a subtraction mask, so that unchanged normal anatomy is suppressed, whereas new abnormalities are enhanced. Though it is not yet a product in the United States, temporal subtraction is available for clinical use in Japan. Temporal subtraction can be combined with energy subtraction to reduce misregistration artifacts, and also has potential to improve computer-aided detection of nodules and other types of lung disease.

Computerized segmentation and measurement of malignant pleural mesothelioma

PURPOSE The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM. METHODS The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots. RESULTS The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations. CONCLUSIONS A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.

Discrete-space versus continuous-space lesion boundary and area definitions

Measurement of the size of anatomic regions of interest in medical images is used to diagnose disease, track growth, and evaluate response to therapy. The discrete nature of medical images allows for both continuous and discrete definitions of region boundary. These definitions may, in turn, support several methods of area calculation that give substantially different quantitative values. This study investigated several boundary definitions (e.g., continuous polygon, internal discrete, and external discrete) and area calculation methods (pixel counting and Greens theorem). These methods were applied to three separate databases: A synthetic image database, the Lung Image Database Consortium database of lung nodules and a database of adrenal gland outlines. Average percent differences in area on the order of 20% were found among the different methods applied to the clinical databases. These results support the idea that inconsistent application of region boundary definition and area calculation may substantially impact measurement accuracy.

The influence of initial outlines on manual segmentation

PURPOSE Initial outlines are often presented as an aid to reduce the time-cost associated with manual segmentation and measurement of structures in medical images. This study evaluated the influence of initial outlines on manual segmentation intraobserver and interobserver precision. METHODS Three observers independently outlined all pleural mesothelioma tumors present in five computed tomography (CT) sections in each of 30 patient scans. After a lapse of time, each observer was presented with the same series of CT sections with the outlines of each observer superimposed as initial outlines. Each observer created altered outlines by altering the initial outlines to reflect their perception of the tumor boundary. Altered outlines were compared to original outlines using the Jaccard similarity coefficient (J). Intraobserver and interobserver precision of observer outlines were calculated by applying linear mixed effects analysis of variance models to the J values. The percent of minor alterations (alterations that resulted in only slight changes in the initial outline) was also recorded. RESULTS The average J value between pairs of observer original outlines was 0.371. The average J value between pairs of observer outlines when altered from an identical initial outline was 0.796, indicating increased interobserver precision. The average difference between J values of an observers segmentation created by altering their own initial outline and when altering a different observers initial outline was 0.476, indicating initial outlines strongly influence intraobserver precision. Observers made minor alterations on 74.5% of initial outlines with which they were presented. CONCLUSIONS Intraobserver and interobserver precision were strongly dependent on the initial outline. These effects are likely due to the tendency of observers to make only minor corrections to initial outlines. This finding could impact observer study design, tumor growth assessment, computer-aided diagnosis system validation, and radiation therapy target volume definition when initial outlines are used as an observer aid.

TU‐D‐L100J‐05: Assessment of Mesothelioma Tumor Response: Correlation of Tumor Thickness and Tumor Area

Purpose: The quantification of pleural mesothelioma tumor extent is required to evaluate the efficacy of clinical trials. The manual acquisition of up to three linear tumor thickness measurements on each of three sections across a series of computed tomography(CT) scans is the current standard for tumor response assessment. The purpose of this study was to determine the correlation of response based on linear tumor thickness measurements and response based on tumor area. Method/MATERIALS: Two CT scans from each of 22 mesothelioma patients were collected. Using a computer interface, a radiologist acquired linear tumor thickness measurements on three sections of each patients baseline scan and on the corresponding sections of each patients follow‐up scan in accordance with our clinical protocol. These linear measurements across 132 CT sections (3 sections per scan, 2 scans per patient, 22 patients) provided the standard for comparison of area measurements. Another radiologist used a computer interface to delineate the tumor border in the same 132 CT sections to obtain tumor area and the changes in tumor area between the baseline and follow‐up scans of each patient. Results: A comparison of the sum of tumor thickness measurements and tumor area yielded a correlation coefficient of 0.59 across the 132 sections. With regard to tumor response, a comparison of change in the sum of tumor thickness measurements and change in the total tumor area between the baseline and follow‐up scans of the 22 patients yielded a correlation coefficient of 0.83. This relatively high correlation, however, does not capture the extent of variability in the data. For example, among patients with RECIST‐based “stable disease,” change in tumor area ranged from a decrease of 58% to an increase of 89%. Conclusions: Although measurements of tumor thickness and tumor area demonstrated moderate correlation, variability in this association requires further investigation.

WE‐E‐304A‐06: The Influence of Initial Outlines On Observers

Purpose: When manually segmenting structures in medical images, an observer identifies the structure and then traces the structures boundary. When an observer instead is presented with an initial segmentation boundary from a computerized method, the expectation is that the observer will alter that outline as necessary to fit their own perception of the boundary, which should not be substantially influenced by the initial, computer‐defined segmentation. This expectation is implicit when observers modify computerized outlines to establish “truth,” measure the area/volume of a structure, or use a computerized system as an initial reader. The goal of this study was to quantifying the extent to which initial outlines influence human observers. Method and Materials: A database of 30 thoracic CT scans from different mesothelioma patients was collected. For each scan, 5 sections were randomly selected for analysis, and three experienced observers independently outlined all mesothelioma tumor in each of these 150 sections to produce “truth outlines.” After a three‐month period, each observer was presented with all 450 truth outlines, which served as “initial outlines” in this second component of the study, and observers could alter these initial outlines to produce “modified outlines” that best captured their perception of tumor boundary. The area‐of‐overlap measure (AOM) between pairwise combinations of outlines was calculated. Results: The average AOM between the truth outlines of a given observer and the modified outlines derived from the initial outline of that same observer was 0.842 ± 0.009 across all observers. In comparison, the average AOM between the truth outlines of a given observer and the modified outlines derived from the initial outlines of the other two observers was 0.443 ± 0.006 across all observers. Conclusion: The substantial difference between the two mean AOM values implies that observers are strongly impacted by the presence of an initial outline.

SU‐GG‐I‐87: Inconsistencies in Discrete Space and Continuous Space Lesion Boundary and Area Definitions

Purpose: Measurement of the size of anatomic regions of interest is used to diagnose disease, track growth, and evaluate response to therapy. The discrete nature of medical images allows for both continuous and discrete definitions of region boundary. These definitions may, in turn, support several methods of area calculation that give substantially different values. This study investigated several boundary definitions and area calculation methods to quantify these differences. Method and Materials: Two sets of region boundaries were investigated, one defined in continuous space and one defined in discrete space. A total 1,764 manual lung nodule boundaries were obtained from the LungImageDatabase Consortium (LIDC) database.Lung nodule area was calculated for each of these discrete‐space boundaries based on four area metrics: boundary‐excluded pixel counting, boundary‐included pixel counting, and two variants of Greens Theorem applied to vertices defined by the center of each boundary pixel. Adrenal gland area was calculated for 71 manual continuous‐space adrenal gland boundaries based of four area metrics: Greens Theorem applied to the original continuous boundary, boundary‐excluded pixel counting after direct conversion to discrete space, boundary‐included pixel counting after direct conversion to discrete space, and pixel counting after pixel‐center conversion to discrete space. Results: Based on the same set of adrenal gland boundaries, mean adrenal gland area ranged from 85.1 ± 35.4 pixels to 126.2 ± 42.8 pixels, depending on the method of area calculation. Based on the same set of lung nodule boundaries, mean lung nodule area ranged from 147.0 ± 212.1 pixels to 208.8 ± 251.7 pixels, depending on the method of calculation. Conclusion: Inconsistent application of region boundary definition and area calculation may substantially impact measurement accuracy. Substantial differences exist among the various area calculation methods supporting the necessity, in both the research and clinical settings, to consistently apply boundary definition and area calculation methods.

SU‐GG‐I‐02: Evolution of Adrenal Gland Perfusion with Anti‐Angiogenic Therapy: A CT‐Based Approach

Purpose: In radiology it is important to understand and interpret anatomical changes which correlate with treatment plans. Several anticancer agents exist that affect tumor vasculature growth and normal vessel growth. Monitoring these effects with CTimaging may be useful to guide dosing of treatment. Observing perfusion of certain organs it possible to monitor vessel competency. If perfusion is constant over time, it can be assumed there is no significant change in vasculature, and no damage due to chemotherapeutics. In contrast, significant changes in perfusion over time may suggest a decrease in normal vessel growth as a result of therapy. Method and Materials: Patients receiving the vascular endothelial growth factor (VEGF) inhibitor sorafenib underwent CTimaging every six weeks, beginning with baseline studies. A “jog scan” tracked perfusion through the highly fenestrated adrenal glands. Sixteen pairs of adrenal images were contoured, with each of the sixteen scans representing different perfusion time intervals from 0–90 seconds. The mean pixel values of each gland were obtained and compared over time for any significant changes in pixel value that could indicate change in vascular perfusion over time. Further calculations were performed isolating the medullary component of the glands because of its high vascularity. Results: The average change in maximum pixel values from baseline to six weeks after treatment initiation demonstrated a 4.58% increase in peak pixel value for both adrenals, with a subsequent decrease of 3.2% in the third scan. Changes in the medullary region demonstrated a 6.1% increase in pixel value in comparison to the entire adrenal area. Conclusion: Manual contouring of adrenal glands in conjunction with calculated maximum pixel values revealed changes in adrenal perfusion between baseline and therapy‐monitoring CT scans. The continued monitoring of perfusion could prove beneficial to the radiologic diagnosis of significant anatomical changes as a result of chemotherapy.

SU‐FF‐I‐05: Evolution of Adrenal Gland Perfusion with Anti‐Angiogenic Therapy: A CT‐Based Study

Purpose: In radiological diagnoses, it is important to understand and interpret anatomical changes, specifically those observed in CT scans, which correlate with treatment plans. There are currently many anticancer agents, which affect tumor vasculature growth as well as normal organ vessel growth. Monitoring these effects with imaging may be useful to guide selection and dosing of different treatments. By observing perfusion of certain organs, it is possible to monitor vessel competency. In cases where perfusion is constant over time, it can be assumed that there is no significant change in vasculature, and thus no damage as an effect of chemotherapeutic agents. In contrast, significant changes in perfusion over time, may suggest decease in normal vessel growth as a result of therapy. Methods & Materials: Patients receiving the VEGF inhibitor sorafenib, underwent CTimaging every six weeks, beginning with a baseline study prior to treatment. A “jog scan” was used to track perfusion through the adrenal glands (chosen due to their significant fenestration). Sixteen pairs of adrenal images were obtained per jog scan, and manually contoured using a contouring program. Each of the sixteen scans represents different perfusion time intervals from 0–150 seconds. The mean pixel values of each gland were obtained, and these values were compared over time for any significant changes in pixel value, and thus change in vasculature perfusion over time. Results: The average change in maximum pixel values from baseline to six weeks after treatment initiation shows a change of 4.58% increase in peak pixel value for both adrenal glands. Conclusion: The manual contouring of adrenal glands in conjunction with calculated maximum pixels values shows changes in adrenal perfusion between baseline and beginning therapy. The continued monitoring of perfusion could prove beneficial to the radiologic diagnosis of significant anatomical changes as a result of continuous chemotherapy.