Philip Caligiuri

Multifractal radiographic analysis of osteoporosis

An important complication of osteoporosis is fracture. Alteration in bone structure, as well as decreased bone mass, contribute to the tendency to fracture in osteoporosis. Current methods that measure bone mass alone show substantial overlap of the measurements of osteoporotic patients who fracture with those that do not. Our aim is to develop noninvasive methods of evaluating bone structure on plain film radiographs to better predict fracture risk in osteoporosis. Regions of interest (ROIs) were selected from digitized lateral lumbar spine radiographs of 43 patients being seen in an osteoporosis clinic. The fractal dimension of these ROIs was estimated using a surface area method. The ability of fractal dimension to distinguish between cases that had fracture elsewhere in the spine from those that did not, was evaluated using receiver operating characteristic (ROC) analysis. These results were compared with ROC analysis for these same patients using bone mineral density (BMD) measurements (bone mass). Significantly larger Az (area under ROC curve) values were obtained using fractal dimension (0.87) than from using BMD (0.58), indicating a better test performance using fractal dimension. Therefore, computerized radiographic methods to evaluate bone structure, such as fractal analysis, may be helpful in better determining fracture risk in osteoporosis.

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.

An Investigation of Radiologists' Perception of Lesion Similarity. Observations with Paired Breast Masses on Mammograms and Paired Lung Nodules on CT Images

RATIONALE AND OBJECTIVES We conducted an observer study to investigate whether radiologists can judge similarities in pairs of breast masses and lung nodules consistently and reproducibly. MATERIALS AND METHODS Institutional review board approval and informed observer consent were obtained. This study was compliant with the Health Insurance Portability and Accountability Act. We used eight pairs of breast masses on mammograms and eight pairs of lung nodules on computed tomographic images, for which subjective similarity ratings ranging from 0 to 1 were determined in our previous studies. From these, four sets of image pairs were created (ie, a set of eight mass pairs, a set of eight nodule pairs, and two mixed sets of four mass and four nodule pairs). Eight radiologists, including four breast radiologists and four chest radiologists, compared all combinations of the eight pairs in each set using a two-alternative forced-choice (2AFC) method to determine the similarity ranking scores by identifying which pair was more similar than the other pair based on the overall impression for diagnosis. RESULTS In the mass set and nodule set, the relationship between the average subjective similarity ratings and the average similarity ranking scores by 2AFC indicated very high correlations (r = 0.91 and 0.88). Moreover, in the two mixed sets, the correlations between the average subjective similarity ratings and the average similarity ranking scores were also very high (r = 0.90 and 0.98). Thus, radiologists were able to compare the similarities for pairs of lesions consistently, even in the unusual comparison of pairs of completely different types of lesions. CONCLUSION The subjective similarity of a pair of lesions in medical images can be quantified consistently by a group of radiologists. The concept of similarity of lesions in medical images can be subjected to rigorous scientific research and investigation in the future.

Temporal subtraction of dual-energy chest radiographs.

Temporal subtraction and dual-energy imaging are two enhanced radiography techniques that are receiving increased attention in chest radiography. Temporal subtraction is an image processing technique that facilitates the visualization of pathologic change across serial chest radiographic images acquired from the same patient; dual-energy imaging exploits the differential relative attenuation of x-ray photons exhibited by soft-tissue and bony structures at different x-ray energies to generate a pair of images that accentuate those structures. Although temporal subtraction images provide a powerful mechanism for enhancing visualization of subtle change, misregistration artifacts in these images can mimic or obscure abnormalities. The purpose of this study was to evaluate whether dual-energy imaging could improve the quality of temporal subtraction images. Temporal subtraction images were generated from 100 pairs of temporally sequential standard radiographic chest images and from the corresponding 100 pairs of dual-energy, soft-tissue radiographic images. The registration accuracy demonstrated in the resulting temporal subtraction images was evaluated subjectively by two radiologists. The registration accuracy of the soft-tissue-based temporal subtraction images was rated superior to that of the conventional temporal subtraction images. Registration accuracy also was evaluated objectively through an automated method, which achieved an area-under-the-ROC-curve value of 0.92 in the distinction between temporal subtraction images that demonstrated clinically acceptable and clinically unacceptable registration accuracy. By combining dual-energy soft-tissue images with temporal subtraction, misregistration artifacts can be reduced and superior image quality can be obtained.

Automated lung segmentation of diseased and artifact-corrupted magnetic resonance sections.

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary step in the computer-based analysis of thoracic MR images. This process is often confounded by image acquisition artifacts and disease-induced morphological deformation. We have developed an automated method for lung segmentation that is insensitive to these complications. The automated method was applied to 23 thoracic MR scans (413 sections) obtained from 10 patients. Two radiologists manually outlined the lung regions in a random sample of 101 sections (n=202 lungs), and the extent to which disease or artifact confounded lung border visualization was evaluated. Accuracy of lung regions extracted by the automated segmentation method was quantified by comparison with the radiologist-defined lung regions using an area overlap measure (AOM) that ranged from 0 (disjoint lung regions) to 1 (complete overlap). The AOM between each observer and the automated method was 0.82 when averaged over all lungs. The average AOM in the lung bases, where lung segmentation is most difficult, was 0.73.

Variability of tumor area measurements for response assessment in malignant pleural mesothelioma

PURPOSE The measurement of malignant pleural mesothelioma is critical to the assessment of tumor response to therapy. Current response assessment standards utilize summed linear measurements acquired on three computed tomography (CT) sections. The purpose of this study was to evaluate manual area measurements as an alternate response assessment metric, specifically through the study of measurement interobserver variability. METHODS Two CT scans from each of 31 patients were collected. Using a computer interface, five observers contoured tumor on three selected CT sections from each baseline scan. Four observers also constructed matched follow-up scan tumor contours for the same 31 patients. Area measurements extracted from these contours were compared using a random effects analysis of variance model to assess relative interobserver variability. The sums of section area measurements were also analyzed, since these area sums are more clinically relevant for response assessment. RESULTS When each observers measurements were compared with those of the other four observers, strong correlation was observed. The 95% confidence interval for relative interobserver variability of baseline scan summed area measurements was [-71%, +240%], spanning 311%. For the follow-up scan summed area measurements, the 95% confidence interval for relative interobserver variability was [-41%, +70%], spanning 111%. At both baseline and follow-up, the variability among observers was a significant component of the total variability in both per-section and summed area measurements (p<0.0001). CONCLUSIONS Despite the ability of tumor area measurements to capture tumor burden with greater fidelity than linear tumor thickness measurements, manual area measurements may not be a robust means of response assessment in mesothelioma patients.

Computer automated algorithm to evaluate cavitary lesions in adults with pulmonary tuberculosis

The presence of a cavitary lesion is a radiographic hallmark of pulmonary tuberculosis (TB). The sizes of the cavitary lesion and its proximity to the bronchial tree have been associated with mycobacterial burden (1).

Radiologic Imaging Techniques for the Diagnosis and Management of Invasive Fungal Disease

Invasive fungal diseases (IFDs) are an important cause of morbidity and mortality, especially in immunocompromised patients. Prompt antifungal therapy is essential for favorable outcomes, but clinical signs and symptoms are nonspecific and mycologic confirmation is often not possible. Radiographic testing is an important adjunct to the diagnosis and management of IFDs. Early imaging has been associated with improved survival, particularly in neutropenic patients with fungal pneumonia or acute invasive fungal sinusitis. This review summarizes common radiologic appearances of IFDs of the lung, sinus, and brain. The advantages and limitations of computed tomography (CT) and magnetic resonance (MR) imaging are discussed as are recent developments in nuclear medicine and proton MR spectroscopy technology.

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.