Adam Starkey

Measurement of mesothelioma on thoracic CT scans: a comparison of manual and computer-assisted techniques.

Our purpose in this study was to evaluate the variability of manual mesothelioma tumor thickness measurements in computed tomography (CT) scans and to assess the relative performance of six computerized measurement algorithms. The CT scans of 22 patients with malignant pleural mesothelioma were collected. In each scan, an initial observer identified up to three sites in each of three CT sections at which tumor thickness measurements were to be made. At each site, five observers manually measured tumor thickness through a computer interface. Three observers repeated these measurements during three separate sessions. Inter- and intra-observer variability in the manual measurement of tumor thickness was assessed. Six automated measurement algorithms were developed based on the geometric relationship between a specified measurement site and the automatically extracted lung regions. Computer-generated measurements were compared with manual measurements. The tumor thickness measurements of different observers were highly correlated (r > or = 0.99); however, the 95% limits of agreement for relative inter-observer difference spanned a range of 30%. Tumor thickness measurements generated by the computer algorithms also correlated highly with the average of observer measurements (r > or = 0.93). We have developed computerized techniques for the measurement of mesothelioma tumor thickness in CT scans. These techniques achieved varying levels of agreement with measurements made by human observers.

Variability in Mesothelioma Tumor Response Classification

OBJECTIVE The objective of our study was to evaluate observer variability in the measurement of temporal change in mesothelioma tumor thickness and in the resulting tumor response classification from CT scans. In addition, the performance of a semiautomated measurement method was evaluated. MATERIALS AND METHODS Four observers individually used an interface that displayed two serial CT scans from the same patient to measure mesothelioma tumor thickness on the follow-up CT scans of 22 patients based on baseline scan measurements. During one session, observers acquired measurements on the follow-up scans based on written reports of baseline scan measurements; in another session, baseline scan measurements were superimposed on the baseline scan for direct visual comparison. Follow-up scan measurements also were obtained from a semiautomated method. Measurement variability and tumor response classification concordance were evaluated for manual measurements acquired in both modes and for semiautomated measurements. RESULTS Although only a small increase in tumor response classification concordance rate was obtained with the visual approach (84.8%) relative to the more standard written-report approach (82.6%), the actual measurements acquired by observers were statistically significantly different between the two approaches (p = 0.03). Both the semiautomated measurements and the resulting tumor response classifications were consistent with manual measurements. CONCLUSION The presentation of baseline scan tumor measurements affects measurements acquired on follow-up scans and could influence tumor response classification. The potential utility of semiautomated tumor thickness measurements was shown in the context of measuring tumor response.

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.

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.

Quantitative measurement of lung reexpansion in malignant pleural mesothelioma patients undergoing pleurectomy/decortication.

RATIONALE AND OBJECTIVES Malignant pleural mesothelioma (MPM) is a neoplasm that grows circumferentially along the pleura. The tumor and concurrent pleural effusion may reduce lung function by restricting or preventing lung expansion. The purpose of this study was to provide objective evidence that pleurectomy/decortication (P/D) allows trapped lung to reexpand, quantify the reexpansion based on computed tomography (CT) scans, and investigate whether the expansion persists after surgery. MATERIALS AND METHODS A database of 12 patients demonstrating unilateral MPM was collected. Each patient underwent a presurgical CT scan, surgical debulking by P/D, and two postsurgical CT scans (at 1 and 4 months). The lung volume was measured in each scan using an automated algorithm and compared for each patient across time. RESULTS An increase in the ipsilateral postsurgical lung volume was observed for 10 of 12 patients (83%) 1 month after surgery. The median ipsilateral volume increase was 44% relative to the presurgical ipsilateral volume and 21% relative to the contralateral volume. A statistically significant change in ipsilateral lung volume was not observed between 1‑month and 4‑month postsurgical scans, implying that the volume improvement persisted months after surgery. CONCLUSIONS Debulking of MPM with P/D substantially increased the ipsilateral lung volume relative to both the presurgical ipsilateral volume and the contralateral lung volume. This improvement persisted months after surgery.

Computer-assisted staging of chronic rhinosinusitis correlates with symptoms

The Lund‐Mackay (LM) staging system for chronic rhinosinusitis (CRS) does not correlate with clinical parameters, likely due to its coarse scale. We developed a “Modified Lund Mackay” (MLM) system, which uses a three‐dimensional (3D), computerized method to quantify the volume of mucosal inflammation in the sinuses, and sought to determine whether the MLM would correlate with symptoms and disease‐specific quality of life.

Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients

PurposeUltrasonography has the potential to accurately stage breast cancer with automated analysis to detect axillary lymph node metastasis. The aim of this study was to develop and test automated quantitative ultrasound image analysis of axillary lymph nodes for breast cancer staging.MethodsFollowing an IRB-approved HIPAA compliant protocol, ultrasound images of 90 breast cancer patients presenting for lymph node assessment were retrospectively collected. There were 51 node-positive and 39 node-negative patients, yielding images of 223 lymph nodes (109 positive for metastasis and 114 negative for metastasis). The analysis was completely automated apart from the manual indication of the approximate center of each lymph node. Mathematical descriptors of the nodes, which served as image-based biomarkers, were computer-extracted and input to a classifier for the task of distinguishing between positive (i.e., metastatic) and negative lymph nodes. The performance of this task was assessed using receiver operating characteristic (ROC) analysis with evaluation by-node and by-patient using the area under the ROC curve (AUC) as the performance metric.ResultsThe AUC was 0.85 (standard error 0.03) for by-node evaluation when distinguishing between positive and negative lymph nodes. The AUC was 0.87 (0.04) for patient-based prognosis, i.e., assessing whether patients were lymph node-positive or lymph node-negative.ConclusionBased on these classification results, we conclude that mathematical descriptors of sonographically imaged lymph nodes may be useful as prognostic biomarkers in breast cancer staging and demonstrate potential for predicting patient lymph node status.

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.

Utilisation of a thoracic oncology database to capture radiological and pathological images for evaluation of response to chemotherapy in patients with malignant pleural mesothelioma

Objective An area of need in cancer informatics is the ability to store images in a comprehensive database as part of translational cancer research. To meet this need, we have implemented a novel tandem database infrastructure that facilitates image storage and utilisation. Background We had previously implemented the Thoracic Oncology Program Database Project (TOPDP) database for our translational cancer research needs. While useful for many research endeavours, it is unable to store images, hence our need to implement an imaging database which could communicate easily with the TOPDP database. Methods The Thoracic Oncology Research Program (TORP) imaging database was designed using the Research Electronic Data Capture (REDCap) platform, which was developed by Vanderbilt University. To demonstrate proof of principle and evaluate utility, we performed a retrospective investigation into tumour response for malignant pleural mesothelioma (MPM) patients treated at the University of Chicago Medical Center with either of two analogous chemotherapy regimens and consented to at least one of two UCMC IRB protocols, 9571 and 13473A. Results A cohort of 22 MPM patients was identified using clinical data in the TOPDP database. After measurements were acquired, two representative CT images and 0–35 histological images per patient were successfully stored in the TORP database, along with clinical and demographic data. Discussion We implemented the TORP imaging database to be used in conjunction with our comprehensive TOPDP database. While it requires an additional effort to use two databases, our database infrastructure facilitates more comprehensive translational research. Conclusions The investigation described herein demonstrates the successful implementation of this novel tandem imaging database infrastructure, as well as the potential utility of investigations enabled by it. The data model presented here can be utilised as the basis for further development of other larger, more streamlined databases in the future.

Research Imaging in an Academic Medical Center

RATIONALE AND OBJECTIVES Managing and supervising the complex imaging examinations performed for clinical research in an academic medical center can be a daunting task. Coordinating with both radiology and research staff to ensure that the necessary imaging is performed, analyzed, and delivered in accordance with the research protocol is nontrivial. The purpose of this communication is to report on the establishment of a new Human Imaging Research Office (HIRO) at our institution that provides a dedicated infrastructure to assist with these issues and improve collaborations between radiology and research staff. MATERIALS AND METHODS The HIRO was created with three primary responsibilities: 1) coordinate the acquisition of images for clinical research per the study protocol, 2) facilitate reliable and consistent assessment of disease response for clinical research, and 3) manage and distribute clinical research images in a compliant manner. RESULTS The HIRO currently provides assistance for 191 clinical research studies from 14 sections and departments within our medical center and performs quality assessment of image-based measurements for six clinical research studies. The HIRO has fulfilled 1806 requests for medical images, delivering 81,712 imaging examinations (more than 44.1 million images) and related reports to investigators for research purposes. CONCLUSIONS The ultimate goal of the HIRO is to increase the level of satisfaction and interaction among investigators, research subjects, radiologists, and other imaging professionals. Clinical research studies that use the HIRO benefit from a more efficient and accurate imaging process. The HIRO model could be adopted by other academic medical centers to support their clinical research activities; the details of implementation may differ among institutions, but the need to support imaging in clinical research through a dedicated, centralized initiative should apply to most academic medical centers.