Temporal image database design for outcome analysis of lung nodule

TimeLine: visualizing integrated patient records

An increasing amount of data is now accrued in medical information systems; however, the organization of this data is still primarily driven by data source, and does not support the cognitive processes of physicians. As such, new methods to visualize patient medical records are becoming imperative in order to assist physicians with clinical tasks and medical decision-making. The TimeLine system is a problem-centric temporal visualization for medical data: information contained with medical records is reorganized around medical disease entities and conditions. Automatic construction of the TimeLine display from existing clinical repositories occurs in three steps: 1) data access, which uses an eXtensible Markup Language (XML) data representation to handle distributed, heterogeneous medical databases; 2) data mapping and reorganization, reformulating data into hierarchical, problemcentric views; and 3) data visualization, which renders the display to a target presentation platform. Leveraging past work, we describe the latter two components of the TimeLine system in this paper, and the issues surrounding the creation of medical problems lists and temporal visualization of medical data. A driving factor in the development of TimeLine was creating a foundation upon which new data types and the visualization metaphors could be readily incorporated.

Mixture distribution analysis of a computer assisted diagnostic method for the evaluation of pulmonary nodules on computed tomography scan

RATIONALE AND OBJECTIVES

To compare the effectiveness of a new computational scheme for pulmonary nodule detection in computed tomography images against human observers.

MATERIALS AND METHODS

The study involved evaluation of 81 potential nodules by four radiologists. Each radiologist separately evaluated the potential nodules and provided a confidence level for the presence of pulmonary nodules. Their performance was compared with that of the new computational scheme by mixture distribution analysis.

RESULTS

Mixture distribution analysis of the results of the four radiologists demonstrated a relative proportion agreement of 0.84. The kappa statistic was used to compare the agreement of the computational scheme with the results of the four radiologists. A kappa value of .65 (se = .11) was shown to be significantly different from chance (P = .99).

CONCLUSION

The new computational scheme correlates well with the radiologists' subjective rankings of pulmonary nodules on computed tomography scans and may prove a useful tool in the evaluation of algorithms for the screening and diagnosis of lung cancer.

A review of medical imaging informatics

This review of medical imaging informatics is a survey of current developments in an exciting field. The focus is on informatics issues rather than traditional data processing and information systems, such as picture archiving and communications systems (PACS) and image processing and analysis systems. In this review, we address imaging informatics issues within the requirements of an informatics system defined by the American Medical Informatics Association. With these requirements as a framework, we review, in four sections: (1) Methods to present imaging and associated data without causing an overload, including image study summarization, content-based medical image retrieval, and natural language processing of text data. (2) Data modeling techniques to represent clinical data with focus on an image data model, including general-purpose time-based multimedia data models, health-care-specific data models, knowledge models, and problem-centric data models. (3) Methods to integrate medical data information from heterogeneous clinical data sources. Advances in centralized databases and mediated architectures are reviewed along with a discussion on our efforts at data integration based on peer-to-peer networking and shared file systems. (4) Visualization schemas to present imaging and clinical data: the large volume of medical data presents a daunting challenge for an efficient visualization paradigm. In this section we review current multimedia visualization methods including temporal modeling, problem-specific data organization, including our problem-centric, context and user-specific visualization interface.

Problem-oriented prefetching for an integrated clinical imaging workstation

Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.

Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation

PURPOSE

To determine the accuracy of high-resolution computed tomographic (CT) volumetric measurements of small pulmonary nodules to assess growth and malignancy status.

MATERIALS AND METHODS

The accuracy of three-dimensional (3D) image extraction and isotropic resampling techniques was assessed by performing three experiments. The first experiment measured volumes in spherical synthetic nodules of two diameters (3.20 and 3.96 mm), the second measured deformable silicone synthetic nodules prior to and after their shape had been altered markedly, and the third measured nodules of various shapes and sizes. Three-dimensional techniques were used to assess growth in 13 patients for whom the final diagnosis was known and whose initial nodule diameters were less than 10 mm. By using the exponential growth model and the calculated nodule volume at two points in time, the doubling time for each subject was calculated.

RESULTS

The three synthetic nodule studies revealed that the volume could be measured accurately to within +/-3%. All five malignant nodules grew, and all had doubling times less than 177 days. Some malignant nodules had asymmetric patterns of growth identified by using the 3D techniques but not the two-dimensional methods. All eight benign nodules had doubling times of 396 days or greater or showed a decrease in volume.

CONCLUSION

CT volumetric measurements are highly accurate for determining volume and are useful in assessing growth of small nodules and calculating their doubling times.

Medical image informatics infrastructure design and applications

Picture archiving and communication systems (PACS) is a system integration of multimodality images and health information systems designed for improving the operation of a radiology department. As it evolves, PACS becomes a hospital image document management system with a voluminous image and related data file repository. A medical image informatics infrastructure can be designed to take advantage of existing data, providing PACS with add-on value for health care service, research, and education. A medical image informatics infrastructure (MIII) consists of the following components: medical images and associated data (including PACS database), image processing, data/knowledge base management, visualization, graphic user interface, communication networking, and application oriented software. This paper describes these components and their logical connection, and illustrates some applications based on the concept of the MIII.