Estimating patient dose from x-ray tube output metrics: automated measurement of patient size from CT images enables large-scale size-specific dose estimates.

PURPOSE: To test the hypothesis that patient size can be accurately calculated from axial computed tomographic (CT) images, including correction for the effects of anatomy truncation that occur in routine clinical CT image reconstruction. MATERIALS AND METHODS: Institutional review board approval was obtained for this HIPAA-compliant study, with waiver of informed consent. Water-equivalent diameter (D(W)) was computed from the attenuation-area product of each image within 50 adult CT scans of the thorax and of the abdomen and pelvis and was also measured for maximal field of view (FOV) reconstructions. Linear regression models were created to compare D(W) with the effective diameter (D(eff)) used to select size-specific volume CT dose index (CTDI(vol)) conversion factors as defined in report 204 of the American Association of Physicists in Medicine. Linear regression models relating reductions in measured D(W) to a metric of anatomy truncation were used to compensate for the effects of clinical image truncation. RESULTS: In the thorax, D(W)versus D(eff) had an R(2) of 0.51 (n = 200, 50 patients at four anatomic locations); in the abdomen and pelvis, R(2) was 0.90 (n = 150, 50 patients at three anatomic locations). By correcting for image truncation, the proportion of clinically reconstructed images with an extracted D(W) within ±5% of the maximal FOV D(W) increased from 54% to 90% in the thorax (n = 3602 images) and from 95% to 100% in the abdomen and pelvis (6181 images). CONCLUSION: The D(W) extracted from axial CT images is a reliable measure of patient size, and varying degrees of clinical image truncation can be readily corrected. Automated measurement of patient size combined with CT radiation exposure metrics may enable patient-specific dose estimation on a large scale.

Automated extraction of BI-RADS final assessment categories from radiology reports with natural language processing.

The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies.

Exposing exposure: enhancing patient safety through automated data mining of nuclear medicine reports for quality assurance and organ dose monitoring.

PURPOSE: To develop and validate an open-source informatics toolkit capable of creating a radiation exposure data repository from existing nuclear medicine report archives and to demonstrate potential applications of such data for quality assurance and longitudinal patient-specific radiation dose monitoring. MATERIALS AND METHODS: This study was institutional review board approved and HIPAA compliant. Informed consent was waived. An open-source toolkit designed to automate the extraction of data on radiopharmaceuticals and administered activities from nuclear medicine reports was developed. After iterative code training, manual validation was performed on 2359 nuclear medicine reports randomly selected from September 17, 1985, to February 28, 2011. Recall (sensitivity) and precision (positive predictive value) were calculated with 95% binomial confidence intervals. From the resultant institutional data repository, examples of usage in quality assurance efforts and patient-specific longitudinal radiation dose monitoring obtained by calculating organ doses from the administered activity and radiopharmaceutical of each examination were provided. RESULTS: Validation statistics yielded a combined recall of 97.6% ± 0.7 (95% confidence interval) and precision of 98.7% ± 0.5. Histograms of administered activity for fluorine 18 fluorodeoxyglucose and iodine 131 sodium iodide were generated. An organ dose heatmap which displays a sample patient's dose accumulation from multiple nuclear medicine examinations was created. CONCLUSION: Large-scale repositories of radiation exposure data can be extracted from institutional nuclear medicine report archives with high recall and precision. Such repositories enable new approaches in radiation exposure patient safety initiatives and patient-specific radiation dose monitoring.

Exposing exposure: automated anatomy-specific CT radiation exposure extraction for quality assurance and radiation monitoring.

PURPOSE: To develop and validate an informatics toolkit that extracts anatomy-specific computed tomography (CT) radiation exposure metrics (volume CT dose index and dose-length product) from existing digital image archives through optical character recognition of CT dose report screen captures (dose screens) combined with Digital Imaging and Communications in Medicine attributes. MATERIALS AND METHODS: This institutional review board-approved HIPAA-compliant study was performed in a large urban health care delivery network. Data were drawn from a random sample of CT encounters that occurred between 2000 and 2010; images from these encounters were contained within the enterprise image archive, which encompassed images obtained at an adult academic tertiary referral hospital and its affiliated sites, including a cancer center, a community hospital, and outpatient imaging centers, as well as images imported from other facilities. Software was validated by using 150 randomly selected encounters for each major CT scanner manufacturer, with outcome measures of dose screen retrieval rate (proportion of correctly located dose screens) and anatomic assignment precision (proportion of extracted exposure data with correctly assigned anatomic region, such as head, chest, or abdomen and pelvis). The 95% binomial confidence intervals (CIs) were calculated for discrete proportions, and CIs were derived from the standard error of the mean for continuous variables. After validation, the informatics toolkit was used to populate an exposure repository from a cohort of 54 549 CT encounters; of which 29 948 had available dose screens. RESULTS: Validation yielded a dose screen retrieval rate of 99% (597 of 605 CT encounters; 95% CI: 98%, 100%) and an anatomic assignment precision of 94% (summed DLP fraction correct 563 in 600 CT encounters; 95% CI: 92%, 96%). Patient safety applications of the resulting data repository include benchmarking between institutions, CT protocol quality control and optimization, and cumulative patient- and anatomy-specific radiation exposure monitoring. CONCLUSION: Large-scale anatomy-specific radiation exposure data repositories can be created with high fidelity from existing digital image archives by using open-source informatics tools.

Multiple Sclerosis Exacerbation Associated With High-Altitude Climbing Exposure.

The spectrum of the neurological effects of high-altitude exposure can range from high-altitude headache and acute mountain sickness, to the more severe end of the spectrum with high-altitude cerebral edema. In general, patients with known unstable preexisting neurological conditions and those patients with residual neurological deficits from a preexisting neurological condition are discouraged from climbing to high altitudes because of the risk of exacerbation or worsening of symptoms. Although multiple sclerosis exacerbations can be triggered by environmental factors, high-altitude exposure has not been reported as a potential trigger. We are reporting the case of a multiple sclerosis exacerbation presenting in an active duty U.S. Air Force serviceman upon ascending and descending Mt. Fuji within the same day.

Leveraging terminologies for retrieval of radiology reports with critical imaging findings.

INTRODUCTION: Communication of critical imaging findings is an important component of medical quality and safety. A fundamental challenge includes retrieval of radiology reports that contain these findings. This study describes the expressiveness and coverage of existing medical terminologies for critical imaging findings and evaluates radiology report retrieval using each terminology. METHODS: Four terminologies were evaluated: National Cancer Institute Thesaurus (NCIT), Radiology Lexicon (RadLex), Systemized Nomenclature of Medicine (SNOMED-CT), and International Classification of Diseases (ICD-9-CM). Concepts in each terminology were identified for 10 critical imaging findings. Three findings were subsequently selected to evaluate document retrieval. RESULTS: SNOMED-CT consistently demonstrated the highest number of overall terms (mean=22) for each of ten critical findings. However, retrieval rate and precision varied between terminologies for the three findings evaluated. CONCLUSION: No single terminology is optimal for retrieving radiology reports with critical findings. The expressiveness of a terminology does not consistently correlate with radiology report retrieval.