Genomic profiling identifies somatic mutations predicting thromboembolic risk in patients with solid tumors.

Venous thromboembolism (VTE) associated with cancer (CAT) is a well-described complication of cancer and a leading cause of death in patients with cancer. The purpose of this study was to assess potential associations of molecular signatures with CAT, including tumor-specific mutations and the presence of clonal hematopoiesis. We analyzed deep-coverage targeted DNA-sequencing data of >14 000 solid tumor samples using the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets platform to identify somatic alterations associated with VTE. End point was defined as the first instance of cancer-associated pulmonary embolism and/or proximal/distal lower extremity deep vein thrombosis. Cause-specific Cox proportional hazards regression was used, adjusting for pertinent clinical covariates. Of 11 695 evaluable individuals, 72% had metastatic disease at time of analysis. Tumor-specific mutations in KRAS (hazard ratio [HR], 1.34; 95% confidence interval (CI), 1.09-1.64; adjusted P = .08), STK11 (HR, 2.12; 95% CI, 1.55-2.89; adjusted P < .001), KEAP1 (HR, 1.84; 95% CI, 1.21-2.79; adjusted P = .07), CTNNB1 (HR, 1.73; 95% CI, 1.15-2.60; adjusted P = .09), CDKN2B (HR, 1.45; 95% CI, 1.13-1.85; adjusted P = .07), and MET (HR, 1.83; 95% CI, 1.15-2.92; adjusted P = .09) were associated with a significantly increased risk of CAT independent of tumor type. Mutations in SETD2 were associated with a decreased risk of CAT (HR, 0.35; 95% CI, 0.16-0.79; adjusted P = .09). The presence of clonal hematopoiesis was not associated with an increased VTE rate. This is the first large-scale analysis to elucidate tumor-specific genomic events associated with CAT. Somatic tumor mutations of STK11, KRAS, CTNNB1, KEAP1, CDKN2B, and MET were associated with an increased risk of VTE in patients with solid tumors. Further analysis is needed to validate these findings and identify additional molecular signatures unique to individual tumor types.

Fast, light, and scalable: harnessing data-mined line annotations for automated tumor segmentation on brain MRI.

OBJECTIVES: While fully supervised learning can yield high-performing segmentation models, the effort required to manually segment large training sets limits practical utility. We investigate whether data mined line annotations can facilitate brain MRI tumor segmentation model development without requiring manually segmented training data. METHODS: In this retrospective study, a tumor detection model trained using clinical line annotations mined from PACS was leveraged with unsupervised segmentation to generate pseudo-masks of enhancing tumors on T1-weighted post-contrast images (9911 image slices; 3449 adult patients). Baseline segmentation models were trained and employed within a semi-supervised learning (SSL) framework to refine the pseudo-masks. Following each self-refinement cycle, a new model was trained and tested on a held-out set of 319 manually segmented image slices (93 adult patients), with the SSL cycles continuing until Dice score coefficient (DSC) peaked. DSCs were compared using bootstrap resampling. Utilizing the best-performing models, two inference methods were compared: (1) conventional full-image segmentation, and (2) a hybrid method augmenting full-image segmentation with detection plus image patch segmentation. RESULTS: Baseline segmentation models achieved DSC of 0.768 (U-Net), 0.831 (Mask R-CNN), and 0.838 (HRNet), improving with self-refinement to 0.798, 0.871, and 0.873 (each p < 0.001), respectively. Hybrid inference outperformed full image segmentation alone: DSC 0.884 (Mask R-CNN) vs. 0.873 (HRNet), p < 0.001. CONCLUSIONS: Line annotations mined from PACS can be harnessed within an automated pipeline to produce accurate brain MRI tumor segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities. KEY POINTS: • A brain MRI tumor detection model trained using clinical line measurement annotations mined from PACS was leveraged to automatically generate tumor segmentation pseudo-masks. • An iterative self-refinement process automatically improved pseudo-mask quality, with the best-performing segmentation pipeline achieving a Dice score of 0.884 on a held-out test set. • Tumor line measurement annotations generated in routine clinical radiology practice can be harnessed to develop high-performing segmentation models without manually segmented training data, providing a mechanism to rapidly establish tumor segmentation capabilities across radiology modalities.

Synoptic Reporting for Pretreatment CT Examination in Patients With Advanced Ovarian Cancer: Impact on Documentation of Disease Sites and Physician Satisfaction.

BACKGROUND. Imaging reports that consistently document all disease sites with a potential to increase surgical complexity or morbidity can facilitate ovarian cancer treatment planning. OBJECTIVE. The aims of this study were to compare simple structured reports and synoptic reports from pretreatment CT examinations in patients with advanced ovarian cancer in terms of completeness of documenting involvement of clinically relevant anatomic sites as well as to evaluate physician satisfaction with synoptic reports. METHODS. This retrospective study included 205 patients (median age, 65 years) who underwent contrast-enhanced abdominopelvic CT before primary treatment of advanced ovarian cancer from June 1, 2018, to January 31, 2022. A total of 128 reports generated on or before March 31, 2020, used a simple structured report (free text organized into sections); 77 reports generated on or after April 1, 2020, used a synoptic report (a list of 45 anatomic sites relevant to ovarian cancer management, each of which was classified in terms of disease absence versus presence). Reports were reviewed for completeness of documentation of involvement of the 45 sites. For patients who underwent neoadjuvant chemotherapy based on diagnostic laparoscopy findings or underwent primary debulking surgery with suboptimal resection, the EMR was reviewed to identify surgically established sites of disease that were unresectable or challenging to resect. Gynecologic oncology surgeons were electronically surveyed. RESULTS. The mean report turnaround time was 29.8 minutes for simple structured reports versus 54.5 minutes for synoptic reports (p < .001). A mean of 17.6 of 45 sites (range, four to 43 sites) were mentioned by simple structured reports versus 44.5 of 45 sites (range, 39-45) for synoptic reports (p < .001). Forty-three patients had surgically established unresectable or challenging-to-resect disease; involvement of anatomic site(s) with such disease was mentioned in 37% (11/30) of simple structured reports versus 100% (13/13) of synoptic reports (p < .001). All eight surveyed gynecologic oncology surgeons completed the survey. CONCLUSION. A synoptic report improved completeness of pretreatment CT reports in patients with advanced ovarian cancer, including for established sites of unresectable or challenging-to-resect disease. CLINICAL IMPACT. The findings indicate the role of disease-specific synoptic reports in facilitating referrer communication and potentially guiding clinical decision-making.

Experiences of Health Departments on Community Engagement and Implementation of a COVID-19 Self-testing Program.

CONTEXT: Health departments (HDs) work on the front lines to ensure the health of their communities, providing a unique perspective to public health response activities. Say Yes! COVID Test (SYCT) is a US federally funded program providing free COVID-19 self-tests to communities with high COVID-19 transmission, low vaccination rates, and high social vulnerability. The collaboration with 9 HDs was key for the program distribution of 5.8 million COVID-19 self-tests between March 31 and November 30, 2021. OBJECTIVE: The objective of this study was to gather qualitative in-depth information on the experiences of HDs with the SYCT program to better understand the successes and barriers to implementing community-focused self-testing programs. DESIGN: Key informant (KI) interviews. SETTING: Online interviews conducted between November and December 2021. PARTICIPANTS: Sixteen program leads representing 9 HDs were purposefully sampled as KIs. KIs completed 60-minute structured interviews conducted by one trained facilitator and recorded. MAIN OUTCOME MEASURES: Key themes and lessons learned were identified using grounded theory. RESULTS: Based on perceptions of KIs, HDs that maximized community partnerships for test distribution were more certain that populations at a higher risk for COVID-19 were reached. Where the HD relied predominantly on direct-to-consumer distribution, KIs were less certain that communities at higher risk were served. Privacy and anonymity in testing were themes linked to higher perceived community acceptance. KIs reported that self-test demand and distribution levels increased during higher COVID-19 transmission levels. CONCLUSION: HDs that build bridges and engage with community partners and trusted leaders are better prepared to identify and link high-risk populations with health services and resources. When collaborating with trusted community organizations, KIs perceived that the SYCT program overcame barriers such as mistrust of government intervention and desire for privacy and motivated community members to utilize this resource to protect themselves against COVID-19.

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations.

Background Artificial intelligence (AI) applications for cancer imaging conceptually begin with automated tumor detection, which can provide the foundation for downstream AI tasks. However, supervised training requires many image annotations, and performing dedicated post hoc image labeling is burdensome and costly. Purpose To investigate whether clinically generated image annotations can be data mined from the picture archiving and communication system (PACS), automatically curated, and used for semisupervised training of a brain MRI tumor detection model. Materials and Methods In this retrospective study, the cancer center PACS was mined for brain MRI scans acquired between January 2012 and December 2017 and included all annotated axial T1 postcontrast images. Line annotations were converted to boxes, excluding boxes shorter than 1 cm or longer than 7 cm. The resulting boxes were used for supervised training of object detection models using RetinaNet and Mask region-based convolutional neural network (R-CNN) architectures. The best-performing model trained from the mined data set was used to detect unannotated tumors on training images themselves (self-labeling), automatically correcting many of the missing labels. After self-labeling, new models were trained using this expanded data set. Models were scored for precision, recall, and F1 using a held-out test data set comprising 754 manually labeled images from 100 patients (403 intra-axial and 56 extra-axial enhancing tumors). Model F1 scores were compared using bootstrap resampling. Results The PACS query extracted 31 150 line annotations, yielding 11 880 boxes that met inclusion criteria. This mined data set was used to train models, yielding F1 scores of 0.886 for RetinaNet and 0.908 for Mask R-CNN. Self-labeling added 18 562 training boxes, improving model F1 scores to 0.935 (P < .001) and 0.954 (P < .001), respectively. Conclusion The application of semisupervised learning to mined image annotations significantly improved tumor detection performance, achieving an excellent F1 score of 0.954. This development pipeline can be extended for other imaging modalities, repurposing unused data silos to potentially enable automated tumor detection across radiologic modalities. © RSNA, 2022 Online supplemental material is available for this article.

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented by means of autopsy series. Purpose To show the feasibility of using natural language processing (NLP) to gather accurate data from radiology reports for assessing spatial and temporal patterns of metastatic spread in a large patient cohort. Materials and Methods In this retrospective longitudinal study, consecutive patients who underwent CT from July 2009 to April 2019 and whose CT reports followed a departmental structured template were included. Three radiologists manually curated a sample of 2219 reports for the presence or absence of metastases across 13 organs; these manually curated reports were used to develop three NLP models with an 80%-20% split for training and test sets. A separate random sample of 448 manually curated reports was used for validation. Model performance was measured by accuracy, precision, and recall for each organ. The best-performing NLP model was used to generate a final database of metastatic disease across all patients. For each cancer type, statistical descriptive reports were provided by analyzing the frequencies of metastatic disease at the report and patient levels. Results In 91 665 patients (mean age ± standard deviation, 61 years ± 15; 46 939 women), 387 359 reports were labeled. The best-performing NLP model achieved accuracies from 90% to 99% across all organs. Metastases were most frequently reported in abdominopelvic (23.6% of all reports) and thoracic (17.6%) nodes, followed by lungs (14.7%), liver (13.7%), and bones (9.9%). Metastatic disease tropism is distinct among common cancers, with the most common first site being bones in prostate and breast cancers and liver among pancreatic and colorectal cancers. Conclusion Natural language processing may be applied to cancer patients' CT reports to generate a large database of metastatic phenotypes. Such a database could be combined with genomic studies and used to explore prognostic imaging phenotypes with relevance to treatment planning. © RSNA, 2021 Online supplemental material is available for this article.

Bag-of-Words Technique in Natural Language Processing: A Primer for Radiologists.

Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. To enable machine learning (ML) techniques in NLP, free-form text must be converted to a numerical representation. After several stages of preprocessing including tokenization, removal of stop words, token normalization, and creation of a master dictionary, the bag-of-words (BOW) technique can be used to represent each remaining word as a feature of the document. The preprocessing steps simplify the documents but also potentially degrade meaning. The values of the features in BOW can be modified by using techniques such as term count, term frequency, and term frequency-inverse document frequency. Experience and experimentation will guide decisions on which specific techniques will optimize ML performance. These and other NLP techniques are being applied in radiology. Radiologists' understanding of the strengths and limitations of these techniques will help in communication with data scientists and in implementation for specific tasks. Online supplemental material is available for this article. ©RSNA, 2021.

An Informatics Approach to Facilitate Clinical Management of Patients With Retrievable Inferior Vena Cava Filters.

OBJECTIVE: Long indwelling times for inferior vena cava (IVC) filters that are used to prevent venous thromboembolism can result in complications. To improve care for patients receiving retrievable IVC filters, we developed and evaluated an informatics-based initiative to facilitate patient tracking, clinical decision-making, and care coordination. MATERIALS AND METHODS: A semiautomated filter-tracking application was custom-built to query our radiology information system to extract and transfer key data elements related to IVC filter insertion procedures into a database. A web-based interface displayed key information and facilitated communication between the interventional radiology clinical team and referring physicians. A set of filter management options was provided depending on each patient's clinical condition. The system was launched in April 2016. Using retrospective observational cohort methods, we compared filter retrieval rates during a test period from July through December 2016 with a control period of the same 6 months in 2015. RESULTS: System development required approximately 100 hours of development time. Two hundred ninety-three IVC filter placements and 83 filter retrievals were tracked during the study periods. The overall filter retrieval rate was 23% in the control period and 34% in the test period. Mean times from filter placement to retrieval in the control and test periods were not significantly different (88.9 and 102.7 days, respectively; p = 0.32). CONCLUSION: A semiautomated approach to tracking patients with IVC filters can facilitate care coordination and clinical decision-making for a device with known potential complications. Similar applications designed to improve provider communication and documentation of filter management plans, including appropriateness for retrieval, can be replicated.

High-Grade Serous Ovarian Cancer: Associations between BRCA Mutation Status, CT Imaging Phenotypes, and Clinical Outcomes.

Purpose To investigate the associations between BRCA mutation status and computed tomography (CT) phenotypes of high-grade serous ovarian cancer (HGSOC) and to evaluate CT indicators of cytoreductive outcome and survival in patients with BRCA-mutant HGSOC and those with BRCA wild-type HGSOC. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 108 patients (33 with BRCA mutant and 75 with BRCA wild-type HGSOC) who underwent CT before primary debulking. Two radiologists independently reviewed the CT findings for various qualitative CT features. Associations between CT features, BRCA mutation status, cytoreductive outcome, and progression-free survival (PFS) were evaluated by using logistic regression and Cox proportional hazards regression, respectively. Results Peritoneal disease (PD) pattern, presence of PD in gastrohepatic ligament, mesenteric involvement, and supradiaphragmatic lymphadenopathy at CT were associated with BRCA mutation status (multiple regression: P < .001 for each CT feature). While clinical and CT features were not associated with cytoreductive outcome for patients with BRCA-mutant HGSOC, presence of PD in lesser sac (odds ratio [OR] = 2.40) and left upper quadrant (OR = 1.19), mesenteric involvement (OR = 7.10), and lymphadenopathy in supradiaphragmatic (OR = 2.83) and suprarenal para-aortic (OR = 4.79) regions were associated with higher odds of incomplete cytoreduction in BRCA wild-type HGSOC (multiple regression: P < .001 each CT feature). Mesenteric involvement at CT was associated with significantly shorter PFS for both patients with BRCA-mutant HGSOC (multiple regression: hazard ratio [HR] = 26.7 P < .001) and those with BRCA wild-type HGSOC (univariate analysis: reader 1, HR = 2.42, P < .001; reader 2, HR = 2.61; P < .001). Conclusion Qualitative CT features differed between patients with BRCA-mutant HGSOC and patients with BRCA wild-type HGSOC. CT indicators of cytoreductive outcome varied according to BRCA mutation status. Mesenteric involvement at CT was an indicator of significantly shorter PFS for both patients with BRCA-mutant HGSOC and those with BRCA wild-type HGSOC. © RSNA, 2017 Online supplemental material is available for this article.

Diagnostic accuracy of intracellular uptake rates calculated using dynamic Gd-EOB-DTPA-enhanced MRI for hepatic fibrosis stage.

PURPOSE: To assess the diagnostic accuracy of intracellular uptake rates (Ki ), and other quantitative pharmacokinetic (PK) parameters, for hepatic fibrosis stage; to compare this accuracy with a previously published semiquantitative metric, contrast enhancement index (CEI); and to assess variability of these parameters between liver regions. MATERIALS AND METHODS: This was a case-control study design. Dynamic Gd-EOB-DTPA-enhanced 1.5T magnetic resonance imaging (MRI) was performed prospectively in 22 subjects with varying known stages of hepatic fibrosis. PK parameters and CEI were derived from the whole livers and from three fixed regions of interest (ROIs) in all subjects. Spearman rank correlation coefficients were computed to assess the relationship between fibrosis stages and each parameter. Receiver operating characteristic (ROC) curves were constructed to discriminate severe fibrosis (stages 3-4) from nonsevere fibrosis (stages 0-2). The coefficient of variation (CV) was calculated to assess variability in parameters between ROIs. RESULTS: Ki and fibrosis stage were significantly correlated (R = -0.55, 95% confidence interval [CI] [-0.79, -0.14], P = 0.01). Area under ROC curve (AUC) in distinguishing severe from nonsevere fibrosis for Ki was 0.84 (95% CI [0.65,1.00]), and for CEI was 0.64 (95% CI [0.39, 0.89]) (P = 0.0248). CV for Ki and CEI were 33.4 and 5.8, respectively. The only other parameter in the PK model having significant correlation with fibrosis stage was absolute arterial blood flow (Fa ) (R = -0.48, 95% CI [-0.75,-0.05], P = 0.03). CONCLUSION: Hepatocyte intracellular uptake rate, Ki , derived from dynamic contrast-enhanced MRI, correlates with fibrosis stage and may contribute to a noninvasive biomarker of hepatic fibrosis. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:1177-1185.

Implementation of a Point-of-Care Radiologist-Technologist Communication Tool in a Quality Assurance Program.

OBJECTIVE: We implemented an Image Quality Reporting and Tracking Solution (IQuaRTS), directly linked from the PACS, to improve communication between radiologists and technologists. MATERIALS AND METHODS: IQuaRTS launched in May 2015. We compared MRI issues filed in the period before IQuaRTS implementation (May-September 2014) using a manual system with MRI issues filed in the IQuaRTS period (May-September 2015). The unpaired t test was used for analysis. For assessment of overall results in the IQuaRTS period alone, all issues filed across all modalities were included. Summary statistics and charts were generated using Excel and Tableau. RESULTS: For MRI issues, the number of issues filed during the IQuaRTS period was 498 (2.5% of overall MRI examination volume) compared with 78 issues filed during the period before IQuaRTS implementation (0.4% of total examination volume) (p = 0.0001), representing a 625% relative increase. Tickets that documented excellent work were 8%. Other issues included images not pushed to PACS (20%), film library issues (19%), and documentation or labeling (8%). Of the issues filed, 55% were MRI-related and 25% were CT-related. The issues were stratified across six sites within our institution. Staff requiring additional training could be readily identified, and 80% of the issues were resolved within 72 hours. CONCLUSION: IQuaRTS is a cost-effective online issue reporting tool that enables robust data collection and analytics to be incorporated into quality improvement programs. One limitation of the system is that it must be implemented in an environment where staff are receptive to quality improvement.

Long term follow up of kidney donors with asymptomatic renal stones.

Patients with asymptomatic kidney stones have a high rate of progression to becoming symptomatic kidney stones when followed for several years. Small kidney stones are often found incidentally on imaging when evaluating patients for kidney donation, and there is a concern that after nephrectomy, the donor may become symptomatic and incur damage to the remaining kidney. We reviewed kidney donors at our institution with asymptomatic stones and surveyed them several years after donation to see if the stones became clinically active.

Exposure to ionizing radiation during liver transplantation evaluation, waitlist time, and in the postoperative period: a cause for concern.

UNLABELLED: Substantial evidence has linked ionizing radiation exposure (RE) to oncogenesis. Patients evaluated for transplantation undergo extensive diagnostic imaging and have increased baseline cancer risk factors. The objective was to examine exposure in a cohort of patients undergoing evaluation and liver transplantation. Radiation exposure from all diagnostic examinations and procedures were retrospectively recorded. Radiation exposure is reported in mSv, a standardized measure of the detrimental biologic effect of radiation which allows for population-level comparisons. Seventy-four patients (69% male, mean 57 years) were evaluated, of which 13 of 35 subsequently listed patients were transplanted; an additional 18 previously evaluated patients were also transplanted during 2010. The most common indications were hepatitis C (55%) and hepatocellular carcinoma (HCC) (30%). The median observation period was 14 months. In all, 1,826 imaging examinations were performed, of which 408 (22%) involved considerable ionizing radiation and were the focus of investigation. Median annualized effective RE was 51 mSv (interquartile range [IQR]: 19,126), with 10% exposed to almost twice the amount of radiation recommended for a 5-year period. Patients with HCC received significantly (P < 0.00001) higher median annualized effective RE than patients without HCC, 137 mSv (IQR: 87,259) versus 32 mSv (IQR: 13,57), respectively. Computed tomography (CT) abdomen (23%) and chest (16%) accounted for the most common exposures, with CT abdomen accounting for 46% of overall cohort RE. CONCLUSION: Patients undergoing evaluation and liver transplantation at our center are exposed to very high levels of ionizing radiation. Although long-term effects in these patients are yet to be defined, the theoretical increased risk of malignancy must be given its due consideration. Routine use of nonradiation imaging and reconsideration of indications may be preferred and justified in this population.

Semiautomated CT-Based Quantification of Donor Kidney Volume Applied to a Predictive Model of Outcomes in Renal Transplantation.

OBJECTIVE: The purpose of this study was to assess the use of semiautomated CT-based quantification of renal graft volume as a preoperative predictor of graft function. MATERIALS AND METHODS: All transplants over a 3-year period in which donors underwent CT and for which recipient outcomes were available were included. Two blinded readers used a commercially available reconstruction tool to independently measure donated kidney cortical volume and total parenchymal kidney volume. Transplant characteristics obtained by chart review included subject demographics, recipient pretransplant weight, immunologic matching, and recipient creatinine values at multiple time points. Intraclass correlation of measurements by the two readers was calculated. The ratios between donated kidney cortical volume and recipient pretransplant weight were correlated with graft function over 24 months and used in logistic regression models to calculate the odds of development of diminished renal function. RESULTS: After application of the inclusion and exclusion criteria, 153 transplants were included in the study. Donated kidney cortical and total parenchymal volume measurements had high correlation (R > 0.9) and high reproducibility (intraclass correlation coefficient, 0.93-0.94). Unadjusted correlations existed between estimated glomerular filtration rate (eGFR) and the ratio between donated kidney cortical volume and recipient pretransplant weight 12 months (R = 0.8489) and 24 months (R = 0.6839) after transplant. After adjustment for transplant parameters, recipients in the highest tertile for ratio between donated kidney cortical volume and recipient pretransplant weight (2.7 mL/kg) had higher mean eGFR values at all time points in the 24 months than did recipients in the lower tertiles (1.2 and 1.6 mL/kg). Recipients in the highest tertile had a significantly lower risk of development of diminished renal function 12 and 24 months after transplant (adjusted odds ratios, 0.25 at 12 months [95% CI, 0.09-0.66]; 0.27 at 24 months [95% CI, 0.10-0.71]). CONCLUSION: The CT-derived ratio between donated kidney cortical volume and recipient pretransplant weight is a noninvasively and readily obtained reproducible biomarker that is predictive of 12- and 24-month renal transplant outcomes.

Radiology Teaching Files: an Assessment of Their Role and Desired Features Based on a National Survey.

A radiology teaching file (TF) is a system containing a collection of cases with teaching value. Given the wide variety of TF solutions available, we conducted a national survey to better understand the need for TFs, TF features desired by users and their current implementation. A 28-question survey was created which explored TF implementation, utilization, and preferences among respondents. The survey was emailed to residents and faculty throughout the USA, with a request for program coordinators to forward the survey to their departments. The survey was completed by 396 respondents from 115 different institutions. These respondents included 60% residents, 21% attendings (non-program directors), 12% program directors, 5% fellows, and 1% medical students. TFs were assigned to one of three categories: personal TFs, shared in-house TFs, and public TFs. Seventy-six percent of respondents kept a personal TF using a variety of media, and 67% used a shared in-house TF. Of the public TFs used, the most popular were those requiring paid subscriptions. The features respondents valued most provided efficient querying of cases, simulated basic PACS functionality, enabled self-directed learning, and facilitated case submissions. There is a trend toward utilizing electronic media for TFs. The media utilized should be understood and reviewed to ensure PHI is properly secured. Contemporary users demand a high degree of functionality from TF solutions, and use both in-house and commercial products to meet their needs.

A mathematical simulation to assess variability in lung nodule size measurement associated with nodule-slice position.

The purpose of this study is to assess the variance and error in nodule diameter measurement associated with variations in nodule-slice position in cross-sectional imaging. A computer program utilizing a standard geometric model was used to simulate theoretical slices through a perfectly spherical nodule of known size, position, and density within a background of "lung" of known fixed density. Assuming a threshold density, partial volume effect of a voxel was simulated using published slice and pixel sensitivity profiles. At a given slice thickness and nodule size, 100 scans were simulated differing only in scan start position, then repeated for multiple node sizes at three simulated slice thicknesses. Diameter was measured using a standard, automated algorithm. The frequency of measured diameters was tabulated; average errors and standard deviations (SD) were calculated. For a representative 5-mm nodule, average measurement error ranged from +10 to -23% and SD ranged from 0.07 to 0.99 mm at slice thicknesses of 0.75 to 5 mm, respectively. At fixed slice thickness, average error and SD decreased from peak values as nodule size increased. At fixed nodule size, SD increased as slice thickness increased. Average error exhibited dependence on both slice thickness and threshold. Variance and error in nodule diameter measurement associated with nodule-slice position exists due to geometrical limitations. This can lead to false interpretations of nodule growth or stability that could affect clinical management. The variance is most pronounced at higher slice thicknesses and for small nodule sizes. Measurement error is slice thickness and threshold dependent.

Implications of surface-rendered facial CT images in patient privacy.

OBJECTIVE: Three-dimensional and multiplanar reconstruction of CT images has become routine in diagnostic imaging. The technology also facilitates surface reconstruction, in which facial features and, as a result, patient identity may be recognized, leading to risk of violations of patient privacy rights. The purpose of this study was to assess whether volunteer viewers can recognize faces on 3D reconstructed images as specific patients. SUBJECTS AND METHODS: A total of 328 participants were included: 29 patients underwent clinically indicated CT of the maxillofacial sinuses or cerebral vasculature and were also photographed (group A); 150 patients volunteered to have their faces photographed (group B); and 149 observers reviewed the images. Surface-reconstructed 3D images of group A were generated from CT data, and digital photographs of both groups A and B were acquired for a total of 179 facial photographs. Image reviewers were recruited with a web-based questionnaire that required observers to match surface-reconstructed images generated from CT data with randomized digital photographs from among the 179 photographs. Data analyses were performed to determine the ability of observers to successfully match surface-reconstructed images with facial photographs. RESULTS: The overall accuracy among the image observers was approximately 61%. No significant differences were found with regard to sex, age, or ethnicity and accuracy of image observers. CONCLUSION: Image reviewers were relatively poor at even side-by-side matching of patient photographs with 3D surface-reconstructed images. This finding suggests that successful identification of patients using surface-rendered faces may be a relatively difficult task for observers.

Corticomedullary strain ratio: a quantitative marker for assessment of renal allograft cortical fibrosis.

OBJECTIVES: To quantitatively assess the correlation between the corticomedullary strain ratio and cortical fibrosis in renal transplants. METHODS: Using quasistatic ultrasound elasticity imaging, we prospectively assessed the corticomedullary strain ratio in renal allografts of 33 patients who underwent renal transplant sonography and biopsy. Based on Banff score criteria for renal cortical fibrosis, 33 allografts were divided into 2 groups: group 1 (n = 19), with mild (<25%) fibrosis; and group 2 (n = 14), with moderate (>26%) fibrosis. We used 2-dimensional speckle-tracking software to perform offline analysis of cortical and medullary strain induced by external compression by the ultrasound transducer. We then calculated the corticomedullary strain ratio (cortical normalized strain/medullary normalized strain; normalized strain = developed strain/applied strain [deformation from the abdominal wall to the pelvic muscles]). An unpaired 2-tailed t test was used to determine differences in normalized strain and the strain ratio between the groups. Receiver operating characteristic curve analysis was performed to determine the best strain ratio cutoff value for identifying moderate fibrosis. RESULTS: Normalized strain differed between the cortex and medulla (mean ± SD: group 1, 4.58 ± 2.02 versus 2.58 ± 1.38; P = .002; group 2, 1.71 ± 0.42 versus 2.60 ± 0.87; P = .0011). The strain ratio in group 1 was higher than in group 2 (2.06 ± 1.33 versus 0.70 ± 0.20; P = .0007). The area under the receiver operating characteristic curve was 0.964. The sensitivity and specificity of a strain ratio cutoff value of 0.975 for determining moderate fibrosis were 92.9% and 94.7%, respectively. CONCLUSIONS: Strain values vary in different compartments of the kidney. The corticomedullary strain ratio on ultrasound elasticity imaging decreases with increasing renal cortical fibrosis, which makes it potentially useful as a noninvasive quantitative marker for monitoring the progression of fibrosis in renal transplants.