Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning.

RATIONALE AND OBJECTIVES: To date, no clinically useful classification system has been developed for reliably differentiating mucinous cystic neoplasm (MCN) from a benign hepatic cyst (BHC) in the liver. The objective was to use machine learning and a multi-center study design to develop and assess the performance of a novel classification system for predicting whether a hepatic cystic lesion represents MCN or BHC. MATERIALS AND METHODS: A multi-center cohort study identified 154 surgically resected hepatic cystic lesions in 154 subjects which were pathologic confirmed as MCN (43) or BHC (111). Readers at each institution recorded seven pre-determined imaging features previously identified as potential differentiating features from prior publications. The contribution of each of these features to differentiating MCN from BHC was assessed by machine learning to develop an optimal classification system. RESULTS: Although several of the assessed imaging features demonstrated statistical significance, only 3 imaging features were found by machine learning to significantly contribute to a potential classification system: (1) solid enhancing nodule (2) all septations arising from an external macro-lobulation (3) whether the lesion was solitary or one of multiple cystic liver lesions. The optimal classification system had only four categories and correctly identified 144/154 lesion (93.5%). CONCLUSION: This multi-center follow-up study was able to use machine learning to develop a highly accurate classification system for differentiation of hepatic MCN from BHC, which could be readily applied to clinical practice.

Non-contrast and portal venous phase computed tomography in breast cancer hepatic metastases: comparison of tumor measurements and impact on response assessment.

BACKGROUND: For many common malignancies, including breast cancer, evaluation for metastatic disease using multiphase computed tomography (CT) has fallen out of favor and been replaced by studies performed only in the portal venous phase. However, differences in tumor vascularity could produce differences in appearance on post-contrast imaging. PURPOSE: To assess non-contrast phase and portal venous phase computed tomography in detection and measurement of hepatic metastases from breast carcinoma. MATERIALS AND METHODS: A total of 75 CT scans from 52 breast cancer patients were independently assessed by three body imagers for lesion presence, number and size. Readers randomly assessed portal venous phase or combined phase images at one session with cross-over reads performed four to six weeks later. RESULTS: In the 58% of cases where index lesions measured larger on combined phase, the mean difference in lesion size was 5.7 mm. In this group, combined phase reads demonstrated an 8.4 mm increase in sum of largest diameters, and a mean percentage sum of largest diameters increase of 19% compared to portal venous phase-only reads. CONCLUSION: Addition of non-contrast phase images results in increased index lesion size in most patients with hepatic metastases from breast cancer. If only the portal venous phase is utilized, there is potential for incorrectly diagnosing disease progression on follow-up due to underestimation of lesion size.

Added value of iodine-specific imaging and virtual non-contrast imaging for gastrointestinal assessment using dual-energy computed tomography.

Dual-energy computed tomography (DECT) has become increasingly available and can be readily incorporated into clinical practice. Although DECT can provide a wide variety of spectral imaging reconstructions, most clinically valuable information is available from a limited number of standard image reconstructions including virtual non-contrast and iodine overlay. The combination of these standard reconstructions can be used for specific diagnostic tasks that provide added value over traditional CT protocols. In this pictorial essay, the added value of these standard reconstructed images will be demonstrated by case examples for diseases specifically related to the gastrointestinal system.

Benefits of Integrated RIS/PACS/Reporting Due to Automatic Population of Templated Reports.

With integration of the Radiological Information Systems (RIS), Picture Archiving and Communication systems (PACS), and reporting systems, patient identifiers and examination information can automatically map into examination reports. There are many potential benefits of report automation to radiologists including improvements in efficiency, accuracy, and fatigue. In this article we describe a 2 part study, with the first part being an anonymous survey of radiologists concerning report automation. A total of 13 staff radiologists and 9 radiology residents at a single institution completed an anonymous survey. Respondents were asked if automatic population of examination description, comparison examination data, indications, computed tomography dose, technique, and copy to physician data saved time, decreased fatigue, and increased accuracy. Respondents were asked if a "copy findings" function saved time. The second objective part of the study was a mock examination experiment to assess time savings of report automation and to assess error rates. Of all, 9 radiologists were asked to dictate fields for 8 mock examinations. Subjects were timed and reporting errors monitored. Estimated daily time savings and error rates were calculated assuming a mix of 80 studies. A total of 95% surveyed responded that report automation saved time; 91% that report automation improved accuracy of dictations; 82% that report automation decreased fatigue. Furthermore, 83% of copy finding function users reported time savings. Average time to dictate these prepopulated fields was 51 seconds per study. Average error rate per report was 0.86, with an average of 0.26 errors remaining uncorrected upon report completion. Estimated average time per day saved per radiologist from report automation was 68 minutes. Estimated average corrected errors was 48 per day. Estimated average uncorrected or missed errors was 21 per day. These estimated benefits from report automation result from tight integration of RIS, PACS, and reporting systems.

Impact of low-kVp scan technique on oral contrast density at abdominopelvic CT.

PURPOSE: To assess quantitative and qualitative effects of kVp on oral contrast density. MATERIALS AND METHODS: Three readers retrospectively reviewed 100 CT scans performed at a range of high- and low-energy settings, independently determining their preferred window and level settings for evaluation of the oral-contrast-opacified bowel. Contrast density was also assessed quantitatively in the stomach, jejunum, and ileum. Subsequently, a range of oral contrast dilutions were imaged at varying kVp's in a commercially available CIRS tissue equivalent phantom model. RESULTS: In the retrospective patient study, mean oral contrast density increased significantly in the ileum compared to the jejunum (455.2 and 308.8 HU, respectively, p < 0.01). Similar findings were seen in patients regardless of patients' weight. Mean oral contrast density was higher on lower-energy scans, requiring more window/level adjustment. An oral contrast iodine concentration of 5.82-7.77 mg I/mL most closely approximated a target oral contrast density of 200 HU. CONCLUSIONS: Oral contrast density is strongly influenced by kVp, supporting use of more dilute oral contrast when using lower-kVp techniques.

Acute Appendicitis Involving a De Garengeot Hernia.

We present a case of appendicitis within a De Garengeot hernia. The patient presented to the emergency department with 4 days of worsening abdominal pain. Computed tomography examination revealed an inflamed appendix within a femoral hernia. The patient underwent appendectomy and repair of the femoral defect. Pathological examination was consistent with acute appendicitis. De Garengeot hernia is a rare phenomenon, with less than 5% of femoral hernias containing the appendix and 0.08% to 0.13% containing an incarcerated acute appendicitis.

Metrix Matrix: A Cloud-Based System for Tracking Non-Relative Value Unit Value-Added Work Metrics.

PURPOSE: In the era of value-based medicine, it will become increasingly important for radiologists to provide metrics that demonstrate their value beyond clinical productivity. In this article the authors describe their institution's development of an easy-to-use system for tracking value-added but non-relative value unit (RVU)-based activities. METHODS: Metrix Matrix is an efficient cloud-based system for tracking value-added work. A password-protected home page contains links to web-based forms created using Google Forms, with collected data populating Google Sheets spreadsheets. Value-added work metrics selected for tracking included interdisciplinary conferences, hospital committee meetings, consulting on nonbilled outside studies, and practice-based quality improvement. Over a period of 4 months, value-added work data were collected for all clinical attending faculty members in a university-based radiology department (n = 39). Time required for data entry was analyzed for 2 faculty members over the same time period. RESULTS: Thirty-nine faculty members (equivalent to 36.4 full-time equivalents) reported a total of 1,223.5 hours of value-added work time (VAWT). A formula was used to calculate "value-added RVUs" (vRVUs) from VAWT. VAWT amounted to 5,793.6 vRVUs or 6.0% of total work performed (vRVUs plus work RVUs [wRVUs]). Were vRVUs considered equivalent to wRVUs for staffing purposes, this would require an additional 2.3 full-time equivalents, on the basis of average wRVU calculations. Mean data entry time was 56.1 seconds per day per faculty member. CONCLUSIONS: As health care reimbursement evolves with an emphasis on value-based medicine, it is imperative that radiologists demonstrate the value they add to patient care beyond wRVUs. This free and easy-to-use cloud-based system allows the efficient quantification of value-added work activities.

Differentiating biliary cystadenomas from benign hepatic cysts: Preliminary analysis of new predictive imaging features.

OBJECTIVE: To identify performance of imaging features in differentiating hepatic cysts from biliary cystadenomas of the liver. METHODS: 25 pathologically confirmed hepatic cystic lesions (hepatic cysts or biliary cystadenomas) were evaluated retrospectively and predetermined imaging features assessed for performance in differentiation. RESULTS: Any septation which arose from a cyst wall without external indentation had a very high association with biliary cystadenoma, while the presence of a thick septation had only a moderate association. CONCLUSIONS: The relationship between septations and the wall of the cystic lesion performed better than previously reported features including thick septations.

Correlate: A PACS- and EHR-integrated Tool Leveraging Natural Language Processing to Provide Automated Clinical Follow-up.

A major challenge for radiologists is obtaining meaningful clinical follow-up information for even a small percentage of cases encountered and dictated. Traditional methods, such as keeping medical record number follow-up lists, discussing cases with rounding clinical teams, and discussing cases at tumor boards, are effective at keeping radiologists informed of clinical outcomes but are time intensive and provide follow-up for a small subset of cases. To this end, the authors developed a picture archiving and communication system-accessible electronic health record (EHR)-integrated program called Correlate, which allows the user to easily enter free-text search queries regarding desired clinical follow-up information, with minimal interruption to the workflow. The program uses natural language processing (NLP) to process the query and parse relevant future clinical data from the EHR. Results are ordered in terms of clinical relevance, and the user is e-mailed a link to results when these are available for viewing. A customizable personal database of queries and results is also maintained for convenient future access. Correlate aids radiologists in efficiently obtaining useful clinical follow-up information that can improve patient care, help keep radiologists integrated with other specialties and referring physicians, and provide valuable experiential learning. The authors briefly review the history of automated clinical follow-up tools and discuss the design and function of the Correlate program, which uses NLP to perform intelligent prospective searches of the EHR. © RSNA, 2017.

Image toggling saves time in mammography.

When two images are perfectly aligned, even subtle differences are readily detected when the images are "toggled" back and forth in the same location. However, substantial changes between two photographs can be missed if the images are misaligned ("change blindness"). Nevertheless, recent work from our lab, testing nonradiologists, suggests that toggling misaligned photographs leads to superior performance compared to side-by-side viewing (SBS). In order to determine if a benefit of toggling misaligned images may be observed in clinical mammography, we developed an image toggling technique where pairs of new and prior breast imaging exam images could be efficiently toggled back and forth. Twenty-three radiologists read 10 mammograms evenly divided in toggle and SBS modes. The toggle mode led to a 6-s benefit in reaching a decision [[Formula: see text], [Formula: see text]]. The toggle viewing mode also led to a 5% improvement in diagnostic accuracy, though in our small sample this effect was not statistically reliable. Time savings were found even though successive mammograms were not perfectly aligned. Given the ever-increasing caseload for radiologists, this simple manipulation of how the images are viewed could save valuable time in clinical practice, allowing radiologists to read more cases or spend more time on difficult cases.

Evaluation of lossy data compression in primary interpretation for full-field digital mammography.

OBJECTIVE. For full-field digital mammography (FFDM), federal regulations prohibit lossy data compression for primary reading and archiving, unlike all other medical images, where reading physicians can apply their professional judgment in implementing lossy compression. Faster image transfer, lower costs, and greater access to expert mammographers would result from development of a safe standard for primary interpretation and archive of lossy-compressed FFDM images. This investigation explores whether JPEG 2000 80:1 lossy data compression affects clinical accuracy in digital mammography. MATERIALS AND METHODS. Randomized FFDM cases (n = 194) were interpreted by six experienced mammographers with and without JPEG 2000 80:1 lossy compression applied. A cancer-enriched population was used, with just less than half of the cases (42%) containing subtle (< 1 cm) biopsy-proven cancerous lesions, and the remaining cases were negative as proven by 2-year follow-up. Data were analyzed using the jackknife alternative free-response ROC (JAFROC) method. RESULTS. The differences in reader performance between lossy-compressed and non-lossy-compressed images using lesion localization (0.660 vs 0.671), true-positive fraction (0.879 vs 0.879), and false-positive fraction (0.283 vs 0.271) were not statistically significant. There was no difference in the JAFROC figure of merit between lossy-compressed and non-lossy-compressed images, with a mean difference of -0.01 (95% CI, -0.03 to 0.01; F1,5 = 2.30; p = 0.189). CONCLUSION. These results suggest that primary interpretation of JPEG 2000 80:1 lossy-compressed FFDM images may be viable without degradation of clinical quality. Benefits would include lower storage costs, faster telemammography, and enhanced access to expert mammographers.