Practical Overview of 123I-Ioflupane Imaging in Parkinsonian Syndromes.

Parkinsonian syndromes are a heterogeneous group of progressive neurodegenerative disorders involving the nigrostriatal dopaminergic pathway and are characterized by a wide spectrum of motor and nonmotor symptoms. These syndromes are quite common and can profoundly impact the lives of patients and their families. In addition to classic Parkinson disease, parkinsonian syndromes include multiple additional disorders known collectively as Parkinson-plus syndromes or atypical parkinsonism. These are characterized by the classic parkinsonian motor symptoms with additional distinguishing clinical features. Dopamine transporter SPECT has been developed as a diagnostic tool to assess the levels of dopamine transporters in the striatum. This imaging assessment, which uses iodine 123 (123I) ioflupane, can be useful to differentiate parkinsonian syndromes caused by nigrostriatal degeneration from other clinical mimics such as essential tremor or psychogenic tremor. Dopamine transporter imaging plays a crucial role in diagnosing parkinsonian syndromes, particularly in patients who do not clearly fulfill the clinical criteria for diagnosis. Diagnostic clarification can allow early treatment in appropriate patients and avoid misdiagnosis. At present, only the qualitative interpretation of dopamine transporter SPECT is approved by the U.S. Food and Drug Administration, but quantitative interpretation is often used to supplement qualitative interpretation. The authors provide an overview of patient preparation, common imaging findings, and potential pitfalls that radiologists and nuclear medicine physicians should know when performing and interpreting dopamine transporter examinations. Alternatives to 123I-ioflupane imaging for the evaluation of nigrostriatal degeneration are also briefly discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Intenzo and Colarossi in this issue.

Diagnosis and Treatment of Cardiac Myxoma.

Cardiac myxoma is the most common primary cardiac neoplasm. It is a benign tumor that typically arises in the left atrium, specifically from the interatrial septum adjacent to the fossa ovalis. We present a case of a 71-year-old male presenting with hematuria that was incidentally found to have a left atrial myxoma on a CT urogram. Follow-up CT and MRI of the heart demonstrated findings compatible with myxoma. Cardiothoracic surgery was consulted, and the patient underwent resection of the left atrial mass, which was confirmed to be a myxoma on pathology.

Altered Biodistribution on 99m Tc-Dimercaptosuccinic Acid Renal Scan.

ABSTRACT: 99m Tc-dimercaptosuccinic acid ( 99m Tc-DMSA) scans are used to evaluate renal cortical defects typically related to parenchymal scarring or pyelonephritis, and ectopic renal parenchyma. 99m Tc-DMSA binds to metalloproteins in proximal tubular cells and typically localizes to the renal cortex, with minimal excretion. Planar and SPECT images are obtained 2 to 4 hours after IV administration of 99m Tc-DMSA. Altered 99m Tc-DMSA biodistribution has been reported in various conditions, including renal injury, technical issues, infiltrative processes, and hematologic disorders. Here, we present a case of altered biodistribution, with hepatic and splenic radiotracer uptake in the setting of hepatosplenomegaly and hematologic abnormalities concerning for a systemic hematologic disorder/lymphohistiocytosis.

Computed Tomography Assessment of Gastric Band Slippage.

Background: The purpose of this study was to develop and validate reliable computed tomography (CT) imaging criteria for the diagnosis of gastric band slippage. Material and Methods: We retrospectively evaluated 67 patients for gastric band slippage using CT. Of these, 14 had surgically proven gastric band slippage (study group), 22 had their gastric bands removed for reasons other than slippage (control group 1), and 31 did not require removal (control group 2). All of the studies were read independently by two radiologists in a blinded fashion. The "O" sign, phi angle, amount of inferior displacement from the esophageal hiatus, and gastric pouch size were used to create CT diagnostic criteria. Standard statistical methods were used. Results: There was good overall interobserver agreement for diagnosis of gastric band slippage using CT diagnostic criteria (kappa = 0.83). Agreement was excellent for the "O" sign (kappa = 0.93) and phi angle (intraclass correlation coefficient = 0.976). The "O" sign, inferior displacement from the hiatus >3.5 cm, and gastric pouch volume >55 cm3 each had 100% positive predictive value. A phi angle <20° or >60° had the highest negative predictive value (NPV) (98%). Of all CT diagnostic criteria, enlarged gastric pouch size was most correlated with band slippage with an AUC of 0.991. Conclusion: All four imaging parameters were useful in evaluating for gastric band slippage on CT, with good interobserver agreement. Of these parameters, enlarged gastric pouch size was most correlated with slippage and abnormal phi angle had the highest NPV.

A novel dual balloon rectal catheter for use in the postoperative assessment of low rectal anastomoses following low anterior resection.

Following low anterior resection (LAR) of the colon, an image-guided assessment of the anastomosis for leak is typically performed using an enema via a rectal catheter, whether by CT or fluoroscopy. However, there is potential for poor assessment due to inappropriate catheter positioning as well as potential risk that the anastomosis becomes compromised by the balloon inflation. This article discusses the adaptation of a novel double-balloon catheter (originally designed by a member of our institution for use in pediatric intussusception reduction) for assessment of low rectal anastomoses. The goal of this technical note is to demonstrate our experience with this catheter, primarily through example cases, and explain its potential for optimizing colon distension, minimizing improper catheter placement, and potentially reducing the risk of iatrogenic anastomosis disruption.

Diagnostic accuracy and performance of artificial intelligence in measuring left atrial volumes and function on multiphasic CT in patients with atrial fibrillation.

OBJECTIVES: To evaluate the effectiveness of a novel artificial intelligence (AI) algorithm for fully automated measurement of left atrial (LA) volumes and function using cardiac CT in patients with atrial fibrillation. METHODS: We included 79 patients (mean age 63 ± 12 years; 35 with atrial fibrillation (AF) and 44 controls) between 2017 and 2020 in this retrospective study. Images were analyzed by a trained AI algorithm and an expert radiologist. Left atrial volumes were obtained at cardiac end-systole, end-diastole, and pre-atrial contraction, which were then used to obtain LA function indices. Intraclass correlation coefficient (ICC) analysis of the LA volumes and function parameters was performed and receiver operating characteristic (ROC) curve analysis was used to compare the ability to detect AF patients. RESULTS: The AI was significantly faster than manual measurement of LA volumes (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p < 0.001) than controls (all ICCs ≥ 0.799; p < 0.001). The AI comparably estimated LA volumes in AF patients (all within 1.3 mL of the manual measurement), but overestimated volumes by clinically negligible amounts in controls (all by ≤ 4.2 mL). The AI's ability to distinguish AF patients from controls using the LA volume index was similar to the expert's (AUC 0.81 vs 0.82, respectively; p = 0.62). CONCLUSION: The novel AI algorithm efficiently performed fully automated multiphasic CT-based quantification of left atrial volume and function with similar accuracy as compared to manual quantification. Novel CT-based AI algorithm efficiently quantifies left atrial volumes and function with similar accuracy as manual quantification in controls and atrial fibrillation patients. KEY POINTS: • There was good-to-excellent agreement between manual and automated methods for left atrial volume quantification. • The AI comparably estimated LA volumes in AF patients, but overestimated volumes by clinically negligible amounts in controls. • The AI's ability to distinguish AF patients from controls was similar to the manual methods.

Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine.

BACKGROUND: Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. OBJECTIVE: To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. METHODS: Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. RESULTS: 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07-1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). CONCLUSION: Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. CLINICAL IMPACT: As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes.

Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.

BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS: We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS: There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 â€‹mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p â€‹< â€‹0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p â€‹< â€‹0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p â€‹= â€‹0.01). CONCLUSION: This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study.

OBJECTIVES: The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments. METHODS: A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance. RESULTS: AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence. CONCLUSION: In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Incidental Serous Cystadenoma Presenting as a Photopenic Pancreatic Defect on 18F-Fluciclovine PET/CT.

ABSTRACT: 18F-fluciclovine (Axumin) PET/CT has been widely used for the evaluation of biochemically recurrent prostate cancer following prior treatment. While lymph node and visceral organ metastases typically show increased radiotracer uptake, altered patterns of normal physiologic activity may also provide insight into other disease processes. We present a case of an incidental pancreatic head mass presenting as a photopenic defect on a staging 18F-fluciclovine PET/CT, which was subsequently confirmed to be a benign serous cystadenoma using multisequence MRI.

Effect of the Ductal Carcinoma In Situ Margin Consensus Guideline Implementation on Re-Excision Rates, Satisfaction, and Cost.

INTRODUCTION: The 2016 consensus guideline on margins for breast-conserving surgery (BCS) with whole-breast irradiation (WBI) for ductal carcinoma in situ (DCIS) recommended 2 mm margins to decrease local recurrence rates. We examined re-excision rates, cost, and patient satisfaction before and after guideline implementation. METHODS: From an Institutional Review Board-approved database, patients with DCIS who underwent BCS with over 1 year of follow-up at one academic institution and one community cancer center were evaluated. Two groups were compared based on when they received treatment, i.e. before (pre-consensus [PRE]) and after November 2016 (post consensus [POST]), with respect to outcome and cost parameters. RESULTS: After consensus guideline implementation, re-excision rate (32.1% vs. 20.0%) and mastectomy conversion (8.3% vs. 2.3%) significantly increased, although total resection volume, operative cost per patient, and satisfaction with breast scores did not differ. Not all patients with <2 mm margins were re-excised, although the re-excision rate among this subset significantly increased (62.4% vs. 31.3%). On multivariable analysis controlling for age, estrogen receptor status, WBI use, and margin status, surgery after consensus guideline publication was independently associated with a higher re-excision rate (odds ratio [OR] 1.97, 95% confidence interval [CI] 1.08-3.59, p = 0.03) and a higher rate of conversion to mastectomy (OR 6.84, 95% CI 1.67-28.00, p = 0.007). CONCLUSIONS: Implementation of the 2016 margin consensus guideline for DCIS resulted in an increase in re-excisions and mastectomy conversions at two institutions. Research is needed for operative tools and strategies to decrease DCIS re-excision rates.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value.

BACKGROUND: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. METHODS: A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen's kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. RESULTS: Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen's kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). CONCLUSION: We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Correlating serum alpha-fetoprotein in hepatocellular carcinoma with response to Yttrium-90 transarterial radioembolization with glass microspheres (TheraSphere™).

BACKGROUND: Few studies have assessed the relationship between serum alpha-fetoprotein (AFP) and yttrium-90 (Y-90) radioembolization response in hepatocellular carcinoma (HCC). The objective of the study was to evaluate whether peri-procedural serum AFP was correlated with Y-90 therapy response in HCC. METHODS: Patients undergoing Y-90 radioembolization with glass microspheres (TheraSphere™) for HCC between 2006 and 2013 at a single center were evaluated. The relationship between AFP and 6-month radiographic improvement (complete or partial response by modified RECIST criteria), overall (OS), and disease-specific survival (DSS) were analyzed. RESULTS: Seventy-four patients underwent a total of 124 Y-90 infusions. Median age was 65 years, median AFP was 37 ng/mL (range: 2-112,593 ng/mL) and median model for end-stage liver disease score was 6.2 (range:1.8-11.2). Increased AFP was not associated with radiographic improvement (odds ratio (OR) = 0.99, 95% confidence interval (CI) = 0.75-1.30, p = 0.92). Median OS was 15.2 months and was increased in patients with low AFP compared to high AFP (30.8 months vs. 7.8 months, p < 0.001). On multivariable regression analysis, increased AFP was associated with worse OS (OR = 1.11, 95%CI = 1.01-1.22, p = 0.034) and DSS (OR = 1.13, 95%CI = 1.03-1.25, p = 0.018). CONCLUSION: Pre-infusion AFP independently predicted survival after Y-90 treatment for HCC, but not radiographic response, and can help guide treatment decisions.

Identifying risk factor for development of perioperative venous thromboembolism in patients with gastrointestinal malignancy.

BACKGROUND: Current data suggests that decreasing VTE incidence may require focus on other factors. This study aimed to identify perioperative risk factors for VTE in patients undergoing surgery for gastrointestinal (GI) malignancy. METHODS: Patients undergoing surgery for GI malignancy from 2013 to 2016 were grouped according to whether or not they developed a postoperative VTE, and groups were compared along demographic, perioperative, and outcome variables. RESULTS: Patients who developed VTE were more likely to be older (67 ±â€¯11 VTE vs. 61 ±â€¯10 no VTE, p = 0.04), male (92% vs. 59%, p = 0.02), and have a history of atrial fibrillation (39% vs. 11%, p = 0.01). They also experienced higher intraoperative blood loss (328 ±â€¯724 mL no VTE vs. 918 ±â€¯1885 mL VTE, p = 0.01). On multivariable analysis, history of atrial fibrillation was independently associated with development of postoperative VTE (odds ratio = 3.83, 95% confidence interval = 1.13-13.05, p = 0.03). CONCLUSION: A prior history of atrial fibrillation independently predicts increased risk of developing VTE after surgery for GI malignancy. Improving understanding of the underlying VTE pathophysiology in these patients can help guide effective prevention strategies.

Evaluating the relationship between ductal carcinoma in situ, calcifications, and margin status in patients undergoing breast conserving surgery.

BACKGROUND: Microcalcifications associated with ductal carcinoma in situ (DCIS-AMC) close to lumpectomy margins could be used as a surrogate for margin involvement and aid in decreasing margin re-excision. We sought to evaluate the histologic factors of DCIS-AMC near lumpectomy margins. METHODS: Women with DCIS treated with breast-conserving surgery (BCS) who had DCIS-AMC on surgical specimens were identified. Pathology slides were reviewed to determine the distance of DCIS-AMC from each margin (six per specimen) and the distance of DCIS from each margin (ie, margin status). RESULTS: Of 35 patients (210 margins), 24 had close/positive margins (39 margins [18%]). DCIS-AMC≤10 mm from a margin was associated with a greater incidence of DCIS≤2 mm from the margin (31.7% DCIS-AMC≤10 mm vs 13.3% no DCIS-AMC≤10 mm, P = 0.003). On multivariable analysis, DCIS≤2 mm from the margin was independently associated with DCIS-AMC≤10 mm from the margin (odds ratio 2.95, 95% confidence interval 1.48-5.86, P = 0.002). CONCLUSIONS: DCIS-AMC≤10 mm from the inked margin is associated with DCIS at or close to the margin (≤2 mm). Using this knowledge, intraoperative techniques like specimen radiography could be utilized to detect microcalcifications≤10 mm from a margin and guide selective margin re-excision in BCS.

The impact of enhanced recovery pathways on cost of care and perioperative outcomes in patients undergoing gastroesophageal and hepatopancreatobiliary surgery.

INTRODUCTION: Enhanced recovery after surgery protocols have been increasingly adopted to standardize patient care and decrease overall costs. This study evaluated the impact of a prospectively implemented enhanced recovery after surgery protocol for patients undergoing surgery for gastroesophageal and hepatopancreatobiliary disease at an academic institution. METHODS: Patients undergoing either hepatopancreatobiliary or gastroesophageal procedures between January 2013 and May 2017 were classified according to whether or not they were placed on an enhanced recovery after surgery protocol. Groups were compared along demographic, perioperative, outcomes, and financial variables. RESULTS: Of a total of 377 patients, 149 were placed on an enhanced recovery after surgery protocol. There was a significant association between enhanced recovery after surgery protocol use and increased perioperative antibiotic use (98.0% enhanced recovery after surgery vs. 87.3% non-enhanced recovery after surgery, P < .001), decreased intraoperative crystalloid use (1,155 ± 705 mL enhanced recovery after surgery vs. 1,576 ± 826 non-enhanced recovery after surgery, P < .001), decreased requirement for intensive care unit stay (20.1% enhanced recovery after surgery vs. 36.4% non-enhanced recovery after surgery, P < .001), and decreased total hospital costs ($10,688.38 ± 10,518.22 vs. $15,439.22 ± 14,201.24, P < .001). On multivariable analysis, enhanced recovery after surgery protocol use was independently associated with decreased rate of intensive care unit admission (odds ratio 0.39, 95% confidence interval 0.23-0.66, P < .001). CONCLUSION: Enhanced recovery after surgery pathways can be safely implemented in patients undergoing hepatopancreatobiliary and gastroesophageal procedures and can help standardize perioperative practices, decrease requirement for intensive care unit admission, and decrease total hospital costs.

Evaluating the Effect of Margin Consensus Guideline Publication on Operative Patterns and Financial Impact of Breast Cancer Operation.

BACKGROUND: This study sought to evaluate re-excision rates, patient satisfaction with their breasts, and healthcare costs before and after publication of 2014 Society of Surgical Oncology/American Society of Radiation Oncology consensus guideline on margins for breast conserving operation with whole-breast irradiation for stage I and II breast cancer at an academic institution. STUDY DESIGN: Patients with stage I and II invasive carcinomas who underwent partial mastectomy were divided into 2 groups based on whether they were treated before (PRE) or after (POST) guideline publication in March 2014. Groups were compared with respect to re-excision rates, conversion to mastectomy, specimen volumes, mean cost per patient of surgical care, and prospectively collected patient post-procedure quality of life. RESULTS: A total of 237 patients who underwent partial mastectomy were examined (n = 126 in the PRE group and n = 111 in the POST group). Patients in the POST group were less likely to require re-excision (9% POST vs 37% PRE; p < 0.001) and were less likely to undergo conversion to mastectomy (5% POST vs 14% PRE; p = 0.02). After consensus guideline publication, mean operative cost per patient decreased ($4,874 POST vs $5,772 PRE; p < 0.001), and patients had improved breast quality of life scores (77 out of 100 POST vs 61 out of 100 PRE; p = 0.03). On multivariable analysis, publication of the consensus statement was an independent predictor of decreased re-excision rates (odds ratio 0.17; 95% CI 0.08 to 0.38; p < 0.001) and operative cost per patient (odds ratio 0.14; 95% CI 0.78 to 0.30; p < 0.001). CONCLUSIONS: Widespread implementation of the consensus guideline on margins for breast conserving operation will likely lead to the intended improvements in operative and financial outcomes, as well as patient satisfaction with breast conserving operation.

Developing a national emergency department data reference set based on SNOMED CT.

Emergency departments around Australia use a range of software to capture data on patients' reason for encounter, presenting problem and diagnosis. The data collected are mainly based on descriptions and codes of the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM), with each emergency department having a tailored list of terms. The National E-Health Transition Authority is introducing a standard clinical terminology, the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT), as one of the building blocks of an e-health infrastructure in Australia. The Australian e-Health Research Centre has developed a software platform, Snapper, which facilitates mapping of existing clinical terms to the SNOMED CT terminology. Using the Snapper software, reference sets of terms for emergency departments are being developed, based on the Australian version of SNOMED CT (SNOMED CT-AU). Existing software systems need to be able to implement these reference sets to support standardised recording of data at the point of care. As the terms collected will be part of a larger terminology, they will be useful for patients' admission and discharge summaries and for computerised clinical decision making. Mapping existing sets of clinical terms to a national emergency department SNOMED CT reference set will facilitate consistency between emergency department data collections and improve the usefulness of the data for clinical and analytical purposes.