CT colonography with computer-aided detection: automated recognition of ileocecal valve to reduce number of false-positive detections

The ileocecal valve (ICV) is a common cause of false-positive detections of polyps at computed tomographic (CT) colonography with computer-aided detection (CAD). The authors developed a CAD algorithm for differentiating the ICV from a true polyp and evaluated this algorithm by using two colonoscopy-confirmed CT colonography data sets. Data sets 1 and 2 consisted of the data obtained at CT colonographic examinations performed in 20 and 40 patients, respectively. Forty of these patients had at least one polyp 1 cm or larger. For data set 1, the proposed ICV recognition algorithm eliminated three of nine (33%; 95% confidence interval [CI]: 8%, 70%) false-positive CAD detections that were attributable to the ICV and none of the true-positive polyp detections. For data set 2, with use of identical parameters, the algorithm eliminated 11 of 18 (61%; 95% CI: 36%, 83%) false-positive detections that were attributable to the ICV and none of the true-positive detections. The thresholds used to recognize the ICV were a mean internal CT attenuation of less than -124 HU and a volume of greater than 1.5 cm(3). The proposed algorithm successfully recognized the ICV and eliminated it in some cases. This result is clinically important because, by reducing the frequency of a common cause of false-positive detections, this algorithm may improve the efficiency of physicians who use CAD.     

Effect of a tele-training programme on radiographers in the interpretation of CT colonography

Objective

To assess the performance of radiographers in CT colonography (CTC) after a tele-training programme, supervised by 2 experienced radiologists.

Materials and methods

Five radiographers underwent training in CTC using a tele-training programme mainly based on the interpretation of 75 training cases performed in the novice department. To evaluate the educational performance, each radiographer was tested on 20 test cases with 27 lesions >6 mm (12: 6–9 mm; 15: > 10 mm). Sensitivity, specificity and PPV for polyps ≥6 mm and ≥10 mm were calculated with point estimates and 95% confidence interval (95% CI). The results were compared by comparing 95% CI with a 5% significance level.

Results

In the training cases overall per-polyp sensitivity was 57% (95% CI 46.1–67.9) and 69.1% (95% CI 50.6–87.5) for lesions ≥6 mm and ≥10 mm, respectively.Overall per patient sensitivity, specificity and PPV were 86.4% (95% CI 76.7–96.1), 85.4% (95% CI 77–93.9) and 78.3% (95% CI 64.9–91.7), respectively.In the test cases overall per-polyp sensitivity was 80.7% (95% CI 69.5–92) and 94.7% (95% CI 85.6–100×) for lesions ≥6 mm and ≥10 mm, respectively. Overal per patient sensitivity, specificity and PPV were 92.9% (95% CI 83.1–100×), 64% (95% CI 13.1–100×) and 87.8% (95% CI 71.7–100×), respectively. There was a statistically significant improvement in per-polyp sensitivity for lesions ≥6 mm in the test cases. No statistically significant differences were found in per patient sensitivity, specificity and PPV, but there was an improvement.

Conclusion

This training programme based on tele-training obtained good performance of radiographers in detecting tumoral lesions in CTC.     

Computer-aided detection for CT colonography: incremental benefit of observer training

The purpose of this study was to investigate the incremental effect of focused training on observer performance when using computer-assisted detection (CAD) software to interpret CT colonography (CTC). Six radiologists who were relatively inexperienced with CTC interpretation underwent 1 day of focused training before reading 20 patient datasets with the assistance of CAD software (ColonCAR 1.3, Medicsight PLC). Sensitivity, specificity and interpretation times were determined and compared with previous performance when reading the same datasets but without the benefit of focused training, using the binomial exact test and Wilcoxon's signed rank test. Per-polyp sensitivity improved after training by 18% overall (95% confidence interval (CI): 14-24%, p<0.001) and was greatest for polyps of 6-9 mm (26%, 95% CI: 18-34%, p<0.001). Absolute sensitivity was 23% (9-36%), 51% (33-71%) and 74% (44-100%) for polyps of or=10 mm, respectively. Specificity fell significantly after focused training (median of 5.5 false positives per 20 datasets (interquartile range (IQR): 4-6) post-training vs median of 2.5 (IQR: 1-5) pre-training, p = 0.03). Interpretation time also increased significantly after training (from a median of 9.3 min (IQR: 9.3-14.5 min) to a median of 17.1 min (IQR: 15.4-19.4 min), p = 0.03). In conclusion, one day of training increases observer polyp sensitivity when using CAD for CTC at the expense of increased reporting time and reduction in specificity.     

CT colonography in the detection of colorectal polyps and cancer: systematic review, meta-analysis, and proposed minimum data set for study level reporting

PURPOSE: To assess the methodologic quality of available data in published reports of computed tomographic (CT) colonography by performing systematic review and meta-analysis. MATERIALS AND METHODS: The MEDLINE database was searched for colonography reports published between 1994 and 2003, without language restriction. The terms colonography, colography, CT colonoscopy, CT pneumocolon, virtual colonoscopy, and virtual endoscopy were used. Studies were selected if the focus was detection of colorectal polyps verified with within-subject reference colonoscopy by using key methodologic criteria based on information presented at the Fourth International Symposium on Virtual Colonoscopy (Boston, Mass). Two reviewers independently abstracted methodologic characteristics. Per-patient and per-polyp detection rates were extracted, and authors were contacted, when necessary. Per-patient sensitivity and specificity were calculated for different lesion size categories, and Forest plots were produced. Meta-analysis of paired sensitivity and specificity was conducted by using a hierarchical model that enabled estimation of summary receiver operating characteristic curves allowing for variation in diagnostic threshold, and the average operating point was calculated. Per-polyp sensitivity was also calculated. RESULTS: Of 1398 studies considered for inclusion, 24 met our criteria. There were 4181 patients with a study prevalence of abnormality of 15%-72%. Meta-analysis of 2610 patients, 206 of whom had large polyps, showed high per-patient average sensitivity (93%; 95% confidence interval [CI]: 73%, 98%) and specificity (97%; 95% CI: 95%, 99%) for colonography; sensitivity and specificity decreased to 86% (95% CI: 75%, 93%) and 86% (95% CI: 76%, 93%), respectively, when the threshold was lowered to include medium polyps. When polyps of all sizes were included, studies were too heterogeneous in sensitivity (range, 45%-97%) and specificity (range, 26%-97%) to allow meaningful meta-analysis. Of 150 cancers, 144 were detected (sensitivity, 95.9%; 95% CI: 91.4%, 98.5%). Data reporting was frequently incomplete, with no generally accepted format. CONCLUSION: CT colonography seems sufficiently sensitive and specific in the detection of large and medium polyps; it is especially sensitive in the detection of symptomatic cancer. Studies are poorly reported, however, and the authors propose a minimum data set for study reporting.     

CT colonography of colorectal polyps: a metaanalysis

OBJECTIVE: For proper evaluation of the accuracy of CT colonography, prospective multiinstitutional trials would be ideal. Until these trials are available, data can be collectively analyzed. The purpose of this study is to use metaanalysis to assess the reported accuracy of CT colonography compared with conventional colonoscopy for detecting colorectal polyps. MATERIALS AND METHODS: Articles comparing CT colonography and conventional colonoscopy were identified, and a standardized form was used to extract relevant study data. Fisher's exact test and the Mantel-Haenszel test were used for pooling of data. A 95% confidence interval (CI) was selected to determine sensitivity and specificity, and the Kruskal-Wallis exact test was used to identify trends relating to polyp size. Meta-analysis methods were used to test strength of results. Comparisons were made for the percentage of polyps detected grouped by size (> or = 10 mm, 6-9 mm, < or = 5 mm) and the percentage of patients identified who had polyps of the same size. RESULTS: Fourteen studies fulfilled all the study inclusion criteria and gave a total of 1,324 patients and 1,411 polyps. The pooled per-patient sensitivity for polyps 10 mm or larger was (sensitivity [95% CI]) 0.88 (0.84-0.93), for polyps 6-9 mm it was 0.84 (0.80-0.89), and for polyps 5 mm or smaller it was 0.65 (0.57-0.73). The pooled per-polyp sensitivity for polyps 10 mm or larger was 0.81 (0.76-0.85), for polyps 6-9 mm it was 0.62 (0.58-0.67), and for polyps 5 mm or smaller it was 0.43 (0.39-0.47). Sensitivity for detection of polyps increased as the polyp size increased (p < 0.00005). The pooled overall specificity for detection of polyps larger than 10 mm was 0.95 (0.94-0.97). CONCLUSION: The specificity and sensitivity of CT colonography are high for polyps larger than 10 mm.     

Imaging Biomarkers of Hypothyroidism on Lung Cancer Screening CT

PurposeTo assess the feasibility of attenuation and size measurement of the thyroid gland as an imaging biomarker for hypothyroidism in patients undergoing lung cancer screening (LCS) with low dose CT.Materials and MethodsWith institutional review board (IRB) approval, we retrospectively reviewed all patients with LCS CT between September 1, 2016 and March 31, 2020, who had at least 1 thyroid-stimulating hormone (TSH) test within 90 days of the patient's most recent screening CT. Hypothyroid patients were identified through billing diagnosis and/or elevated TSH or those on treatment with thyroxine; normal patients were identified as those without a diagnosis of hypothyroidism and normal TSH. For each hypothyroid patient, an age- and sex-matched normal control was included. The diameters and attenuation of both lobes of the thyroid gland were measured for each case; patients in whom the thyroid gland could not be seen to measure were excluded.ResultsA total of 304 patients were included. The areas under the receiver operating characteristic curve for size and attenuation of the left lobe were 0.774 (95% confidence interval [CI] 0.714-0.825) and 0.812 (95% CI 0.759-0.861), respectively; and for the right lobe were 0.776 (95% CI 0.719-0.827) and 0.794 (95% CI 0.740-0.847), respectively. We developed a decision tree algorithm to predict hypothyroidism combining the minimum size and attenuation of either lobe of the thyroid gland, with sensitivity, specificity, and accuracy of 76%, 87%, and 82%, respectively.ConclusionSize and attenuation of the thyroid gland can be used to identify potential hypothyroid patients undergoing LCS.     

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs

ObjectiveTo develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.Materials and MethodsThis retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs).ResultsThe AI model showed an AUROC of 0.922 (95% CI, 0.842–0.969) in the internal test set and 0.870 (95% CI, 0.785–0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%–92.0%) and specificity of 91.3% (95% CI, 79.2%–97.6%) for the internal test set and 78.9% (95% CI, 54.4%–93.9%) and 88.2% (95% CI, 78.7%–94.4%), respectively, for the external test set. With the model’s assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020–0.168; p = 0.012) and 0.069 (95% CI, 0.002–0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074–0.090; p = 0.850).ConclusionA deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.     

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors

ObjectiveThe mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm.Materials and MethodsAbdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria.ResultsAt the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834–0.877), specificity 67.5% (95% CI: 0.636–0.712), PPV 82.1% (95% CI: 0.797–0.843), NPV 73.0% (95% CI: 0.691–0.766), and AUC 0.771 (95% CI: 0.750–0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541–0.995), specificity 70.0% (95% CI: 0.354–0.919), PPV 75.0% (95% CI: 0.428–0.933), NPV 87.5% (95% CI: 0.467–0.993), and AUC 0.800 (95% CI: 0.563–0.943).ConclusionWe developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.     

Does a computer-aided detection algorithm in a second read paradigm enhance the performance of experienced computed tomography colonography readers in a population of increased risk?

We prospectively determined whether computer-aided detection (CAD) could improve the performance characteristics of computed tomography colonography (CTC) in a population of increased risk for colorectal cancer. Therefore, we included 170 consecutive patients that underwent both CTC and colonoscopy. All findings >or=6 mm were evaluated at colonoscopy by segmental unblinding. We determined per-patient sensitivity and specificity for polyps >or=6 mm and >or=10 mm without and with computer-aided detection (CAD). The McNemar test was used for comparison the results without and with CAD. Unblinded colonoscopy detected 50 patients with lesions >or=6 mm and 25 patients with lesions >or=10 mm. Sensitivity of CTC without CAD for these size categories was 80% (40/50, 95% CI: 69-81%) and 64% (16/25, 95% CI: 45-83%), respectively. CTC with CAD detected one additional patient with a lesion >or=6 mm and two with a lesion >or=10 mm, resulting in a sensitivity of 82% (41/50, 95% CI: 71-93%) (p = 0.50) and 72% (18/25, 95% CI: 54-90%) (p = 1.0), respectively. Specificity without CAD for polyps >or=6 mm and >or=10 mm was 84% (101/120, 95% CI: 78-91%) and 94% (136/145, 95% CI: 90-98%), respectively. With CAD, the specificity remained (nearly) unchanged: 83% (99/120, 95% CI: 76-89%) and 94% (136/145, 95% CI: 90-98%), respectively. Thus, although CTC with CAD detected a few more patients than CTC without CAD, it had no statistically significant positive influence on CTC performance.     

Characterization of normal ileocecal valve density on CT colonography

The objective of this study was to evaluate normal ileocecal valve (ICV) attenuation and composition on CT colonography (CTC). Two hundred twelve patients who underwent CTC at the authors' institution were retrospectively identified. Two independent readers recorded three region-of-interest (ROI) measurements, which were used to determine mean ICV density. ICV attenuation measurements were compared with age and abdominal body fat using Pearson correlation. A subjective classification system was applied to characterize the relative fat and soft tissue density of the ICV. Mean ICV density was -26.3+/-14 HU (range -60.1 to 18.3 HU). There was no correlation between the fat content of the ICV and age (r=0.06) or body fat (r=-0.47). The majority of valves (83.5%) were classified as heterogeneously low density or heterogeneously high density, whereas a small fraction of valves (6.1%) were categorized as homogeneously low density. The results of this study suggest that ICVs have a varying range of densities, and this feature alone cannot be used to distinguish the ICV from a polyp or neoplastic lesion on CTC.     

Ventilation-perfusion scanning and helical CT in suspected pulmonary embolism: meta-analysis of diagnostic performance

PURPOSE: To perform meta-analysis of literature about the role of helical computed tomography (CT) and ventilation-perfusion (V-P) scanning in detection of acute pulmonary embolism (PE) by using summary receiver operating characteristic (ROC) curve analysis. MATERIALS AND METHODS: V-P scanning articles published from January 1985 to March 2003 and helical CT articles published from January 1990 to March 2003 in MEDLINE and EMBASE databases were included if (a) tests were performed for evaluation of acute PE, (b) conventional angiography was the reference standard, and (c) absolute numbers of true-positive, false-negative, true-negative, and false-positive results were available. Sensitivity analysis was conducted by excluding articles published before 1995. RESULTS: A total of 12 articles discussing helical CT and/or V-P scanning were included. With a random-effects model, pooled sensitivity for helical CT was 86.0% (95% confidence interval [CI]: 80.2%, 92.1%), and specificity was 93.7% (95% CI: 91.1%, 96.3%). V-P scanning yielded low sensitivity of 39.0% (95% CI: 37.3%, 40.8%) but high specificity of 97.1% (95% CI: 96.0%, 98.3%) with high probability threshold. V-P scanning yielded high sensitivity of 98.3% (95% CI: 97.2%, 99.5%) and low specificity of 4.8% (95% CI: 4.7%, 4.9%) with normal threshold. Regression coefficients for helical CT angiography were 0.588 (95% CI: -1.55, 2.74) and 4.14 (95% CI: -0.002, 8.28) versus V-P scanning with high and normal thresholds, respectively. Regression coefficients for helical CT angiography were 0.588 (95% CI: -1.55, 2.74) and 4.14 (95% CI: -0.002, 8.28) versus V-P scanning with high and normal thresholds, respectively. CONCLUSION: Helical CT has greater discriminatory power than V-P scanning with normal and/or near-normal threshold to exclude PE, while helical CT and V-P scanning with high probability threshold had similar discriminatory power in the diagnosis of PE.     

Colorectal polyps and cancers in asymptomatic average-risk patients: evaluation with CT colonography

PURPOSE: To compare thin-section multi-detector row computed tomographic (CT) colonography with conventional colonoscopy in the evaluation of colorectal polyps and cancer in asymptomatic average-risk patients. MATERIALS AND METHODS: Sixty-eight asymptomatic men (age > 50 years) scheduled to undergo screening colonoscopy were enrolled in this study. CT colonography was followed by conventional colonoscopy, performed on the same day. Supine and prone CT colonography were performed after colonic insufflation with room air. A gastroenterologist measured all polyps, which were categorized as 1-5, 6-9, or over 10 mm. Biopsy and histologic evaluation were performed of all polyps. CT colonography and colonoscopy results were compared for location, size, and morphology of detected lesions. Point estimates and 95% CIs were provided for specificity and sensitivity of CT by using results at conventional colonoscopy as the reference standard. RESULTS: At colonoscopy, 98 polyps were identified in 39 patients; 21 (21.4%) of 98 were detected at CT colonography. Sensitivity was 11.5% (nine of 78) for polyps 1-5 mm, 52.9% (nine of 17) for polyps 6-9 mm, and 100% (three of three) for polyps over 10 mm. Results at colonoscopy were normal in 29 (42.6%) of 68 patients; at CT colonography, results were correctly identified as normal in 26 of these 29 patients. In one of these patients, a lesion larger than 10 mm was detected at CT colonography. The per-patient specificity of CT was 89.7% (26 of 29; 95% CI: 72.7%, 97.8%). The mean time for CT image interpretation was 9 minutes. CONCLUSION: In patients at average risk for colorectal cancer, CT colonography is a sensitive and specific screening test for detecting polyps 10 mm or larger; the sensitivity for detecting smaller polyps is decreased. Examination findings can be interpreted in a clinically feasible amount of time.     

Dual-energy computed tomography imaging to determine atherosclerotic plaque composition: A prospective study with tissue validation

BackgroundIdentifying vulnerable coronary plaque with coronary CT angiography is limited by overlap between attenuation of necrotic core and fibrous plaque. Using x-rays with differing energies alters attenuation values of these components, depending on their material composition.ObjectivesWe sought to determine whether dual-energy CT (DECT) improves plaque component discrimination compared with single-energy CT (SECT).MethodsTwenty patients underwent DECT and virtual histology intravascular ultrasound (VH-IVUS). Attenuation changes at 100 and 140 kV for each plaque component were defined, using 1088 plaque areas co-registered with VH-IVUS. Hounsfield unit thresholds that best detected necrotic core were derived for SECT (conventional attenuation values) and for DECT (using dual-energy indices, defined as difference in Hounsfield unit values at the 2 voltages/their sum). Sensitivity of SECT and DECT to detect plaque components was determined in 77 segments from 7 postmortem coronary arteries. Finally, we examined 60 plaques in vivo to determine feasibility and sensitivity of clinical DECT to detect VH-IVUS–defined necrotic core.ResultsIn contrast to conventional SECT, mean dual-energy indices of necrotic core and fibrous tissue were significantly different with minimal overlap of ranges (necrotic core, 0.007 [95% CI, –0.001 to 0.016]; fibrous tissue, 0.028 [95% CI, 0.016–0.050]; P < .0001). DECT increased diagnostic accuracy to detect necrotic core in postmortem arteries (sensitivity, 64%; specificity, 98%) compared with SECT (sensitivity, 50%; specificity, 94%). DECT sensitivity to detect necrotic core was lower when analyzed in vivo, although still better than SECT (45% vs 39%).ConclusionsDECT improves the differentiation of necrotic core and fibrous plaque in ex vivo postmortem arteries. However, much of this improvement is lost when translated to in vivo imaging because of a reduction in image quality.     

Postmenopausal bone density referral decision rules: correlation with clinical fractures

OBJECTIVE: To test decision rules for bone mineral density (BMD) against fractures. METHODS: We surveyed postmenopausal women in a military primary care clinic and tested three national clinical decision rules (Osteoporosis Risk Assessment Instrument; age, body size, no estrogen; weight) for correlation with fracture history. Outcome measures included relative risk (RR), area under the receiver operating characteristics curve (aROC), sensitivity, and specificity. RESULTS: Patients were 69 years old on average, 53% were Caucasian, 38% were African American, and 15% had a history of fractures. Caucasian women (RR, 1.8; 95% confidence interval [CI], 1.1-3.1) and those older than 65 years (RR, 2.0; 95% CI, 1.2-3.5) had higher prevalence of fractures. The Osteoporosis Risk Assessment Instrument decision rule had the highest aROC (0.65; 95% CI, 0.57-0.731 and sensitivity (sensitivity, 0.83; specificity, 0.31). Age, Body Size, No Estrogen had the next highest aROC (0.63; 95% CI, 0.54-0.71) and sensitivity (sensitivity, 0.74; specificity, 0.46). Weight criterion was the most specific (aROC, 0.60; 95% CI, 0.52-0.68; sensitivity, 0.64; specificity, 0.56). CONCLUSIONS: Current postmenopausal bone density referral decision rules only modestly correlate with clinical fractures.     

Assessment of polyp and mass histopathology by intravenous contrast-enhanced CT colonography

RATIONALE AND OBJECTIVES: We sought to demonstrate that intravenous contrast-enhanced CT colonography (CTC) can distinguish colonic adenomas from carcinomas. METHODS: Supine intravenous contrast-enhanced CTC with colonoscopic and/or surgical correlation was performed on 25 patients with colonic adenomas or carcinomas. Standard deviation of mean polyp CT attenuation was computed and assessed using ANOVA and receiver-operating characteristic analyses. RESULTS: Colonoscopy confirmed 32 polyps or masses 1 to 8 cm in size. The standard deviations of CT attenuation were carcinomas (n = 13; 36 +/- 6 HU; range 28-48 HU) and adenomas (n = 19; 49 +/- 14 HU; range 31-100 HU) (P = 0.005). At a standard deviation threshold of 42 HU, the sensitivity and specificity for classifying a polyp or mass as a carcinoma were 92% and 79%, respectively. The area under the receiver-operating characteristic curve was 0.89 +/- 0.06 (95% confidence interval 0.73-0.96). CONCLUSIONS: Measurement of the standard deviation of CT attenuation on intravenous contrast-enhanced CTC permits histopathologic classification of polyps 1 cm or larger as carcinomas versus adenomas. The presence of ulceration or absence of muscular invasion in carcinomas creates overlap with adenomas, reducing the specificity of carcinoma classification.     

^18F-FDG PET显像用于脑胶质瘤分级的Meta分析

目的用Meta分析法综合评价18F—FDGPET显像对脑胶质瘤分级的能力。方法搜索Medline、中国期刊网关于”F-FDGPET显像诊断脑胶质瘤的中英文文献。提取文献中用半定量法或目测法判定胶质瘤恶性程度的数据,同时按照Cochrane工作组推荐的诊断评价标准进行文献的质量方法学评估。用Meta分析软件（Meta—Disc）对纳入文献汇总分析,获得汇总灵敏度（Se）和特异性（Sp）及其95％可信区间（a）,并绘制汇总受试者工作特征（SROC）曲线,计算曲线下面积（AUC）。结果共获取文献17篇（英文16篇、中文1篇）,其中11篇可获取用半定量法[肿瘤／皮质放射性比值（T／C）组及肿瘤／白质放射性比值（T／W）组]判定胶质瘤恶性程度的数据,共272例患者;9篇可获取以目测法判定胶质瘤恶性程度的数据,共481例患者（目测组,部分文献包含2种以上判定方法）。通过异质性检验,分别采用不同效应模型进行分析。T／C组、T／W组和目测组的汇总Se分别为0．952,0．857和0．810;Sp分别为0．409,0．538和0．870;诊断比值（DOR）分别为11．746,22．066和15．282。汇总Se的95％CI分别为0．903—0．980、0．768～0．922和0．757—0．855;汇总Sp的95％凹分别为0．318～0．504,0．431—0．642和0．819～0．911;汇总DOR的95％口分别为5．368—25．702,7．077～68．800和3．716～62．851。3组AUC分别为0．8604,0．8373和0．8724。结论利用18F-FDGPET显像对脑胶质瘤进行恶性程度预测时,半定量组的Se较高,有利于阳性病例的检出,目测组sp较高,有利于阴性病例的排除。根据AUC判断,目测组总体诊断效能最高,T／C组次之,T／W组再次。     

Meta-analysis: comparison of F-18 Fluorodeoxyglucose-positron emission tomography and bone scintigraphy in the detection of bone metastases in patients with breast cancer

PURPOSE: To evaluate the diagnostic properties of FDG-PET and bone scintigraphy in the detection of osseous metastases in patients with breast cancer. MATERIALS AND METHODS: Studies evaluating the diagnostic accuracy of FDG-PET and bone scintigraphy in the diagnosis of osseous metastasis were systematically searched for in the MEDLINE, CINAHL, and EBM Review databases from January 1995 to November 2006. Two reviewers independently abstracted data including research design, sample size, imaging technique and technical characteristics, reference standard, method of image interpretation, and totals of true positives, false positives, true negatives, and false negatives. Per-patient and per-lesion pooled sensitivity and specificity, and area under summary receiver operating characteristic curves were calculated using Meta-Test software. RESULTS: The pooled patient-based sensitivity for FDG-PET was 81% (95% CI: 70%-89%), specificity was 93% (95% CI: 84%-97%), and the area under the curve (AUC) was 0.08. The pooled sensitivity of bone scan was 78% (95% CI: 67%-86%), specificity was 79% (95% CI: 40%-95%), and the AUC was 0.43. The pooled lesion-based sensitivity for FDG-PET was 69% (95% CI: 28%-93%), specificity was 98% (95% CI: 87%-100%), and the AUC was 0.09. The pooled sensitivity for bone scan was 88% (95% CI: 82%-92%), specificity was 87% (95% CI: 29%-99%), and the AUC was 0.81. CONCLUSIONS: It remains inconclusive whether FDG-PET or bone scintigraphy is superior in detecting osseous metastasis from breast cancer. However, FDG-PET does have a higher specificity and may better serve as a confirmatory test than bone scintigraphy and used to monitor response to therapy.     

Prospective study on bright lumen magnetic resonance colonography in comparison with conventional colonoscopy

The aim of this prospective trial was to evaluate sensitivity and specificity of bright lumen magnetic resonance colonography (MRC) in comparison with conventional colonoscopy (CC). A total of 120 consecutive patients with clinical indications for CC were prospectively examined using MRC (1.5 Tesla) which was then followed by CC. Prior to MRC, the cleansed colon was filled with a gadolinium-water solution. A 3D GRE sequence was performed with the patient in the prone and supine position, each acquired during one breathhold period. After division of the colon into five segments, interactive data analysis was carried out using three-dimensional post-processing, including a virtual intraluminal view. The results of CC served as a reference standard. In all patients MRC was performed successfully and no complications occurred. Image quality was diagnostic in 92% (574/620 colonic segments). On a per-patient basis, the results of MRC were as follows: sensitivity 84% (95% CI 71.7-92.3%), specificity 97% (95% CI 89.0-99.6%). Five flat adenomas and 6/16 small polyps (< or =5 mm) were not identified by MRC. MRC offers high sensitivity and excellent specificity rates in patients with clinical indications for CC. Improved MRC techniques are needed to detect small polyps and flat adenomas.     

An optimized method for estimating intracranial volume from magnetic resonance images.

The accuracy and efficiency of protocols to measure intracranial volume (ICV) from volumetric magnetic resonance imaging (MRI) studies has not been formally analyzed. The ICV of 30 control participants was obtained by tracing every slice of a MRI data set on which the cranial cavity appeared, and compared with estimated ICVs calculated by progressively selecting one of every x slices (i.e., "1-in-x") as a sampling strategy. The reliability and precision of each sampling strategy was then determined. There was virtually no reduction in reliability at the 1-in-10 sampling strategy, with a reliability exceeding 0.999. ICV can be confidently traced using a 1-in-10 sampling strategy, which should result in significant time savings.     

Interpretation errors in focused cardiac ultrasound by novice pediatric emergency medicine fellow sonologists

Background

Focused cardiac ultrasound (FOCUS) is a core competency for pediatric emergency medicine (PEM) fellows. The objectives of this study were (1) to evaluate test characteristics of PEM-fellow-performed FOCUS for pericardial effusion and diminished cardiac function and (2) to assess image interpretation independent of image acquisition.

Methods

PEM fellows performed and interpreted FOCUS on patients who also received cardiology service echocardiograms, the reference standard. Subsequently, eight different PEM fellows remotely interpreted a subset of the PEM-acquired and cardiology-acquired echocardiograms.

Results

Eight PEM fellows performed 54 FOCUS exams, of which two had pericardial effusion and four had diminished function. PEM fellow FOCUS had a sensitivity of 50.0% (95% CI 9.19–90.8) and specificity of 100.0% (95% CI 91.1–100.0) for detecting diminished function, and sensitivity of 50.0% (95% CI 2.67–97.33) and specificity of 98.1% (95% CI 88.42–99.9) for detecting pericardial effusions. When PEM fellows remotely interpreted 15 echocardiograms, the sensitivity was 81.3% (95% CI 70.7–88.8) and specificity 75% (95% CI 67.0–81.0) for detecting diminished function, and sensitivity of 76.3% (95% CI 65.0–85.0) and specificity 94.4% (95% CI 89.0–97.0) for detecting pericardial effusion. There were no differences in sensitivity and specificity of PEM fellows’ interpretation of FOCUS studies compared to their interpretation of cardiology echocardiograms. Interrater reliability for interpretation of remote images (kappa) was 0.66 (95% CI 0.59–0.73) for effusion and 0.31 (95% CI 0.24–0.38) for function among the fellows.

Conclusion

Novice PEM fellow sonologists (a physician who performs and interprets ultrasound) in the majority of instances were able to acquire and remotely interpret FOCUS images with limited training. However, they made real-time interpretation errors and likely need further training to incorporate real-time image acquisition and interpretation into their practice.