Power Doppler Quantification in Assessing Gestational Trophoblastic Neoplasia.

PURPOSE: The FIGO score cannot accurately stratify low-risk gestational trophoblastic neoplasia (GTN) patients who develop chemoresistance to single agent methotrexate chemotherapy. Tumour vascularisation is a key risk factor and its quantification may provide non-invasive way of complementing risk assessment. MATERIALS AND METHODS: 187 FIGO-staged, low-risk GTN patients were prospectively recruited. Power Doppler ultrasound was analysed using a quantification program. Four diagnostic indicators were obtained comprising the number of colour pixels (NCP), mean dB, power Doppler quantification (PDQ), and percentage of colour pixels (%CP). Each indicator performance was assessed to determine if they could distinguish the subset of low-risk patients who became chemoresistant. RESULTS: There were 111 non-resistant and 76 resistant patients. NCP performed best at distinguishing these two groups where the non-resistant group had an average 3435 (±â€Š2060) pixels and the resistant group 6151 (±â€Š3192) pixels (p < 0.001). PDQ and %CP showed significant differences (p < 0.001) but had poorer performance (area under ROC curves were 72 % and 67 % respectively compared with 75 % for NCP). The mean dB index was not significantly different (p = 0.133). CONCLUSION: Power Doppler ultrasound quantification shows potential for non-invasive assessment of tumour vascularity and can distinguish low-risk GTN patients who become chemoresistant from those who have an uncomplicated course with first line treatment.

Pleural tuberculosis: a key differential diagnosis for pleural thickening, even without obvious risk factors for tuberculosis in a low incidence setting.

We report the case of a 64-year-old woman, presenting with pleuritic chest pain and weight loss. She had a previous history of breast malignancy and no clear risk factors for tuberculosis (TB). Initial investigations showed a right-sided pleural effusion and pleural thickening suggestive of malignancy, which would have been in keeping with the clinical presentation. Initial pleural biopsy showed features suggestive of possible TB infection, though no growth on cultures. A repeat biopsy was negative on initial microscopy, but was culture positive for Mycobacterium tuberculosis, also identifying isoniazid resistance. This case highlights that TB remains an important differential even in the absence of classical risk factors, and illustrates the diagnostic challenges it poses. It also highlights the value of culture positivity in identification of drug resistance and facilitation of appropriate treatment.

Computer assisted detection software for CT colonography: effect of sphericity filter on performance characteristics for patients with and without fecal tagging.

The aim of this study is to investigate the effect of changing sphericity filter values on performance of a computer assisted detection (CAD) system for CT colonography for data with and without fecal tagging. Colonography data from 138 patients with 317 validated polyps were divided into those with (86) and without (52) fecal tagging. Polyp coordinates were established by three observers and datasets analysed subsequently by a proprietary CAD system used at four discrete sphericity filter settings. Prompts were compared with the known coordinates in order to determine sensitivity and specificity. Sensitivity was highest at low sphericity; of 164 polyps 6 mm or more, 144 (87.8%) were detected at sphericity 0.3, and 132 (80.1%) at sphericity 0.9. Of 42 polyps measuring 10 mm or more, 40 (95.2%) were detected at sphericity 0.3, and 36 (85.7%) at sphericity 0.9. There was no significant difference in sensitivity for tagged and un-tagged data but specificity was reduced in tagged data at low sphericity and significantly reduced in untagged data at high sphericity. CAD had a sensitivity of 95.2% for polyps measuring 1 cm or more and 87.8% for polyps 6 mm or more when used at a sphericity setting of 0.3. Higher sphericity settings increased specificity while reducing sensitivity. The bowel preparation used significantly impacts on specificity.

CT colonography: automated measurement of colonic polyps compared with manual techniques--human in vitro study.

PURPOSE: To prospectively investigate the relative accuracy and reproducibility of manual and automated computer software measurements by using polyps of known size in a human colectomy specimen. MATERIALS AND METHODS: Institutional review board approval was obtained for the study; written consent for use of the surgical specimen was obtained. A colectomy specimen containing 27 polyps from a 16-year-old male patient with familial adenomatous polyposis was insufflated, submerged in a container with solution, and scanned at four-section multi-detector row computed tomography (CT). A histopathologist measured the maximum dimension of all polyps in the opened specimen. Digital photographs and line drawings were produced to aid CT-histologic measurement correlation. A novice (radiographic technician) and an experienced (radiologist) observer independently estimated polyp diameter with three methods: manual two-dimensional (2D) and manual three-dimensional (3D) measurement with software calipers and automated measurement with software (automatic). Data were analyzed with paired t tests and Bland-Altman limits of agreement. RESULTS: Seven polyps (

Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography.

OBJECTIVE: The purpose of our study was to assess the sensitivity of computer-assisted reader (CAR) software for polyp detection compared with the performance of expert reviewers. MATERIALS AND METHODS: A library of colonoscopically validated CT colonography cases were collated and separated into training and test sets according to the time of accrual. Training data sets were annotated in consensus by three expert radiologists who were aware of the colonoscopy report. A subset of 45 training cases containing 100 polyps underwent batch analysis using ColonCAR version 1.2 software to determine the optimum polyp enhancement filter settings for polyp detection. Twenty-five consecutive positive test data sets were subsequently interpreted individually by each expert, who was unaware of the endoscopy report, and before generation of the annotated reference via an unblinded consensus interpretation. ColonCAR version 1.2 software was applied to the test cases, at optimized polyp enhancement filter settings, to determine diagnostic performance. False-positive findings were classified according to importance. RESULTS: The 25 test cases contained 32 nondiminutive polyps ranging from 6 to 35 mm in diameter. The ColonCAR version 1.2 software identified 26 (81%) of 32 polyps compared with an average sensitivity of 70% for the expert reviewers. Eleven (92%) of 12 polyps > or = 10 mm were detected by ColonCAR version 1.2. All polyps missed by experts 1 (n = 4) and 2 (n = 3) and 12 (86%) of 14 polyps missed by expert 3 were detected by ColonCAR version 1.2. The median number of false-positive highlights per case was 13, of which 91% were easily dismissed. CONCLUSION: ColonCAR version 1.2 is sensitive for polyp detection, with a clinically acceptable false-positive rate. ColonCAR version 1.2 has a synergistic effect to the reviewer alone, and its standalone performance may exceed even that of experts.

Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection.

BACKGROUND & AIMS: In isolation, computer-aided detection (CAD) for computed tomographic (CT) colonography is as effective as optical colonoscopy for detection of significant adenomas. However, the unavoidable interaction between CAD and the reader has not been addressed. METHODS: Ten readers trained in CT but without special expertise in colonography interpreted CT colonography images of 107 patients (60 with 142 polyps), first without CAD and then with CAD after temporal separation of 2 months. Per-patient and per-polyp detection were determined by comparing responses with known patient status. RESULTS: With CAD, 41 (68%; 95% confidence interval [CI], 55%-80%) of the 60 patients with polyps were identified more frequently by readers. Per-patient sensitivity increased significantly in 70% of readers, while specificity dropped significantly in only one. Polyp detection increased significantly with CAD; on average, 12 more polyps were detected by each reader (9.1%, 95% CI, 5.2%-12.8%). Small- (< or =5 mm) and medium-sized (6-9 mm) polyps were significantly more likely to be detected when prompted correctly by CAD. However, overall performance was relatively poor; even with CAD, on average readers detected only 10 polyps (51.0%) > or =10 mm and 24 (38.2%) > or =6 mm. Interpretation time was shortened significantly with CAD: by 1.9 minutes (95% CI, 1.4-2.4 minutes) for patients with polyps and by 2.9 minutes (95% CI, 2.5-3.3 minutes) for patients without. Overall, 9 readers (90%) benefited significantly from CAD, either by increased sensitivity and/or by reduced interpretation time. CONCLUSIONS: CAD for CT colonography significantly increases per-patient and per-polyp detection and significantly reduces interpretation times but cannot substitute for adequate training.

Polyp detection with CT colonography: primary 3D endoluminal analysis versus primary 2D transverse analysis with computer-assisted reader software.

PURPOSE: To retrospectively compare primary three-dimensional (3D) endoluminal analysis with primary two-dimensional (2D) transverse analysis supplemented by computer-assisted reader (CAR) software for computed tomographic (CT) polyp detection and reader reporting times. MATERIALS AND METHODS: Ethical permission and patient consent were obtained from all donor institutions for use of CT colonography data sets. Twenty CT colonography data sets from 14 men (median age, 61 years; age range, 52-78 years) with 48 endoscopically proved polyps were selected. Polyp coordinates were documented in consensus by three unblinded radiologists to create a reference standard. Two radiologists read the data sets, which were randomized between primary 3D endoluminal views with 2D problem solving and 2D views supplemented by CAR software. Reading times and diagnostic confidence were documented. The CAR software highlighted possible polyps by superimposing circles on the 2D transverse images. Data sets were reread after 1 month by using the opposing analysis method. Detection rates were compared by using the McNemar test. Reporting times and diagnostic confidence were compared by using the paired t test and Mann-Whitney U test, respectively. RESULTS: Mean sensitivity values for polyps measuring 1-5, 6-9, and 10 mm or larger were 14%, 53%, and 83%, respectively, for 2D CAR analysis and 16%, 53%, and 67%, respectively, for primary 3D analysis. Overall sensitivity values were 41% for 2D CAR analysis and 39% for primary 3D analysis (P=.77). Reader 1 detected more polyps than reader 2, particularly when using the 3D fly-through method (P=.002). Mean reading times were significantly longer with the 3D method (P=.001). Mean false-positive findings were 1.5 for 2D analysis and 5.5 for 3D analysis. Reader confidence was not significantly different between analysis methods (P=.42). CONCLUSION: Two-dimensional CAR analysis is quicker and at least matches the sensitivity of primary 3D endoluminal analysis, with fewer false-positive findings.

Computed tomography colonography: automated diameter and volume measurement of colonic polyps compared with a manual technique--in vitro study.

OBJECTIVE: To investigate inter- and intraobserver agreement of automated measurement of polyp diameter in vitro. METHODS: Two phantoms ("QRM" and "Whiting") containing simulated polyps of known diameter and volume were scanned using 16-detector row computed tomography. Two observers estimated polyp diameter using 3 methods: software calipers ("manual"), freehand boundary identification ("semiautomatic"), and automated software segmentation ("fully automatic"). RESULTS: Intraobserver 95% limits of agreement for diameter were narrowest for the fully automatic method (QRM span: 0.39 mm, 0.48 mm; Whiting span: 0.24 mm, 0 mm). Manual estimates were approximately 10 times wider (QRM span: 3.57 mm, 3.21 mm; Whiting span: 3.2 mm, 2.02 mm). Volume estimates were narrowest for the fully automatic method (span: 24.2 mm, 24.1 mm vs. 97.9 mm, 102.9 mm for semiautomatic measurement). Interobserver agreement for diameter was narrowest for the fully automatic method (QRM span: 0.12 mm, Whiting span: 0.16 mm), with the manual method approximately 18 times wider (QRM span: 2.87 mm, Whiting span: 2.18 mm). CONCLUSION: Fully automated measurement of polyp diameter and volume is technically feasible and results in superior inter- and intraobserver agreement.