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Lactococcus garvieae, Yersinia ruckeri and Flavobacterium psychrophilum are three of the most important pathogens in worldwide rainbow trout aquaculture (Oncorrhynchus mykiss Walbaum). In this work, a multiplex quantitative PCR (qPCR) method for the simultaneous detection of the three pathogens was developed and tested in a commercial trout farm. Fifty-four spleen and brain samples from a trout farm were analyzed using both multiplex qPCR and classic microbiological methods. When comparing qPCR results and classic plate diagnostic techniques no negative deviations were observed, which resulted in a 100% relative sensitivity of the multiplex qPCR method developed. In addition, efficiency of the qPCR method ranged between 97.5% and 108.8%. The specificity of the combination of primers and probes was successfully tested in 57 target and non-target bacterial strains Hence, the multiplex qPCR method developed in the present study could be used as a reliable diagnostic tool for the detection of L. garvieae, Y. ruckeri and F. psychrophilum in rainbow trout farms, enabling faster diagnostics (only a few hours) and contributing to a quick response and management of bacterial disease outbreaks.

Contains fulltext : 48240.pdf (Publisher’s version ) (Closed access) The purpose of this study was to determine the importance of using prior mammograms for classification of benign and malignant masses. Five radiologists and one resident classified mass lesions in 198 mammograms obtained from a population-based screening program. Cases were interpreted twice, once without and once with comparison of previous mammograms, in a sequential reading order using soft copy image display. The radiologists' performances in classifying benign and malignant masses without and with previous mammograms were evaluated with receiver operating characteristic (ROC) analysis. The statistical significance of the difference in performances was calculated using analysis of variance. The use of prior mammograms improved the classification performance of all participants in the study. The mean area under the ROC curve of the readers increased from 0.763 to 0.796. This difference in performance was statistically significant (P = 0.008).

The purpose of this article is to evaluate the impact on the diagnosis of breast cancer of implementing full-field digital mammography (FFDM) in a multidisciplinary breast pathology unit and, 1 year later, the addition of a computer-aided detection (CAD) system.A total of 13,453 mammograms performed between January and July of the years 2004, 2006, and 2007 were retrospectively reviewed using conventional mammography, digital mammography, and digital mammography plus CAD techniques. Mammograms were classified into two subsets: screening and diagnosis. Variables analyzed included cancer detection rate, rate of in situ carcinoma, tumor size at detection, biopsy rate, and positive predictive value of biopsy.FFDM increased the cancer detection rate, albeit not statistically significantly. The detection rate of in situ carcinoma increased significantly using FFDM plus CAD compared with conventional technique (36.8% vs 6.7%; p = 0.05 without Bonferroni statistical correction) for the screening dataset. Relative to conventional mammography, tumor size at detection decreased with digital mammography (T1, 61.5% vs 88%; p = 0.018) and with digital mammography plus CAD (T1, 79.7%; p = 0.03 without Bonferroni statistical correction). Biopsy rates in the general population increased significantly using CAD (10.6/1000 for conventional mammography, 14.7/1000 for digital mammography, and 17.9/1000 for digital mammography plus CAD; p = 0.02). The positive predictive value of biopsy decreased slightly, but not significantly, for both subsets.The incorporation of new techniques has improved the performance of the breast unit by increasing the overall detection rates and earlier detection (smaller tumors), both leading to an increase in interventionism.

Contains fulltext : 89520.pdf (Publisher’s version ) (Closed access) The purpose of this study was to evaluate the effect of independent reading with computer-aided diagnosis (CAD) and independent double reading on radiologists' performance to characterize mass lesions on serial mammograms. Six radiologists rated 198 cases, 99 benign and 99 malignant. For each case, the mammograms from two consecutive screening rounds were available. The mass was visible on the prior view in 40% of the cases. Independently, a CAD programe also rated each mass lesion making use of information from prior and current views. The following reading situations were compared: single reading, independent reading with CAD, and independent double reading. Independent reading with CAD was implemented by averaging the scaled ratings from each radiologist and the scaled CAD scores. We implemented independent double reading by averaging the scaled scores from two radiologists. Results were evaluated using receiver-operating characteristic (ROC) methodology and multiple reader multiple case analysis. The average performance, measured as the area under the ROC curve (A(z) value), was 0.80 for the single-reading mode. For independent double reading, the average performance improved to 0.81. This improvement was not significant. For independent interpretation with CAD, the average performance significantly increased to 0.83 (P < 0.05). We conclude that CAD technology with temporal analysis has the potential to help radiologists with the task of discriminating between benign and malignant masses. 01 mei 2010

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.

In mammography, computer-aided diagnosis (CAD) techniques for mass detection and classification mainly use local image information to determine whether a region is abnormal or not. There is a lot of interest in developing CAD methods that use context, asymmetry, and multiple view information. However, it is not clear to what extent this may improve CAD results. In this study, we made use of human observers to investigate the potential benefit of using context information for CAD. We investigated to what extent human readers make use of context information derived from the whole breast area and from asymmetry for the tasks of mass detection and classification. Results showed that context information can be used to improve CAD programs for mass detection. However, there is still a lot to be gained from improvement of local feature extraction and classification. This is demonstrated by the fact that the observers did much better in classifying true positive (TP) and false positive (FP) regions than the CAD program. For classification of benign and malignant masses context seems to be less important.

For radiologists lesion margin appearance is of high importance when classifying breast masses as malignant or benign lesions. In this study, we developed different measures to characterize the margin of a lesion. Towards this goal, we developed a series of algorithms to quantify the degree of sharpness and lobulation of a mass margin. Besides, to estimate spiculation of a margin, features previously developed for mass detection were used. Images selected from the publicly available data set "Digital Database for Screening Mammography" were used for development and evaluation of these algorithms. The data set consisted of 777 images corresponding to 382 patients. To extract lesions from the mammograms a segmentation algorithm based on dynamic programming was used. Features were extracted for each lesion. A k-nearest neighbor algorithm was used in combination with a leave-one-out procedure to select the best features for classification purposes. Classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. The average test Az value for the task of classifying masses on a single mammographic view was 0.79. In a case-based evaluation we obtained an Az value of 0.84.

An automatic computer-aided diagnosis algorithm to classify malignant and benign tumors in digitized mammograms was developed. Images selected from the publicly available data set “Digital Database for Screening Mammography” were used for evaluation of this algorithm. A new segmentation algorithm was used to segment the lesion. To represent spiculation we used features previously developed for mass detection. Features based on margin sharpness, location and contrast were also extracted. A k-nearest neighbor algorithm was used for feature selection. The average test results corresponded to Az value of 0.84 and 0.79 in a case and in an image based classification scheme respectively.

A previously developed computerized scheme to detect masses has been further revised and several improvements were intended. Mammograms were digitized at a higher resolution with a mammographic laser scanner providing 12 bits. Some steps of the scheme, based on bilateral subtraction technique, were modified. Several new features were designed and a BPN neural network was used to reduce the number of false positives. Results obtained with the training set were encouraging, yielding a sensitivity of 85% and 1.54 mean number of false positives per image before applying false positive reduction. After applying false positive reduction, a sensitivity of 78.3% at a mean number of 0.4 false positives per image was obtained. The area under the AFROC curve was A1 = 0.808.

Mammographic screening is widely used for early detection of breast cancer. Despite the success of screening programs, negative effects should not be underestimated. In many countries, only 15%–40% of detected lesions which are biopsied are subsequently determined malignant. Radiologists might improve their performance, when they could use objective computer-aided diagnosis programs developed with the aim of reducing false positives.