Radiologists In Detection Research Articles

Evaluation of esophageal varices and predicting the risk of esophageal varices bleeding with multi-detector CT in patients with portal hypertension

The objective of this study was to evaluate the performance of 320-row multi-detector CT (MDCT) in the detection and grading of esophageal varices and to evaluate the ability of MDCT in predicting the risk of hemorrhage in comparison with upper endoscopy in patients with portal hypertension. A total of 69 patients, with clinically confirmed portal hypertension, underwent epigastric triphasic enhancement scans using 320-row MDCT 1 weeks or less before upper endoscopies were performed. Two blinded abdominal imagers retrospectively interpreted all CT images to detect the presence of esophageal varices and divided EV into large EV (≥ 5 mm) and small EV (< 5 mm). The correlation between CT measurements and endoscopic grading was assessed by kappa values. With endoscopy as standard, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the two radiologists in detection of EV were calculated. Correlations between CT measurements of varix size and red color sign were assessed by correlation analysis. Of the total of 69 patients, 56 patients had esophageal varices, 41 had large EV, and 15 had small EV according to the endoscopic findings. MDCT showed an excellent interobserver reliability with regard to the diagnosis of the EV (k = 0.94). Agreement between CT and endoscopy with regard to the grading of EV were excellent (k = 0.77). The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of radiologist 1 in the detection of EV was 95%, 69%, 87%, 93% and 75% respectively; the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of radiologist 2 in the detection of EV was 93%, 77%, 87%, 95% and 71%, respectively. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the two radiologists in the detection of large EV was 95%, 100%, 97%, 100% and 93%, respectively. CT variceal grading showed a strong correlation with endoscopic grading for both observers (P < 0.01). Correlations between CT measurements of varix size and red color sign were significant in both radiologists with a correlation coefficient r of 0.731 for radiologist 1 (P < 0.01) and 0.718 for radiologist 2 (P < 0.01). 320-row MDCT is useful for the detection and grading of EV, and it is useful in evaluation of EV for predicting a risk of hemorrhage.

Breast cancer image assessment using an adaptative network-based fuzzy inference system

In Brazil breast cancer is the foremost cause of fatality by cancer for women. Given that the causes are unidentified, it cannot be prevented. Mammography is one of the most reliable exams for breast cancer detection and it is based on image analysis by radiologists. Early detection is the key issue for breast cancer control and computer-aided diagnosis system can help ra diologists in detection and diagnosing breast abnormalities. Hybrid neuro-fuzzy systems are suitable for pattern recognition tasks and therefore useful for medical diagnosis support through pattern identification in mammographic images. This study presents an Adaptative Network-based Fuzzy Inference System (ANFIS) that classifies the mammographic images calcification region of interest as benign or malign and provides an important tool for breast cancer image assessment. The ANFIS model, utilized in the mammogram region of interest’s classification phase, reached a maximum accuracy rate of 99.75%.

Performance of Radiologists in Detection of Small Pulmonary Nodules on Chest Radiographs: Effect of Rib Suppression With a Massive-Training Artificial Neural Network

A massive-training artificial neural network is a nonlinear pattern recognition tool used to suppress rib opacity on chest radiographs while soft-tissue contrast is maintained. We investigated the effect of rib suppression with a massive-training artificial neural network on the performance of radiologists in the detection of pulmonary nodules on chest radiographs. We used 60 chest radiographs; 30 depicted solitary pulmonary nodules, and 30 showed no nodules. A stratified random-sampling scheme was used to select the images from the standard digital image database developed by the Japanese Society of Radiologic Technology. The mean diameter of the 30 pulmonary nodules was 14.7 +/- 4.1 (SD) mm. Receiver operating characteristic analysis was used to evaluate observer performance in the detection of pulmonary nodules first on the chest radiographs without and then on the radiographs with rib suppression. Seven board-certified radiologists and five radiology residents participated in this observer study. For all 12 observers, the mean values of the area under the best-fit receiver operating characteristic curve for images without and with rib suppression were 0.816 +/- 0.077 and 0.843 +/- 0.074; the difference was statistically significant (p = 0.019). The mean areas under the curve for images without and with rib suppression were 0.848 +/- 0.059 and 0.883 +/- 0.050 for the seven board-certified radiologists (p = 0.011) and 0.770 +/- 0.081 and 0.788 +/- 0.074 for the five radiology residents (p = 0.310). In the detection of pulmonary nodules, evaluation of a combination of rib-suppressed and original chest radiographs significantly improved the diagnostic performance of radiologists over the use of chest radiographs alone.

Computer-aided diagnosis in breast tomosynthesis and chest CT.

Computer-aided diagnosis (CAD), in the general sense, includes computer-aided detection and characterization of abnormalities on medical images. The usefulness of CAD for assisting radiologists in detection of breast cancer in screening mammography has been demonstrated by a number of prospective clinical trials in recent years. The development of CAD in other areas is also being actively pursued by researchers. In this talk, the recent work in two areas of CAD, digital breast tomosynthesis (DBT) and chest computed tomography (CT), in the CAD Research Laboratory at the University of Michigan will be reviewed. DBT is a new modality under development for breast imaging. The quasi-3D information in DBT alleviates the problem of overlapping tissue in mammography and holds the promise to improve the sensitivity for cancer detection. DBT image analysis can be performed in the 3D reconstructed volume of the 2D projection view (PV) images. DBT image quality depend on the image acquisition parameters, reconstruction method and parameters. The flexibility in image processing approaches makes CAD development for DBT interesting and challenging. out early experiences in the development of image segmentation and features extraction technique for mass detection and characterization in DBT will be discussed. The performances of the CAD systems using the 2D, 3D, and combined 2D and 3D approaches will be compared. CT has been shown to be superior to chest x-ray in detection of small lung nodules and thus lung cancer screening with CT is still being debated, many research groups are developing CAD methods for detection and characterization of lung nodules in chest CT scans. The specific prescreening, segmentation, and feature extraction techniques designed for our lung nodule detection and characterization systems will be discussed. The effects of CAD on radiologists' accuracy in nodules detection and characterization in CT scans will be demonstrated by results of observer ROC studies. There are similarities in the approaches to developing CAD methods in 3D image volumes such as DBT and CT, these experiences will facilitate the development of CAD systems for other diseases in 3D modalities.

Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

The high incidence of breast cancer in women has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcification. Mammograms-breast X-ray-are considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this paper, two techniques are proposed based on wavelet analysis and fuzzy-neural approaches. These techniques are mammography classifier based on globally processed image and mammography classifier based on locally processed image (region of interest). The system is classified normal from abnormal, mass for microcalcification and abnormal severity (benign or malignant). The evaluation of the system is carried out on Mammography Image Analysis Society (MIAS) dataset. The accuracy achieved is satisfied.