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Ellen B Mendelson

Publications and source records attributed to Ellen B Mendelson.

13 recordsLinked to original sources
PubMed2006

Lesion detection and characterization in a breast US phantom: results of the ACRIN 6666 Investigators.

PURPOSE: To prospectively evaluate ultrasonographic (US) lesion detection and characterization in a breast phantom by potential investigators in a screening US protocol, American College of Radiology Imaging Network (ACRIN) 6666. MATERIALS AND METHODS: National Cancer Institute Cancer Experimental Therapeutic Protocol review and ACRIN internal institutional review board approved the protocol; potential investigators were informed of the study purpose prior to participation. Six equivalent anthropomorphic phantoms were prepared with 17 masses (2-10 mm in mean diameter) in different locations at different depths. Sixty-six investigators, experienced in breast US, from 23 institutions scanned a phantom with high-frequency linear-array transducers (12-5 MHz). Lesion location, diameters, echogenicity, shape, and posterior features were recorded. Reader-specific phantom maps were generated and compared with known lesion locations and features. Results from 64 observers could be analyzed and were masked to investigator identity. Agreement on US features was measured with kappa statistics. A generalized linear model generated log relative risks for detection rates as a function of lesion diameter, depth, and features. RESULTS: Of 17 lesions, a median of 14 (82%) were detected (range, 9-16), and 86% of observers detected at least 12 lesions. Of 1088 potential detections, 861 (79.1%) were made. Among 5-10-mm lesions, 499 (97.5%) of 512 detections were made (excluding a 6-mm "skin" lesion seen by only seven observers [11%]). One 4-mm mass was seen by 53 observers (83%). Among 3-mm lesions, 274 (71.4%) of 384 detections were made. One 2-mm lesion was seen by 28 (44%) observers. Relative risk of detection decreased to 0.55 (95% confidence interval: 0.51, 0.59) for each centimeter increase in lesion depth. Agreement was slight for lesion shape (kappa=0.14), substantial for echogenicity (kappa=0.61), and moderate for posterior features (kappa=0.45). Feature description errors were common for 2-4-mm lesions; only 33% of 3-mm anechoic masses were so characterized. Among eight 6-10-mm lesions, investigators erred in feature description of a median of 1 lesion (mean, 1.3; range, 0-4). CONCLUSION: US detection and description of lesions in a breast phantom were highly consistent for lesions 5-10 mm in diameter; those smaller than 5 mm were less reliably identified or characterized by experienced investigators.

Breast Cyst↗
PubMed2006

Anthropomorphic breast phantoms for qualification of Investigators for ACRIN Protocol 6666.

The purpose of this study was to evaluate various ultrasonic properties of breast phantoms developed for use in qualifying investigators for participation in the American College of Radiology Imaging Network (ACRIN) protocol 6666, "Screening Breast Ultrasound in High-Risk Women." Specifically, a tool was sought to consistently measure the performance of radiology personnel in detecting and characterizing lesions similar to those expected with screening breast ultrasonography (US). The phantoms are equivalent to one another except for the randomization of positions of 14 of the 17 simulated lesions. The lesions differ in depth and ultrasonic properties. Representative values of propagation speed, attenuation, relative echogenicity, and mass density are reported for all tissue-mimicking components. Beam refraction occurs at the interface between the subcutaneous fat layer and the glandular parenchyma and can result in beam distortion artifacts similar to those encountered in clinical breast US.

Adipose Tissue↗
PubMed2006

Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set.

PURPOSE: To evaluate a computer-aided diagnosis multimodality intelligent workstation as an aid to radiologists in the interpretation of mammograms and breast sonograms. MATERIALS AND METHODS: An institutional review board approved the protocol for an observer study with signed consent, as well as the retrospective use of the mammograms, sonograms, and clinical data with waiver of consent. The HIPAA-compliant observer study was conducted with five breast radiologists and five breast imaging fellows, all of whom gave confidence ratings and patient management decisions, both without and with the computer aid, for 97 lesions that were unknown to both the observers and the computer. The performance of each observer without and with the computer aid was quantified by using four performance measures: area under the receiver operating characteristic curve (A(z)) value, partial A(z) value, sensitivity, and specificity. The statistical significance of the differences in the performance measures without and with the computer aid was determined by using a two-tailed t test for paired data. RESULTS: Use of the computer aid resulted in an improvement of the average performance of the 10 observers, as measured by means of a statistically significant increase in A(z) value (0.87-0.92; P < .001), partial A(z) value (0.47-0.68; P < .001), and sensitivity (0.88-0.93; P = .005). A statistically significant difference was not found in the specificity without and with the computer aid (0.66-0.69; P = .20). CONCLUSION: Use of multimodality intelligent workstations can improve the performance of radiologists in the task of differentiating malignant and benign lesions at mammography and sonography.

Adult↗
PubMed2006

Operator dependence of physician-performed whole-breast US: lesion detection and characterization.

PURPOSE: To prospectively examine operator dependence of lesion detection, description, and interpretation when experienced breast radiologists perform whole-breast ultrasonography (US). MATERIALS AND METHODS: Institutional review board approval was obtained for the HIPAA-compliant study. Ten women (aged 19-53 years; mean, 37.4 years; 20 breasts) with numerous known breast lesions consented to participate. Eleven breast radiologists, who passed experience and qualification requirements for a screening breast US trial and consented to participate, scanned both breasts in all participants and documented images of each detected lesion and its size, location, features, palpability, and Breast Imaging Reporting and Data System final assessment. Intraclass correlation coefficients (ICCs) were used to measure agreement on lesion size and location, and kappa statistics were calculated for agreement on features and final assessments compared with consensus. RESULTS: Eighty-eight unique lesions were identified by at least two investigators (five to 13 lesions per participant). Mean diameter was 6.7 mm (standard error, 0.4; range, 2-22 mm), and eight lesions (9%) were palpable. Of 968 potential detections (88 lesions, 11 investigators), 536 (55%) detections were made. Individual investigators detected between 43 (49%) and 58 (66%) lesions. Larger lesions were more consistently detected: Detection rates were six of 33 lesions (18%) at 3 mm or smaller; 164 of 374 (43.9%) at 3.1-5 mm; 145 of 275 (52.7%) at 5.1-7 mm; 119 of 176 (67.6%) at 7.1-9 mm; 38 of 44 (86%) at 9.1-11 mm; and 64 of 66 (97%) lesions larger than 11 mm (P < .001). ICCs for clockface, distance from nipple, and individual lesion diameter all exceeded 0.7, indicating high reliability. For shape, margins, and final assessments of solid lesions, kappa values were 0.62, 0.67 (substantial agreement), and 0.52 (moderate agreement), respectively. Of 110 detections of consensus cysts 8 mm and smaller, 15 (14%) detections were considered to be of solid lesions by at least one reader. CONCLUSION: Larger lesions (>11 mm) are most consistently detected, with fewer than half of lesions 5 mm or smaller in mean diameter identified; substantial agreement was found for description of lesion size, location, and key features, and moderate agreement was found for lesion management.

Adult↗
PubMed2005

Incorporating new imaging models in breast cancer management.

Mammography is the only screening test proven to decrease breast cancer morbidity and mortality. Although mammography is an effective screening tool, it does have limitations, particularly in women with dense breasts. New imaging techniques are emerging to overcome these limitations and enhance cancer detection, improving patient outcome. Digital mammography, computer aided detection, breast ultrasound and breast magnetic resonance imaging (MRI) are frequently used adjuncts to mammography in today's clinical practice. Recent studies have shown that these techniques can enhance the radiologist's ability to detect cancer and assess disease extent, which is crucial in treatment planning and staging. Positron emission tomography (PET) also plays an important role in staging breast cancer and monitoring treatment response. Other modalities such as tomosynthesis and MR lymphangiography show promise in overcoming the problems related to dense breast tissue and the lack of noninvasive methods to assess lymph node status. Imaging-guided, minimally invasive therapies are also emerging as alternatives to surgical biopsy for breast lesions. As imaging techniques improve, the role of imaging will continue to evolve with the goal remaining a decrease in breast cancer morbidity and mortality.

Adult↗
PubMed2004

Problem-solving ultrasound.

This article addresses the accepted indications for using ultrasound alone or in conjunction with other imaging techniques in solving problems of breast diagnosis and guiding patient management. The indications include evaluation of palpable and mammographic masses; guidance of interventional procedures; evaluating problems with implants; radiation treatment planning; and as the initial imaging technique in young,pregnant, and lactating patients. Evolving indications and controversial applications for breast ultrasound are also discussed.

Breast Implants↗
PubMed2004

Computerized detection and classification of cancer on breast ultrasound.

RATIONALE AND OBJECTIVES: To develop and evaluate a two-stage computerized method that first detects suspicious regions on ultrasound images, and subsequently distinguishes among different lesion types. MATERIALS AND METHODS: The first stage of detecting potential lesions was based on expected lesion shape and margin characteristics. After the detection stage, all candidate lesions were classified by a Bayesian neural net based on computer-extracted lesion features. Two separate tasks were performed and evaluated at the classification stage: the first classification task was the distinction between all actual lesions and false-positive detections; the second classification task was the distinction between actual cancer and all other detected lesion candidates (including false-positive detections). The neural nets were trained on a database of 400 cases (757 images), consisting of complex cysts and benign and malignant lesions, and tested on an independent database of 458 cases (1,740 images including 578 normal images). RESULTS: In the distinction between all actual lesions and false-positive detections, Az values of 0.94 and 0.91 were obtained with the training and testing data sets, respectively. Sensitivity by patient of 90% at 0.45 false-positive detections per image was achieved for this detection-plus-classification scheme for the testing data set. Distinguishing cancer from all other detections (false-positives plus all benign lesions) proved to be more challenging, and Az values of 0.87 and 0.81 were obtained during training and testing, respectively. Sensitivity by patient of 100% at 0.43 false-positive malignancies per image was achieved in the detection and classification of cancerous lesions for the testing dataset. CONCLUSION: The results show promising performance of the computerized lesion detection and classification method, and indicate the potential of such a system for clinical breast ultrasound.

Breast Neoplasms↗
PubMed2003

Computerized analysis of shadowing on breast ultrasound for improved lesion detection.

Sonography is being considered for the screening of women at high risk for breast cancer. We are developing computerized detection methods to aid in the localization of lesions on breast ultrasound images. The detection scheme presented here is based on the analysis of posterior acoustic shadowing, since posterior acoustic shadowing is observed for many malignant lesions. The method uses a nonlinear filtering technique based on the skewness of the gray level distribution within a kernel of image data. The database used in this study included 400 breast ultrasound cases (757 images) consisting of complicated cysts, solid benign lesions, and malignant lesions. At a false-positive rate of 0.25 false positives per image, a detection sensitivity of 80% by case (66% by image) was achieved for malignant lesions. The performance for the overall database (at 0.25 false positives per image) was less at 42% sensitivity by case (30% by image) due to the more limited presence of posterior acoustic shadowing for benign solid lesions and the presence of posterior acoustic enhancement for cysts. Our computerized method for the detection of lesion shadows alerts radiologists to lesions that exhibit posterior acoustic shadowing. While this is not a characterization method, its performance is best for lesions that exhibit posterior acoustic shadowing such as malignant and, to a lesser extent, benign solid lesions. This method, in combination with other computerized sonographic detection methods, may ultimately help facilitate the use of ultrasound for breast cancer screening.

Algorithms↗
PubMed2002

Computerized lesion detection on breast ultrasound.

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false-positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false-positives by a Bayesian neural network. The round robin analysis yielded an Az value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false-positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.

Bayes Theorem↗