Showing papers by "Craig K. Abbey published in 2006"

Rapamycin inhibits growth of premalignant and malignant mammary lesions in a mouse model of ductal carcinoma in situ.

Abstract: Purpose: Rapamycin has been shown to have antitumor effects in various tumor models. To study the effect of rapamycin at different stages of breast cancer development, we used two unique mouse models of breast cancer with activated phosphatidylinositol 3-kinase (PI3K) pathway. Met-1 tumors are highly invasive and metastatic, and mammary intraepithelial neoplasia-outgrowths (MIN-O), a model for human ductal carcinoma in situ , are transplantable premalignant mammary lesions that develop invasive carcinoma with predictable latencies. Both of these models were derived from mammary lesions in Tg( MMTV-PyV-mT ) mice. Experimental Design: Met-1 tumors were used to study the effect of rapamycin treatment on invasive disease. Transplanted MIN-O model was used to study the effect of rapamycin on premalignant mammary lesions. Animals were in vivo micro–positron emission tomography imaged to follow the lesion growth and transformation to tumor during the treatment. Cell proliferation, angiogenesis, and apoptosis was assayed by immunohistochemistry. Results: Rapamycin inhibited in vitro tumor cell proliferation and in vivo Met-1 tumor growth. The growth inhibition was correlated with dephosphorylation of mammalian target of rapamycin (mTOR) targets. Rapamycin treatment significantly reduced the growth of the premalignant MIN-O lesion, as well as tumor incidence and tumor burden. Growth inhibition was associated with reduced cell proliferation and angiogenesis and increased apoptosis. Conclusions: In PyV-mT mouse mammary models, rapamycin inhibits the growth of premalignant lesions and invasive tumors. Although the inhibitory effect of rapamycin was striking, rapamycin treatment did not completely obliterate the lesions.

Observer efficiency in discrimination tasks Simulating Malignant and benign breast lesions imaged with ultrasound

Abstract: We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image.

Classification images for detection, contrast discrimination, and identification tasks with a common ideal observer

Abstract: We consider three simple forced-choice visual tasks--detection, contrast discrimination, and identification--in Gaussian white noise. The three tasks are designed so that the difference signal in all three cases is the same difference-of-Gaussians (DOG) profile. The distribution of the image noise implies that the ideal observer uses the same DOG filter to perform all three tasks. But do human observers also use the same visual strategy to perform these tasks? We use classification image analysis to evaluate the visual strategies of human observers. We find significantly different subject classification images across the three tasks. The domain of greatest variability appears to be low spatial frequencies [<5 cycles per degree (cpd)]. In this range, we find frequency enhancement in the detection task, and frequency suppression and reversal in the contrast discrimination task. In the identification task, subject classification images agree reasonably well with the ideal observer filter. We evaluate the effect of nonlinear transducers and intrinsic spatial uncertainty to explain divergence from the ideal observer found in detection and contrast discrimination tasks.

Analyzing the effect of dose reduction on the detection of mammographic lesions using mathematical observer models

Abstract: The purpose of this study was to determine the effect of dose reduction on the detectability of breast lesions in mammograms Mammograms with dose levels corresponding to 50% and 25% of the original clinically-relevant exposure levels were simulated Detection of masses and microcalicifications embedded in these mammograms was analyzed by four mathematical observer models, namely, the Hotelling Observer, Non-prewhitening Matched Filter with Eye Filter (NPWE), and Laguerre-Gauss and Gabor Channelized Hotelling Observers Performance was measured in terms of ROC curves and Area under ROC Curves (AUC) under Signal Known Exactly but Variable Tasks (SKEV) paradigm Gabor Channelized Hotelling Observer predicted deterioration in detectability of benign masses The other algorithmic observers, however, did not indicate statistically significant differences in the detectability of masses and microcalcifications with reduction in dose Detection of microcalcifications was affected more than the detection of masses Overall, the results indicate that there is a potential for reduction of radiation dose level in mammographic screening procedures without severely compromising the detectability of lesions

The efficiency of reading around learned backgrounds

Abstract: Most metrics of medical image quality typically treat all variability components of the background as a Gaussian noise process. This includes task based model observers (non-prewhitening matched filter without and with an eye filter, NPW and NPWE; Hotelling and Channelized Hotelling) as well as Fourier metrics of medical image quality based on the noise power spectra. However, many investigators have observed that unlike many of the models/metrics, physicians often can discount signal-looking structures that are part of the normal anatomic background. This process has been referred to as reading around the background or noise. The purpose of this paper is to develop an experimental framework to systematically study the ability of human observers to read around learned backgrounds and compare their ability to that of an optimal ideal observer which has knowledge of the background. We measured human localization performance of one of twelve targets in the presence of a fixed background consisting of randomly placed Gaussians with random contrasts and sizes, and white noise. Performance was compared to a condition in which the test images contained only white noise but with higher contrast. Human performance was compared to standard model observers that treat the background as a Gaussian noise process (NPW, NPWE and Hotelling), a Fourier-based prewhitening matched filter, and an ideal observer. The Hotelling, NPW, NPWE models as well as the Fourier-based prewhitening matched filter predicted higher performance for the white noise test images than the background plus white noise. In contrast, ideal and human performance was higher for the background plus white noise condition. Furthermore, human performance exceeded that of the NPW, NPWE and Hotelling models and reached an efficiency of 19% relative to the ideal observer. Our results demonstrate that for some types of images human signal localization performance is consistent with use of knowledge about the high order moments of the backgrounds to discount signal-looking structures that belong to the background. In such scenarios model observers and metrics that either ignore the background or treat the background as a Gaussian process (Hotelling, Channelized Hotelling, Task-based SNR) under predict human performance.

Observer efficiency in boundary discrimination tasks related to assessment of breast lesions with ultrasound

Abstract: The statistical efficiency of human observers in diagnostic tasks is an important measure of how effectively task relevant information in the image is being utilized. Most efficiency studies have investigated efficiency in terms of contrast or size effects. In many cases, malignant lesions will have similar contrast to normal or benign objects, but can be distinguished by properties of their boundary. We investigate this issue in the framework of malignant/benign discrimination tasks for the breast with ultrasound. In order to identify effects in terms of specific features and to control for other effects such as aberration or specular reflections, we simulate the formation of beam-formed radio-frequency (RF) data. We consider three tasks related to lesion boundaries including boundary eccentricity, boundary sharpness, and detection of boundary spiculations. We also consider standard detection and contrast discrimination tasks. We find that human observers exhibit surprisingly low efficiency with respect to the Ideal observer acting on RF data in boundary discrimination tasks (0.08%-3.3%), and that efficiency of human observers is substantially increased by Wiener-filtering RF frame data. We also find a limitation in efficiency is the computation of an envelope image from the RF data recorded by the transducer. Approximations to the Ideal observer acting on the envelope images indicate that humans may be substantially more efficient (10%-75%) with respect to the envelope Ideal observers. Our work suggests that significant diagnostic information may be lost in standard envelope processing in the formation of ultrasonic images.

Observer performance detecting signals in globally non-stationary oriented noise

Abstract: Most of the studies on signal detection task for medical images have used backgrounds that are or assumed to be statistically stationary. However, medical images usually present statistically non-stationary properties. Fewer studies have addressed how humans detect signals in non-stationary backgrounds. In particular, it is unknown whether humans can adapt their strategy to different local statistical properties in non-stationary backgrounds. In this paper, we measured human performance detecting a signal embedded in statistically non-stationary noise and in statistically stationary noise. Test images were designed so that performance of model observers that assumed statistically stationary and made no use of differences in local statistics would be constant across both conditions. In contrast, performance of an ideal model observer that uses local statistics is about 140% higher with the non-stationary backgrounds than the stationary ones. Human performance was 30% higher in the non-stationary backgrounds. We conclude that humans can adapt their strategy to the local statistical properties of non-stationary backgrounds (although suboptimally compared to the ideal observer) and that model observers that derive their templates based on stationary assumptions might be inadequate to predict human performance in some non-stationary backgrounds.