&lt;title&gt;Digital mammography: observer performance study of the effects of pixel size on radiologists' characterization of malignant and benign microcalcifications&lt;/title&gt;

Chan, Heang-Ping; Helvie, Mark A.; Petrick, Nicholas; Sahiner, Berkman; Adler, Dorit D.; Blane, Caroline E.; Joynt, Lynn K.; Paramagul, Chintana; Roubidoux, Marilyn A.; Wilson, Todd E.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

doi:10.1117/12.349514

Cited by 9 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The availability of commercial full-field digital mammography systems for clinical use has made it possible to directly compare the performance of screen-film and full-field digital imaging systems [6][7][8][9]. Several recent studies have shown the ability of full-field digital mammography to detect and characterize calcifications [10][11][12], but few studies have specifically assessed the ability of full-field digital mammography to detect and characterize breast masses [13,14].…”

mentioning

confidence: 99%

Comparison of Full-Field Digital Mammography and Screen-Film Mammography for Detection and Characterization of Simulated Small Masses

Yang

Lai

Whitman

et al. 2006

American Journal of Roentgenology

View full text Add to dashboard Cite

OBJECTIVE-The two objectives of this study were to create an ex vivo phantom model that closely mimics human breast cancer for detection tasks and to compare the performance of full-field digital mammography with screen-film mammography in detecting and characterizing small breast masses in a phantom with a spectrum of complex tissue backgrounds.MATERIALS AND METHODS-Sixteen phantom breast masses of varying sizes (0.3-1.2 cm), shapes (round and irregular), and densities (high and low) were created from shaved tumor specimens and imaged using both full-field digital and screen-film mammography techniques. We created 408 detection tasks that were captured on 68 films. On each radiograph, six detection tasks were partially obscured by areas of varying breast-pattern complexity, including low (predominantly fatty), mixed (scattered fibroglandular densities and heterogeneously dense), and high (extremely dense) density patterns. Each detection task was scored using a five-point confidence scale by three mammographers. Receiver operating characteristic (ROC) curve analysis was performed to analyze differences in detection of masses between the two imaging systems, and sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were computed.RESULTS-Full-field digital mammography showed higher area under the ROC curve than screenfilm mammography for detecting masses in each breast background and performed significantly better than screen-film mammography in mixed (p = 0.010), dense (p = 0.029), and all breast backgrounds combined (p = 0.004). Full-field digital mammography was superior to screen-film mammography for characterizing round and irregular masses and low-and high-density masses.CONCLUSION-Full-field digital mammography was significantly superior to screen-film technique for detecting and characterizing small masses in mixed and dense breast backgrounds in a phantom model. Keywords breast neoplasms; breast radiography; digital radiography; phantoms Results of both randomized controlled clinical trials and meta-analyses have shown that screenfilm mammographic screening in women 40 years old and older may reduce the breast cancer mortality rate by up to 45% [1][2][3]. This decrease in the mortality rate has been attributed to the ability of screen-film mammography to identify cancers early when they appear as small, nonpalpable masses, calcifications, or both. The effectiveness of screen-film mammography is limited by its narrow dynamic range, low contrast resolution, image noise, and film

show abstract

mentioning

confidence: 99%

Comparison of Full-Field Digital Mammography and Screen-Film Mammography for Detection and Characterization of Simulated Small Masses

Yang

Lai

Whitman

et al. 2006

American Journal of Roentgenology

View full text Add to dashboard Cite

show abstract

“…Reducing the pixel size below 100µm does not improve their detection. It is interesting to remark that the observer performance studies for the characterisation of malignant and benign microcalcification recently presented by Heang-Ping Chan [8] suggest the same optimum pixel size of either 70µm or 105µm and that 35µm pixel size gives worse results -even though they did not achieve sufficient statistical evidence in their study yet.…”

Section: Influence Of the Pixel Sizementioning

confidence: 76%

Quantitative Modelling of Microcalcification Detection in Digital Mammography

Rick¹,

Serge²,

Bothorel³

et al. 1999

Medical Image Computing and Computer-Assisted Intervention – MICCAI’99

View full text Add to dashboard Cite

Abstract. This article presents a simulation framework for the image acquisition on digital mammography systems. The framework is used to analyse the performance of a previously developed method for the detection of microcalcifications by a series of top-hat operators. The framework allows to determine the theoretical number of false positives and true positives for a given set of acquisition parameters and size of microcalcifications. A minimal size of microcalcification that can be detected with sufficient certitude is analysed as a function of the pixel size of the detector and the acquisition conditions. An improved selection scheme for the detection threshold is developed which uses the model and the acquisition parameters to obtain a near-optimal detection for a wide range of acquisition conditions. Finally the model is evaluated on clinical images.

show abstract

“…While a summary ROC curve is not a substitute for the information in all the individual-reader curves, a summary ROC curve is often required and provided in scientific publications as a companion to the rigorous statistical inference results on the AUC in MRMC reader studies. 9,10 However, although some methods for generating average ROC curves are suggested, 7,11 the properties of these methods have not been formally investigated. Section 2.3 in Swets and Pickett 11 briefly mentioned that an average ROC curve can be obtained by averaging ROC parameters.…”

Section: Resultsmentioning

confidence: 99%

The average receiver operating characteristic curve in multireader multicase imaging studies

Chen¹,

Samuelson²

2014

BJR

View full text Add to dashboard Cite

Objective: In multireader, multicase (MRMC) receiver operating characteristic (ROC) studies for evaluating medical imaging systems, the area under the ROC curve (AUC) is often used as a summary metric. Owing to the limitations of AUC, plotting the average ROC curve to accompany the rigorous statistical inference on AUC is recommended. The objective of this article is to investigate methods for generating the average ROC curve from ROC curves of individual readers. Methods: We present both a non-parametric method and a parametric method for averaging ROC curves that produce a ROC curve, the area under which is equal to the average AUC of individual readers (a property we call area preserving). We use hypothetical examples, simulated data and a real-world imaging data set to illustrate these methods and their properties. Results:We show that our proposed methods are area preserving. We also show that the method of averaging the ROC parameters, either the conventional bi-normal parameters (a, b) or the proper bi-normal parameters (c, d a ), is generally not area preserving and may produce a ROC curve that is intuitively not an average of multiple curves. Conclusion: Our proposed methods are useful for making plots of average ROC curves in MRMC studies as a companion to the rigorous statistical inference on the AUC end point. The software implementing these methods is freely available from the authors. Advances in knowledge: Methods for generating the average ROC curve in MRMC ROC studies are formally investigated. The area-preserving criterion we defined is useful to evaluate such methods.Multireader, multicase (MRMC) receiver operating characteristic (ROC) studies have been widely used to evaluate medical imaging devices and computer-assisted detection/ diagnosis devices in medical imaging. 1 Typically, multiple readers (i.e. radiologists) read images of multiple cases (i.e. patients), and, for each case, each reader rates his/her level of suspicion that a lesion is present based on the image. The diagnostic performance of each reader using an imaging device is thus characterized by a ROC curve constructed using his/her rating data. A ROC curve plots the true-positive fraction (TPF or sensitivity) as a function of the false-positive fraction (FPF or 1-specificity) as the decision threshold varies and thus illustrates the trade-off between the sensitivity and the specificity of a reader across all possible thresholds. 2The area under the ROC curve (AUC) is widely used to summarize the diagnostic performance of imaging systems.3 By analysing the sample AUCs that are obtained from a sample of readers reading a sample of cases, one makes statistical inference on the "population AUC", which represents the performance of the device expected (or averaged) over the population of readers and the population of cases. 4 As such, it characterizes the device performance itself independent of the particular readers and cases used in the study. Methods are well established for the design and analysis of such studies and tutorial...

show abstract

<title>Digital mammography: observer performance study of the effects of pixel size on radiologists' characterization of malignant and benign microcalcifications</title>

Cited by 9 publications

References 0 publications

Comparison of Full-Field Digital Mammography and Screen-Film Mammography for Detection and Characterization of Simulated Small Masses

Comparison of Full-Field Digital Mammography and Screen-Film Mammography for Detection and Characterization of Simulated Small Masses

Quantitative Modelling of Microcalcification Detection in Digital Mammography

The average receiver operating characteristic curve in multireader multicase imaging studies

Contact Info

Product

Resources

About