Why is microcalcification missed on mammography?

Peters,; Jones, C. T.; Daniels, Katie

doi:10.1111/1754-9485.12011

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…21,22 Considering that the features of cancer lesions are varied, past research has shown that cancers that present with mixed appearances (more than one mammographic feature) are more likely to be missed and readers are more susceptible to omission error when cancers display mixed mammographic appearances. 23 Our study supports this, whereby the mammographic features of stellate lesions were associated with spiked linear extensions radiating outwards and ill-defined spicules from the central lesion which indicate the demosplastic reaction of breast cancer into surrounding tissue. 24 The distinctive features of stellate lesions have a positive-predictive value of 84-91% and are most common mammographic features of invasive breast cancer.…”

Section: Discussionsupporting

confidence: 84%

An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced

Norsuddin

Mello‐Thoms

Reed

et al. 2017

BJR

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Cancers with a stellate mass were more easily detected and were more likely to continue to be recalled, even at lower recall rates. Cancers with non-specific density and mixed features were most likely to be missed at reduced recall rates. Advances in knowledge: Internationally, recall rates vary within screening mammography programs considerably, with a range between 1% and 15%, and very little is known about the type of breast cancer appearances found when radiologists interpret screening mammograms at these various recall rates. Therefore, understanding the lesion types and the mammographic appearances of breast cancers that are affected by readers' recall decisions should be investigated.

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Section: Discussionsupporting

confidence: 84%

An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced

Norsuddin

Mello‐Thoms

Reed

et al. 2017

BJR

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“…In our study, the proportion of DCIS was comparable to that in other pooled results of initial screening (17% vs 18%) (5); data obtained from 12 countries indicated that the overall DCIS detection rate in initial screening was lower from age 50 to 59 Third, data involving breast density at mammography were not available for evaluation, and this might be a confounding factor. However, about 92% of all cases of DCIS detected with digital mammography screening are associated with calcifications as a mammographic abnormality (20), which has been reported to be poorly correlated with breast density (21). Recent results seem to confirm that breast density at mammography is an important risk factor for all types of breast cancer and that it strongly increases the risk of developing an interval tumor, while this excess risk is not completely explained by a possible masking effect (22).…”

Section: Discussionmentioning

confidence: 98%

Digital Mammography Screening: Does Age Influence the Detection Rates of Low-, Intermediate-, and High-Grade Ductal Carcinoma in Situ?

Weigel¹,

Hense²,

Heidrich³

et al. 2016

Radiology

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“…The digital database for screening mammography (DDSM) [35] was built by the University of South Florida, which is available for research at [36]. In our experiments, all images manually selected from this database are intensity images, digitized at 43.5 μ m/pixel and 12-bit gray scale.…”

Section: Methodsmentioning

confidence: 99%

A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

Zhang

2014

The Scientific World Journal

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In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the l P-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods.

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Why is microcalcification missed on mammography?

Abstract: Breast screening reading for microcalcification is poorly correlated to mammographic or histological features. The majority of errors were perceptive rather than interpretative. Double reading is advocated as standard practice to reduce perceptive error.

Cited by 10 publications

References 13 publications

An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced

An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced

Digital Mammography Screening: Does Age Influence the Detection Rates of Low-, Intermediate-, and High-Grade Ductal Carcinoma in Situ?

A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

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