The halo sign and malignant breast lesions

Swann, C A; Kopans, D. B.; Koerner, F C; McCarthy, Kathleen A.; White, G. Edward; Hall, DA

doi:10.2214/ajr.149.6.1145

Cited by 16 publications

(4 citation statements)

References 3 publications

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“…3) and was first considered to be a strong indicator of benignity of a lesion. Historically, the halo sign was attributed to compressed fat or an illusory Mach band [19,[23][24][25]. However, Cupples et al [19] used an optical magnification method and suggested that a halo is not always a perceptual illusion that can be seen around both benign and malignant lesions.…”

Section: Discussionmentioning

confidence: 99%

Comparison of visibility of circumscribed masses on Digital Breast Tomosynthesis (DBT) and 2D mammography: are circumscribed masses better visualized and assured of being benign on DBT?

et al. 2016

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Section: Discussionmentioning

confidence: 99%

Comparison of visibility of circumscribed masses on Digital Breast Tomosynthesis (DBT) and 2D mammography: are circumscribed masses better visualized and assured of being benign on DBT?

et al. 2016

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“…Although well-circumscribed cancers are relatively uncommon, they do exist (22). Medullary, colloid (mucinous), and papillary carcinoma commonly manifest as well-circumscribed masses (Fig 13).…”

Section: Subtle Signs Of Malignancymentioning

confidence: 99%

Missed Breast Carcinoma: Pitfalls and Pearls

Majid

Paredes²,

Doherty³

et al. 2003

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Mammography is the standard of reference for the detection of breast carcinoma, yet 10%-30% of breast cancers may be missed at mammography. Possible causes for missed breast cancers include dense parenchyma obscuring a lesion, poor positioning or technique, perception error, incorrect interpretation of a suspect finding, subtle features of malignancy, and slow growth of a lesion. Recent studies have emphasized the use of alternative imaging modalities to detect and diagnose breast carcinoma, including ultrasonography (US), magnetic resonance imaging, and nuclear medicine studies. However, the radiologist can take a number of steps that will significantly enhance the accuracy of image interpretation at mammography and decrease the false-negative rate. These steps include performing diagnostic as well as screening mammography, reviewing clinical data and using US to help assess a palpable or mammographically detected mass, strictly adhering to positioning and technical requirements, being alert to subtle features of breast cancers, comparing recent images with earlier mammograms to look for subtle increases in lesion size, looking for additional lesions when one abnormality is seen, and judging a lesion by its most malignant features.

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“…Our CAD system is designed to detect both benign and malignant masses on screening mammograms because all suspicious findings will be recalled for diagnostic work-up or short-term follow-up. [36][37][38] However, it will be important to evaluate the performance of the CAD system for detection of malignant masses. Figure 10 shows a comparison of the four CAD systems for the subset of 168 malignant masses in the data set.…”

Section: Iiib Froc Analysis For Evaluation Of Detection Performancementioning

confidence: 99%

Computer‐aided detection of breast masses: Four‐view strategy for screening mammography

et al. 2011

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Purpose:To improve the performance of a computer-aided detection ͑CAD͒ system for mass detection by using four-view information in screening mammography. Methods: The authors developed a four-view CAD system that emulates radiologists' reading by using the craniocaudal and mediolateral oblique views of the ipsilateral breast to reduce false positives ͑FPs͒ and the corresponding views of the contralateral breast to detect asymmetry. The CAD system consists of four major components: ͑1͒ Initial detection of breast masses on individual views, ͑2͒ information fusion of the ipsilateral views of the breast ͑referred to as two-view analysis͒, ͑3͒ information fusion of the corresponding views of the contralateral breast ͑referred to as bilateral analysis͒, and ͑4͒ fusion of the four-view information with a decision tree. The authors collected two data sets for training and testing of the CAD system: A mass set containing 389 patients with 389 biopsy-proven masses and a normal set containing 200 normal subjects. All cases had four-view mammograms. The true locations of the masses on the mammograms were identified by an experienced MQSA radiologist. The authors randomly divided the mass set into two independent sets for cross validation training and testing. The overall test performance was assessed by averaging the free response receiver operating characteristic ͑FROC͒ curves of the two test subsets. The FP rates during the FROC analysis were estimated by using the normal set only. The jackknife free-response ROC ͑JAFROC͒ method was used to estimate the statistical significance of the difference between the test FROC curves obtained with the single-view and the four-view CAD systems. Results: Using the single-view CAD system, the breast-based test sensitivities were 58% and 77% at the FP rates of 0.5 and 1.0 per image, respectively. With the four-view CAD system, the breast-based test sensitivities were improved to 76% and 87% at the corresponding FP rates, respectively. The improvement was found to be statistically significant ͑p Ͻ 0.0001͒ by JAFROC analysis. Conclusions:The four-view information fusion approach that emulates radiologists' reading strategy significantly improves the performance of breast mass detection of the CAD system in comparison with the single-view approach.

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The halo sign and malignant breast lesions

Cited by 16 publications

References 3 publications

Comparison of visibility of circumscribed masses on Digital Breast Tomosynthesis (DBT) and 2D mammography: are circumscribed masses better visualized and assured of being benign on DBT?

Comparison of visibility of circumscribed masses on Digital Breast Tomosynthesis (DBT) and 2D mammography: are circumscribed masses better visualized and assured of being benign on DBT?

Missed Breast Carcinoma: Pitfalls and Pearls

Computer‐aided detection of breast masses: Four‐view strategy for screening mammography

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