Microcalcificações mamárias na mamografia de rastreio: uma revisão bibliográfica/  Breast microcalcifications in screening mammography: a literature review

Abreu, Mariana Santos de Andrès; Ferreira, Ana Carolina Matos; Lanza, Camila Cardoso; Santiago, João Henrique Vilaça; Tanure, Luciana Coelho; Oliveira, Mayra Feitosa de; Nogueira, Paola Falcão Moreira; Silveira, Rachel Pimentel Romano

doi:10.34119/bjhrv4n6-292

Cited by 1 publication

(2 citation statements)

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“…The most comprehensive large-scale screening method available is the mammographic exam. Unfortunately, about 20% of mammograms are false negative and up to 50% are false positive [1,2]. To minimize diagnostic errors, two radiologists usually review multiple images, and when this is not available, a Computer-Aided Detection or Diagnosis (CADe\Dx) scheme can be used [3].…”

Section: Introductionmentioning

confidence: 99%

“…Most computer-aided systems in this field are actually CADe (Computer-Aided Detection), ie., they detect and inform the location of suspicious signals on the image. They have limited use, as tests have shown that most mammography errors are caused by misinterpretation and not by missed detections [2,4,5]. Experienced radiologists tend to be more assertive about CAD schemes to help their analysis rather than their ability to detect suspicious signals, mainly due to the value of information about quantitative density data and and other findings [5].…”

Section: Introductionmentioning

confidence: 99%

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Real time mass classification for mammographic images: a Driven CADx scheme

Schiabel¹,

Matheus²,

Cardoso³

2023

Braz. J. Hea. Rev.

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Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%