BI-RADS Density Classification From Areometric and Volumetric Automatic Breast Density Measurements

Østerås, Bjørn Helge; Martinsen, Anne Catrine Trægde; Brandal, Siri Helene Bertelsen; Chaudhry, Khalida Nasreen; Eben, Ellen B.; Haakenaasen, Unni; Falk, Ragnhild Sørum; Skaane, Per

doi:10.1016/j.acra.2015.12.016

Cited by 8 publications

(8 citation statements)

References 25 publications

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“…Similarly, the number of women with heterogeneously dense was reduced by 8.2 % and the number of women with extremely dense breasts were reduced by 3.5 % (a 34 % decrease in number of extremely dense breast compared to BI-RADS density assessment). Consequently, about a third of the women are considered dense compared to about half using BI-RADS density assessment (207). Similar changes in density distribution has been reported by other authors (86,91).…”

Section: Potential Transition To Volumetric Breast Density Assessmentsupporting

confidence: 83%

“…In paper II, two different methods for determining cutoff values were investigated (207). One where sensitivity and specificity compared to BI-RADS density assessment was maximized (using Youden's index).…”

Section: Automatic Assessment Of Breast Densitymentioning

confidence: 99%

“…The performance was improved, using areabased assessment for the threshold between almost entirely fatty and scattered fibroglandular breasts. Since it is the denser breasts that are potentially interesting with respect to personalized screening and breast density legislation, volumetric density was concluded as preferable (207). The ROC method produced density distributions which were different from the radiologist's distribution, due to the excessive numbers of women classified as almost entirely fatty and extremely dense.…”

Section: Automatic Assessment Of Breast Densitymentioning

confidence: 99%

“…Automatic volumetric breast density has proved to be feasible in both DM and DBT mammographic screening, producing density scores similar to 4 th and 5 th edition of BI-RADS density (86,91,207). Still, a transition to implementing automatic assessment would introduce a shift in breast density assessment comparable to the inter-observer variation currently present in subjective interpretation for some radiologists.…”

Section: Automatic Volumetric Assessmentmentioning

confidence: 99%

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Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

et al. 2018

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The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. All received craniocaudal and mediolateral oblique views of each breasts with paired DM and DBT acquisitions. Exposure parameters were extracted from DICOM metadata. Air kerma and HVL were measured for all beam qualities used to acquire the mammograms. Volumetric breast density was estimated using Quantra. AGD was estimated using the Dance model. AGD reported directly from the DICOM metadata was also assessed. Mean AGD was 1.74 and 2.10 mGy for DM and DBT, respectively. Mean DBT/DM AGD ratio was 1.24. For fatty breasts: mean AGD was 1.74 and 2.27 mGy for DM and DBT, respectively. For dense breasts: mean AGD was 1.73 and 1.79 mGy, for DM and DBT, respectively. For breasts of similar thickness, dense breasts had higher AGD for DM and similar AGD for DBT. The DBT/DM dose ratio was substantially lower for dense compared to fatty breasts (1.08 versus 1.33). The average c-factor was 1.16. Using previously published polynomials to estimate glandularity from thickness underestimated the c-factor by 5.9% on average. Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.

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Section: Potential Transition To Volumetric Breast Density Assessmentsupporting

confidence: 83%

Section: Automatic Assessment Of Breast Densitymentioning

confidence: 99%

Section: Automatic Assessment Of Breast Densitymentioning

confidence: 99%

Section: Automatic Volumetric Assessmentmentioning

confidence: 99%

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Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

et al. 2018

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“…h) A hybrid method based on artificial immune system and fuzzy c-means was proposed for medicine diagnosis such as breast cancer [13]. i) A hybrid comparator system to compare radiologists' comments and output of a density-based image assessment system; using density thresholds and bootstrapping [39]. j) A text classification system based using the Naïve Bayesian (NB) learning algorithm to transforms the probability estimation problem into an optimization scheme [40].…”

Section: Related Workmentioning

confidence: 99%

Automated classification of BI-RADS in textual mammography reports

Boroumandzadeh¹,

Parvinnia²

2021

Turk J Elec Eng & Comp Sci

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The main purpose of this paper is to process key information in medical text records and also classify patients, per different levels of Breast Imaging-Reporting and Data system (BI-RADS). The BI-RADS is a scheme for the standardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reports supported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction from textual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosing keywords from medical text reports. Word2vec and Term Frequency Inverse Document Frequency (TFIDF) techniques are used for extracting features, finally, they're combined with the Hospital Information System (HIS) reports and called With-HIS. The different classifiers like multi-class Support Vector Machine (SVM), Naïve Bayesian (NB), Extreme Gradient Boosting (XGBoost), and Multi-Level Fuzzy Min-Max Neural Network (MLF) are used so as to compare the accuracy of With-HIS and Without HIS (called Without-HIS). The results are confirmed that using HIS beside the proposed approach (Word2vec +TFIDF) encompasses a significant effect on the accuracy of medical text classification. Accuracy within the proposed method with MLF classifier is 0.89% but without-HIS is 0.85%.

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Evaluating the Efficacy of Multi‐resolution Texture Features for Prediction of Breast Density Using Mammographic Images

Kaur

Virmani

2017

Hybrid Intelligence for Image Analysis and Understanding

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BI-RADS Density Classification From Areometric and Volumetric Automatic Breast Density Measurements

Cited by 8 publications

References 25 publications

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

Average glandular dose in paired digital mammography and digital breast tomosynthesis acquisitions in a population based screening program: effects of measuring breast density, air kerma and beam quality

Automated classification of BI-RADS in textual mammography reports

Evaluating the Efficacy of Multi‐resolution Texture Features for Prediction of Breast Density Using Mammographic Images

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