Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches

Mendes, João; Matela, Nuno

doi:10.3390/jimaging7060098

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…Clinical DBT images result from heavy processing. There is little published work with measures derived from clinical DBT images for risk factor purposes at this time [24, 31]. It is reasonable to assume that a more precise measure of breast density would result from analyzing volumetric images in comparison with conventional 2D mammograms.…”

Section: Introductionmentioning

confidence: 99%

“…There are many methods under investigation for measuring both breast density and more generally texture [24][25][26][27]. The percentage of breast density measure (PD) has been studied for many years and has repeatedly shown to be significantly associated with breast cancer risk [28].…”

Section: Introductionmentioning

confidence: 99%

“…At present for screening in many high-income countries, risk is based primarily on age [22]. Risk prediction methods that use some type of image derived information (simple modeling through deep learning) show that texture may be an important factor, yet it is not commonly used in practice [4, 23, 24].…”

Section: Introductionmentioning

confidence: 99%

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Breast Density Analysis Using Digital Breast Tomosynthesis

Heine

Fowler

Weinfurtner

et al. 2023

Preprint

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Background: Breast density (BD) is a significant breast cancer risk factor, traditionally assessed from two-dimensional (2D) mammograms. Mammography has shifted to digital breast tomosynthesis (DBT). BD measures developed for traditional 2D mammograms will require translation to DBT images. Objectives: We evaluated an anutomated percentage of BD measure (PDa) using several methods as risk factors with DBT data: (1) normalized volumetric measure; (2) total dense volume (Dv); (3) 2D measure applied to DBT volume images (slices) and averaged (slice-mean); and (4) applied to synthetic 2D images. For the DBT volume analysis, measure were derived theoretically and evaluated. PDawas modeled as a function of compressed breast thickness. An alternative method for constructing synthetic 2D mammograms was evaluated using the PDavolume results as a basis. Other measures such as the mean and standard deviations of the pixel values were also investigated as risk factors. Methods: A case-control study (n = 426 pairs) design was investigated with matching on age, hormone replacement, imaging unit, and screening history. Conditional logistic regression modeling, controlling body mas index and ethnicity, was used to estimate odds ratios (ORs) for the various image measures with 95% confidence intervals provided parenthetically. ORs were estimated per standard deviation increment of the respective image measurement. Results: Volumetic PDawas significantly associated with breast cancer risk [OR = 1.43 (1.18, 1.72)] and produced an identical OR as the slice-mean measure. PDavalues from the slices (function of compressed breast thickness) were modeled as 2nddegree polynomial (concave-down). The maximum PDavalue occurred at approximately 0.41×(compressed breast thickness), and this position was similar across case-control groups; PDafrom this slice location was significant [OR = 1.47 (1.21, 1.78)]. PDaproduced significant findings when applied to the 2D synthetic DBT images: [OR = 1.44 (1.18, 1.75)]. The mean of the pixel values taken from either the volume or synthetic 2D images were similar and significant [ORs ~ 1.31 (1.09, 1.57)]. Dvdid not produce a significant finding. Discussion: This study produced several findings. The PDaalgorithm provided significant ORs when applied to the volume and synthetic 2D images. AS predicted by the derivations, the volumetric and slice-mean measures are equivalent and produced an identical OR. The maximum PDaslice position found, empirically agreed with the polynomial model prediction. Dvwas not significant until normalized by the breast volume. The volumetric analysis enabled the construction of an alternative standardized 2D synthetic image, where each pixel value represents the percentage of BD above its location. Conclusion: Several BD measurements derived from DBT images produced significant breast cancer associations, showing the validity of the automated techniques. Theoretical derivations supported by empirical analyses led to the construction of a new synthetic 2D standardized image derived from the volume measurements. Ancillary to the study objectives, these volumetric findings provide evidence for understanding the nature of the percentage of breast density measure applied to traditional 2D mammograms. The study will require replication in different populations. Notwithstanding the findings, the study design provides a template for investigating other possible risk measures such as texture from DBT.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Breast Density Analysis Using Digital Breast Tomosynthesis

Heine

Fowler

Weinfurtner

et al. 2023

Preprint

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“…The false-positive findings affect women's lives, particularly in terms of daily welfare and medicine expenses. However, false positives are not ultrasound's sole disadvantage [8]. Sure researchers have studied Nucleus analysis, who have extracted nucleus characteristics that can categorize cells as benign or malignant [9].…”

Section: Introductionmentioning

confidence: 99%

Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

et al. 2021

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Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

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“…Literature reviews are useful in providing a comprehensive vision of computer-assisted approaches to support the clinical process, especially for young scientists [12,13]. In particular, [14] reviews extraction methods of textural features from mammograms, where machine learning and deep learning algorithms are used to infer knowledge from the features and assess breast cancer risk. The accurate diagnosis of breast cancer is very challenging due to the increasing disease complexity, such as changes in treatment procedures and patient population samples.…”

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confidence: 99%