Mammography in Breast Disease Screening and Diagnosis

Tari, Daniele Ugo; Pinto, Fábio

doi:10.3390/jpm13020228

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In this regard, mammography (MG) is among the most widespread techniques to perform screening exams for the detection of small nodules, potentially dangerous calcifications, and other types of suspicious lesions [12,13]. The screening exam consists of an X-ray scan of each breast in two projections (or views), namely the craniocaudal (CC) and the medial lateral oblique (MLO) projection, to improve anomaly visualization, thus obtaining four images per patient.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions

Ponsiglione

Angelone

Amato

et al. 2023

JPM

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Despite mammography (MG) being among the most widespread techniques in breast cancer screening, tumour detection and classification remain challenging tasks due to the high morphological variability of the lesions. The extraction of radiomics features has proved to be a promising approach in MG. However, radiomics features can suffer from dependency on factors such as acquisition protocol, segmentation accuracy, feature extraction and engineering methods, which prevent the implementation of robust and clinically reliable radiomics workflow in MG. In this study, the variability and robustness of radiomics features is investigated as a function of lesion segmentation in MG images from a public database. A statistical analysis is carried out to assess feature variability and a radiomics robustness score is introduced based on the significance of the statistical tests performed. The obtained results indicate that variability is observable not only as a function of the abnormality type (calcification and masses), but also among feature categories (first-order and second-order), image view (craniocaudal and medial lateral oblique), and the type of lesions (benign and malignant). Furthermore, through the proposed approach, it is possible to identify those radiomics characteristics with a higher discriminative power between benign and malignant lesions and a lower dependency on segmentation, thus suggesting the most appropriate choice of robust features to be used as inputs to automated classification algorithms.

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Section: Introductionmentioning

confidence: 99%

A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions

Ponsiglione

Angelone

Amato

et al. 2023

JPM

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“…Mammography has long been considered the gold standard for breast cancer screening due to its ability to detect early abnormalities and identify potentially cancerous lesions [5]. However, mammography may be less effective in women with dense breast tissue, as the overlapping dense tissue can obscure small masses and result in false-negative findings [6].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

LAGHMATI,

HICHAM,

CHERRADI

et al. 2023

IJACSA

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Breast cancer (BC) is one of the most prevailing and life-threatening types of cancer impacting women worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages the Breast Ultrasound Images BUSI dataset to develop two variations of a segmentation model using the Attention U-Net architecture. In this study, we trained the Attention3 U-Net and the Attention4 U-net on the BUSI dataset, consisting of normal, benign, and malignant breast lesions. We evaluated the model's performance based on standard segmentation metrics such as the Dice coefficient and Intersection over Union (IoU). The results demonstrate the effectiveness of the Attention U-Net in accurately segmenting breast lesions, with high overall performance, indicating agreement between predicted and ground truth masks. The successful application of the Attention U-Net to the BUSI dataset holds promise for improving breast cancer diagnosis and treatment. It highlights the potential of deep learning in medical image analysis, paving the way for more efficient and reliable diagnostic tools in breast cancer management.

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New Advances in Mammography as Guidance for Vacuum-assisted Breast Biopsy: A Complete Tool for Radiologists

Tari

2024

CRP

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Mammography in Breast Disease Screening and Diagnosis

Abstract: Female breasts can suffer from a wide spectrum of pathologies, ranging from inflammatory diseases to benign and malignant tumors [...]

Cited by 3 publications

References 13 publications

A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions

A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

New Advances in Mammography as Guidance for Vacuum-assisted Breast Biopsy: A Complete Tool for Radiologists

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