Breast Imaging

Benito, Marina Álvarez; Herrero, Julia Camps

doi:10.1007/978-3-540-71207-7_1

Cited by 1 publication

(3 citation statements)

References 97 publications

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“…In particular, one of the most important points that breast surgeons pay attention, during the diagnostic process through MAMMO, is whether the detected tumor or lesion is a new finding compared to a previous relevant examination. Another feature that is emphasized in the explanation of MAMMO findings is whether the breasts have micronodular morphology and calcifications [82]. The architectural disorder in the breast and the breast density based on the ACR scale, detected on MAMMO examination, is a very important risk factor for mammographic findings, as highlighted in numerous published studies [82], [83].…”

Section: Data Preprocessing and Feature Vectormentioning

confidence: 97%

“…Another feature that is emphasized in the explanation of MAMMO findings is whether the breasts have micronodular morphology and calcifications [82]. The architectural disorder in the breast and the breast density based on the ACR scale, detected on MAMMO examination, is a very important risk factor for mammographic findings, as highlighted in numerous published studies [82], [83]. Complementary to MAMMO findings, US can provide critical information regarding the shape of the tumor detected in the examination (e.g., oval shape, radial shape, etc).…”

Section: Data Preprocessing and Feature Vectormentioning

confidence: 99%

“…Another important feature that US report contains, is the characterization of the boundaries of the localized lesion in terms of their smoothness. Additionally, the vascularization of the tumor as well as the condition of the lymph nodes, are frequently described in the US report, information of particular concern to the clinicians as they can be the tell-tale signs of a malignant lesion [82].…”

Section: Data Preprocessing and Feature Vectormentioning

confidence: 99%

See 2 more Smart Citations

Development of a Cost-Effective Intelligent Clinical Decision Support System for Breast Cancer Early Diagnosis and Triage

Tarousi

Bountris

Daskalakis³

et al. 2022

Int. J. Onl. Eng.

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Women population screening using mammography has dramatically reduced breast cancer rates worldwide. Nowadays, in many countries, the prevention of breast cancer policy is based on frequent and repeated mammographies, followed by breast ultrasound and if necessary, by histological examination in the biological material of the biopsy. However, evaluating mammography findings is considered as a difficult process which can properly performed only by a highly experienced and well-trained medical staff. Subsequently, the interpretation of those findings could be easily influenced by subjective factors and therefore can be prone to diagnostic errors as evidenced in the present study. Breast MRI, is a diagnostic practice indicated in cases of high breast density and at the same time, a high-cost examination for healthcare systems. Furthermore, genetic testing that is used to diagnose hereditary breast cancer, represents a small proportion of breast cancers and at the same time it is a high-cost specialized test. Even the most widely used biopsy, Fine Needle Aspirate (FNA), in some cases involves risks and provides false negative results. This study presents a novel intelligent Clinical Decision Support System (CDSS), which uses data from common practice, non-invasive and low-cost diagnostic tests, together with medical health record, in order to provide clinicians with a viable, cost-effective and accurate diagnostic solution. After implementing several algorithms, Random Forest classifier showed the highest values of sensitivity 96,2 %, specificity 94,6%, PPV 96,2% and NPV 94,6%, being thus an effective algorithm in the development of our innovative CDSS model aiming to constitute a very useful tool in clinical practice for breast cancer early diagnosis.

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Section: Data Preprocessing and Feature Vectormentioning

confidence: 97%