The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review

Henriksen, E; Carlsen, Jonathan Frederik; Vejborg, Ilse; Nielsen, Michael Bachmann; Lauridsen, Carsten Ammitzbøl

doi:10.1177/0284185118770917

Cited by 98 publications

(71 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, CAD systems should only be used as support for radiologists; the responsibility lies solely with the physician. CAD systems are nowadays widely accepted and used throughout the field of radiology [16, 40]. The image analysis of CAD systems is based on image acquisition, segmentation, processing, detection, and output [15, 16, 41].…”

Section: Computer Assistance For Image-based Diagnosis Of Bcmentioning

confidence: 99%

Digital Analysis in Breast Imaging

Ingrisch

Fallenberg

2019

Breast Care

View full text Add to dashboard Cite

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

show abstract

Section: Computer Assistance For Image-based Diagnosis Of Bcmentioning

confidence: 99%

Digital Analysis in Breast Imaging

Ingrisch

Fallenberg

2019

Breast Care

View full text Add to dashboard Cite

show abstract

“…Over the past few decades, several CAD systems have been approved by the US Food and Drug Administration to help in imaging situations [13]. Many investigators have studied CAD for diagnosis of various types of diseases, including lung diseases [14,15], breast cancer [16,17], stroke [18], liver cancer [19], microcalcifications [20], and artery disease [21].…”

Section: Biomedical Applications Of Cadmentioning

confidence: 99%

Introductory Chapter: Computer-Aided Diagnosis for Biomedical Applications

Wang¹,

Yu²

2019

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

View full text Add to dashboard Cite

“…It is the most frequent cancer among females and the most frequent cause of cancer-associated death comprising 24.2% of total cancer new cases and 15% of all cancer death (Bray et al 2018). The incidence rate increases day by day, therefore, research ongoing on the development of a superior method for early diagnosis of BC is a must to overcome this serious problem and to reduce mortality (Joshi, 2019;Henriksen et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Diagnostic role of collagen III in the diagnosis of breast cancer in Egyptian women

Attallah¹,

El‐Far

Sayes³

et al. 2020

Journal of Bioscience and Applied Research

View full text Add to dashboard Cite

Mammography, magnetic resonance imaging (MRI), and ultrasound are useful tools that can be used in breast cancer (BC) screening but these methods have various limitations. Thus, finding diagnostic biomarkers may be a useful alternative choice. A total of 142 females with breast diseases (40 females with benign conditions and 102 females with BC) were included in the study. In addition to age-and sex-matched 29 normal females as a negative control. The level of collagen III was detected using the ELISA technique. It was (21.29±3.14 µg/ml) in sera of females with BC higher than in benign (13.91±2.65 µg/ml) and in normal (5.69±0.43 µg/ml) females. Collagen III yielded an AUC of 0.86 to differentiate BC from normal females and an AUC of 0.62 to differentiate BC females from those with benign conditions. Moreover, AUC was risen to 0.72 to differentiate females with BC from those without BC. While the index provided values that were significantly (P<0.01) higher in BC (9.1±1.3) than benign (6.3±1.1) and normal (2.5±1.1) females. It yielded an AUC of 0.98 (sensitivity = 90.0 %) to differentiate BC from normal females and an AUC of 0.84 to differentiate BC females from those with benign conditions. Moreover, AUC was risen to 0.87 to differentiate females with BC from those without BC (sensitivity = 84.0 %). In conclusion, serum collagen III concentration represents a candidate biomarker for BC. Moreover, its combination with cancer antigen 15-3 (CA 15.3) and age provided a valuable index for BC diagnosis with high accuracy.

show abstract

The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review

Cited by 98 publications

References 36 publications

Digital Analysis in Breast Imaging

Digital Analysis in Breast Imaging

Introductory Chapter: Computer-Aided Diagnosis for Biomedical Applications

Diagnostic role of collagen III in the diagnosis of breast cancer in Egyptian women

Contact Info

Product

Resources

About