Three-Dimensional Digital Breast Tomosynthesis in the Early Diagnosis and Detection of Breast Cancer

2006 ◽  
pp. 152-159 ◽  
Author(s):  
Mari Varjonen
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Three Dimensional ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals Tecnica di Imaging tridimensionale (Digital Breast Tomosynthesis) per la diagnosi del tumore alla mammella

2019 ◽  
Author(s):  
Di Guida Lisa ◽  
De Rosa Salvatore
Keyword(s):  
Breast Cancer ◽  
Early Diagnosis ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Digital Breast Tomosynthesis ◽  
Recurrence Time ◽  
Digital Tomosynthesis ◽  
Breast Tomosynthesis ◽  
X Ray Imaging ◽  
Projective Image

Breast cancer affects one in eight women over a lifetime. It is the most common cancer in women and represents 29% of all cancers affecting women, with a mortality rate of 17% of all deaths due to cancer on women. Sooner the cancer is identified with an early diagnosis, higher are the possibilities to treat it completely and longer is the recurrence time. Mammography is the most common method for early diagnosis. is a two-dimensional X-ray imaging technique and this involves the overlapping of the tissues in the projective image inability to visualize cancer in the first stage. In recent years, three-dimensional imaging techniques have been introduced, including digital tomosynthesis for the diagnosis of breast cancer, this technique has the advantages to perform dozens of projections, and not just one, from various angular views around the compressed breast. The major benefits of tomosynthesis are a lower stratification of breast tissues, better visibility of tumor masses especially for small tumors, breast tomosynthesis provides the ability to visualize 3D images to obtain a more accurated evaluation of lesions allowing better differentiation between overlapping fabrics.

scholarly journals Diagnostic Value of Digital Breast Tomosynthesis Combined with Magnetic Resonance Imaging for Breast Cancer in Dense Breast Tissues

2021 ◽  
Vol 18 (4) ◽  
Author(s):  
Hongfang Xu ◽  
Wei Zeng ◽  
Zehong Fu ◽  
Qing Cui
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Early Diagnosis ◽  
Digital Breast Tomosynthesis ◽  
Diagnostic Value ◽  
Diagnostic Efficiency ◽  
Breast Cancer Patients ◽  
Resonance Imaging ◽  
Breast Tomosynthesis

Background: Early diagnosis and timely treatment are crucial for breast cancer patients. Objectives: This study aimed to investigate the diagnostic value of full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and magnetic resonance imaging (MRI) for breast cancer. Patients and Methods: This study was performed on 210 patients diagnosed with breast cancer and benign breast lesions (n = 105) by FFDM, DBT, MRI, and pathological examination from January 2019 to December 2020. The patients’ imaging and clinical data were retrospectively analyzed. The lesions were evaluated according to the breast imaging-reporting and data system, with pathological diagnosis as the gold standard. The diagnostic efficiency of the examination methods was analyzed by plotting the receiver operating characteristic (ROC) curves. The DBT and MRI results were finally compared. Results: In 210 patients, 105 benign and 105 malignant lesions were detected. The area under the ROC curve (AUC) of FFDM, DBT, MRI, FFDM + DBT, and FFDM + MRI was 0.734, 0.857, 0.883, 0.865, and 0.924, respectively. Based on the results, the AUC values were significantly higher for DBT, MRI, FFDM + DBT, and FFDM + MRI compared to FFDM (P < 0.05), while similar values were reported for the former methods (P > 0.05). The diagnostic sensitivity of MRI was higher than that of DBT and FFDM; the sensitivity of DBT was higher than that of FFDM; and the specificity and positive predictive value were higher for DBT compared to MRI and FFDM. Conclusion: Compared to FFDM, DBT and FFDM + DBT could significantly improve the diagnostic efficiency of breast cancer; the diagnostic efficiency of these modalities was comparable to that of MRI and FFDM + MRI. The sensitivity of DBT was lower than that of MRI and higher than that of FFDM, while its specificity and positive predictive value were higher than those of MRI. Overall, FFDM + DBT and FFDM + MRI are conducive to early diagnosis.

Fusion Techniques for the Breast Cancer Detection

2020 ◽  
Vol 17 (9) ◽  
pp. 4036-4040
Author(s):  
M. Veena ◽  
M. C. Padma
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Computer Technology ◽  
Medical Science ◽  
Three Dimensional ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Breast Image ◽  
Cancerous Cells

Breast cancer is a considerable prime cause which affects the lives of women’s and leads to death all around the world. A tumor is said to be malignancy if a number of cancerous cells can spread to other organs. There has been great impact on the field of medical imaging by the advancement of the computer technology as new and improved techniques of data acquisition, analysis, processing and visualization has evolved. A Digital Breast Tomosynthesis is used for the detection of breast cancer. This is also called as three-dimensional mammography, which eliminates the overlapping tissue problem. Digital Breast Tomosynthesis (DBT) provides information about potential abnormal tissues necessary for medical follow up and diagnosis. DBT gets additional importance in medical science as it is the only preliminary method of diagnosing a breast cancer with the dense breast. Early and accurate diagnosis can be sufficient in resolving various complications and guiding the patient with timely and proper treatment. By considering the above factors there is a great requirement to explore for DBT. The proposed work develops a computer aided methodology for automatic tumor detection and diagnosing in tomosynthesis Patient’s image. This method is incredibly helpful for doctors or the radiologist automatically locates the tumor space within the breast image for further surgery.

A Survey on Female Breast Cancer

2019 ◽  
pp. 209-226
Author(s):  
Kalaivani Anbarasan ◽  
Ramya S.
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Research Work ◽  
Three Dimensional ◽  
Digital Breast Tomosynthesis ◽  
Female Breast Cancer ◽  
Breast Tomosynthesis ◽  
Imaging Model ◽  
Image Set ◽  
Aided Diagnosis

The mortality rate of breast cancer can be effectively reduced by early diagnosis. Imaging modalities are used to diagnose through computer for women breast cancer. Digital mammography is the best imaging model for breast cancer screening technique and diagnosis. Digital breast tomosynthesis (DBT), a three-dimensional (3-D) mammography, is an advanced form of breast imaging where multiple images of the breast from different angles are captured and reconstructed (synthesized) into a three-dimensional image set. This chapter discusses the research work carried out on the computer diagnosis of women breast cancer through digital breast tomosynthesis and concludes with further improvement in the computer-aided diagnosis.

scholarly journals Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

2021 ◽  
Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Support System ◽  
Reading Time ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Screening Programs ◽  
The Impact

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

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