FIGURE 1 - uploaded by Anahita Tavana
Content may be subject to copyright.
Pulmonary angiogram chest CT performed in the emergency department Axial (A) and coronal (B) images demonstrate incidental finding of isoattenuating mass with subtle rim enhancement (red arrows) in the right breast.
Source publication
There are many benign breast lesions that mimic breast cancer on breast imaging. Postlumpectomy scar, hematoma, fat necrosis, diabetic mastopathy, and granulomatous mastitis are examples of benign breast lesions that have suspicious breast imaging findings.Mammogram and breast ultrasound are the imaging studies to evaluate breast findings.CT scan i...
Contexts in source publication
Context 1
... upon arrival included a chest x-ray and CT chest pulmonary angiogram. Pertinent findings included a circumscribed, isoattenuating mass in the right breast measuring 2.4 x 2.5 cm with subtle rim enhancement (anteroposterior by transverse dimension) (Figure 1). Most recent screening mammogram two weeks prior to the abnormal CT scan was Breast Imaging Reporting and Data System (BI-RADS) category 2 considered as benign for postlumpectomy changes ( Figure 2). ...
Context 2
... upon arrival included a chest x-ray and CT chest pulmonary angiogram. Pertinent findings included a circumscribed, isoattenuating mass in the right breast measuring 2.4 x 2.5 cm with subtle rim enhancement (anteroposterior by transverse dimension) (Figure 1). Most recent screening mammogram two weeks prior to the abnormal CT scan was Breast Imaging Reporting and Data System (BI-RADS) category 2 considered as benign for postlumpectomy changes ( Figure 2). ...
Similar publications
Objective
To explore the application values of deep-learning based artificial intelligence (AI) automatic classification system, on the differential diagnosis of non-lactating mastitis (NLM) and malignant breast tumors, via its comparation with traditional ultrasound interpretations and the following interpretation conclusions made by the sonograph...
Citations
Objective:
This study was performed to investigate the accuracy of conventional ultrasound (US), contrast-enhanced US (CEUS), and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing the size of breast cancer.
Methods:
In total, 49 breast cancer lesions of 48 patients were included in this study. The inclusion criteria were the performance of total mastectomy or breast-conserving surgery for treatment of breast cancer in our hospital from January 2017 to December 2020 with complete pathological results, as well as the performance of conventional US, CEUS, and DCE-MRI examinations with complete results. The exclusion criteria were non-mass breast cancer shown on conventional US or DCE-MRI, including that found on CEUS with no boundary with surrounding tissues and no confirmed tumor scope; a tumor too large to be completely displayed in the US section, thus affecting the measurement results; the presence of two nodules in the same breast that were too close to each other to be distinguished by any of the three imaging methods; and treatment with preoperative chemotherapy. Preoperative conventional US, CEUS, and DCE-MRI examinations were performed. The postoperative pathological results were taken as the gold standard. The lesion size was represented by its maximum diameter. The accuracy, overestimation, and underestimation rates of conventional US, CEUS, and DCE-MRI were compared.
Results:
The maximum lesion diameter on US, CEUS, DCE-MRI and pathology were 1.62±0.63 cm (range, 0.6-3.5 cm), 2.05±0.75 cm (range, 1.0-4.0 cm), 1.99±0.74 cm (range, 0.7-4.2 cm) and 1.92±0.83 cm (range, 0.5-4.0 cm), respectively. The lesion size on US was significantly smaller than that of postoperative pathological tissue (P < 0.05). However, there was no significant difference between the CEUS or DCE-MRI results and the pathological results. The underestimation rate of conventional US (55.1%, 27/49) was significantly higher than that of CEUS (20.4%, 10/49) and DCE-MRI (24.5%, 12/49) (P < 0.001 and P = 0.002, respectively). There was no significant difference in the accuracy of CEUS (36.7%, 18/49) and DCE-MRI (34.7%, 17/49) compared with conventional US (26.5%, 13/49); however, the accuracy of both groups tended to be higher than that of conventional US. The overestimation rate of CEUS (42.9%, 21/49) and DCE-MRI (40.8%, 20/49) was significantly higher than that of conventional US (18.4%, 9/49) (P = 0.001 and P = 0.015, respectively).
Conclusions:
CEUS and DCE-MRI show similar performance when evaluating the size of breast cancer. However, CEUS is more convenient, has a shorter operation time, and has fewer restrictions on its use. Notably, conventional US is more prone to underestimate the size of lesions, whereas CEUS and DCE-MRI are more prone to overestimate the size.
Breast vasculature is visualised on all screening and diagnostic imaging of the breast. Various vascular breast lesions exist, spanning from chronic systemic processes, congenital malformations, post-traumatic sequela, benign masses, to malignant tumours. Accurate diagnosis of vascular lesions on breast imaging can be difficult due to overlapping characteristics. Radiologists should be aware of key multi-technique imaging features to make an accurate diagnosis and to avoid unnecessary biopsies. This article reviews the normal vascular anatomy of the breast, commonly used imaging techniques to diagnose vascular lesions, and an in-depth review of various vascular breast lesions. Finally, management recommendations and clinical guidance are discussed so that the radiologist can appropriately triage these patients.
Breast Cancer is most common disease in worldwide leads to high rate in mortality. Detection of symptoms at early stage is difficult to identify the breast cancer. Diagnosis through General Medical Examination cannot detect the disease. This paper represents various common methods to detect the Breast Cancer and detection of tumors using mammogram images with Artificial Intelligence (AI). Algorithms helps Images in preprocessed using various feature extraction methods and classification algorithms to predict the class of tumors is benign or malignant. This article insight the recent approaches used in detection of tumors and stage of breast cancer.
