Impact of Addition of Digital Breast Tomosynthesis to Digital Mammography in Lesion Characterization in Breast Cancer Patients

Classification of asymmetry in mammography via the DenseNet convolutional neural network

Purpose

To investigate the effectiveness of a deep learning system based on the DenseNet convolutional neural network in diagnosing benign and malignant asymmetric lesions in mammography.

Methods

Clinical and image data from 460 women aged 23-82 years (47.57 ± 8.73 years) with asymmetric lesions who underwent mammography at Shenzhen People's Hospital, Shenzhen Luohu District People's Hospital, and Shenzhen Hospital of Peking University from December 2019 to December 2020 were retrospectively analyzed. Two senior radiologists, two junior radiologists, and the DL system read the mammographic images of 460 patients, respectively, and finally recorded the BI-RADS classification of asymmetric lesions. We then used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the diagnostic efficacy and the difference between AUCs by the Delong method.

Results

Specificity (0.909 vs. 0.835, 0.790, χ2=8.21 and 17.22, p＜0.05) and precision (0.872 vs. 0.763, 0.726, χ2=9.23 and 5.22, p＜0.05) of the DL system in the diagnosis of benign and malignant asymmetric lesions were higher than those of junior radiologist A and B, and there was a statistically significant difference between AUCs (0.778 vs. 0.579, 0.564, Z = 4.033 and 4.460, p＜0.05). Furthermore, the AUC (0.778 vs. 0.904, 0.862, Z = 3.191, and 2.167, p＜0.05) of benign and malignant asymmetric lesions diagnosed by the DL system was lower than that of senior radiologist A and senior radiologist B.

Conclusions

The DL system based on the DenseNet convolution neural network has high diagnostic efficiency, which can help junior radiologists evaluate benign and malignant asymmetric lesions more accurately. It can also improve diagnostic accuracy and reduce missed diagnoses caused by inexperienced junior radiologists.

Bright Is Not Always Better: A Pictorial Review of Hyperechoic Malignant Breast Masses

Hyperechogenic breast lesions are a relatively rare finding at breast ultrasonography and are traditionally thought to be benign. However, hyperechogenicity on the ultrasound alone does not provide enough evidence to rule out malignancy completely. We herein reported a short series of nine cases of echogenic malignant breast lesions, which include invasive ductal carcinoma, ductal carcinoma in situ, invasive lobular carcinoma, angiosarcoma, lymphoma, and metastasis to the breast. Echogenic breast lesions should be carefully evaluated and properly categorized based on any other suspicious sonographic characteristics and must be correlated with mammographic findings and clinical history to lower the threshold for biopsy and avoid delay in diagnosis. Hyperechogenicity should not be considered as a characteristically benign feature and should not supersede the less specifically benign features of the same lesion on the other examination.

Three-dimensional tomosynthesis versus two-dimensional mammography in detection and characterization of different breast lesions

Background

Breast cancer is considered the most serious lesion among different breast lesions. Mammography is the corner stone for screening for detection of breast cancer. It has been modified to digital mammography (DM) and then to digital breast tomosynthesis (DBT). Tomosynthesis is an emerging technique for diagnosis and screening of breast lesions.

The aim of this study is to interrogate whether the addition of DBT to DM helps in better detection and characterization of different breast lesions.

Methods

This is a prospective study carried on 38 female patients according to our inclusion criteria. All patients were evaluated by using DM alone and thereafter with the addition of DBT to DM. Recall rate was calculated, and the imaging findings of each case were correlated with the final diagnosis and follow-up.

Results

DM identified 32 lesions while DBT with DM identified 37 lesions. On DM alone, 17 lesions were characterized as masses, 5 as focal asymmetry, 2 as architectural distortion, 7 as microcalcification and 1 as macrocalcification. With the addition of DBT, 27 lesions were characterized as masses, 1 as focal asymmetry, 1 as architectural distortion, 7 as microcalcification and 1 as macrocalcification. So, there were better detection and characterization of lesions with the addition of DBT than DM alone. The sensitivity, specificity, AUC, positive and negative predictive values were significantly higher with the addition of DBT to DM (100%, 90.5%, 0.952, 90% and 100%, respectively) than with DM (77.8%, 80.9%, 0.794, 77.8% and 80.9%, respectively) for all breast lesions.

Conclusions

The addition of DBT to DM helps in better detection and characterization of different breast lesions. This leads to early detection of breast cancer, improvement of the performance of radiologists and saving time by reduction of recall rate.

SCREENING MAMMOGRAPHY: DIAGNOSTIC EFFICACY-ISSUES AND CONSIDERATIONS FOR THE 2020S

Diagnostic efficacy in medical imaging is ultimately a reflection of radiologist performance. This can be influenced by numerous factors, some of which are patient related, such as the physical size and density of the breast, and machine related, where some lesions are difficult to visualise on traditional imaging techniques. Other factors are human reader errors that occur during the diagnostic process, which relate to reader experience and their perceptual and cognitive oversights. Given the large-scale nature of breast cancer screening, even small increases in diagnostic performance equate to large numbers of women saved. It is important to identify the causes of diagnostic errors and how detection efficacy can be improved. This narrative review will therefore explore the various factors that influence mammographic performance and the potential solutions used in an attempt to ameliorate the errors made.