Breast cancer research and treatment reconstruction of unilateral breast structure using three-dimensional ultrasound imaging to assess breast neoplasm

How to accurately preoperative screen nipple-sparing mastectomy candidate-a nomogram for predicting nipple-areola complex involvement risk in breast cancer patients

BACKGROUND

Nipple-sparing mastectomy (NSM) offers superior cosmetic outcomes and has been gaining wide acceptance. It has always been difficult to objectively quantify the risk of nipple-areola complex involvement (NACi). The goal was to develop a prediction model for clinical application.

METHODS

Patients who had a total mastectomy (TM) between January 2016 and January 2020 at a single institute formed the development cohort (n = 578) and those who had NSM + immediate breast reconstruction (IBR) between January 2020 and January 2021 formed the validation cohort (n = 112). The prediction model was developed using univariate and multivariate logistic regression studies. Based on NACi risk variables identified in the development cohort, a nomogram was created and evaluated in the validation cohort. Meanwhile, stratified analysis was performed based on the model's risk levels and was combined with intraoperative frozen pathology (IFP) to optimize the model.

RESULTS

Tumor central location, clinical tumor size (CTS) > 4.0 cm, tumor-nipple distance (TND) ≤ 1.0 cm, clinical nodal status positive (cN +), and KI-67 ≥ 20% were revealed to be good predictive indicators for NACi. A nomogram based on these major clinicopathologic variables was employed to quantify preoperative NACi risk. The accuracy was verified internally and externally. The diagnostic accuracy of IFP was 92.9%, sensitivity was 64.3%, and specificity was 96.9% in the validation group. Stratified analysis was then performed based on model risk. The diagnostic accuracy rates of IFP and NACiPM in low-risk, intermediate-risk, and high-risk respectively were 96.0%, 93.3%, 83.9%, 61.3%, 66.7%, and 83.3%.

CONCLUSION

We created a visual nomogram to predict NACi risk in breast cancer patients. The NACiPM can be used to distinguish the low, intermediate, and high risk of NAC before surgery. Combined with IFP, we can develop a decision-making system for the implementation of NSM.

An Intelligent Breast Ultrasound System for Diagnosis and 3D Visualization

Background: Ultrasonography is the main examination method for breast diseases. Ultrasound imaging is currently relied upon by doctors to form statements of characteristics and locations of lesions, which severely limits the completeness and effectiveness of ultrasound image information. Moreover, analyzing ultrasonography requires experienced ultrasound doctors, which are not common in hospitals. Thus, this work proposes a 3D-based breast ultrasound system, which can automatically diagnose ultrasound images of the breasts and generate a representative 3D breast lesion model through typical ultrasonography. Methods: In this system, we use a weighted ensemble method to combine three different neural networks and explore different combinations of the neural networks. On this basis, a breast locator was designed to measure and transform the spatial position of lesions. The breast ultrasound software generates a 3D visualization report through the selection and geometric transformation of the nodular model. Results: The ensemble neural network improved in all metrics compared with the classical neural network (DenseNet, AlexNet, GoogLeNet, etc.). It proved that the ensemble neural network proposed in this work can be used for intelligent diagnosis of breast ultrasound images. For 3D visualization, magnetic resonance imaging (MRI) scans were performed to achieve their 3D reconstructions. By comparing two types of visualized results (MRI and our 3D model), we determined that models generated by the 3D-based breast ultrasound system have similar nodule characteristics and spatial relationships with the MRI. Conclusions: In summary, this system implements automatic diagnosis of ultrasound images and presents lesions through 3D models, which can obtain complete and accurate ultrasound image information. Thus, it has clinical potential.

Ultrasound Image Features under Deep Learning in Breast Conservation Surgery for Breast Cancer

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences (P < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences (P < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups (P < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast.

Accurate angle estimation based on moment for multirotation computation imaging

In a multirotation computation imaging system, the fidelity of the reconstructed result is limited by the accuracy of the estimated rotation angles. Here, an accurate angle detection method using image moment is proposed to estimate angles of diffraction images. The second moment of a digital image is adopted as the rotational inertia in order to estimate angles of diffraction images. Compared with previous versions based on Radon/Hough transform, it has higher accuracy and is simultaneously time-saving, which is verified in both simulation and experiment. The angle error of moment method is narrowed down within 0.1°, or even less, and it also can perform well in sample diversity or when slightly out of focus.

Hsa_circ_0008945 promoted breast cancer progression by targeting miR-338-3p

PURPOSE

To detect the expression and function of circ_0008945 in breast cancer (BC) and to explore its potential molecular mechanisms in BC tumorigenesis.

MATERIALS AND METHODS

We measured expression levels of circ_0008945, miR-338-3p and homeobox A3 (HOXA3) in BC tissue specimens and cells using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). We examined the effects of all three genes on BC cell proliferation using Cell Counting Kit-8 (CCK-8) and colony formation assays. We also performed a Transwell assay to assess the migratory and invasive ability of treated BC cells. BC cell apoptosis was assessed using flow cytometric (FCM) analysis; interaction between miR-338-3p and circ_0008945 or HOXA3 was verified by dual-luciferase reporter assay as well as by ribonucleic-acid (RNA) pulldown. Finally, we used an in vivo tumor growth assay to assess the role of circ_0008945 overexpression in BC tumor growth.

RESULTS

We found that circ_0008945 expression was significantly increased in both BC tissue specimens and cells. This increase was correlated with poor prognosis in BC patients. Knockdown of circ_0008945 inhibited BC cell proliferation, migration and invasion while promoting BC cell apoptosis in vitro. Overexpression of circ_0008945 remarkably promoted BC tumor growth in vivo. Mechanistically, circ_0008945 acted as a miRNA sponge for miR-338-3p and inhibited its expression in BC cells. Moreover, miR-338-3p targeted and inhibited HOXA3.

CONCLUSION

We found that circ_0008945 acted as a BC oncogene by physically binding miR-338-3p, which further targeted and regulated HOXA3.