In this study, we aimed to evaluate the clinical and pathological factors that associated with sonographic appearances of triple-negative (TN) invasive breast carcinoma. With the ethical approval, 560 patients who were pathologically confirmed as invasive breast carcinoma were reviewed for ultrasound, clinical, and pathological data. Logistic regression analysis was used to identify the typical sonographic features for TN invasive breast carcinomas. The effect of clinical and pathological factors on the sonographic features of TN invasive breast carcinoma was studied. There were 104 cases of TN invasive breast carcinoma. The independent sonographic features for the TN subgroup included regular shape (odds ratio, OR = 2.14, p = 0.007), no spiculated/angular margin (OR = 1.93, p = 0.035), posterior acoustic enhancement (OR = 2.14, p = 0.004), and no calcifications (OR = 2.10, p = 0.008). Higher pathological grade was significantly associated with regular tumor shape of TN breast cancer (p = 0.012). Higher Ki67 level was significantly associated with regular tumor shape (p = 0.023) and absence of angular/spiculated margin (p = 0.005). Higher human epidermal growth factor receptor 2 (HER2) score was significantly associated with the presence of calcifications (p = 0.033). We conclude that four sonographic features are associated with TN invasive breast carcinoma. Heterogeneity of sonographic features was associated with the pathological grade, Ki67 proliferation level and HER2 score of TN breast cancers.

Background: To compare the abilities of ultrasonography (US) and Computed Tomography (CT) to identify calcifications and to predict probability of malignancy for Papillary Thyroid Carcinoma (PTC) and Papillary Thyroid Microcarcinoma (PTMC). Methods: We reviewed 1008 cases of PTC/PTMC with calcifications reported by pre-operative US, CT, or post-operative pathology. The size of the thyroid nodule was obtained from the US report and the maximum diameter (d) was documented. According to the nodule size (d), the PTC and PTMC groups were each divided into two subgroups, as follows: large PTC group (d ≥ 2 cm), small PTC group (1 cm < d < 2 cm), large PTMC group (0.6 cm ≤ d ≤ 1 cm), and small PTMC group (d < 0.6 cm). Results: In the 1008 patients, the ratio of females to males was 2.29 and the mean age was 40.9 years (standard deviation: 11.7 years). Of the 1008 records, 92.8% were found to have calcifications according to the US report, while 50.4% showed calcifications according to the CT report. This difference between US and CT reports was statistically significant (p < 0.0005). The percentages of US reports showing calcifications were similar for all four PTC and PTMC subgroups (93.7%, 94.3%, 92.1%, and 85.1%, respectively; p = 0.052), while the percentages of CT reports showing calcifications were significantly different among the PTC and PTMC subgroups (62.3%, 52.2%, 45.4%, and 31.3%, respectively; p < 0.0005). As for the prediction of malignancy, US was superior to CT in all four subgroups (large PTC group: 97.1% vs. 54.1%, small PTC group: 94.8% vs. 42.9%, large PTMC group: 97.2% vs. 32.0%, small PTMC group: 95.5% vs. 14.9%; p < 0.0005 for all pairwise comparisons). No significant difference was observed in terms of the ability of US to predict the malignancy of PTC versus PTMC (p = 0.31), while CT showed significant superiority in diagnosing PTC versus PTMC (p < 0.0005). The predictive value of CT for PTC declined as the nodule size decreased (p < 0.05 for all pairwise comparisons). Conclusion: Our results showed that US detected calcifications and predicted the malignancy of all nodule sizes of thyroid papillary carcinoma equally well, while the performance of CT declined with the reduction of nodule size.

Acoustic signals have attracted increasing attention in mechanical fault diagnosis due to the advantage of non-invasive measurement. However, the acoustic signal has low signal-to-noise ratio and weak fault characteristics, which brings difficulty for fault feature extraction. To address the above deficiencies, a novel sparse filtering (SF) method based on generalized matrix norm SF (GMNSF) is proposed in this paper, which uses the matrix norm to determine the optimal sparse feature distribution. Specifically, principal component analysis is employed on the overlapping segments of the acquired sound signal first. Then, the GMNSF model is trained by principal component matrix and sparse features are mapped from the trained weight matrix. Finally, softmax regression is used as a classifier to categorize different fault types. The diagnostic performance of the proposed method is verified by the bearing and planetary gear datasets. Results show that the GMNSF model has good feature extraction performance than other traditional methods that can be used for mechanical fault diagnosis under acoustic signals.

The independent sonographic features for the TN subgroup included regular shape (odds ratio, OR = 2.14, p = 0.007), no spiculated/angular margin (OR = 1.93, p = 0.035), posterior acoustic enhancement (OR = 2.14, p = 0.004), and no calcifications (OR = 2.10, p = 0.008). " now reads: "The independent sonographic features for the TN subgroup included regular shape (odds ratio, OR = 1.73, p = 0.033), no spiculated/angular margin (OR = 2.09, p = 0.01), posterior acoustic enhancement (OR = 2.09, p = 0.004), and no calcifications (OR = 2.11, p = 0.005). " Additionally, this Article contained an error in section (B) of the Figure 6 legend. "(B) The uncircumscribed margin with cellular prominence (HE stain, original magnification, ×4). " now reads: "(B) The uncircumscribed margin with cellular prominence (HE stain, original magnification, ×40).