Clinical Significance of Ultrasound Elastography and Fibrotic Focus and Their Association in Breast Cancer

(1) Background: Ultrasound (US) elastography is an imaging technology that reveals tissue stiffness. This study aimed to investigate whether fibrotic focus (FF) affects elastographic findings in breast cancer, and to evaluate the clinical significance of US elastography and FF in breast cancer. (2) Methods: In this study, 151 patients with breast cancer who underwent surgery were included. Strain elastography was performed and an elasticity scoring system was used to assess the findings. The elasticity scores were classified as negative, equivocal, or positive. FF was evaluated in the surgical specimens. Medical records were reviewed for all patients. (3) Results: Elastographic findings were equivocal in 30 patients (19.9%) and positive in 121 patients (80.1%). FF was present in 68 patients (46.9%). There was no correlation between elastographic findings and FF. Older age, larger tumor size, lymph node metastasis, and higher tumor stage were associated with positive elastographic results. FF showed a positive correlation with age, postmenopausal status, tumor size, lymphovascular invasion, lymph node metastasis, tumor stage, and intratumoral and peritumoral inflammation. (4) Conclusions: Our study showed that positive elastographic results and FF were associated with poor prognostic factors for breast cancer. FF did not affect the elastographic findings of this study.

Ultrasonic multimodality imaging features and the classification value of nonpuerperal mastitis

OBJECTIVE

To explore the value of ultrasonic multimodality imaging for characterizing nonpuerperal mastitis (NPM) lesions and feasibility of distinguishing different subtypes.

METHODS

Thirty-eight NPM lesions were assessed using conventional ultrasonography (US), strain elastography (SE), and contrast-enhanced ultrasound (CEUS). The lesions were confirmed pathologically and classified as granulomatous lobular mastitis (GLM), plasma cell mastitis (PCM), or nonspecific mastitis (NSM). Furthermore, diagnostic indicators were evaluated. The diagnostic performances of the modalities were compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

The overall morphological features on US differed significantly between the GLM and PCM groups (p = 0.002). Lesion size (≤10 mm) (p = 0.003) and mean SE score (p = 0.001) differed significantly between the PCM and NSM groups. The frequent NPM characteristic on CEUS was hyperenhancement with (or without) increased lesion size; intergroup differences were not significant. Breast Imaging Reporting and Data System > 3 was considered to indicate malignancy; accordingly, the accuracy of US alone, US with CEUS, and US with SE was 10.5%, 21.1%, and 65.8%, respectively. Moreover, the AUC for US with SE for classifying GLM and PCM was 0.616.

CONCLUSION

CEUS cannot accurately classify NPM subtypes, while US and SE are valuable for classification.

Deep learning identification of stiffness markers in breast cancer

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.