To Evaluate the Accuracy of Axillary Staging Using Ultrasound and Ultrasound-Guided Fine-Needle Aspiration Cytology (USG-FNAC) in Early Breast Cancer Patients-a Prospective Study

Utility of perioperative ultrasonography and fine-needle aspiration cytology in differentiation between benign and malignant cervical lymphadenopathy: a retrospective cohort study

Background

The preoperative differentiation of benign form malignant cervical lymphadenopathy (CLA) is crucial in determining the need for surgical intervention. This study aims to assess the diagnostic performance of ultrasonography (US), fine-needle aspiration cytology (FNAC), and their combination with the postoperative histopathological diagnoses of CLA.

Method

In a retrospective study between April 2021 and May 2023, 214 patients with CLA were assessed with preoperative US and FNAC. The morphological parameters, including tissue margins, vascularity, and fatty hilum echogenicity, were collected and analyzed retrospectively. The diagnostic efficacies of US, FNAC, and their combined use were compared to the postoperative histopathological findings.

Result

In the final histopathological examination, 185 cases (86.4%) were found to be benign, while 29 cases (13.6%) were determined to be malignant. The US features of fatty hilum, echogenicity, and vascularity pattern had the highest diagnostic accuracy in characterizing CLA patterns, with values of 88.3%, 85.5%, and 85.0%, respectively. The receiver operating characteristic (ROC) curve showed a significantly higher area under the curve (AUC) value of 0.883 (95% CI: 0.832-0.923; P<0.0001) for the combined use of all US parameters with better sensitivity (93.10%) and specificity (68.65%) than individual parameters. The overall sensitivity, specificity, and accuracy of FNAC were 97.3%, 82.8%, and 95.3%, respectively. Additionally, US parameters and FNAC together showed a significantly higher AUC value of 0.924 (95% CI: 0.880-0.956; P<0.0001) and achieved a sensitivity of 86.21% and specificity of 88.65%.

Conclusions

The combined use of US and FNAC provides high sensitivity, specificity, and diagnostic accuracy in characterizing CLA patterns. In limited-resources settings, this approach is feasible, less invasive, and cost-effective, thereby enabling clear management strategies and avoiding additional surgical interventions.

Node Positivity Among Sonographically Suspicious but FNA-Negative Axillary Nodes

BACKGROUND

Fine needle aspiration (FNA) of sonographically suspicious axillary lymph nodes is helpful to clinically stage patients and guide consideration of neoadjuvant therapy in breast cancer. However, data are limited for suspicious nodes that are FNA negative. Our goal is to compare the frequency of node positivity between patients with negative axillary ultrasound (AUSneg) versus suspicious AUS with negative FNA (FNAneg).

METHODS

With IRB approval, we identified all clinically node-negative (cN0) patients with invasive breast cancer treated with upfront surgery at our tertiary care center between 2016 and 2021. AUS is routinely performed with FNA of suspicious lymph node(s). We compared clinicopathologic characteristics and nodal positivity rates between AUSneg and FNAneg groups.

RESULTS

A total of 1580 cN0 patients with invasive breast cancer were analyzed, including 1240 AUSneg and 340 FNAneg patients. The FNAneg group was younger (median age 59.7 years versus 63.5 years, p < 0.001) and had higher clinical T (cT) category (29.1% versus 21.7% with cT2-cT4 disease, p = 0.005). Final axillary pathologic node positivity did not differ significantly between the AUSneg and FNAneg groups (16.5% versus 19.1%, p = 0.25). Among FNAneg patients, 58/340 (17.1%) had a clip placed, with retrieval confirmed in 28/58 (48.3%). Of the 28 retrieved clipped nodes, 27 were sentinel nodes. Final pathologic nodal status (pN+%) did not differ between patients in whom retrieval of the clipped node was confirmed versus not confirmed (28.6% versus 16.7%, p = 0.28).

CONCLUSIONS

Both patients with sonographically suspicious node(s) and negative FNA and patients with negative AUS have a similarly low chance of positive nodes. Additionally, routine targeted excision of FNA-negative clipped nodes is not warranted.

Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system

Objective Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. Methods The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. Result Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. Conclusion The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.