Roles of artificial intelligence and high frame-rate contrast-enhanced ultrasound in the differential diagnosis of Breast Imaging Reporting and Data System 4 breast nodules.

Abstract

Background: Breast cancer prevalence and mortality are rising, emphasizing the need for early, accurate diagnosis. Contrast-enhanced ultrasound (CEUS) and artificial intelligence (AI) show promise in distinguishing benign from malignant breast nodules. We compared the diagnostic values of AI, high frame-rate CEUS (HiFR-CEUS), and their combination in Breast Imaging Reporting and Data System (BI-RADS) 4 nodules, using pathology as the gold standard.

Methods: Patients with BI-RADS 4 breast nodules who were hospitalized at the Department of Thyroid and Breast Surgery, Taizhou People's Hospital from December 2021 to June 2022 were enrolled in the study.80 female patients (80 lesions) underwent preoperative AI and/or HiFR-CEUS. We assessed diagnostic outcomes of AI, HiFR-CEUS, and their combination, calculating sensitivity (SE), specificity (SP), accuracy (ACC), positive/negative predictive values (PPV/NPV). Reliability was compared using Kappa statistics, and AI-HiFR-CEUS correlation was analyzed with Pearson's test. Receiver operating characteristic curves were plotted to compare diagnostic accuracy of AI, HiFR-CEUS, and their combined approach in differentiating BI-RADS 4 lesions.

Results: Of the 80 lesions, 18 were pathologically confirmed to be benign, while the remaining 62 were malignant. The SE, SP, ACC, PPV, and NPV were 75.81%, 94.44%, 80.00%, 97.92%, and 53.13% in the AI group, 74.20%, 94.44%, 78.75%, 97.91%, and 51.51% in the HiFR-CEUS group, and 98.39%, 88.89%, 96.25%, 96.83%, and 94.12% in the combination group, respectively. Thus, the SE, ACC, and NPV of the combination group were significantly higher than those of the AI and HiFR-CEUS groups, and the SP of the combination group was lower (all P<0.05); however, no significant difference was found between the groups in terms of the PPV (P>0.05). No statistically significant difference was observed in the diagnostic performance of the AI and HiFR-CEUS groups (all P>0.05). The AI and HiFR-CEUS groups had moderate agreement with the "gold standard" (Kappa =0.551, Kappa =0.530, respectively), while the combination group had high agreement (Kappa =0.890). AI was positively correlated with HiFR-CEUS (r=0.249, P<0.05). The area under the curves (AUCs) of AI, HiFR-CEUS, and both in combination were 0.851±0.039, 0.815±0.047, and 0.936±0.039, respectively. Thus, the AUC of the combination group was significantly higher than those of the AI and HiFR-CEUS groups (Z1=2.207, Z2=2.477, respectively, both P<0.05). The AI group had a higher AUC than the HiFR-CEUS group, but the difference was not statistically significant (Z3=0.554, P>0.05).

Conclusions: Compared with AI alone or HiFR-CEUS alone, the combined use of these two methods had higher diagnostic performance in distinguishing between benign and malignant BI-RADS 4 breast nodules. Thus, our combination method could further improve the diagnostic accuracy and guide clinical decision making.