Section Discrepancy and Diagnostic Performance of Breast Lesions in Two-dimensional Ultrasound by Dynamic Videos versus Static Images

Abstract

Background: Benign or malignant breast lesions with typical ultrasonic characteristics could be easily and correctly diagnosed with two-dimensional ultrasound (2D US). However, diagnosis of atypical lesions remains a challenge. Most atypical lesions have different ultrasonographic features with probe direction variation. Thus, the interpretation of ultrasonographic features based on static images empirically collected by sonographers might be inaccurate. We aimed to investigate the section discrepancy and diagnostic performance of breast lesions in 2D US by dynamic videos versus static images.Methods: Static images and dynamic videos based on two perpendicular planes of 468 breast lesions were collected and evaluated. The Breast Imaging and Reporting Data System (BI-RADS®) US lexicon was used. Category 3 was used as the cut-off point, and section discrepancy was defined as two perpendicular planes showing different BI-RADS categories (3 versus 4A, 4B, 4C, and 5).Results: This retrospective study included 315 benign and 153 malignant lesions. There were 53 and 50 lesions with section discrepancy during static and dynamic observations, respectively. The proportion of benign lesions with section discrepancy was significantly higher than that of malignant lesions (P < 0.05) either in dynamic or static observation, and the contingency coefficient was 0.2 between section discrepancy and histopathology. Duct changes were more clearly depicted in dynamic videos than in static images (P < 0.05) both in malignant and benign lesions. Calcification and architectural distortion were more sensitively detected by dynamic videos than with static images (P < 0.05) in malignant lesions. The interpretation of “margin” significantly differed in benign lesions between static images and dynamic videos (P < 0.05). The areas under the curve of static image-horizontal, static image-sagittal, dynamic video-horizontal, and dynamic video-sagittal were 0.807, 0.820, 0.837, and 0.846, respectively. The specificities of dynamic videos were higher than those of static images (P < 0.05).Conclusion: Breast lesions have section discrepancy in 2D US. Observations based on dynamic videos could more accurately reflect lesion features and increase the specificity of US in the differentiation of atypical breast lesions.

Relationship of Amniotic Fluid Sludge and Short Cervix With a High Rate of Preterm Birth in Women After Cervical Cerclage

“…The exact composition of AFS in this population is not confirmed in this study. Because of the lack of dynamic videos, we did not act a double‐check on the AFS cases 34 . Finally, whether post‐cerclage CL rather than pre‐cerclage CL can effectively predict preterm birth after cerclage is uncertain.…”

“…Because of the lack of dynamic videos, we did not act a double‐check on the AFS cases. 34 Finally, whether post‐cerclage CL rather than pre‐cerclage CL can effectively predict preterm birth after cerclage is uncertain. These limitations and further expectations of the present study are aims of another prospective studies.…”

Masked Video Modeling with Correlation-Aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound

“…Its real-time imaging capability allows rapid acquisition of tissue information and produces corresponding US image sequences (i.e., videos) on the screen instantaneously. Compared with the single conventional 2D static image, the original US video of a lesion contains richer spatial-temporal information and is beneficial for diagnosis [14,15]. However, deciphering raw US videos faces several challenges, including heavy data-oriented dependency, scarce supervision signals, inter-class similarity and intra-class variance (see Fig.…”