Application of Microflow Imaging in Neoplasm Diagnosis: A Systematic Review and Meta-analysis

Tumor angiogenesis is a hallmark of malignant transformation. Abnormal vascular structure and blood flow characteristics provide crucial information for differentiating benign from malignant lesions. Conventional ultrasound relies primarily on morphological features, lesion boundaries, and blood flow patterns. However, some tumors exhibit overlapping grayscale characteristics between benign and malignant types. Moreover, color Doppler flow imaging (CDFI) has limitations in detecting microvascular flow. Superb microvascular imaging (SMI) utilizes adaptive algorithms to display intratumoral microvascular morphology and distribution with high resolution, potentially improving early differentiation. This research therefore conducted a diagnostic test accuracy meta-analysis to systematically assess and compare the diagnostic performance of SMI against conventional CDFI for distinguishing benign from malignant tumors.

An extensive literature retrieval was performed across the Web of Science, PubMed, Cochrane Library, and Embase up to February 26, 2025. The retrieval focused on studies using SMI and CDFI for diagnosing tumors across various organ systems. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was employed to evaluate methodological quality. Review Manager 5.4 was used to generate quality assessment figures. Meta-DiSc 1.4 was used for threshold effect testing (Spearman correlation between sensitivity and 1-specificity). STATA software was then utilized for heterogeneity testing and to compute pooled sensitivity, negative likelihood ratio (LR-), specificity, diagnostic odds ratio (DOR), and positive likelihood ratio (LR+). Summary receiver operating characteristic (SROC) curves were constructed.

Twenty-eight studies were included: 22 focused on superficial organ tumors (17 breast, 5 other) and 6 on abdominal organ tumors (Five kidney, One liver). Pooled analysis showed that SMI yielded an overall sensitivity of 0.84 (95% CI: 0.80-0.87), a specificity of 0.79 (0.74-0.84), and an area under the curve (AUC) of 0.88. In the superficial organ group, SMI yielded a sensitivity of 0.84 (0.79-0.88), a specificity of 0.79 (0.73-0.84), and an AUC of 0.89. In the abdominal organ group, it yielded a sensitivity of 0.83 (0.77-0.88), a specificity of 0.81 (0.71-0.88), and an AUC of 0.89.

SMI demonstrates high specificity and sensitivity for differentiating benign from malignant tumors compared to conventional CDFI. It thus represents a promising novel imaging technique for clinical adoption. Future research should expand sample sizes for abdominal organs and investigate the specific performance of SMI across different lesion types.