Empowering Breast Cancer Precision Diagnosis with Advanced Ultrasound: From Radiomics to Radio-multiomics

doi:10.26599/AUDT.2026.260041

Abstract

Abstract:

Numerous research studies have revealed that breast cancer is characterized by substantial biological and molecular heterogeneity, which can neither be fully captured by conventional clinical medical imaging nor biopsy which is typically limited by sampling error and invasiveness. Despite the exhibited complexity, developments in ultrasound imaging technologies from morphological imaging only to multi-dimensional functional imaging coupled with quantitative analysis have significantly improved the diagnostic accessibility and efficiency for breast cancer patients. This review first summarizes recent advances in multi-modal ultrasound imaging encompassing microvascular and super-resolution ultrasound imaging with contrast enhancement, elastography and viscoelastic imaging, as well as quantitative analysis consisting of radiofrequency signal analysis, radiomics, and artificial intelligence-based image analysis for precision breast cancer diagnosis. Specific attention is given to their applications in benign-malignant differentiation, molecular subtype characterization, lymph node metastasis assessment. Beyond image-based analysis, the integration of ultrasound radiomics with genomic, transcriptomic, and other multi-omics data is emerging as a promising strategy for non-invasive molecular phenotyping, although its clinical application is still at its infancy. This review then discusses the transition from radiomics alone through radiogenomics to radio-multiomics, highlighting the potential of advanced ultrasound imaging technologies to bridge imaging phenotypes with underlying tumor biological behaviour in order to facilitate individualized diagnosis and treatment planning. Most radio-multiomics approaches remain at a preclinical or proof-of-concept stage and still require validation in large-scale paired cohorts. In addition, issues such as standardization, reproducibility, interpretability and data heterogeneity remain to be resolved. Finally, this review discusses current challenges that should be addressed before ultrasound-based radio-multi-omics can be translated to a viable clinical tool.

Key words: Breast cancer; Advanced ultrasound; Ultrasound radiomics; Artificial intelligence; Radio-multiomics; Precision diagnosis

Li Mo, Jin Letian, Xue Ge, Chen Jiye, Benariba Mohamed Aimene, Wang Vicky Yang, Teng Gaojun, Xu Dong. Empowering Breast Cancer Precision Diagnosis with Advanced Ultrasound: From Radiomics to Radio-multiomics. Advanced Ultrasound in Diagnosis and Therapy, 2026, 10(4): 277-297.

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Figures/Tables 3

Figure 1

Table 1

Summary of representative AI/CAD models for breast ultrasound diagnosis"

Author	Year	Country	Model and architecture	Study design	Centers	Input	No. of cases	Age (years)	Task	AUC	External‑ validation status
AI, artificial intelligence; AUC, area under the curve; BI-RADS, Breast Imaging Reporting and Data System; B-mode, grayscale ultrasound; BU-CAD, breast ultrasound computer-aided diagnosis; CAD, computer-aided diagnosis; CDFI, color Doppler flow imaging; DCNN, deep convolutional neural network; DL, deep learning; NR, not reported; SWE, shear wave elastography. ※Commercially available CAD system.
Wei et al. [74]	2022	China	S-Detect (deep-learning CAD)※	Prospective	9	B-mode	901	45.2 ± 10.8	Differentiation of benign and malignant breast masses	0.906	YES
Gu et al. [75]	2023	China	DL-BI-RADS (RepVGG)	Prospective	32	B-mode	5,012	44.1 ± 13.1	Benign/malignant classification and 6-category BI-RADS	0.980	YES
Shen et al. [76]	2021	United States	Breast ultrasound AI (attention mechanism)	Retrospective	20	B-mode + Color Doppler	143,203	53.7 ± 13.7	Breast cancer detection and triage of low-risk examinations	0.976	YES
Mango et al. [77]	2020	United States	Koios DS※	Retrospective	> 20	B-mode	900	53.6	Lesion risk stratification and management recommendations	0.88	YES
Lai et al. [78]	2022	Taiwan, China	BU-CAD (deep-learning CAD)※	Retrospective	1	B-mode	172	54.1 ± 12.0	Lesion detection and diagnosis	0.859	NO
Chen et al. [79]	2025	China	AINet	Retrospective	7	B-mode + SWE	15,223	N/A	Benign/malignant classification and BI-RADS grading	0.942	NO
Cai et al. [80]	2025	International	AI-SWE (EfficientNetB1)	Retrospective	12	SWE	1,294	46.5 ± 16.3	Benign/malignant stratification of BI-RADS 3/4 lesions	0.94/ 0.93	YES
Qian et al. [81]	2024	China	BMU-Net (ResNet-18 + Transformer)	Prospective	≥ 3	MMG + US (three-modal) + clinical	5,025	48.8	Coarse-/fine-grained breast cancer risk stratification	0.948	YES
Jiang et al. [82]	2021	China	ResNet50-DCNN	Retrospective	3	B-mode	2,120	49	Breast cancer molecular subtype classification	0.87/ 0.83	YES
Zhou et al. [83]	2020	China	Inception V3	Retrospective	2	B-mode	834	48	Prediction of axillary lymph node metastasis	0.89	YES

Table 1

Figure 2

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