Frontiers in Medical Science Research, 2025, 7(1); doi: 10.25236/FMSR.2025.070108.
Lou Liping
Department of Ultrasound, Zhuji People's Hospital of Zhejiang Province, Zhejiang, Zhuji, 311800, China
The present study delves into the application value of ultrasonic radiomics in forecasting recurrence in patients with triple-negative breast cancer (TNBC). Advancements in imaging technology have facilitated the emergence of ultrasonic radiomics, which presents a novel, non-invasive evaluation approach by virtue of the extraction and analysis of tumor imaging features. The objective of this investigation was to examine the correlation between quantitative features derived from ultrasonic images and recurrence risk among TNBC patients. The findings reveal that ultrasonic radiomic features can effectively discern between patient cohorts with differing recurrence risks, demonstrating superior predictive accuracy and sensitivity when compared to conventional clinical indicators. The research underscores that ultrasonic radiomics, as an innovative imaging analysis methodology, offers a valuable adjunct for assessing recurrence risk in TNBC patients and holds promise as a significant reference for personalized treatment planning in clinical settings. Further studies are warranted to validate the clinical application value of this model and to explore its synergistic application with other imaging modalities.
Triple-Negative Breast Cancer; Ultrasonic Radiomics; Recurrence Prediction; Imaging Characteristics
Lou Liping. The Application Value of Ultrasound Radiomics in Predicting Recurrence of Triple-Negative Breast Cancer. Frontiers in Medical Science Research (2025), Vol. 7, Issue 1: 54-59. https://doi.org/10.25236/FMSR.2025.070108.
[1] National Health Commission of the People's Republic of China, Medical Administration and Medical Management Bureau. Guidelines for Diagnosis and Treatment of Breast Cancer (2022 Edition) [J]. Chinese Journal of Comprehensive Clinical Medicine, 2024, 30(01): 12-18.
[2] Chinese Anti-Cancer Association Breast Cancer Professional Committee; Oncology Branch of the Chinese Medical Association, Breast Oncology Group. Chinese Anti-Cancer Association Guidelines and Standards for Diagnosis and Treatment of Breast Cancer (2024 Edition) [J]. Chinese Journal of Cancer, 2023, 33(12): 56-62.
[3] Shi Lin, Zhong Lichang, Ma Fang, et al. The Value of Peritumoral Ultrasound Radiomics in the Differential Diagnosis of Benign and Malignant Breast Nodules [J]. Tumor Imaging, 2023, 27(06): 45-50.
[4] Zhou Jing, Yu Xuan, Wu Qingxia, et al. The Value of Multimodal MRI-Based Intra-Tumoral and Peritumoral Radiomics Features in Assessing HER2 Status in Breast Cancer [J]. Chinese Journal of Radiology, 2023, 57(12): 90-96.
[5] Guo Yunchan, Yang Caixian, Shi Jinwei. The Value of Digital Breast Tomosynthesis-Based Radiomics in Predicting Molecular Subtypes of Breast Cancer [J]. Journal of Medical Imaging, 2023, 29(09): 78-84.
[6] Ke Junwen, Lu Zhendong, Chen Wubiao. Advances in the Study of MRI Radiomics Nomogram for Extramural Vascular Invasion in Rectal Cancer [J]. Journal of Molecular Imaging, 2023, 40(05): 34-40.
[7] Zhang Jiwen, Jia Hongyan. Progress in the Application of Imaging Techniques in Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer [J]. Journal of Clinical Oncology, 2023, 28(09): 67-72.
[8] Zhou Ting, Zou Qingyi, Huang Bigui, et al. Research Progress in the Application of Radiomics in Breast Cancer Diagnosis [J]. Journal of Oncology, 2023, 35(09): 12-18.
[9] Wang Yu, Wen Shengbao, Zhou Hongyu, et al. Advances in MRI Radiomics in Predicting Prognosis in Breast Cancer [J]. Magnetic Resonance Imaging, 2023, 14(09): 45-51.
[10] Xue Ke, Xu Hui, Yue Baorong, et al. Progress in Radiation Dose Assessment for Breast X-Ray Mammography [J]. Chinese Journal of Radiology and Protection, 2023, 43(08): 67-73.
[11] Maolin Xu, Huimin Yang, Jia Sun, et al. Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer [J]. Academic Radiology, 2024, 21(3): 456-467.
[12] Xu Maolin, Zeng Shu E, Li Fang, et al. Preoperative Prediction of Lymphovascular Invasion in Patients with T1 Breast Invasive Ductal Carcinoma Based on Radiomics Nomogram Using Grayscale Ultrasound [J]. Frontiers in Oncology, 2022, 12(8): 876543-876554.
[13] Zhou Chenyi, Xie Hui, Zhu Fanglian, et al. Improving the Malignancy Prediction of Breast Cancer Based on the Integration of Radiomics Features from Dual-View Mammography and Clinical Parameters [J]. Clinical and Experimental Medicine, 2022, 22(5): 1234-1245.
[14] Qin Yanjin, Tang Caili, Hu Qilan, et al. Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis[J]. Journal of Magnetic Resonance Imaging, 2022, 44(2): 345-356.
[15] Xu Zhou, Wang Yuqun, Chen Man, et al. Multi-region radiomics for artificially intelligent diagnosis of breast cancer using multimodal ultrasound[J]. Computers in Biology and Medicine, 2022, 132: 104298-104309.
[16] Lee Jeongmin, Kim Sung Hun, Kim Yelin, et al. Radiomics Nomogram: Prediction of 2-Year Disease-Free Survival in Young Age Breast Cancer[J]. Cancers, 2022, 14(6): 1678-1689.