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Objective: This study aims to explore whether intratumoral and peritumoral ultrasound radiomics of ultrasound images can predict the low expression status of human epidermal growth factor receptor 2 (HER2) in HER2-negative breast cancer patients.
Methods: HER2-negative breast cancer patients were recruited retrospectively and randomly divided into a training cohort (n = 303) and a test cohort (n = 130) at a ratio of 7:3. The region of interest within the breast ultrasound image was designated as the intratumoral region, and expansions of 3 mm, 5 mm, and 8 mm from this region were considered as the peritumoral regions for the extraction of ultrasound radiomic features. Feature extraction and selection were performed, and radiomics scores (Rad-score) were obtained in four ultrasound radiomics scenarios: intratumoral only, intratumoral + peritumoral 3 mm, intratumoral + peritumoral 5 mm, and intratumoral + peritumoral 8 mm. An optimal combined nomogram radiomic model incorporating clinical features was established and validated. Subsequently, the diagnostic performance of the radiomic models was evaluated.
Results: The results indicated that the intratumoral + peritumoral (5 mm) ultrasound radiomics exhibited the excellent diagnostic performance in evaluated the HER2 low expression. The nomogram combining intratumoral + peritumoral (5 mm) and clinical features showed superior diagnostic performance, achieving an area under the curve (AUC) of 0.911 and 0.869 in the training and test cohorts, respectively.
Conclusion: The combination of intratumoral + peritumoral (5 mm) ultrasound radiomics and clinical features possesses the capability to accurately predict the low-expression status of HER2 in HER2-negative breast cancer patients.
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http://dx.doi.org/10.1007/s12672-025-02752-4 | DOI Listing |
J Ultrasound Med
September 2025
Department of Ultrasound, Donghai Hospital Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Objective: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics (DLRN) features derived from ultrasound and multi-sequence magnetic resonance imaging (MRI) for predicting survival, recurrence, and metastasis in patients diagnosed with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC).
Methods: This retrospective, multicenter study included 103 patients with histopathologically confirmed TNBC across four institutions. The training group comprised 72 cases from the First People's Hospital of Lianyungang, while the validation group included 31 cases from three external centers.
Technol Cancer Res Treat
September 2025
Department of Nephrology, Dongyang People's Hospital, Dongyang, China.
ObjectiveTo evaluate the diagnostic performance of a combined model incorporating ultrasound video-based radiomics features and clinical variables for distinguishing between benign and malignant breast lesions.MethodsA total of 346 patients (173 benign and 173 malignant) were retrospectively enrolled. Breast ultrasound videos were acquired and processed using semi-automatic segmentation in 3D Slicer.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
September 2025
Department of Cardiovascular Center, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People's Republic of China.
Objective: This study aimed to develop and validate a deep learning radiomics (DLR) nomogram for individualized CHD risk assessment in the COPD population.
Methods: This retrospective study included 543 COPD patients from two different centers. Comprehensive clinical and imaging data were collected for all participants.
Front Endocrinol (Lausanne)
September 2025
Department of Ultrasound, Shengjing Hospital, China Medical University, Shenyang, Liaoning, China.
This research aimed to investigate the preoperative risk factors for lymph node metastasis (LNM) in medullary thyroid carcinoma (MTC) using clinical, pathological, serological, ultrasound, and radiomics characteristics. Additionally, it aimed to explore the diagnostic precision of ultrasound (US) for MTC and LNM. A retrospective analysis of 111 nodules was eligible from 104 patients from January 1, 2000, to December 28, 2024.
View Article and Find Full Text PDFPhys Imaging Radiat Oncol
July 2025
Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Background And Purpose: Predicting hepatocellular carcinoma (HCC) response to Stereotactic Body Radiation Therapy (SBRT) can be challenging. Here, we assessed the value of a radiomics-based machine learning (ML) approach for predicting HCC response to SBRT, using pre-treatment and early post-treatment magnetic resonance imaging (MRI).
Materials And Methods: This retrospective single-center study included 87 patients (M 67, mean age 65.