Mag Intro

Mag Intro Mag Intro

Responsible Institution:
Fudan University
Sponsored by:
Fudan University Shanghai Cancer Center
Edited by:
Editorial Board of Oncoradiology
Editors-in-Chief:
CHANG Cai
PENG Weijun
FAN Wei
Editorial Director: NI Ming
Published by:
Editorial Office of Oncoradiology
Address:
270 Dong’an Road, Shanghai 200032, China
Tel: (021)64188274
Website: www.zhongliuyingxiangxue.com
E-mail：
zlyxx@zhongliuyingxiangxue.com
ISSN 2096-6210　
CN 31-2087/R

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Volume 35 期 4，2026 2026年第35卷第4期

  • Specialists' Commentary

    XIAO Qing, WANG Simin, GU Yajia

    DOI：10.19732/j.cnki.2096-6210.2026.04.001
    摘要：The 2025 edition of the Breast Imaging Reporting and Data System (BI-RADS), the first major revision in 12 years, integrates multiple emerging breast imaging techniques including mammography, ultrasound, magnetic resonance imaging (MRI), digital breast tomosynthesis (DBT) and contrast-enhanced mammography (CEM), with enhanced clinical applicability. This article presents a tabular comparison of the key differences between the 2025 and 2013 editions, focusing on mammography and MRI. The new edition introduces unified standards terminology across modalities, optimizes lesion descriptors, lymph node assessment, and reporting structure, and revises terminology definitions, criteria and evaluation items specific to each technique. Furthermore, it refines the BI-RADS assessment categories and improves corresponding malignancy risk stratification and clinical management recommendations. This update standardizes breast imaging diagnostic criteria, enhances the precision of lesion risk stratification, and holds significant value for standardizing radiology reports and guiding clinical decision-making.  
    关键词：Breast Imaging Reporting and Data System;Mammography;Magnetic resonance imaging   
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    ZHENG Yuhan, YOU Chao

    DOI：10.19732/j.cnki.2096-6210.2026.04.002
    摘要：Breast cancer is one of the most common malignancies among women worldwide. Fibroglandular tissue (FGT) constitutes the non-adipose component of the breast parenchyma. Its amount, distribution, and physiologic activity affect lesion conspicuity and have important implications for breast cancer screening, risk stratification, and treatment response assessment. Different imaging modalities provide complementary information on tissue composition, spatial distribution, and functional activity. Mammography is the most established modality for FGT assessment and primarily evaluates FGT in terms of mammographic density, which reflects the relative amount of FGT and its associated masking effect; however, tissue superimposition resulting from two-dimensional projection imaging may impair lesion detection. Breast ultrasound can characterize the glandular tissue component and background echotexture, providing complementary information on variations in the composition of mammographically dense breasts. Breast magnetic resonance imaging (MRI) enables assessment of FGT amount and spatial distribution, while background parenchymal enhancement provides information on tissue perfusion and hormone-related physiologic activity. Molecular breast imaging and positron emission tomography can further characterize background parenchymal uptake and metabolic activity, although these FGT-related applications remain investigational. With advances in artificial intelligence, FGT assessment is expanding beyond visual grading to automated quantification and quantitative imaging feature extraction. Current research has focused primarily on automated mammographic density assessment, three-dimensional segmentation of the whole breast and FGT on MRI, and quantitative background parenchymal enhancement assessment, with further extensions to analyses of texture, spatial distribution, and topological features. Artificial intelligence research in other imaging modalities remains at an early stage, and further work is needed to standardize these methods and validate their clinical utility. Focusing on FGT-related indicators, including mammographic density, the glandular tissue component on breast ultrasonography, MRI-assessed fibroglandular tissue volume, background parenchymal enhancement, and background parenchymal uptake, this review summarizes the current status and recent advances in multimodal imaging evaluation, artificial intelligence-based quantitative analysis, and clinical applications of FGT.  
    关键词：Breast neoplasm;Fibroglandular tissue;Background parenchymal enhancement;Artificial intelligence   
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  • Specialists' Article

    XUE Qi, LIU Chunling, MA Zhiying, LIU Zaiyi

    DOI：10.19732/j.cnki.2096-6210.2026.04.003
    摘要：ObjectiveTo investigate the predictive value of the breast magnetic resonance imaging (MRI)-based Node Reporting and Data System (Node-RADS) scoring system for pathological complete response (pCR) of axillary lymph nodes before and after neoadjuvant therapy in patients with hormone receptor (HR)-positive breast cancer, compare the diagnostic efficacy of single-index versus multi-index combined models.MethodsPatients with HR-positive breast cancer who received neoadjuvant therapy and underwent breast MRI examinations both before and after treatment at Guangdong Provincial People’s Hospital from January 2023 to December 2024 were retrospectively enrolled. Postoperative pathological axillary lymph node pCR was used as the gold standard. The axillary lymph node status was evaluated pre- and post-treatment using the Node-RADS 1.0, and pre-treatment molecular subtype was incorporated as a predictive indicator. Five predictive models (M1-5) were constructed: M1, pre-treatment Node-RADS score; M2, post-treatment Node-RADS score; M3, pre-treatment molecular subtype; M4, post-treatment Node-RADS score combined with pre-treatment molecular subtype; M5, pre-treatment Node-RADS score + post-treatment Node-RADS score + pre-treatment molecular subtype. Receiver operating characteristic (ROC) curves were plotted to calculate the area under curve (AUC). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of each model were compared to evaluate their performance in predicting pCR.ResultsOf the 148 patients,80 achieved pCR and 68 did not. Among single‑index models, the post‑treatment Node‑RADS score (M2) showed the best predictive performance, with an AUC of 0.753 (95% CI 0.676-0.830, P<0.001) and a specificity of 91.7%. Pre‑treatment molecular subtype (M3) ranked second, with an AUC of 0.644 (95% CI 0.555-0.733, P<0.001). Pre‑treatment Node‑RADS score (M1) yielded an AUC of 0.579 (95% CI 0.487-0.671, P=0.226), with no statistical significance. For combined models, post‑treatment Node‑RADS score plus pre‑treatment molecular subtype (M4) achieved the highest AUC of 0.806 (95% CI 0.735-0.877), with a sensitivity of 56.6%, specificity of 92.4%, and accuracy of 79.3%. The AUC of M5 (pre‑treatment Node‑RADS + post‑treatment Node‑RADS + pre‑treatment molecular subtype) was 0.804, which was slightly lower than that of M4, suggesting that adding pre‑treatment Node‑RADS score did not improve predictive performance.ConclusionPost-neoadjuvant therapy axillary lymph node Node-RADS score shows favorable predictive value for axillary pCR in patients with HR-positive breast cancer. The combination of post-treatment Node-RADS score and pre-treatment molecular subtype can further improve predictive efficacy, and may serve as a reliable imaging tool for clinically evaluating axillary lymph nodes response and guiding axillary surgical strategies in breast cancer patients.  
    关键词：Breast cancer;Magnetic resonance imaging;Hormone receptor-positive;Neoadjuvant therapy;Axillary lymph node;Node Reporting and Data System;Pathological complete response;Predictive model   
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  • Article

    YAN Huimin, YUAN Ying, TAO Xiaofeng

    DOI：10.19732/j.cnki.2096-6210.2026.04.004
    摘要：ObjectiveTo construct a fusion model based on multiparametric magnetic resonance imaging (mpMRI) radiomics for predicting occult cervical lymph node metastasis (OCLNM) in patients with oropharyngeal squamous cell carcinoma (OPSCC), and validate the diagnostic performance of the model.MethodsThis study retrospectively enrolled patients with OPSCC who had pathologically confirmed cervical lymph node status after surgery at the Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2018 to August 2025. The patients were randomly divided into training and validation sets at a 7∶3 ratio. Regions of interest (ROIs) were delineated and radiomic features were extracted from preoperative mpMRI sequences, including T2-weighted fat-suppressed sequence (T2WI-FS), contrast-enhanced T1-weighted imaging (CE-T1WI), and diffusion-weighted imaging (DWI). The least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen features with predictive value for OCLNM. An OCLNM prediction model was constructed based on the selected features, and the model performance and clinical applicability were evaluated using the area under the curve (AUC) and decision curve analysis (DCA).ResultsA total of 151 patients with OPSCC were enrolled, including 105 cases in the validation set and 46 cases in the training set. Male gender and tumor originating in the tonsils were independent risk factors for cervical lymph node metastasis in OPSCC patients (P<0.05). The multiparameter model integrating three sequence features achieved the optimal performance. In the training set, the integrated model yielded the highest AUC of 0.860 (95% CI 0.788-0.931) among all models; in the validation set, the AUC of the integrated model was 0.808 (95% CI 0.661-0.955).ConclusionA mpMRI-based radiomics fusion model can effectively predict the risk of preoperative OCLNM in patients with OPSCC.  
    关键词：Oropharyngeal squamous cell carcinoma;Cervical lymph node status;Radiomics;Multi-parameter;Magnetic resonance imaging   
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