一、文章
1. Xu S, Cao M, Chen L, et al. Evaluation of Splenic Involvement in Lymphomas Using Extracellular Volume Fraction Computed Tomography. J Comput Assist Tomogr 2025, 49(2): 225-233.
2. Xiaoxia W, Yao H, Ying C, et al. Time-Dependent Diffusion MRI-Based Microstructural Mapping for Characterizing HER2-Zero, -Low, -Ultra-Low, and -Positive Breast Cancer. J Magn Reson Imaging 2025(0).
3. Wang X, Huang Y, Shi J, et al. Biomechanical parameters quantified by MR elastography for predicting response to neoadjuvant chemotherapy and disease-free survival in breast cancer: a prospective longitudinal study. Breast Cancer Res 2025, 27(1): 72.
4. Wang X, Hu X, Wang C, et al. Automatic Segmentation and Molecular Subtype Classification of Breast Cancer Using an MRI-based Deep Learning Framework. Radiol Imaging Cancer 2025, 7(3): e240184.
5. Tang S, Li L, Wang X, et al. Habitat imaging based on dual-energy computed tomography for predicting axillary lymph node metastasis in breast cancer. Acta Radiol 2025, 66(9): 919-928.
6. Shi D, Wang X, Li S, et al. Comprehensive characterization of tumor therapeutic response via simultaneous mapping of cell size, density, and transcytolemmal water exchange. Magn Reson Imaging 2025, 122: 110433.
7. Huang Y, Wang X, Cao Y, et al. Nomogram for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using MRI-based Intratumoral Heterogeneity Quantification. Radiology 2025, 315(1): e241805.
8. Huang Y, Gong H, Hu W, et al. Fractal analysis of longitudinal MRI for predicting response to neoadjuvant chemotherapy in breast cancer. NPJ Precis Oncol 2025.
9. Huang Y, Cao Y, Chen H, et al. Quantifying tumor morphological complexity based on pretreatment MRI fractal analysis for predicting pathologic complete response and survival in breast cancer: a retrospective, multicenter study. Breast Cancer Res 2025, 27(1): 86.
10. Gong X, Wang X, Wang L, et al. Comparing Multi-b-Value Diffusion MRI Models for Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology 2025, 316(1): e242969.
11. Fang J, Wang X, Wang L, et al. APTWI-differential analysis for breast cancer: Association with histopathologic characteristics and early prediction of neoadjuvant chemotherapy response. Magn Reson Imaging 2025, 127: 110590.
12. Chen H, Wang X, Huang Y, et al. Nomograms Integrating MRI-derived Apparent Diffusion Coefficient and Clinicopathologic Features for Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Radiol Imaging Cancer 2025, 7(2): e240202.
13. Cao Y, Gong X, Huang Y, et al. Early-phase semi-quantitative analysis versus full time-course quantitative modeling of ultrafast dynamic contrast-enhanced MRI for breast cancer diagnosis, molecular subtyping, and treatment response prediction. Insights Imaging 2025, 16(1): 279.
14. Yu T, Li L, Shi J, et al. Predicting histopathological types and molecular subtype of breast tumors: A comparative study using amide proton transfer-weighted imaging, intravoxel incoherent motion and diffusion kurtosis imaging. Magn Reson Imaging 2024, 105: 37-45.
15. Wang X, Pan X, Zhou W, et al. Quantification of Hepatic Steatosis on Dual-Energy CT in Comparison With MRI mDIXON-Quant Sequence in Breast Cancer. J Comput Assist Tomogr 2024, 48(1): 64-71.
16. Wang X, Du L, Cao Y, et al. Comparing extracellular volume fraction with apparent diffusion coefficient for the characterization of breast tumors. Eur J Radiol 2024, 171: 111268.
17. Wang X, Ba R, Huang Y, et al. Time-Dependent Diffusion MRI Helps Predict Molecular Subtypes and Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer. Radiology 2024, 313(1): e240288.
18. Wang L, Wang X, Jiang F, et al. Adding quantitative T1rho-weighted imaging to conventional MRI improves specificity and sensitivity for differentiating malignant from benign breast lesions. Magn Reson Imaging 2024, 108: 98-103.
19. Shen H, Zhou W, ChunrongTu, et al. Thoracic aorta injury detected by 4D flow MRI predicts subsequent main adverse cardiovascular events in breast cancer patients receiving anthracyclines: A longitudinal study. Magn Reson Imaging 2024, 109: 67-73.
20. Long L, Liu M, Deng X, et al. Tumor Stiffness Measurement at Multifrequency MR Elastography to Predict Lymphovascular Space Invasion in Endometrial Cancer. Radiology 2024, 311(3): e232242.
21. Jiang F, Liu S, Wang L, et al. ROS-Responsive Nanoprobes for Bimodal Imaging-Guided Cancer Targeted Combinatorial Therapy. Int J Nanomedicine 2024, 19: 8071-8090.
22. Huang Y, Wang X, Cao Y, et al. Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis. Diagn Interv Imaging 2024, 105(5): 191-205.
23. Huang Y, Cao Y, Hu X, et al. Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study. J Magn Reson Imaging 2024, 60(4): 1325-1337.
24. Huang J, Liu D, Chen J, et al. Differential diagnosis of thyroid nodules by DCE-MRI based on compressed sensing volumetric interpolated breath-hold examination: A feasibility study. Magn Reson Imaging 2024, 111: 138-147.
25. Cao Y, Huang Y, Chen X, et al. Optimizing ultrafast dynamic contrast-enhanced MRI scan duration in the differentiation of benign and malignant breast lesions. Insights Imaging 2024, 15(1): 112.
26. Shi J, Huang H, Xu S, et al. XGBoost-based multiparameters from dual-energy computed tomography for the differentiation of multiple myeloma of the spine from vertebral osteolytic metastases. Eur Radiol 2023, 33(7): 4801-4811.
27. Luo Y, Liu L, Liu D, et al. Extracellular volume fraction determined by equilibrium contrast-enhanced CT for the prediction of the pathological complete response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Eur Radiol 2023, 33(6): 4042-4051.
28. Liu Y, Cheng F, Wang L, et al. Quantitative parameters derived from dual-energy computed tomography for the preoperative prediction of early recurrence in patients with esophageal squamous cell carcinoma. Eur Radiol 2023, 33(11): 7419-7428.
29. Cheng F, Liu Y, Du L, et al. Evaluation of optimal monoenergetic images acquired by dual-energy CT in the diagnosis of T staging of thoracic esophageal cancer. Insights Imaging 2023, 14(1): 33.
30. Cao Y, Wang X, Shi J, et al. Multiple parameters from ultrafast dynamic contrast-enhanced magnetic resonance imaging to discriminate between benign and malignant breast lesions: Comparison with apparent diffusion coefficient. Diagn Interv Imaging 2023, 104(6): 275-283.
31. Cao Y, Wang X, Li L, et al. Early prediction of pathologic complete response of breast cancer after neoadjuvant chemotherapy using longitudinal ultrafast dynamic contrast-enhanced MRI. Diagn Interv Imaging 2023, 104(12): 605-614.
32. Ba R, Wang X, Zhang Z, et al. Diffusion-time dependent diffusion MRI: effect of diffusion-time on microstructural mapping and prediction of prognostic features in breast cancer. Eur Radiol 2023, 33(9): 6226-6237.
33. Zhang J, Liu M, Liu D, et al. Low-dose CT with tin filter combined with iterative metal artefact reduction for guiding lung biopsy. Quant Imaging Med Surg 2022, 12(2): 1359-1371.
34. Wang X, Tan Y, Liu D, et al. Chemotherapy-associated steatohepatitis was concomitant with epicardial adipose tissue volume increasing in breast cancer patients who received neoadjuvant chemotherapy. Eur Radiol 2022, 32(7): 4898-4908.
35. Shen H, Yin J, Niu R, et al. MRI-based radiomics to compare the survival benefit of induction chemotherapy plus concurrent chemoradiotherapy versus concurrent chemoradiotherapy plus adjuvant chemotherapy in locoregionally advanced nasopharyngeal carcinoma: A multicenter study. Radiother Oncol 2022, 171: 107-113.
36. Shen H, Huang Y, Yuan X, et al. Using quantitative parameters derived from pretreatment dual-energy computed tomography to predict histopathologic features in head and neck squamous cell carcinoma. Quant Imaging Med Surg 2022, 12(2): 1243-1256.
37. Li X, Zhang J, Shen H, et al. Initial Investigation of Clinical Value of Noise-Optimized Virtual Monoenergetic Images Derived From Dual-Energy Computed Tomography Angiography in Preoperative Perforator Planning of Anterolateral Thigh Flap Transplantation. J Comput Assist Tomogr 2022, 46(4): 560-567.
38. Li L, Yu T, Sun J, et al. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI. Acta Radiol 2022, 63(8): 1014-1022.
39. Lan X, Wang X, Qi J, et al. Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions. Quant Imaging Med Surg 2022, 12(1): 810-822.
40. Chen H, Lan X, Yu T, et al. Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study. Front Oncol 2022, 12: 1076267.
41. Wang X, Liu D, Zeng X, et al. Dual-energy CT quantitative parameters for the differentiation of benign from malignant lesions and the prediction of histopathological and molecular subtypes in breast cancer. Quant Imaging Med Surg 2021, 11(5): 1946-1957.
42. Wang X, Liu D, Zeng X, et al. Dual-energy CT quantitative parameters for evaluating Immunohistochemical biomarkers of invasive breast cancer. Cancer Imaging 2021, 21(1): 4.
43. Wang X, Liu D, Jiang S, et al. Subjective and Objective Assessment of Monoenergetic and Polyenergetic Images Acquired by Dual-Energy CT in Breast Cancer. Korean J Radiol 2021, 22(4): 502-512.
44. Tu C, Shen H, Liu D, et al. Simultaneous multi-slice readout-segmentation of long variable echo-trains for accelerated diffusion-weighted imaging of nasopharyngeal carcinoma: A feasibility and optimization study. Clin Imaging 2021, 79: 119-124.
45. Shen H, Yuan X, Liu D, et al. Multiparametric dual-energy CT to differentiate stage T1 nasopharyngeal carcinoma from benign hyperplasia. Quant Imaging Med Surg 2021, 11(9): 4004-4015.
46. Deng X, Liu M, Sun J, et al. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur J Radiol 2021, 134: 109429.
47. Zeng X, Wang X, Chen H, et al. Evaluating the Image Quality of Monoenergetic Images From Dual-Energy Computed Tomography With Low-Concentration and Low-Flow-Rate Contrast Media for the Arterials Supply to the Nipple-Areola Complex in Breast Cancer Compared With Conventional Computed Tomography Angiography. J Comput Assist Tomogr 2020, 44(6): 921-927.
二、承担和参与的科学研究项目
[1]重庆市自然科学基金，影像组学联合病理组学精准预测三阴性乳腺癌新辅助化疗疗效的研究（cstc2021jcyj-msxmX0387）；项目负责人（2021.10-2024.09）
[2]中央高校基本科研业务费“医工融合项目”，MRI联合深度学习解析乳腺癌时空异质性及预测疗效（2023CDJYGRH-YB04）；项目负责人（2023.10-2025.10）
[3]重庆市科卫联合医学科研面上项目，基于多参数MRI异质性精准识别HER2状态及预测新辅助治疗疗效（2024MSXM171）；项目负责人（2024.01-2025.12）
[4]重庆市自然科学基金，基于MR断层弹性成像纹理分析探讨肿瘤物理微环境介导HER2阳性乳腺癌耐药机制及疗效预测研究（CSTB2024NSCQ-MSX0217）；项目负责人（2024.07-2027.6）
[5]重庆市卫生健康委医学科研项目，基于影像组学联合病理组学构建精准预测乳腺癌新辅助化疗疗效模型的研究（2022WSJK027）；项目负责人（2022.01-2022.12）
