Research progress on clinical application of oscillating gradient spin echo sequence in breast cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHENG Zihan ¹ ,  YIN Longlin ^1,2,3

1. School of Medicine, University of Electronic Science and Technology of China, Chengdu 610072, P. R. China;
2. Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, P. R. China;
3. Institute of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, P. R. China;

Corresponding?author：

YIN Longlin, Email: yinlonglin@163.com

Keywords：

breast cancer; oscillating gradient spin echo sequence; diffusion weighted imaging; progress

DOI：

10.7507/1007-9424.202512079

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To review the research progress on the clinical application of the oscillating gradient spin echo (OGSE) sequence in breast cancer, aiming to provide new insights for clinical diagnosis and treatment of breast cancer. Method By searching relevant literature in recent years, the research articles of OGSE sequence in breast cancer were summarized and analyzed. Results As an emerging diffusion weighted imaging method, OGSE sequence can measure cell diameter, extracellular diffusivity, intracellular water molecule volume fraction, and cell density of living tissue, thereby quantifying the microstructural characteristics of tumors. OGSE sequence may be of certain values in differential diagnosis of benign and malignant breast diseases, prediction of breast cancer molecular typing and histological grading. Conclusion With the continuous development and improvement of OGSE, the application of this technology will become increasingly extensive in diagnosis and treatment of breast cancer.

Citation： ZHENG Zihan, YIN Longlin. Research progress on clinical application of oscillating gradient spin echo sequence in breast cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2026, 33(5): 719-725. doi: 10.7507/1007-9424.202512079 Copy

1.	樊怡豪, 周逸倩, 楊玉丹, 等. 我國惡性腫瘤發病現狀及流行趨勢. 中國普外基礎與臨床雜志, 2025, 32(6): 669-676.
2.	Aristokli N, Polycarpou I, Themistocleous SC, et al. Comparison of the diagnostic performance of magnetic resonance imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient’s history: a review. Radiography (Lond), 2022, 28(3): 848-856.
3.	Jang H, Du J. Optimizing diffusion-weighted MRI of peripheral nerves. Radiology, 2022, 302(1): 162-163.
4.	Jiang X, Li H, Xie J, et al. Quantification of cell size using temporal diffusion spectroscopy. Magn Reson Med, 2016, 75(3): 1076-1085.
5.	張新利, 汪晶. 基于振蕩梯度自旋回波的擴散磁共振成像研究進展. 磁共振成像, 2023, 14(9): 198-202.
6.	Zhu A, Shih R, Huang RY, et al. Revealing tumor microstructure with oscillating diffusion encoding MRI in pre-surgical and post-treatment glioma patients. Magn Reson Med, 2023, 90(5): 1789-1801.
7.	Gore JC, Xu J, Colvin DC, et al. Characterization of tissue structure at varying length scales using temporal diffusion spectroscopy. NMR Biomed, 2010, 23(7): 745-756.
8.	Xu J. Probing neural tissues at small scales: recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans. J Neurosci Methods, 2021, 349: 109024. doi: 10.1016/j.jneumeth.2020.109024.
9.	Tanner JE. Self diffusion of water in frog muscle. Biophys J, 1979, 28(1): 107-116.
10.	Schachter M, Does MD, Anderson AW, et al. Measurements of restricted diffusion using an oscillating gradient spin-echo sequence. J Magn Reson, 2000, 147(2): 232-237.
11.	Xu J, Jiang X, Devan SP, et al. MRI-cytometry: mapping nonparametric cell size distributions using diffusion MRI. Magn Reson Med, 2021, 85(2): 748-761.
12.	Devan SP, Jiang X, Luo G, et al. Selective cell size MRI differentiates brain tumors from radiation necrosis. Cancer Res, 2022, 82(19): 3603-3613.
13.	Xing S, Levesque IR. A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion-weighted MRI. Magn Reson Med, 2021, 86(2): 1029-1044.
14.	Hoffmann E, Gerwing M, Niland S, et al. Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI. J Immunother Cancer, 2023, 11(3): e006092. doi: 10.1136/jitc-2022-006092.
15.	Zhang H, Liu K, Ba R, et al. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping. Neuro Oncol, 2023, 25(6): 1146-1156.
16.	Bonet-Carne E, Johnston E, Daducci A, et al. VERDICT-AMICO: ultrafast fitting algorithm for non-invasive prostate microstructure characterization. NMR Biomed, 2019, 32(1): e4019. doi: 10.1002/nbm.4019.
17.	Panagiotaki E, Chan RW, Dikaios N, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol, 2015, 50(4): 218-227.
18.	Reynaud O, Winters KV, Hoang DM, et al. Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas. NMR Biomed, 2016, 29(10): 1350-1363.
19.	Xu J, Jiang X, Li H, et al. Magnetic resonance imaging of mean cell size in human breast tumors. Magn Reson Med, 2020, 83(6): 2002-2014.
20.	Lin Z, Zhang X, Guo L, et al. Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing. J Magn Reson Imaging, 2019, 50(6): 1843-1851.
21.	Zhang N, Kang J, Wang H, et al. Differentiation of fibroadenomas versus malignant breast tumors utilizing three-dimensional amide proton transfer weighted magnetic resonance imaging. Clin Imaging, 2022, 81: 15-23.
22.	Wu D, Jiang K, Li H, et al. Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer. Radiology, 2022, 303(3): 578-587.
23.	Iima M, Kataoka M, Honda M, et al. The rate of apparent diffusion coefficient change with diffusion time on breast diffusion-weighted imaging depends on breast tumor types and molecular prognostic biomarker expression. Invest Radiol, 2021, 56(8): 501-508.
24.	Wang X, Zhang Y, Cheng J, et al. Microstructural diffusion MRI for differentiation of breast tumors and prediction of prognostic factors in breast cancer. Front Oncol, 2025, 15: 1498691. doi: 10.3389/fonc.2025.1498691.
25.	Suo S, Zhang K, Cao M, et al. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging, 2016, 43(4): 894-902.
26.	Peng S, Sun P, Liu J, et al. Imaging microstructural parameters of breast tumor in patient using time-dependent diffusion: a feasibility study. Diagnostics (Basel), 2025, 15(7): 823. doi: 10.3390/diagnostics15070823.
27.	Iima M, Yamamoto A, Kataoka M, et al. Time-dependent diffusion MRI to distinguish malignant from benign head and neck tumors. J Magn Reson Imaging, 2019, 50(1): 88-95.
28.	武鑫培, 羅銀義, 王世明. HER2低表達乳腺癌患者的臨床病理特征及其預后分析. 中國普外基礎與臨床雜志, 2023, 30(6): 691-697.
29.	孫正魁, 江澤飛. 2022版《中國臨床腫瘤學會乳腺癌診療指南》更新解讀. 中國腫瘤外科雜志, 2022, 14(3): 212-218.
30.	Perou CM, S?rlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature, 2000, 406(6797): 747-752.
31.	Katsura C, Ogunmwonyi I, Kankam HK, et al. Breast cancer: presentation, investigation and management. Br J Hosp Med (Lond), 2022, 83(2): 1-7.
32.	吳曉琴, 鄒麗, 胡慧, 等. 術前超聲造影聯合細胞學檢查判斷乳腺癌前哨淋巴結狀態122例分析. 臨床外科雜志, 2020, 28(1): 49-52.
33.	Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3. 0 Tesla. J Magn Reson Imaging, 2015, 41(1): 175-182.
34.	Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol, 2012, 22(7): 1519-1528.
35.	Choi SY, Chang YW, Park HJ, et al. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol, 2012, 85(1016): e474-e479.
36.	陸玨, 汪晶. 擴散磁共振成像振蕩梯度自旋回波原理及其在腦膠質瘤中的應用進展. 磁共振成像, 2024, 15(6): 185-189.
37.	Makkat S, Luypaert R, Stadnik T, et al. Deconvolution-based dynamic contrast-enhanced MR imaging of breast tumors: correlation of tumor blood flow with human epidermal growth factor receptor 2 status and clinicopathologic findings–preliminary results. Radiology, 2008, 249(2): 471-482.
38.	王曉艷, 張焱, 程敬亮, 等. 酰胺質子轉移加權成像與時間依賴性擴散MRI診斷乳腺惡性病變的效能比較. 中華放射學雜志, 2024, 58(6): 611-619.
39.	Wu L, Liu F, Li S, et al. Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer. Radiol Oncol, 2025, 59(3): 337-348.
40.	楊雁雯, 李偉偉, 陶玲玲, 等. 小乳腺癌的超聲造影特征與病理組織學分級的相關性研究. 中國醫學計算機成像雜志, 2024, 30(5): 594-598.
41.	Schwartz AM, Henson DE, Chen D, et al. Histologic grade remains a prognostic factor for breast cancer regardless of the number of positive lymph nodes and tumor size: a study of 161 708 cases of breast cancer from the SEER program. Arch Pathol Lab Med, 2014, 138(8): 1048-1052.
42.	Fowler AM, Strigel RM. Clinical advances in PET-MRI for breast cancer. Lancet Oncol, 2022, 23(1): e32-e43.
43.	Xu J, Li K, Smith RA, et al. Characterizing tumor response to chemotherapy at various length scales using temporal diffusion spectroscopy. PLoS One, 2012, 7(7): e41714. doi: 10.1371/journal.pone.0041714.
44.	Connolly S, McGourty K, Newport D. The in vitro inertial positions and viability of cells in suspension under different in vivo flow conditions. Sci Rep, 2020, 10(1): 1711. doi: 10.1038/s41598-020-58161-w.
45.	Shashni B, Ariyasu S, Takeda R, et al. Size-based differentiation of cancer and normal cells by a particle size analyzer assisted by a cell-recognition PC software. Biol Pharm Bull, 2018, 41(4): 487-503.
46.	Young JS, Al-Adli N, Scotford K, et al. Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know. J Neurosurg, 2023, 139(3): 748-759.
47.	Sidibe I, Tensaouti F, Gilhodes J, et al. Pseudoprogression in GBM versus true progression in patients with glioblastoma: a multiapproach analysis. Radiother Oncol, 2023, 181: 109486. doi: 10.1016/j.radonc.2023.109486.
48.	Riedel F, Schaefgen B, Sinn HP, et al. Diagnostic accuracy of axillary staging by ultrasound in early breast cancer patients. Eur J Radiol, 2021, 135: 109468. doi: 10.1016/j.ejrad.2020.109468.
49.	Zhang-Yin J, Mauel E, Talpe S. Update on sentinel lymph node methods and pathology in breast cancer. Diagnostics (Basel), 2024, 14(3): 252. doi: 10.3390/diagnostics14030252.
50.	Zhao M, Wu Q, Guo L, et al. Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer. Eur J Radiol, 2020, 129: 109093. doi: 10.1016/j.ejrad.2020.109093.
51.	Chen Y, Wang L, Dong X, et al. Deep learning radiomics of preoperative breast MRI for prediction of axillary lymph node metastasis in breast cancer. J Digit Imaging, 2023, 36(4): 1323-1331.
52.	Qu M, Feng W, Liu X, et al. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer. Eur J Radiol, 2024, 175: 111452. doi: 10.1016/j.ejrad.2024.111452.
53.	Guvenc I, Whitman GJ, Liu P, et al. Diffusion-weighted MR imaging increases diagnostic accuracy of breast MR imaging for predicting axillary metastases in breast cancer patients. Breast J, 2019, 25(1): 47-55.
54.	Chen S, Bai J, Guo X, et al. Utility of time-dependent diffusion MRI based microstructural parameters in predicting lymph node metastasis in breast cancer. Eur J Radiol, 2026, 194: 112515. doi: 10.1016/j.ejrad.2025.112515.
55.	Li H, Jiang X, Xie J, et al. Impact of transcytolemmal water exchange on estimates of tissue microstructural properties derived from diffusion MRI. Magn Reson Med, 2017, 77(6): 2239-2249.
56.	Zhu A, Michael ES, Li H, et al. Engineering clinical translation of OGSE diffusion MRI. Magn Reson Med, 2025, 94(3): 913-936.
57.	Davids M, Guerin B, Klein V, et al. Optimization of MRI gradient coils with explicit peripheral nerve stimulation constraints. IEEE Trans Med Imaging, 2021, 40(1): 129-142.
58.	Sung H, Ferlay J, Siegel RL, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
59.	Wu J, Kang T, Lan X, et al. IMPULSED model based cytological feature estimation with U-Net: application to human brain tumor at 3T. Magn Reson Med, 2023, 89(1): 411-422.
60.	Diao Y, Jelescu I. Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network. Magn Reson Med, 2023, 89(3): 1193-1206.
61.	鄭孫易, 劉佳鑫, 崔效楠, 等. 人工智能在腫瘤影像學中的應用進展. 放射學實踐, 2025, 40(9): 1093-1097.

1. 樊怡豪, 周逸倩, 楊玉丹, 等. 我國惡性腫瘤發病現狀及流行趨勢. 中國普外基礎與臨床雜志, 2025, 32(6): 669-676.
2. Aristokli N, Polycarpou I, Themistocleous SC, et al. Comparison of the diagnostic performance of magnetic resonance imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient’s history: a review. Radiography (Lond), 2022, 28(3): 848-856.
3. Jang H, Du J. Optimizing diffusion-weighted MRI of peripheral nerves. Radiology, 2022, 302(1): 162-163.
4. Jiang X, Li H, Xie J, et al. Quantification of cell size using temporal diffusion spectroscopy. Magn Reson Med, 2016, 75(3): 1076-1085.
5. 張新利, 汪晶. 基于振蕩梯度自旋回波的擴散磁共振成像研究進展. 磁共振成像, 2023, 14(9): 198-202.
6. Zhu A, Shih R, Huang RY, et al. Revealing tumor microstructure with oscillating diffusion encoding MRI in pre-surgical and post-treatment glioma patients. Magn Reson Med, 2023, 90(5): 1789-1801.
7. Gore JC, Xu J, Colvin DC, et al. Characterization of tissue structure at varying length scales using temporal diffusion spectroscopy. NMR Biomed, 2010, 23(7): 745-756.
8. Xu J. Probing neural tissues at small scales: recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans. J Neurosci Methods, 2021, 349: 109024. doi: 10.1016/j.jneumeth.2020.109024.
9. Tanner JE. Self diffusion of water in frog muscle. Biophys J, 1979, 28(1): 107-116.
10. Schachter M, Does MD, Anderson AW, et al. Measurements of restricted diffusion using an oscillating gradient spin-echo sequence. J Magn Reson, 2000, 147(2): 232-237.
11. Xu J, Jiang X, Devan SP, et al. MRI-cytometry: mapping nonparametric cell size distributions using diffusion MRI. Magn Reson Med, 2021, 85(2): 748-761.
12. Devan SP, Jiang X, Luo G, et al. Selective cell size MRI differentiates brain tumors from radiation necrosis. Cancer Res, 2022, 82(19): 3603-3613.
13. Xing S, Levesque IR. A simulation study of cell size and volume fraction mapping for tissue with two underlying cell populations using diffusion-weighted MRI. Magn Reson Med, 2021, 86(2): 1029-1044.
14. Hoffmann E, Gerwing M, Niland S, et al. Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI. J Immunother Cancer, 2023, 11(3): e006092. doi: 10.1136/jitc-2022-006092.
15. Zhang H, Liu K, Ba R, et al. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping. Neuro Oncol, 2023, 25(6): 1146-1156.
16. Bonet-Carne E, Johnston E, Daducci A, et al. VERDICT-AMICO: ultrafast fitting algorithm for non-invasive prostate microstructure characterization. NMR Biomed, 2019, 32(1): e4019. doi: 10.1002/nbm.4019.
17. Panagiotaki E, Chan RW, Dikaios N, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol, 2015, 50(4): 218-227.
18. Reynaud O, Winters KV, Hoang DM, et al. Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space (POMACE) in mouse gliomas. NMR Biomed, 2016, 29(10): 1350-1363.
19. Xu J, Jiang X, Li H, et al. Magnetic resonance imaging of mean cell size in human breast tumors. Magn Reson Med, 2020, 83(6): 2002-2014.
20. Lin Z, Zhang X, Guo L, et al. Clinical feasibility study of 3D intracranial magnetic resonance angiography using compressed sensing. J Magn Reson Imaging, 2019, 50(6): 1843-1851.
21. Zhang N, Kang J, Wang H, et al. Differentiation of fibroadenomas versus malignant breast tumors utilizing three-dimensional amide proton transfer weighted magnetic resonance imaging. Clin Imaging, 2022, 81: 15-23.
22. Wu D, Jiang K, Li H, et al. Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer. Radiology, 2022, 303(3): 578-587.
23. Iima M, Kataoka M, Honda M, et al. The rate of apparent diffusion coefficient change with diffusion time on breast diffusion-weighted imaging depends on breast tumor types and molecular prognostic biomarker expression. Invest Radiol, 2021, 56(8): 501-508.
24. Wang X, Zhang Y, Cheng J, et al. Microstructural diffusion MRI for differentiation of breast tumors and prediction of prognostic factors in breast cancer. Front Oncol, 2025, 15: 1498691. doi: 10.3389/fonc.2025.1498691.
25. Suo S, Zhang K, Cao M, et al. Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient. J Magn Reson Imaging, 2016, 43(4): 894-902.
26. Peng S, Sun P, Liu J, et al. Imaging microstructural parameters of breast tumor in patient using time-dependent diffusion: a feasibility study. Diagnostics (Basel), 2025, 15(7): 823. doi: 10.3390/diagnostics15070823.
27. Iima M, Yamamoto A, Kataoka M, et al. Time-dependent diffusion MRI to distinguish malignant from benign head and neck tumors. J Magn Reson Imaging, 2019, 50(1): 88-95.
28. 武鑫培, 羅銀義, 王世明. HER2低表達乳腺癌患者的臨床病理特征及其預后分析. 中國普外基礎與臨床雜志, 2023, 30(6): 691-697.
29. 孫正魁, 江澤飛. 2022版《中國臨床腫瘤學會乳腺癌診療指南》更新解讀. 中國腫瘤外科雜志, 2022, 14(3): 212-218.
30. Perou CM, S?rlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature, 2000, 406(6797): 747-752.
31. Katsura C, Ogunmwonyi I, Kankam HK, et al. Breast cancer: presentation, investigation and management. Br J Hosp Med (Lond), 2022, 83(2): 1-7.
32. 吳曉琴, 鄒麗, 胡慧, 等. 術前超聲造影聯合細胞學檢查判斷乳腺癌前哨淋巴結狀態122例分析. 臨床外科雜志, 2020, 28(1): 49-52.
33. Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3. 0 Tesla. J Magn Reson Imaging, 2015, 41(1): 175-182.
34. Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol, 2012, 22(7): 1519-1528.
35. Choi SY, Chang YW, Park HJ, et al. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol, 2012, 85(1016): e474-e479.
36. 陸玨, 汪晶. 擴散磁共振成像振蕩梯度自旋回波原理及其在腦膠質瘤中的應用進展. 磁共振成像, 2024, 15(6): 185-189.
37. Makkat S, Luypaert R, Stadnik T, et al. Deconvolution-based dynamic contrast-enhanced MR imaging of breast tumors: correlation of tumor blood flow with human epidermal growth factor receptor 2 status and clinicopathologic findings–preliminary results. Radiology, 2008, 249(2): 471-482.
38. 王曉艷, 張焱, 程敬亮, 等. 酰胺質子轉移加權成像與時間依賴性擴散MRI診斷乳腺惡性病變的效能比較. 中華放射學雜志, 2024, 58(6): 611-619.
39. Wu L, Liu F, Li S, et al. Comparison of MR cytometry methods in predicting immunohistochemical factor status and molecular subtypes of breast cancer. Radiol Oncol, 2025, 59(3): 337-348.
40. 楊雁雯, 李偉偉, 陶玲玲, 等. 小乳腺癌的超聲造影特征與病理組織學分級的相關性研究. 中國醫學計算機成像雜志, 2024, 30(5): 594-598.
41. Schwartz AM, Henson DE, Chen D, et al. Histologic grade remains a prognostic factor for breast cancer regardless of the number of positive lymph nodes and tumor size: a study of 161 708 cases of breast cancer from the SEER program. Arch Pathol Lab Med, 2014, 138(8): 1048-1052.
42. Fowler AM, Strigel RM. Clinical advances in PET-MRI for breast cancer. Lancet Oncol, 2022, 23(1): e32-e43.
43. Xu J, Li K, Smith RA, et al. Characterizing tumor response to chemotherapy at various length scales using temporal diffusion spectroscopy. PLoS One, 2012, 7(7): e41714. doi: 10.1371/journal.pone.0041714.
44. Connolly S, McGourty K, Newport D. The in vitro inertial positions and viability of cells in suspension under different in vivo flow conditions. Sci Rep, 2020, 10(1): 1711. doi: 10.1038/s41598-020-58161-w.
45. Shashni B, Ariyasu S, Takeda R, et al. Size-based differentiation of cancer and normal cells by a particle size analyzer assisted by a cell-recognition PC software. Biol Pharm Bull, 2018, 41(4): 487-503.
46. Young JS, Al-Adli N, Scotford K, et al. Pseudoprogression versus true progression in glioblastoma: what neurosurgeons need to know. J Neurosurg, 2023, 139(3): 748-759.
47. Sidibe I, Tensaouti F, Gilhodes J, et al. Pseudoprogression in GBM versus true progression in patients with glioblastoma: a multiapproach analysis. Radiother Oncol, 2023, 181: 109486. doi: 10.1016/j.radonc.2023.109486.
48. Riedel F, Schaefgen B, Sinn HP, et al. Diagnostic accuracy of axillary staging by ultrasound in early breast cancer patients. Eur J Radiol, 2021, 135: 109468. doi: 10.1016/j.ejrad.2020.109468.
49. Zhang-Yin J, Mauel E, Talpe S. Update on sentinel lymph node methods and pathology in breast cancer. Diagnostics (Basel), 2024, 14(3): 252. doi: 10.3390/diagnostics14030252.
50. Zhao M, Wu Q, Guo L, et al. Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer. Eur J Radiol, 2020, 129: 109093. doi: 10.1016/j.ejrad.2020.109093.
51. Chen Y, Wang L, Dong X, et al. Deep learning radiomics of preoperative breast MRI for prediction of axillary lymph node metastasis in breast cancer. J Digit Imaging, 2023, 36(4): 1323-1331.
52. Qu M, Feng W, Liu X, et al. Investigation of synthetic MRI with quantitative parameters for discriminating axillary lymph nodes status in invasive breast cancer. Eur J Radiol, 2024, 175: 111452. doi: 10.1016/j.ejrad.2024.111452.
53. Guvenc I, Whitman GJ, Liu P, et al. Diffusion-weighted MR imaging increases diagnostic accuracy of breast MR imaging for predicting axillary metastases in breast cancer patients. Breast J, 2019, 25(1): 47-55.
54. Chen S, Bai J, Guo X, et al. Utility of time-dependent diffusion MRI based microstructural parameters in predicting lymph node metastasis in breast cancer. Eur J Radiol, 2026, 194: 112515. doi: 10.1016/j.ejrad.2025.112515.
55. Li H, Jiang X, Xie J, et al. Impact of transcytolemmal water exchange on estimates of tissue microstructural properties derived from diffusion MRI. Magn Reson Med, 2017, 77(6): 2239-2249.
56. Zhu A, Michael ES, Li H, et al. Engineering clinical translation of OGSE diffusion MRI. Magn Reson Med, 2025, 94(3): 913-936.
57. Davids M, Guerin B, Klein V, et al. Optimization of MRI gradient coils with explicit peripheral nerve stimulation constraints. IEEE Trans Med Imaging, 2021, 40(1): 129-142.
58. Sung H, Ferlay J, Siegel RL, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
59. Wu J, Kang T, Lan X, et al. IMPULSED model based cytological feature estimation with U-Net: application to human brain tumor at 3T. Magn Reson Med, 2023, 89(1): 411-422.
60. Diao Y, Jelescu I. Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network. Magn Reson Med, 2023, 89(3): 1193-1206.
61. 鄭孫易, 劉佳鑫, 崔效楠, 等. 人工智能在腫瘤影像學中的應用進展. 放射學實踐, 2025, 40(9): 1093-1097.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Research progress on clinical application of oscillating gradient spin echo sequence in breast cancer

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