Page 210 - 磁共振成像2024年7期电子刊
P. 210
磁共振成像 2024年7月第15卷第7期 Chin J Magn Reson Imaging, Jul, 2024, Vol. 15, No. 7 综 述||Reviews
https://pubmed.ncbi.nlm.nih.gov/37835774/. DOI: 10.3390/diagnostics axillary lymph node metastasis in patients with breast cancer[J]. Acad
13193031. Radiol, 2024, 31(3): 788-799. DOI: 10.1016/j.acra.2023.10.026.
[29] 徐海敏, 戴瑶, 马雨竹, 等 . MRT1WI 瘤体及瘤周影像组学联合临床 [43] ZHANG B Y, YU Y M, MAO Y, et al. Development of MRI-based
特征预测乳腺癌新辅助化疗疗效[J]. 中国医学影像技术, 2023, deep learning signature for prediction of axillary response after NAC in
39(10): 1520-1525. DOI: 10.13929/j.issn.1003-3289.2023.10.016. breast cancer[J]. Acad Radiol, 2024, 31(3): 800-811. DOI: 10.1016/j.
XU H M, DAI Y, MA Y Z, et al. MR T1WI intratumoral and peritumoral acra.2023.10.004.
radiomics combined with clinical features for predicting effect of [44] 张丽, 黄小华, 沈梦伊, 等 . 基于不同机器学习算法的影像组学模型
neoadjuvant chemotherapy for breast cancer[J]. Chin J Med Imag Technol, 预测浸润性乳腺癌 Ki-67 表达的价值[J]. 中国医学计算机成像杂志,
2023, 39(10): 1520-1525. DOI: 10.13929/j.issn.1003-3289.2023.10.016. 2024, 30(1): 39-44. DOI: 10.19627/j.cnki.cn31-1700/th.2024.01.016.
[30] ZHENG G Y, PENG J X, SHU Z Y, et al. Predicting pathological ZHANG L, HUANG X H, SHEN M Y, et al. The value of radiomics
complete response to neoadjuvant chemotherapy in breast cancer models based on different machine learning in predicting ki-67
patients: use of MRI radiomics data from three regions with multiple expression in invasive breast cancer[J]. Chin Comput Med Imag, 2024,
machine learning algorithms[J/OL]. J Cancer Res Clin Oncol, 2024, 30(1): 39-44. DOI: 10.19627/j.cnki.cn31-1700/th.2024.01.016.
150(3): 147 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38512406/. [45] 明洁, 陈莹, 刘莹, 等 . 基于 DCE-MRI瘤内联合瘤周影像组学模型术
DOI: 10.1007/s00432-024-05680-y. 前预测乳腺癌 Ki-67 表达状态的价值[J]. 磁共振成像, 2022, 13(10):
[31] GUO L C, DU S Y, GAO S, et al. Delta-radiomics based on dynamic 132-137, 149. DOI: 10.12015/issn.1674-8034.2022.10.020.
contrast-enhanced MRI predicts pathologic complete response in breast MING J, CHEN Y, LIU Y, et al. Value of preoperative prediction of Ki-67
cancer patients treated with neoadjuvant chemotherapy[J/OL]. Cancers, expression in breast cancer based on DCE-MRI intratumoral combined with
2022, 14(14): 3515 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/ peritumoral radiomics model[J]. Chin J Magn Reson Imag, 2022, 13(10):
35884576/. DOI: 10.3390/cancers14143515. 132-137, 149. DOI: 10.12015/issn.1674-8034.2022.10.020.
[32] 余雅丽, 王晓, 查小明, 等 . 基线 ADC 图全容积 ROI 影像组学模型预 [46] 刘晓东, 王新宇, 宁刚 . MRI 影像组学术前预测乳腺浸润性导管癌
测肿块样乳腺癌新辅助化疗后获得病理完全缓解的价值[J]. 放射学 Ki-67 表 达 [J]. 中 国 医 学 影 像 技 术 , 2022, 38(2): 210-214. DOI:
实践, 2022, 37(8): 987-994. DOI: 10.13609/j.cnki.1000-0313.2022.08.012. 10.13929/j.issn.1003-3289.2022.02.012.
YU Y L, WANG X, ZHA X M, et al. Whole volume ROI radiomics LIU X D, WANG X Y, NING G. MRI radiomics for preoperative
analysis of mass-like breast cancer based on pretreatment ADC images predicting Ki-67 expression of breast invasive ductal carcinoma[J].
for the prediction of pathological complete response to neoadjuvant Chin J Med Imag Technol, 2022, 38(2): 210-214. DOI: 10.13929/j.
chemotherapy[J]. Radiol Pract, 2022, 37(8): 987-994. DOI: 10.13609/j. issn.1003-3289.2022.02.012.
cnki.1000-0313.2022.08.012. [47] FENG S Q, YIN J D. Radiomics of dynamic contrast-enhanced
[33] SHI Z W, HUANG X M, CHENG Z L, et al. MRI-based quantification magnetic resonance imaging parametric maps and apparent diffusion
of intratumoral heterogeneity for predicting treatment response to coefficient maps to predict Ki-67 status in breast cancer[J/OL]. Front
neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2023, Oncol, 2022, 12: 847880 [2024-04-01]. https://pubmed. ncbi. nlm. nih.
308(1): e222830 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37432083/. gov/36895526/. DOI: 10.3389/fonc.2022.847880.
DOI: 10.1148/radiol.222830. [48] ZHANG L, SHEN M Y, ZHANG D Y, et al. Radiomics nomogram
[34] HWANG K P, ELSHAFEEY N A, KOTROTSOU A, et al. A radiomics based on dual-sequence MRI for assessing ki-67 expression in breast
model based on synthetic MRI acquisition for predicting neoadjuvant cancer[J/OL]. J Magn Reson Imaging, 2023 [2024-04-01]. https://
systemic treatment response in triple-negative breast cancer[J/OL]. pubmed.ncbi.nlm.nih.gov/38088478/. DOI: 10.1002/jmri.29149.
Radiol Imaging Cancer, 2023, 5(4): e230009 [2024-04-01]. https:// [49] TABNAK P, HAJIESMAILPOOR Z, BARADARAN B, et al.
pubmed.ncbi.nlm.nih.gov/37505106/. DOI: 10.1148/rycan.230009. MRI-based radiomics methods for predicting ki-67 expression in breast
[35] 王贇霞, 尚怡研, 郭亚欣, 等 . DCE-MRI 影像组学特征在预测乳腺癌 cancer: a systematic review and meta-analysis[J]. Acad Radiol, 2024,
腋窝淋巴结转移中的价值[J]. 磁共振成像, 2023, 14(3): 21-27. DOI: 31(3): 763-787. DOI: 10.1016/j.acra.2023.10.010.
10.12015/issn.1674-8034.2023.03.005. [50] MA M M, GAN L Y, LIU Y H, et al. Radiomics features based on
WANG Y X, SHANG Y Y, GUO Y X, et al. Value of DCE-MRI based automatic segmented MRI images: prognostic biomarkers for
radiomics features for prediction of axillary lymph node metastasis in triple-negative breast cancer treated with neoadjuvant chemotherapy[J/OL].
breast carcinoma[J]. Chin J Magn Reson Imag, 2023, 14(3): 21-27. Eur J Radiol, 2022, 146: 110095 [2024-04-01]. https://pubmed.ncbi.nlm.nih.
DOI: 10.12015/issn.1674-8034.2023.03.005. gov/34890936/. DOI: 10.1016/j.ejrad.2021.110095.
[36] LI L, YU T, SUN J Q, et al. Prediction of the number of metastatic [51] LEE J, KIM S H, KIM Y, et al. Radiomics nomogram: prediction of
axillary lymph nodes in breast cancer by radiomic signature based on 2-year disease-free survival in young age breast cancer[J/OL]. Cancers,
dynamic contrast-enhanced MRI[J]. Acta Radiol, 2022, 63(8): 1014-1022. 2022, 14(18): 4461 [2024-04-01]. https://pubmed. ncbi. nlm. nih. gov/
DOI: 10.1177/02841851211025857. 36139620/. DOI: 10.3390/cancers14184461.
[37] 赵楠楠, 朱芸, 汤晓敏, 等 . 基于瘤内及瘤周 MRI 影像组学列线图预 [52] CHO H H, KIM H, NAM S Y, et al. Measurement of perfusion
测乳腺癌腋窝淋巴结转移[J]. 磁共振成像, 2023, 14(3): 81-87, 94. heterogeneity within tumor habitats on magnetic resonance imaging
DOI: 10.12015/issn.1674-8034.2023.03.014. and its association with prognosis in breast cancer patients[J/OL].
ZHAO N N, ZHU Y, TANG X M, et al. Prediction of axillary lymph Cancers, 2022, 14(8): 1858 [2024-04-01]. https://pubmed.ncbi.nlm.nih.
node metastasis in breast cancer based on intra-tumoral and peri-tumoral gov/35454768/. DOI: 10.3390/cancers14081858.
MRI radiomics nomogram[J]. Chin J Magn Reson Imag, 2023, 14(3): [53] PARK G E, KIM S H, LEE E B, et al. Ipsilateral recurrence of DCIS in
81-87, 94. DOI: 10.12015/issn.1674-8034.2023.03.014. relation to radiomics features on contrast enhanced breast MRI[J].
[38] ZHAN C N, HU Y Q, WANG X R, et al. Prediction of Axillary Lymph Tomography, 2022, 8(2): 596-606. DOI: 10.3390/tomography8020049.
Node Metastasis in Breast Cancer using Intra-peritumoral Textural [54] YU Y F, REN W, HE Z F, et al. Machine learning radiomics of
Transition Analysis based on Dynamic Contrast-enhanced Magnetic magnetic resonance imaging predicts recurrence-free survival after
Resonance Imaging[J/OL]. Acad Radiol, 2022, 29(Suppl 1): S107-S115 surgery and correlation of LncRNAs in patients with breast cancer: a
[2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/33712393/. DOI: 10.1016/ multicenter cohort study[J/OL]. Breast Cancer Res, 2023, 25(1): 132
j.acra.2021.02.008. [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37915093/. DOI: 10.1186/
[39] LIN G H, CHEN W Y, FAN Y Y, et al. Machine learning s13058-023-01688-3.
radiomics-based prediction of non-sentinel lymph node metastasis in [55] 崔雅静, 范明, 厉力华 . DCE-MRI 影像联合临床信息预测乳腺癌复
Chinese breast cancer patients with 1-2 positive sentinel lymph nodes: 发风险评分[J]. 杭州电子科技大学学报(自然科学版), 2022, 42(1):
a multicenter study[J/OL]. Acad Radiol, 2024: S1076-S6332(24) 67-73. DOI: 10.13954/j.cnki.hdu.2022.01.011.
00080-1 [2024-04-01]. https://pubmed. ncbi. nlm. nih. gov/38490840/. DOI: CUI Y J, FAN M, LI L H. Prediction of Oncotype DX RS in breast
10.1016/j.acra.2024.02.010. cancer by integrating of DCE-MRI radiomics and clinicopathologic
[40] HARAGUCHI T, KOBAYASHI Y, HIRAHARA D, et al. Radiomics data[J]. J Hangzhou Dianzi Univ Nat Sci, 2022, 42(1): 67-73. DOI:
model of diffusion-weighted whole-body imaging with background 10.13954/j.cnki.hdu.2022.01.011.
signal suppression (DWIBS) for predicting axillary lymph node status [56] CHEN Y, TANG W, LIU W, et al. Multiparametric MR imaging
in breast cancer[J]. J Xray Sci Technol, 2023, 31(3): 627-640. DOI: radiomics signatures for assessing the recurrence risk of ER+/HER2-
10.3233/XST-230009. breast cancer quantified with 21-gene recurrence score[J]. J Magn
[41] SONG S E, WOO O H, CHO Y, et al. Prediction of axillary lymph Reson Imaging, 2023, 58(2): 444-453. DOI: 10.1002/jmri.28547.
node metastasis in early-stage triple-negative breast cancer using [57] PAQUIER Z, CHAO S L, ACQUISTO A, et al. Radiomics software
multiparametric and radiomic features of breast MRI[J/OL]. Acad comparison using digital phantom and patient data: IBSI-compliance
Radiol, 2023, 30(Suppl 2): S25-S37 [2024-04-01]. https://pubmed.ncbi. does not guarantee concordance of feature values[J/OL]. Biomed Phys
nlm.nih.gov/37331865/. DOI: 10.1016/j.acra.2023.05.025. Eng Express, 2022, 8(6) [2024-04-01]. https://pubmed. ncbi. nlm. nih.
[42] CHEN Y S, LI J P, ZHANG J, et al. Radiomic nomogram for predicting gov/36049399/. DOI: 10.1088/2057-1976/ac8e6f.
https://www.chinesemri.com ·203 ·