Page 209 - 磁共振成像2024年7期电子刊

综 述｜｜Reviews 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

良好的应用前景。本文简要论述了 MRI影像组学的 Radiol, 2022, 32(7): 4845-4856. DOI: 10.1007/s00330-022-08539-3.
[11] FAN W, SUN W, XU M Z, et al. Diagnosis of benign and malignant
工作流程以及在乳腺肿瘤中的临床应用，包括鉴别 nodules with a radiomics model integrating features from nodules and
mammary regions on DCE-MRI[J/OL]. Front Oncol, 2024, 14:
良恶性肿瘤、预测乳腺癌分子分型、对 NAC 的反应、 1307907 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38450180/. DOI:
10.3389/fonc.2024.1307907.
ALN 状态、Ki-67 表达、预后评估及复发风险的预测。 [12] SUN K, JIAO Z C, ZHU H, et al. Radiomics-based machine learning
乳腺 MP-MRI 可以提供丰富的定量信息，基于乳腺 analysis and characterization of breast lesions with multiparametric
diffusion-weighted MR[J/OL]. J Transl Med, 2021, 19(1): 443
MRI 的影像组学在乳腺癌患者的诊断、预测、决策和 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34689804/. DOI: 10.1186/
s12967-021-03117-5.
治疗支持方面将会提供更精准的信息。然而，它仍 [13] LIU Y L, JIA X X, ZHAO J Q, et al. A machine learning-based
处于研究阶段，在临床应用前仍存在许多问题，尤其 unenhanced radiomics approach to distinguishing between benign and
malignant breast lesions using T2-weighted and diffusion-weighted
在癌症治疗中的应用还需要一些努力。基因组学和 MRI[J/OL]. J Magn Reson Imaging, 2023 [2024-04-01]. https://
pubmed.ncbi.nlm.nih.gov/37933890/. DOI: 10.1002/jmri.29111.
DL 的迅速发展将鼓励影像组学研究人员探索新的 [14] WANG G S, GUO Q, SHI D F, et al. Clinical breast MRI-based
radiomics for distinguishing benign and malignant lesions: an analysis
可能性，并增强我们对乳腺癌患者的管理能力。未 of sequences and enhanced phases[J/OL]. J Magn Reson Imaging,
来的放射科医生有望将从乳腺 MP-MRI 中提取的信 2023 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38006286/. DOI:
10.1002/jmri.29150.
息与临床决策联系起来，并为精准医疗建立重要的 [15] TURNER K M, YEO S K, HOLM T M, et al. Heterogeneity within
molecular subtypes of breast cancer[J/OL]. Am J Physiol Cell Physiol,
生物标志物。 2021, 321(2): C343-C354 [2024-04-01]. https://pubmed.ncbi.nlm.nih.
gov/34191627/. DOI: 10.1152/ajpcell.00109.2021.
作者利益冲突声明：全体作者均声明无利益冲突。 [16] HUANG T, FAN B, QIU Y Y, et al. Application of DCE-MRI
作者贡献声明：王毅设计本研究的方案，并对稿 radiomics signature analysis in differentiating molecular subtypes of
luminal and non-luminal breast cancer[J/OL]. Front Med, 2023, 10:
件重要内容进行了修改；李晓光起草和撰写稿件，获 1140514 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37181350/. DOI:
10.3389/fmed.2023.1140514.
取、分析和解释本研究的数据；田静、张春来、谢宗玉 [17] YUE W Y, ZHANG H T, GAO S, et al. Predicting breast cancer
subtypes using magnetic resonance imaging based radiomics with
构思和设计本研究，获取、分析本研究的数据，对稿 automatic segmentation[J]. J Comput Assist Tomogr, 2023, 47(5): 729-737.
DOI: 10.1097/RCT.0000000000001474.
件重要内容进行了修改；王毅获得陆军军医大学临 [18] ZHANG L L, FAN M, WANG S W, et al. Radiomic analysis of
床医学研究项目资助，李晓光获得重庆市卫生健康 pharmacokinetic heterogeneity within tumor based on the unsupervised
decomposition of dynamic contrast-enhanced MRI for predicting
委医学科研项目资助；全体作者都同意发表最后的 histological characteristics of breast cancer[J]. J Magn Reson Imaging,
2022, 55(6): 1636-1647. DOI: 10.1002/jmri.27993.
修改稿，同意对本研究的所有方面负责，确保本研究 [19] ZHANG L, ZHOU X X, LIU L, et al. Comparison of dynamic
的准确性和诚信。 contrast-enhanced MRI and non-mono-exponential model-based
diffusion-weighted imaging for the prediction of prognostic biomarkers
and molecular subtypes of breast cancer based on radiomics[J]. J Magn
Reson Imaging, 2023, 58(5): 1590-1602. DOI: 10.1002/jmri.28611.
参考文献[References] [20] ZHOU J, TAN H N, LI W, et al. Radiomics signatures based on
multiparametric MRI for the preoperative prediction of the HER2
[1] WEKKING D, PORCU M, SILVA P D, et al. Breast MRI: clinical status of patients with breast cancer[J]. Acad Radiol, 2021, 28(10):
indications, recommendations, and future applications in breast cancer 1352-1360. DOI: 10.1016/j.acra.2020.05.040.
diagnosis[J]. Curr Oncol Rep, 2023, 25(4): 257-267. DOI: 10.1007/ [21] ZHANG W L, LIANG F R, ZHAO Y, et al. Multiparametric MR-based
s11912-023-01372-x. feature fusion radiomics combined with ADC maps-based tumor
[2] KATAOKA M, IIMA M, MIYAKE K K, et al. Multiparametric imaging proliferative burden in distinguishing TNBC versus non-TNBC[J/OL].
of breast cancer: an update of current applications[J]. Diagn Interv Imaging, Phys Med Biol, 2024, 69(5) [2024-04-01]. https://pubmed.ncbi.nlm.nih.
2022, 103(12): 574-583. DOI: 10.1016/j.diii.2022.10.012. gov/38306970/. DOI: 10.1088/1361-6560/ad25c0.
[3] GUIOT J, VAIDYANATHAN A, DEPREZ L, et al. A review in [22] XU R, YU D, LUO P, et al. Do habitat MRI and fractal analysis help
radiomics: making personalized medicine a reality via routine imaging distinguish triple-negative breast cancer from non-triple-negative breast
[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846. carcinoma[J/OL]. J L'association Can Des Radiol, 2024: 8465371241231573
[4] GILLIES R J, ANDERSON A R, GATENBY R A, et al. The biology [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38389194/. DOI: 10.1177/
underlying molecular imaging in oncology: from genome to anatome 08465371241231573.
and back again[J]. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j. [23] VEMURU S, HUANG J, COLBORN K, et al. Clinical implications of
crad.2010.04.005. receptor conversions in breast cancer patients who have undergone
[5] LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: neoadjuvant chemotherapy[J]. Breast Cancer Res Treat, 2023, 200(2):
extracting more information from medical images using advanced 247-256. DOI: 10.1007/s10549-023-06978-0.
feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j. [24] LIU H Q, LIN S Y, SONG Y D, et al. Machine learning on MRI
ejca.2011.11.036. radiomic features: identification of molecular subtype alteration in
[6] ZHAO X, BAI J W, GUO Q, et al. Clinical applications of deep breast cancer after neoadjuvant therapy[J]. Eur Radiol, 2023, 33(4):
learning in breast MRI[J/OL]. Biochim Biophys Acta Rev Cancer, 2965-2974. DOI: 10.1007/s00330-022-09264-7.
2023, 1878(2): 188864 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/ [25] ZHENG S Y, YANG Z H, DU G Z, et al. Discrimination between
36822377/. DOI: 10.1016/j.bbcan.2023.188864. HER2-overexpressing, -low-expressing, and -zero-expressing statuses
[7] ZHANG X, SU G H, CHEN Y, et al. Decoding Intratumoral in breast cancer using multiparametric MRI-based radiomics[J/OL]. Eur
Heterogeneity: clinical Potential of Habitat Imaging based on Radiol, 2024 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38363315/.
Radiomics[J/OL]. Radiology, 2023, 309(3): e232047 [2024-04-01]. DOI: 10.1007/s00330-024-10641-7.
https://pubmed.ncbi.nlm.nih.gov/38085080/. DOI: 10.1148/radiol.232047. [26] YU Y M, WANG Z B, WANG Q, et al. Radiomic model based on
[8] JI Y, LI H, EDWARDS A V, et al. Independent validation of machine magnetic resonance imaging for predicting pathological complete
learning in diagnosing breast Cancer on magnetic resonance imaging response after neoadjuvant chemotherapy in breast cancer patients[J/OL].
within a single institution[J/OL]. Cancer Imaging, 2019, 19(1): 64 Front Oncol, 2023, 13: 1249339 [2024-04-01]. https://pubmed. ncbi.
[2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36991474/. DOI: 10.1186/ nlm.nih.gov/38357424/. DOI: 10.3389/fonc.2023.1249339.
s40644-019-0252-2. [27] LI Q, XIAO Q, LI J W, et al. Value of machine learning with
[9] DEBBI K, HABERT P, GROB A, et al. Radiomics model to classify multiphases CE-MRI radiomics for early prediction of pathological
mammary masses using breast DCE-MRI compared to the BI-RADS complete response to neoadjuvant therapy in HER2-positive invasive breast
classification performance[J/OL]. Insights Imaging, 2023, 14(1): 64 cancer[J/OL]. Cancer Manag Res, 2021, 13: 5053-5062 [2024-04-01]. https:
[2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37052738/. DOI: 10.1186/ //pubmed.ncbi.nlm.nih.gov/34234550/. DOI: 10.2147/CMAR.S304547.
s13244-023-01404-x. [28] PARK J, KIM M J, YOON J H, et al. Machine learning predicts
[10] XU H, LIU J K, CHEN Z, et al. Intratumoral and peritumoral pathologic complete response to neoadjuvant chemotherapy for ER+
radiomics based on dynamic contrast-enhanced MRI for preoperative HER2- breast cancer: integrating tumoral and peritumoral MRI
prediction of intraductal component in invasive breast cancer[J]. Eur radiomic features[J/OL]. Diagnostics, 2023, 13(19): 3031 [2024-04-01].

·202 · https://www.chinesemri.com