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Clinical Article
A preliminary clinical application of T2 mapping-based radiomics on MRI in breast diseases
HUANG Wenping  WANG Fen  LIU Hongli  YU Yali  LOU Jianjuan  ZOU Qigui  WANG Siqi  JIANG Yanni 

Cite this article as: HUANG W P, WANG F, LIU H L, et al. A preliminary clinical application of T2 mapping-based radiomics on MRI in breast diseases[J]. Chin J Magn Reson Imaging, 2023, 14(2): 50-55. DOI:10.12015/issn.1674-8034.2023.02.009.


[Abstract] Objective To investigate the diagnostic performance of radiomic features based on breast MRI T2 mapping in differentiating benign and malignant lesions.Materials and Methods This retrospective study included T2 mapping images of breast MRI from 113 patients (113 breast lesions: 51 benign lesions, 62 malignant lesions) confirmed by pathology. Breast lesions were segmented manually on the T2 mapping images, and radiomic features were then extracted and selected. They were divided into two groups according to the pathological results. The Kappa was measured by the intra-class correlation coefficients. The training set and test set were selected according to the ratio of 7∶3. Z-score, Pearson correlation coefficients, recursive feature elimination were used to select features in the training set. A radiomics-based predictive model using logistic regression was developed and calibrated with five-fold cross-validation. The receiver operating characteristic (ROC) curves were drawn in the training set and test set respectively to evaluate the diagnostic performance of the model. The model efficiency was evaluated using the clinical decision curve.Results A total of 107 features were extracted from T2 mapping images for each patient. Finally, 6 features (original_shape_Sphericity, original_glcm_InverseVariance, original_glrlm_GrayLevelNonUniformityNormalized, original_glrlm_ShortRunEmphasis, original_glszm_GrayLevelNonUniformityNormalized and original_ngtdm_Coarseness) were selected to construct the model for differentiating benign from malignant lesions. The area under the curve, sensitivity, specificity and accuracy of model in test set were 0.895 (95% confidence interval: 0.768-0.990), 94.7%, 80.0% and 88.2%.Conclusions T2 mapping-based radiomics method can be used to preoperatively discriminate benign and malignant lesions with high accuracy.
[Keywords] breast;T2 mapping;magnetic resonance imaging;radiomics;texture features;heterogeneity

HUANG Wenping   WANG Fen   LIU Hongli   YU Yali   LOU Jianjuan   ZOU Qigui   WANG Siqi   JIANG Yanni*  

Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China

*Correspondence to: Jiang YN, E-mail: jyn_njmu@163.com

Conflicts of interest   None.

Received  2022-09-28
Accepted  2023-02-08
DOI: 10.12015/issn.1674-8034.2023.02.009
Cite this article as: HUANG W P, WANG F, LIU H L, et al. A preliminary clinical application of T2 mapping-based radiomics on MRI in breast diseases[J]. Chin J Magn Reson Imaging, 2023, 14(2): 50-55. DOI:10.12015/issn.1674-8034.2023.02.009.

[1]
SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
ZHU L H, LIU H, ZHOU J J. Research progress of magnetic resonance T2-mapping in body malignant tumors[J]. Chin J Magn Reson Imaging, 2020, 11(5): 398-400. DOI: 10.12015/issn.1674-8034.2020.05.019.
[3]
LÜSSE S, CLAASSEN H, GEHRKE T, et al. Evaluation of water content by spatially resolved transverse relaxation times of human articular cartilage[J]. Magn Reson Imaging, 2000, 18(4): 423-430. DOI: 10.1016/s0730-725x(99)00144-7.
[4]
LAMBIN P, RIOS-VELAZQUEZ E, LEIJENAAR R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[5]
CROOIJMANS H J, SCHEFFLER K, BIERI O. Finite RF pulse correction on DESPOT2[J]. Magn Reson Med, 2011, 65(3): 858-862. DOI: 10.1002/mrm.22661.
[6]
JUNG Y, GHO S M, BACK S N, et al. The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method[J/OL]. Br J Radiol, 2018, 92(1093): 20180479 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/30215550/. DOI: 10.1259/bjr.20180479.
[7]
KOO T K, LI M Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research[J]. J Chiropr Med, 2016, 15(2): 155-163. DOI: 10.1016/j.jcm.2016.02.012.
[8]
SONG Y, ZHANG J, ZHANG Y D, et al. FeAture Explorer (FAE): A tool for developing and comparing radiomics models[J/OL]. PLoS One, 2020, 15(8): e0237587 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/32804986/. DOI: 10.1371/journal.pone.0237587.
[9]
YAMAUCHI F I, PENZKOFER T, FEDOROV A, et al. Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence[J]. Magn Reson Imaging, 2015, 33(5): 525-530. DOI: 10.1016/j.mri.2015.02.006.
[10]
MAI J, ABUBRIG M, LEHMANN T, et al. T2 Mapping in Prostate Cancer[J]. Invest Radiol, 2019, 54(3): 146-152. DOI: 10.1097/RLI.0000000000000520.
[11]
LEE C H, TAUPITZ M, ASBACH P, et al. Clinical utility of combined T2-weighted imaging and T2-mapping in the detection of prostate cancer: a multi-observer study[J]. Quant Imaging Med Surg, 2020, 10(9): 1811-1822. DOI: 10.21037/qims-20-222.
[12]
GHOSH A, SINGH T, BAGGA R, et al. T2 relaxometry mapping in demonstrating layered uterine architecture: parameter optimization and utility in endometrial carcinoma and adenomyosis: a feasibility study[J/OL]. Br J Radiol, 2018, 91(1081): 20170377 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/28936889/. DOI: 10.1259/bjr.20170377.
[13]
LI S, LIU J, ZHANG F, et al. Novel T2 Mapping for Evaluating Cervical Cancer Features by Providing Quantitative T2 Maps and Synthetic Morphologic Images: A Preliminary Study[J]. J Magn Reson Imaging, 2020, 52(6): 1859-1869. DOI: 10.1002/jmri.27297.
[14]
MENG F X, LU Z M, YU B, et al. MR T2 mapping in differential diagnosis between benign and malignant breast tumors[J]. J Chin Clin Med Imaging, 2013, 24(5): 317-320. DOI: 10.3969/j.issn.1008-1062.2013.05.004.
[15]
LIU L, YIN B, SHEK K, et al. Role of quantitative analysis of T2 relaxation time in differentiating benign from malignant breast lesions[J]. J Int Med Res, 2018, 46(5): 1928-1935. DOI: 10.1177/0300060517721071.
[16]
MENG T, HE N, HE H, et al. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI[J/OL]. Cancer Imaging, 2020, 20(1): 88 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/33317609/. DOI: 10.1186/s40644-020-00365-4.
[17]
GAO W, ZHANG S, GUO J, et al. Investigation of Synthetic Relaxometry and Diffusion Measures in the Differentiation of Benign and Malignant Breast Lesions as Compared to BI-RADS[J]. J Magn Reson Imaging, 2021, 53(4): 1118-1127. DOI: 10.1002/jmri.27435.
[18]
GANESHAN B, GOH V, MANDEVILLE H C, et al. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT[J]. Radiology, 2013, 266(1): 326-336. DOI: 10.1148/radiol.12112428.
[19]
ZHANG L W, FANG M J, ZANG Y L, et al. Development and application of radiomics[J]. Chin J Radiol, 2017, 51(1): 75-77. DOI: 10.3760/cma.j.issn.1005-1201.2017.01.017.
[20]
GAN L, MA M, LIU Y, et al. A Clinical-Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases[J/OL]. Front Oncol, 2021, 11: 786346 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/34993145/. DOI: 10.3389/fonc.2021.786346.
[21]
GAO W B, DENG P F, YANG Q X, et al. A Feasibility Study of Radiomics Based on Dynamic Contrast Enhanced Magnetic Resonance Imaging in Identifying Benign and Malignant Breast Mass[J]. J Clin Radiol, 2020, 39(4): 674-679. DOI: 10.13437/j.cnki.jcr.2020.04.011.
[22]
WU P Q, LIN T W, MAO X M. The application of radiomic features based on MR in the differentiation between breast cancer and breast fibroadenoma[J]. J Pract Radiol, 2019, 35(12): 1934-1939. DOI: 10.3969/j.issn.1002-1671.2019.12.012.
[23]
ZHANG Q, PENG Y, LIU W, et al. Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions[J]. J Magn Reson Imaging, 2020, 52(2): 596-607. DOI: 10.1002/jmri.27098.
[24]
TRUHN D, SCHRADING S, HAARBURGER C, et al. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI[J]. Radiology, 2019, 290(2): 290-297. DOI: 10.1148/radiol.2018181352.
[25]
REN C, WANG S, ZHANG S. Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors[J/OL]. Cancer Imaging, 2020, 20(1): 5 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/31931874/. DOI: 10.1186/s40644-019-0284-7.
[26]
YU Y L, WANG X, ZHA X M, et al. Whole volume ROI radiomics analysis of mass-like breast cancer based on pretreatment ADC images for the prediction of pathological complete response to neoadjuvant chemotherapy[J]. Radiol Practice, 2022, 37(8): 987-994. DOI: 10.13609/j.cnki.1000-0313.2022.08.012.
[27]
CHU H, LIN X, HE J, et al. Value of MRI Radiomics Based on Enhanced T1WI Images in Prediction of Meningiomas Grade[J]. Acad Radiol, 2021, 28(5): 687-693. DOI: 10.1016/j.acra.2020.03.034.
[28]
CONTI A, DUGGENTO A, INDOVINA I, et al. Radiomics in breast cancer classification and prediction[J]. Semin Cancer Biol, 2021, 72: 238-250. DOI: 10.1016/j.semcancer.2020.04.002.
[29]
YE D M, WANG H T, YU T. The Application of Radiomics in Breast MRI: A Review[J/OL]. Technol Cancer Res Treat, 2020, 19: 1533033820916191 [2022-09-27]. https://pubmed.ncbi.nlm.nih.gov/32347167/. DOI: 10.1177/1533033820916191.
[30]
CHITALIA R D, ROWLAND J, MCDONALD E S, et al. Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence[J]. Clin Cancer Res, 2020, 26(4): 862-869. DOI: 10.1158/1078-0432.CCR-18-4067.

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