Share：
Share this content in WeChat
X
[Chinese][PDF]2179106
• Clinical Article •
Predictive value of random forest based on MRI radiomics in evaluating the invasion depth of endometrial carcinoma
GUO Ran  SHEN Xiuzhi  XIN Ruiqiang  SHI Qinglei  WANG Jinjie  ZHONG Jiali  PENG Ruchen 

Cite this article as: Guo R, Shen XZ, Xin RQ, et al. Predictive value of random forest based on MRI radiomics in evaluating the invasion depth of endometrial carcinoma. Chin J Magn Reson Imaging, 2020, 11(12): 1133-1137. DOI:10.12015/issn.1674-8034.2020.12.011.


[Abstract] Objective: To explore the predictive value of random forest based on MRI plain T2WI and contrast-enhanced T1WI radiomics in evaluating the invasion depth of endometrial carcinoma.Materials and Methods: We retrospectively analyzed one hundred and fourteen (eighty-six cases of stage ⅠA and twenty-eight cases of stage ⅠB) patients with endometrial carcinoma confirmed by surgical pathology and all patients underwent pelvic MRI plain and contrast-enhanced examination. All MRI data were divided into training and testing set by stratified sampling method with the ratio of 4∶1. The ITK-SNAP software was used to manually delineate the region of interest layer by layer on the sagittal T2WI images and the second phase of the multi-phase T1WI contrast-enhanced images. The radiomics features were extracted based on an open soured tool named pyradiomics (https://github.com/Radiomics/pyradiomics), and the model was established based on scikit-learn (https://www.sklearn.org/). Predictive performance was evaluated by the receiver operating characteristics (ROC) curve.Results: In the testing set, the area under the curve (AUC) of the RF model based on the plain T2WI images predicting the depth of myometrial invasion for endometrial carcinoma was 0.938, and the accuracy, sensitivity and specificity were 91.3%, 87.5%, and 93.3%, respectively. The top three most important features of the model were shape flatness, GLSZM zone variance, and GLRLM run variance; The AUC of the RF model based on contrast-enhanced T1WI images was 0.818, the accuracy, sensitivity and specificity were 81.8%, 100%, and 75.0%, respectively. The top three most important features of the model were shape flatness, GLDM large dependence high gray level emphasis, and GLCM correlation.Conclusions: The algorithm of random forest based on MRI radiomics demonstrated great potential in predicting the invasion depth of endometrial carcinoma, and the model based on T2WI images demonstrated more diagnostic value than that contrast-enhanced T1WI images.
[Keywords] endometrial neoplasms；myometrial invasion；radiomics；magnetic resonance imaging

GUO Ran Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

SHEN Xiuzhi Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

XIN Ruiqiang Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

SHI Qinglei MR Scientific, Siemens Healthcare, Beijing 100102, China; Machine Learning and Data Mining Lab. Software Colledge, Shandong University, Ji’nan 250101, China

WANG Jinjie Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

ZHONG Jiali Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

PENG Ruchen* Department of Radiology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China

*Correspondence to: Peng RC, E-mail: 13501271260@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of Beijing Tongzhou District Science and Technology Project No. KJ2020CX004-19
Received  2020-06-08
Accepted  2020-11-13
DOI: 10.12015/issn.1674-8034.2020.12.011
Cite this article as: Guo R, Shen XZ, Xin RQ, et al. Predictive value of random forest based on MRI radiomics in evaluating the invasion depth of endometrial carcinoma. Chin J Magn Reson Imaging, 2020, 11(12): 1133-1137. DOI:10.12015/issn.1674-8034.2020.12.011.

[1]
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin, 2019, 69(1): 7-34. DOI: 10.3322/caac.21551.
[2]
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132. DOI: 10.3322/caac.21338.
[3]
中国抗癌协会妇科肿瘤专业委员会.子宫内膜癌诊断与治疗指南(第四版).中国实用妇科与产科杂志, 2018, 34(8): 880-886. DOI: 10.19538/j.fk2018080114.
[4]
Zhang Q, Yu X, Lin M, et al. Multi-b-value diffusion weighted imaging for preoperative evaluation of risk stratification in early-stage endometrial cancer. Eur J Radiol, 2019, 119: 108637. DOI: 10.1016/j.ejrad.2019.08.006.
[5]
Larson DM, Connor GP, Broste SK, et al. Prognostic significance of gross myometrial invasion with endometrial cancer. Obstet Gynecol, 1996, 88(3): 394-398. DOI: 10.1016/0029-7844(96)00161-5.
[6]
Lewin SN, Herzog TJ, Barrena Medel LI, et al. Comparative performance of the 2009 international federation of gynecology and obstetrics’ staging system for uterine corpus cancer. Obstet Gynecol, 2010, 116(5): 1141-1149. DOI: 10.1097/AOG.0b013e3181f39849.
[7]
Nougaret S, Reinhold C, Alsharif SS, et al. Endometrial cancer: combined MR volumetry and diffusion-weighted imaging for assessment of myometrial and lymphovascular invasion and tumor grade. Radiology, 2015, 276(3): 797-808. DOI: 10.1148/radiol.15141212.
[8]
Rechichi G, Galimberti S, Signorelli M, et al. Endometrial cancer: correlation of apparent diffusion coefficient with tumor grade, depth of myometrial invasion, and presence of lymph node metastases. AJR Am J Roentgenol, 2011, 197(1): 256-262. DOI: 10.2214/AJR.10.5584.
[9]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[10]
段亚阳，周坤鹏，边杰,等.基于非增强MRI的影像组学术前预测肝细胞癌微血管浸润的研究.磁共振成像, 2020, 11(3): 195-200. DOI: 10.12015/issn.1674-8034.2020.03.007.
[11]
Nie K, Shi L, Chen Q, et al. Rectal cancer: Assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res, 2016, 22(21): 5256-5264. DOI: 10.1158/1078-0432.CCR-15-2997.
[12]
Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med, 2017, 38: 122-139. DOI: 10.1016/j.ejmp.2017.05.071.
[13]
Ueno Y, Forghani B, Forghani R, et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-A preliminary analysis. Radiology, 2017, 284(3): 748-757. DOI: 10.1148/radiol.2017161950.
[14]
Sigmund YH, Dybvik JA, Arvid L, et al. Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging, 2018, 48(6): 1637-1647. DOI: 10.1002/jmri.26184.
[15]
Yan B, Liang X, Zhao T, et al. Preoperative prediction of deep myometrial invasion and tumor grade for stage I endometrioid adenocarcinoma: a simple method of measurement on DWI. Eur Radiol, 2019, 29(2): 838-848. DOI: 10.1007/s00330-018-5653-2.
[16]
Nie P, Yang G, Wang Z, et al. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma. Eur Radiol, 2020, 30(2): 1274-1284. DOI: 10.1007/s00330-019-06427-x.
[17]
Zhang GMY, Sun H, Shi B, et al. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. Abdom Radiol, 2017, 42(2): 561-568. DOI: 10.1007/s00261-016-0897-2.
[18]
王国蓉，王志伟，薛华丹,等. CT纹理分析对鉴别直肠癌患者K-ras基因突变的可行性研究.放射学实践, 2017, 32(12): 1215-1220. DOI: 10.13609/j.cnki.1000-0313.2017.12.002.

PREV MRI features of the submandibular gland in Sjögren syndrome
NEXT A Meta analysis of quantitative evaluation of lumbar disc degeneration by diffusion weighted magnetic resonance imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn