Share：
Share this content in WeChat
X
[Chinese][PDF]94265
• Clinical Article •
Application study of MRI T2WI texture baseline predicting the efficacy of advanced rectal cancer transformation therapy for primary tumors
WANG Zheng  MENG Linghou  LI Qiang  LI Liya  TIAN Lianfen  LIANG Binling  ZHOU Chuanji 

Cite this article as: Wang Z, Meng LH, Li Q, et al. Application study of MRI T2WI texture baseline predicting the efficacy of advanced rectal cancer transformation therapy for primary tumors[J]. Chin J Magn Reson Imaging, 2022, 13(1): 42-47, 53. DOI:10.12015/issn.1674-8034.2022.01.009.


[Abstract] Objective To explore the predictive value of baseline magnetic resonance imaging (MRI) T2WI image texture analysis in the treatment of advanced rectal cancer for the primary tumor.Materials and Methods: Retrospective analysis of 66 patients with advanced rectal cancer confirmed clinically and pathologically. All patients underwent pelvic MRI scan, enhancement and diffusion weighted imaging (DWI) examinations before operation. According to the plain scan and enhanced images, the location and range of the tumor were identified, and the Mazda software was used to extract the region of interest (ROI) texture in the T2WI image, and linear discriminant analysis (LDA) and nonlinear discriminant analysis (linear discriminant analysis) were used respectively. Discriminant analysis (NDA) and principal component analysis (PCA) are three extraction methods for discriminative classification, and the best method is selected for texture extraction. Combined with postoperative pathology, the baseline morphological characteristics of the primary focus of patients with advanced rectal cancer were compared between the sensitive group and the insensitive group, and the texture characteristics of the T2WI sequence images of the two groups were compared to construct a curative effect prediction model.Results The pathological tumor regression grade (pTRG) of 66 patients with advanced rectal cancer showed that 9 cases were pTRG 0, 8 cases were pTRG 1, 35 cases were pTRG 2, and 14 cases were pTRG 3. Among them, 52 cases were in the sensitive group (pTRG 0～2) and 14 cases were in the insensitive group (pTRG 3). There was no significant difference between the two groups of patients between the primary tumor involving the intestinal segment, the relationship with the peritoneum reflexion, the length of the longitudinal involvement, the proportion of the circumference of the intestinal cavity, the maximum thickness of the oblique axis, and the distance between the lower edge of the tumor and the anal edge (all P＞0.05); the NDA classification method under the Fisher texture feature extraction method has the lowest misjudgment rate, so this method is used to extract the image texture. The univariate analysis of texture characteristics in different treatment groups of the primary tumor of advanced rectal cancer showed: the first percentile (Perc 1%), S (2, 0) DifEntrp, S (3, 0) InvDfMom, S (3, -3) SumAverg, S (4, 0) InvDfMom, S (4, -4) SumAverg, S (5, 0) InvDfMom, S (5, -5) SumAverg, S (2, 2) SumVarnc, all indicators were statistically different (P＜0.05), S (2, 2) SumVarnc, S (3, 0) DifEntrp were not statistically different (P=0.05, 0.052); the indicators with differences in univariate analysis were included in Logistic multivariate analysis of the model showed that Perc 1% and S (5, 0) InvDfMom were independent predictors of insensitivity to transformation treatment of primary tumors of advanced rectal cancer, and the above factors were used to construct the prediction of insensitivity of primary tumors of advanced rectal cancer to transformation therapy. The area under the curve (AUC) of the model is 0.812, the sensitivity is 92.90%, and the specificity was 60.80%.Conclusions T2WI image texture features extracted based on MRIFisher extraction method can help predict the efficacy of primary tumor transformation therapy for advanced rectal cancer, and provide valuable reference information for the formulation of individualized treatment plans for patients.
[Keywords] advanced rectal cancer；magnetic resonance imaging；translational therapy；pathological grading；texture analysis；curative effect prediction

WANG Zheng1, 2, 3   MENG Linghou1#   LI Qiang1, 2, 3*   LI Liya4   TIAN Lianfen4   LIANG Binling4   ZHOU Chuanji4  

1 Affiliated Cancer Hospital of Guangxi Medical University, Nanning 530021, China

2 Guangxi Imaging Medicine Clinical Medical Research Center, Nanning 530021, China

3 Key Clinical Specialties in Guangxi (Medical Imaging Department), Nanning 530021, China

4 Graduate School of Guangxi Medical University, Nanning 530021, China

Li Q, E-mail: 448954904@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Guangxi Medical and Health Appropriate Technology Development, Promotion and Application Project (No. S2020093); Guangxi Key Reseach and Development Project (No. AB19110015); Guangxi Medical and Health Self-financing Project (No. Z20200403, Z20200445, Z20210418); Guangxi Medical University Education and Teaching Reform Project (No. 2020XJGZ05, 2020XJGB16, 2021XJGA14, 2021XJGB56); Youth Science Fund Project of Guangxi Medical University (No. GXMUYSF202226).
Received  2021-05-20
Accepted  2021-11-09
DOI: 10.12015/issn.1674-8034.2022.01.009
Cite this article as: Wang Z, Meng LH, Li Q, et al. Application study of MRI T2WI texture baseline predicting the efficacy of advanced rectal cancer transformation therapy for primary tumors[J]. Chin J Magn Reson Imaging, 2022, 13(1): 42-47, 53. DOI:10.12015/issn.1674-8034.2022.01.009.

[1]
Park SH, Cho SH, Choi SH, et al. MRI Assessment of Complete Response to Preoperative Chemoradiation Therapy for Rectal Cancer: 2020 Guide for Practice from the Korean Society of Abdominal Radiology[J]. Korean J Radiol, 2020, 21(7): 812-828. DOI: 10.3348/kjr.2020.0483.
[2]
Liu QZ, Zhang H, Hao LQ, et al. Analysis of influencing factors of pathological complete remission after neoadjuvant radiotherapy and chemotherapy for middle and low rectal cancer[J]. Chin J Gastrointestinal Surg, 2020, 23(12): 1159-1163. DOI: 10.3760/cma.j.cn.441530-20200106-00009.
[3]
Bulens P, Couwenberg A, Intven M, et al. Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics[J]. Radiother Oncol, 2020, 142: 246-252. DOI: 10.1016/j.radonc.2019.07.033.
[4]
Oh JE, Kim MJ, Lee J, et al. Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer[J]. Cancer Res Treat, 2020, 52(1): 51-59. DOI: 10.4143/crt.2019.050.
[5]
Antunes J, Viswanath S, Brady JT, et al. Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings[J]. Acad Radiol, 2018, 25(7): 833-841. DOI: 10.1016/j.acra.2017.12.006.
[6]
Yin JD, Song LR, Lu HC, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps[J]. World J Gastroenterol, 2020, 26(17): 2082-2096. DOI: 10.3748/wjg.v26.i17.2082.
[7]
Liu YH, Chen AL, Wu JJ, et al. The value of dynamic contrast-enhanced magnetic resonance imaging texture multi-parameter analysis to predict the expression of P53 in the differential diagnosis of rectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 33-37, 74. DOI: 10.12015/issn.1674-8034.2021.08.007.
[8]
Atre ID, Eurboonyanun K, Noda Y, et al. Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort[J]. Abdom Radiol (NY), 2021, 46(2): 459-468. DOI: 10.1007/s00261-020-02653-w.
[9]
Park H, Kim KA, Jung JH, et al. MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer[J]. Eur Radiol, 2020, 30(8): 4201-4211. DOI: 10.1007/s00330-020-06835-4.
[10]
Crimì F, Capelli G, Spolverato G, et al. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC)[J]. Radiol Med, 2020, 125(12): 1216-1224. DOI: 10.1007/s11547-020-01215-w.
[11]
Lu HC, Wang F, Yin JD. Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer[J]. Front Oncol, 2020, 10: 1476. DOI: 10.3389/fonc.2020.01476.
[12]
Song LR, Yin JD. Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer[J]. Front Oncol, 2020, 10: 1364. DOI: 10.3389/fonc.2020.01364.
[13]
Yin JD, Song LR, Lu HC, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps[J]. World J Gastroenterol, 2020, 26(17): 2082-2096. DOI: 10.3748/wjg.v26.i17.2082.
[14]
Nardone V, Reginelli A, Scala F, et al. Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation[J]. Gastroenterol Res Pract, 2019, 2019: 8505798. DOI: 10.1155/2019/8505798.
[15]
Horvat N, Veeraraghavan H, Pelossof RA, et al. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations[J]. Eur J Radiol, 2019, 113: 174-181. DOI: 10.1016/j.ejrad.2019.02.022.
[16]
Petresc B, Lebovici A, Caraiani C, et al. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study[J]. Cancers (Basel), 2020, 12(7): 1894. DOI: 10.3390/cancers12071894.
[17]
Meng YK, Zhang CD, Zou SM, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer[J]. Oncotarget, 2017, 9(15): 11999-12008. DOI: 10.18632/oncotarget.23813.
[18]
Shayesteh SP, Alikhassi A, Farhan F, et al. Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients[J]. J Gastrointest Cancer, 2020, 51(2): 601-609. DOI: 10.1007/s12029-019-00291-0.
[19]
Yang LQ, Qiu M, Xia CC, et al. Value of High-Resolution DWI in Combination With Texture Analysis for the Evaluation of Tumor Response After Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer[J]. AJR Am J Roentgenol, 2019,1-8. DOI: 10.2214/AJR.18.20689.
[20]
Shayesteh SP, Alikhassi A, Farhan F, et al. Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients[J]. J Gastrointest Cancer, 2020, 51(2): 601-609. DOI: 10.1007/s12029-019-00291-0.
[21]
Zou HH, Yu J, Wei Y, et al. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49(3): 885-893. DOI: 10.1002/jmri.26254.

PREV Study on the relationship between signal intensity parameters of liver lobes and albumin-bilirubin grades in magnetic resonance images enhanced with Gd-EOB-DTPA
NEXT The clinical application value of DWI in quantitative evaluation of Crohn′s disease lesions
  


LINK


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