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• Clinical Article •
The diagnostic value of radiomics based on HRT2WI and DWI in the breakthrough of the muscularis propria layer of rectal cancer
SHENG Fangting  TIAN Weizhong  FENG Zemeng 

Cite this article as: SHENG F T, TIAN W Z, FENG Z M. The diagnostic value of radiomics based on HRT2WI and DWI in the breakthrough of the muscularis propria layer of rectal cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 102-106, 131. DOI:10.12015/issn.1674-8034.2023.04.017.


[Abstract] Objective To evaluate the diagnostic value of radiomics models based on high-resolution T2-weighted imaging (HRT2WI) and diffusion-weighted imaging (DWI) in the breakthrough of the muscularis propria of rectal cancer.Materials and Methods A retrospective analysis was performed on rectal cancer patients who underwent preoperative 3.0 T MRI scans including HRT2WI and DWI (b value of 800 s/mm2), and were confirmed by surgical pathology at Taizhou People's Hospital affiliated of Nanjing Medical University from January 2019 to December 2021. Patients with T1 and T2 staging were classified as the non-breakthrough group, and those with T3 and T4 staging were classified as the breakthrough group based on pathological staging. Radiomics features were extracted after manually delineating the volume of interest (VOI) on the lesion, and then independent sample t-tests and support vector machine (SVM) with a linear kernel were used for feature selection and dimensionality reduction, respectively, to select valuable radiomics features. The selected samples were randomly divided into training and validation sets at a ratio of 7∶3 for machine learning to build the SVM classifier model. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different models in terms of the area under the curve (AUC), sensitivity, specificity, and accuracy for detecting rectal cancer invasion beyond the muscularis propria. The DeLong test was used to compare the differences in AUC between different models.Results A total of 1142 radiomics features were extracted from the HRT2WI and DWI images of each patient's tumor tissue and screened by independent sample t-tests and SVM with a linear kernel. The SVM model constructed based on the radiomics features of HRT2WI images had a validation AUC value of 0.894, sensitivity of 90.0%, and specificity of 70.6%. The SVM model constructed based on the radiomics features of DWI images had a validation AUC value of 0.774, sensitivity of 60.0%, and specificity of 76.5%. The final predictive model combining HRT2WI and DWI had significantly better diagnostic performance than other models, with a validation AUC value of 0.927, sensitivity of 80.0%, and specificity of 88.2%. The DeLong test showed significant differences in predictive performance between the combined model and the single sequence models (P＜0.05).Conclusions The radiomics model combining HRT2WI and DWI can effectively evaluate the breakthrough of the muscularis propria of rectal cancer, which may provide assistance for individualized clinical treatment.
[Keywords] rectal cancer；magnetic resonance imaging；radiomics；muscularis propria；diagnostic performan

SHENG Fangting1   TIAN Weizhong2*   FENG Zemeng3  

1 Graduate School of Dalian Medical University, Dalian 116000, China

2 Department of Radiology, Affiliated Taizhou Hospital of Nanjing Medical University, Taizhou 225300, China

3 School of Flexible Electronics (Future Technologies), Nanjing Tech University, Nanjing 210000, China

Corresponding author: Tian WZ, E-mail: jstztwz@163.com

Conflicts of interest   None.

Received  2022-11-15
Accepted  2023-04-07
DOI: 10.12015/issn.1674-8034.2023.04.017
Cite this article as: SHENG F T, TIAN W Z, FENG Z M. The diagnostic value of radiomics based on HRT2WI and DWI in the breakthrough of the muscularis propria layer of rectal cancer[J]. Chin J Magn Reson Imaging, 2023, 14(4): 102-106, 131. DOI:10.12015/issn.1674-8034.2023.04.017.

[1]
Chinese knotting Colorectal Cancer Diagnosis and Treatment Standards (2020 Edition) Expert Group. National health and wellness committee's diagnostic and therapeutic guidelines for colorectal cancer in China (2020 edition)[J]. Chin J Gastrointest Surg, 2020, 23(6): 521-540. DOI: 10.3760/cma.j.cn.441530-20200520-00289.
[2]
BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.
[3]
DESANTIS C E, LIN C C, MARIOTTO A B, et al. Cancer treatment and survivorship statistics, 2014[J]. CA Cancer J Clin, 2014, 64(4): 252-271. DOI: 10.3322/caac.21235.
[4]
GOLDENBERG B A, HOLLIDAY E B, HELEWA R M, et al. Rectal cancer in 2018: a primer for the gastroenterologist[J]. Am J Gastroenterol, 2018, 113(12): 1763-1771. DOI: 10.1038/s41395-018-0180-y.
[5]
SEBAG-MONTEFIORE D, STEPHENS R J, STEELE R, et al. Preoperative radiotherapy versus selective postoperative chemoradiotherapy in patients with rectal cancer (MRC CR07 and NCIC-CTG C016): a multicentre, randomised trial[J]. Lancet, 2009, 373(9666): 811-820. DOI: 10.1016/S0140-6736(09)60484-0.
[6]
KIM H, LIM J S, CHOI J Y, et al. Rectal cancer: comparison of accuracy of local-regional staging with two- and three-dimensional preoperative 3-T MR imaging[J]. Radiology, 2010, 254(2): 485-492. DOI: 10.1148/radiol.09090587.
[7]
STANZIONE A, VERDE F, ROMEO V, et al. Radiomics and machine learning applications in rectal cancer: current update and future perspectives[J]. World J Gastroenterol, 2021, 27(32): 5306-5321. DOI: 10.3748/wjg.v27.i32.5306.
[8]
COPPOLA F, GIANNINI V, GABELLONI M, et al. Radiomics and magnetic resonance imaging of rectal cancer: from engineering to clinical practice[J/OL]. Diagnostics (Basel), 2021, 11(5): 756 [2022-09-29]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146913. DOI: 10.3390/diagnostics11050756.
[9]
CHETAN M R, GLEESON F V. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives[J]. Eur Radiol, 2021, 31(2): 1049-1058. DOI: 10.1007/s00330-020-07141-9.
[10]
SCHURINK N W, LAMBREGTS D M J, BEETS-TAN R G H. Diffusion-weighted imaging in rectal cancer: current applications and future perspectives[J/OL]. Br J Radiol, 2019, 92(1096): 20180655 [2022-09-02]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540856. DOI: 10.1259/bjr.20180655.
[11]
SOFIC A, HUSIC-SELIMOVIC A, EFENDIC A, et al. MRI evaluation of extramural venous invasion (EMVI) with rectal carcinoma using high resolution T2 and combination of high resolution T2 and contrast enhanced T1 weighted imaging[J]. Acta Inform Med, 2021, 29(2): 113-117. DOI: 10.5455/aim.2021.29.113-117.
[12]
WANG J, LI Z H, SHEN F, et al. Application value of high-resolution T2WI-based imaging in preoperative staging of rectal cancer[J]. Radiol Pract, 2019, 34(11): 1251-1254. DOI: 10.13609/j.cnki.1000-0313.2019.11.016.
[13]
LIN X, ZHAO S, JIANG H J, et al. A radiomics-based nomogram for preoperative T staging prediction of rectal cancer[J]. Abdom Radiol (NY), 2021, 46(10): 4525-4535. DOI: 10.1007/s00261-021-03137-1.
[14]
YOU J, YIN J D. Performances of whole tumor texture analysis based on MRI: predicting preoperative T stage of rectal carcinomas[J/OL]. Front Oncol, 2021, 11: 678441 [2022-10-30]. https://www.ncbi.nlm.nih.gov/pmc/artic-les/PMC8369414. DOI: 10.3389/fonc.2021.678441.
[15]
HUANG Y H, WEI L H, HU Y L, et al. Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer[J/OL]. Front Oncol, 2021, 11: 706733 [2022-10-30]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416497/. DOI: 10.3389/fonc.2021.706733.
[16]
LIU L H, LIU Y H, XU L, et al. Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer[J]. J Magn Reson Imaging, 2017, 45(6): 1798-1808. DOI: 10.1002/jmri.25460.
[17]
SA S, LI J, LI X D, et al. Application of random forest model based on CT images and clinical data in preoperative T staging of colorectal cancer[J]. Chin J Radiol, 2017, 51(12): 933-938. DOI: 10.3760/cma.j.issn.1005-1201.2017.12.009.
[18]
MA X L, SHEN F, JIA Y, et al. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features[J/OL]. BMC Med Imaging, 2019, 19(1): 86 [2022-09-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864926. DOI: 10.1186/s12880-019-0392-7.
[19]
LING R N, YANG R F, YI Q Q, et al. Prediction of lymph node metastasis of cervical cancer based on neural network model[J]. Chin J Magn Reson Imaging, 2021, 12(10): 16-21. DOI: 10.12015/issn.1674-8034.2021.10.004.
[20]
FELISAZ P F, COLELLI G, BALLANTE E, et al. Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy[J/OL]. Eur J Radiol, 2021, 134: 109460 [2022-11-03]. https://www.sciencedirect.com/science/article/pii/S0720048X20306501. DOI: 10.1016/j.ejrad.2020.109460.
[21]
MENG X L, WEI Q P, MENG L, et al. Feature fusion and detection in alzheimer's disease using a novel genetic multi-kernel SVM based on MRI imaging and gene data[J/OL]. Genes, 2022, 13(5): 837 [2022-09-13]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140721. DOI: 10.3390/genes13050837.
[22]
SIAKALLIS L, SUDRE C H, MULHOLLAND P, et al. Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance[J]. Neuroradiology, 2021, 63(12): 2047-2056. DOI: 10.1007/s00234-021-02719-6.
[23]
CUI Y F, LIU H H, REN J L, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer[J]. Eur Radiol, 2020, 30(4): 1948-1958. DOI: 10.1007/s00330-019-06572-3.
[24]
XUE K, DING Y Y, LI Z H, et al. Dynamic contrast-enhanced MRI texture analysis for distinguishing different molecular subtypes of breast cancer[J]. J Pract Radiol, 2020, 36(8): 1235-1239. DOI: 10.3969/j.issn.1002-1671.2020.08.015.
[25]
XIONG H, HE X J, GUO D J. Value of MRI texture analysis for predicting high-grade prostate cancer[J]. Clin Imaging, 2021, 72: 168-174. DOI: 10.1016/j.clinimag.2020.10.028.
[26]
EUN N L, KANG D, SON E J, et al. Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy[J]. Eur Radiol, 2021, 31(9): 6916-6928. DOI: 10.1007/s00330-021-07816-x.
[27]
FAN T W, MALHI H, VARGHESE B, et al. Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma[J]. Abdom Radiol (NY), 2019, 44(1): 201-208. DOI: 10.1007/s00261-018-1694-x.
[28]
MENG J, LIU S L, ZHU L J, et al. Texture Analysis as Imaging Biomarker for recurrence in advanced cervical cancer treated with CCRT[J/OL]. Sci Rep, 2018, 8(1): 11399 [2022-09-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065361. DOI: 10.1038/s41598-018-29838-0.
[29]
XING P Y, CHEN L G, YANG Q S, et al. Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging[J/OL]. Cancer Imaging, 2021, 21(1): 54 [2022-09-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8477463. DOI: 10.1186/s40644-021-00423-5.
[30]
NKETIAH G A, ELSCHOT M, SCHEENEN T W, et al. Utility of T2-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study[J/OL]. Sci Rep, 2021, 11: 2085 [2022-09-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7822867. DOI: 10.1038/s41598-021-81272-x.
[31]
YIN J D, SONG L R, LU H C, 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.
[32]
HOU M, ZHOU L, SUN J H. Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer[J]. Eur Radiol, 2023, 33(1): 1-10. DOI: 10.1007/s00330-022-08952-8.
[33]
WEN D G, HU S X, LI Z L, et al. Application of automated machine learning based on radiomics features of T2WI and RS-EPI DWI to predict preoperative T staging of rectal cancer[J]. J Sichuan Univ Med Sci Ed, 2021, 52(4): 698-705. DOI: 10.12182/20210460201.

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