Share：
Share this content in WeChat
X
[Chinese][PDF]96373
• Clinical Article •
Texture features derived from intravoxel incoherent motion diffusion-weighted imaging for predicting the pathological response to chemoradiotherapy in rectal cancer
LIU Si-ye  WEN Lu  HOU Jing  NIE Shao-lin  ZHOU Ju-mei  CAO Fang  LU Qiang  QIN Yu-hui  YU Xiao-ping 

DOI:10.12015/issn.1674-8034.2018.07.007.


[Abstract] Objective: To investigate the performance of texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).Materials and Methods: Pretreatment IVIM-DWI was performed on 38 LARC patients receiving nCRT. Nine first-order texture features (TFs) and eleven gray level co-occurrence matrix (GLCM) TFs were derived from four IVIM-DWI parameter maps (ADC, D, D* and f) respectively. The first-order TFs included Mean, Kurtosis, Skewness, Variance, Perc01%, Perc10%, Perc50%, Perc90% and Perc99%, and the GLCM features included Angular Second Moment (AngScMom), Contrast, Correlat, Difference Entropy (DifEntrp), Difference Variance (DifVarnc), Entropy, Inverse Difference Moment (InvDfMom), Sum Average (SumAverg), Sum Entropy (SumEntrp), Sum of Squares (SumOfSqs) and Sum Variance (SumVarnc). The values of first-order and GLCM TFs were compared between the pCR (n=8) and non-pathological responder (non-pCR, n=30) groups, which was classified according to tumor regression grade system. Receiver operating characteristic (ROC) curve in univariate and multivariate Logistic regression analysis was generated to determine the efficiency for identifying pCR.Results: The pCR group had lower AngScMomD, AngScMomD*, AngScMomf, DifVarncADC, DifVarncD, ContrastADC and ContrastD* values. Higher Perc10%ADC, Perc10%D, Perc99%D*, CorrelatD*, Correlatf, DifEntrpADC, InvDfMomADC, SumAvergD, SumVarncD* and SumOfSqsD* values were observed in the pCR group. The area under the ROC curve (AUC) values for the predictors in univariate analysis ranged from 0.662 to 0.829, with sensitivities from 33.33% to 100.00% and specificities from 37.50% to 100.00%. In multivariate Logistic regression analysis based on the first-order TFs, Perc10%ADC (P=0.032) and Perc10%D (P=0.028) were the independent predictors to pCR, with an AUC value of 0.754 (95% confidence interval, 0.588—0.879), a sensitivity of 50% and a specificity of 100.00%. DifVarncD (P=0.003) and SumVarncD* (P=0.002) were the independent predictors to pCR in the multivariate models that were based on either the GLCM TFs or the combination of the first-order and GLCM TFs, with an AUC of 0.929 (95% confidence interval, 0.797-0.987), a sensitivity of 83.33% and a specificity of 100.00%.Conclusions: GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC before starting chemoradiotherapy.
[Keywords] Rectal neoplasms；Chemoradiotherapy；Pathological response；Intravoxel incoherent motion；Diffusion magnetic resonance imaging；texture analysis

LIU Si-ye Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

WEN Lu Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

HOU Jing Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

NIE Shao-lin Department of Colorectal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

ZHOU Ju-mei Department of Radiotherapy, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

CAO Fang Department of Pathology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

LU Qiang Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

QIN Yu-hui Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

YU Xiao-ping* Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410006, China

*Correspondence to: Yu XP, E-mail: yuxiaoping@hnszlyy.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This study was supported by the Provincial Key Clinical Specialty (Medical Imaging) Development Program from Health and Family Planning Commission of Hunan Province, China No. 2015/43 by funding from Health and Family Planning Commission of Hunan Province, China No. B2017099 and by funding from National Cancer Centre of China No. NCC2017A19
Received  2018-03-19
Accepted  2018-05-20
DOI: 10.12015/issn.1674-8034.2018.07.007
DOI:10.12015/issn.1674-8034.2018.07.007.

[1]
Sauer R, Becker H, Hohenberger W, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med, 2004, 351(17): 1731-1740.
[2]
Valentini V, Aristei C, Glimelius B, et al. Multidisciplinary rectal cancer management: 2nd European rectal cancer consensus conference (EURECA-CC2). Radiother Oncol, 2009, 92(2): 148-163.
[3]
Lu W, Jing H, Ju-Mei Z, et al. Intravoxel incoherent motion diffusion-weighted imaging for discriminating the pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Sci Rep, 2017, 7(1): 8496.
[4]
Zhu HB, Zhang XY, Zhou XH, et al. Assessment of pathological complete response to preoperative chemoradiotherapy by means of multiple mathematical models of diffusion-weighted MRI in locally advanced rectal cancer: a prospective single-center study. J Magn Reson Imaging, 2017, 46(1): 175-183.
[5]
Nougaret S, Vargas HA, Lakhman Y, et al. Intravoxel incoherent motion-derived histogram metrics for assessment of response after combined chemotherapy and radiation therapy in rectal cancer: initial experience and comparison between single-section and volumetric analyses. Radiology, 2016, 280(2): 446-454.
[6]
Davnall F, Yip CS, Ljungqvist G, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging, 2012, 3(6): 573-589.
[7]
Ng F, Ganeshan B, Kozarski R, et al. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology, 2013, 266(1): 177-184.
[8]
Yang X, Knopp MV. Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: a review. J Biomed Biotechnol, 2011, 2011: 732848.
[9]
De Cecco CN, Ciolina M, Caruso D, et al. Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience. Abdom Radiol (NY), 2016, 41(9): 1728-1735.
[10]
De Cecco CN, Ganeshan B, Ciolina M, et al. Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance. Invest Radiol, 2015, 50(4): 239-245.
[11]
Liu M, Lv H, Liu LH, et al. Locally advanced rectal cancer: predicting non-responders to neoadjuvant chemoradiotherapy using apparent diffusion coefficient textures. Int J Colorectal Dis, 2017, 32(2): 1009-1012.
[12]
Dworak O, Keilholz L, Hoffmann A. Pathological features of rectal cancer after preoperative radiochemotherapy. Int J Colorectal Dis, 1997,12(1): 19-23.
[13]
Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging, 2013, 13(1): 140-149.
[14]
Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
[15]
Ahmed A, Gibbs P, Pickles M, et al. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging, 2013, 38(1): 89-101.
[16]
Liu J, Mao Y, Li Z, et al. Use of texture analysis based on contrast-enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma. J Magn Reson Imaging, 2016, 44(2): 445-455.
[17]
Ben Bouallègue F, Tabaa YA, Kafrouni M, et al. Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas. Med Phys, 2017, 44(9): 4608-4619.
[18]
Thibault G, Tudorica A, Afzal A, et al. DCE-MRI texture features for early prediction of breast cancer therapy response. Tomography, 2017, 3(1): 23-32.
[19]
Donati OF, Mazaheri Y, Afaq A, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology, 2014, 271(1): 143-152.
[20]
Meng J, Zhu L, Zhu L, et al. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget, 2017, 8(54): 92442-92453.
[21]
Kolarevic D, Tomasevic Z, Dzodic R, et al. Early prognosis of metastasis risk in inflammatory breast cancer by texture analysis of tumour microscopic images. Biomed Microdevices, 2015, 17(5): 92.
[22]
Haralick R, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 1973, 3(6): 610-621.
[23]
Torheim T, Malinen E, Kvaal K, et al. Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging, 2014, 33(8): 1648-1656.
[24]
Chee CG, Kim YH, Lee KH, et al. CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: a potential imaging biomarker for treatment response and prognosis. PLoS One, 2017, 12(8): e0182883.

PREV Preliminary application of gadolinium-based capsule in MR colon transit times
NEXT The value of MRI intravoxel incoherent motion imaging (IVIM) and diffusion kurtosis imaging (DKI) in the differential diagnosis of benign and malignant bone and soft tissue tumors of lower extremity
  


LINK


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