Abstract
Texture analysis has been used to characterize and measure tissue heterogeneity in medical images. The purpose of this study was to investigate the potential of texture features derived from apparent diffusion coefficient (ADC) maps, to serve as imaging markers for predicting important histopathologic prognostic factors in rectal cancer. One hundred patients of rectal cancer received 3 T preoperative magnetic resonance imaging including diffusion-weighted imaging (DWI). Skewness, kurtosis, uniformity from the histogram and entropy, energy, inertia, correlation from gray-level co-occurrence matrix (GLCM) derived from whole-lesion volumes were measured. Independent sample t-test or Mann-Whitney U-test and receiver operating characteristic (ROC) curves were used for statistical analysis. Uniformity, energy and entropy were significantly different (p = 0.026, 0.001, and 0.006, respectively) between stage pT1–2 and pT3–4 tumors. Skewness, kurtosis and correlation were significantly different (p = 0.000, 0.006, and 0.041, respectively) between grade 1–2 and grade 3 tumors. Energy and entropy (p = 0.008 and 0.033, respectively) could significantly differentiate negative circumferential resection margin (CRM) from positive CRM. Furthermore, predicted probabilities derived by logistic regression analysis yielded greater area under the curve (AUC) in differentiating pT3–4 stage and grade 3 grade tumors. Texture features derived from ADC maps may useful to predict important histopathologic prognostic factors of rectal cancer.
Similar content being viewed by others
References
Schmoll, H. J., Van Cutsem, E., Stein, A., Valentini, V., Glimelius, B., Haustermans, K. et al., ESMO consensus guidelines for management of patients with colon and rectal cancer. A personalized approach to clinical decision making. Ann Oncol 23:2479–2516, 2012. https://doi.org/10.1093/annonc/mds236.
Boras, Z., Kondza, G., Sisljagić, V., Busić, Z., Gmajnić, R., and Istvanić, T., Prognostic factors of local recurrence and survival after curative rectal cancer surgery: A single institution experience. Coll Antropol 36:1355–1361, 2012.
Brown, G., Radcliffe, A. G., Newcombe, R. G., Dallimore, N. S., Bourne, M. W., and Williams, G. T., Preoperative assessment of prognostic factors in rectal cancer using high-resolution magnetic resonance imaging. Br J Surg 90:355–364, 2003. https://doi.org/10.1002/bjs.4034.
Lee, E. S., Kim, M. J., Park, S. C., Hur, B. Y., Hyun, J. H., Chang, H. J., Baek, J. Y., Kim, S. Y., Kim, D. Y., and Oh, J. H., Magnetic resonance imaging-detected extramural venous invasion in rectal Cancer before and after preoperative Chemoradiotherapy: Diagnostic performance and prognostic significance. Eur Radiol 28:496–505, 2018. https://doi.org/10.1007/s00330-017-4978-6.
Cienfuegos, J. A., Rotellar, F., Baixauli, J., Beorlegui, C., Sola, J. J., Arbea, L., Pastor, C., Arredondo, J., and Hernández-Lizoáin, J. L., Impact of perineural and lymphovascular invasion on oncological outcomes in rectal cancer treated with neoadjuvant chemoradiotherapy and surgery. Ann Surg Oncol 22:916–923, 2015. https://doi.org/10.1245/s10434-014-4051-5.
Bammer, R., Basic principles of diffusion-weighted imaging. Eur J Radiol 45:169–184, 2003. https://doi.org/10.1016/S0720-048X(02)00303-0.
Padhani, A. R., Liu, G., Koh, D. M., Chenevert, T. L., Thoeny, H. C., Takahara, T. et al., Diffusion-weighted magnetic resonance imaging as a cancer biomarker: Consensus and recommendations. Neoplasia 11:102–125, 2009.
Curvo-Semedo, L., Lambregts, D. M., Maas, M., Beets, G. L., Caseiro-Alves, F., and Beets-Tan, R. G., Diffusion-weighted MRI in rectal cancer: Apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness. J Magn Reson Imaging 35:1365–1371, 2012. https://doi.org/10.1002/jmri.23589.
Attenberger, U. I., Pilz, L. R., Morelli, J. N., Hausmann, D., Doyon, F., Hofheinz, R., Kienle, P., Post, S., Michaely, H. J., Schoenberg, S. O., and Dinter, D. J., Multi-parametric MRI of rectal cancer - do quantitative functional MR measurements correlate with radiologic and pathologic tumor stages? Eur J Radiol 83:1036–1043, 2014. https://doi.org/10.1016/j.ejrad.2014.03.012.
Oh, J. W., Rha, S. E., Oh, S. N., Park, M. Y., Byun, J. Y., and Lee, A., Diffusion-weighted MRI of epithelial ovarian cancers: Correlation of apparent diffusion coefficient values with histologic grade and surgical stage. Eur J Radiol 84:590–595, 2015. https://doi.org/10.1016/j.ejrad.2015.01.005.
Hecht, E. M., Liu, M. Z., Prince, M. R., Jambawalikar, S., Remotti, H. E., Weisberg, S. W., Garmon, D., Lopez-Pintado, S., Woo, Y., Kluger, M. D., and Chabot, J. A., Can diffusion-weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma? J Magn Reson Imaging 46:393–402, 2017. https://doi.org/10.1002/jmri.25581.
Barnes, S. L., Sorace, A. G., Whisenant, J. G., McIntyre, J. O., Kang, H., and Yankeelov, T. E., DCE- and DW-MRI as early imaging biomarkers of treatment response in a preclinical model of triple negative breast cancer. NMR Biomed 30:e3799, 2017. https://doi.org/10.1002/nbm.3799.
Just, N., Improving tumor heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213, 2014. https://doi.org/10.1038/bjc.2014.512.
Gillies, R. J., Kinahan, P. E., and Hricak, H., Radiomics: Images are more than pictures, they are data. Radiology 278:563–577, 2016. https://doi.org/10.1148/radiol.2015151169.
Becker, A. S., Ghafoor, S., Marcon, M., Perucho, J. A., Wurnig, M. C., Wagner, M. W., Khong, P. L., Lee, E. Y., and Boss, A., MRI texture features may predict differentiation and nodal stage of cervical cancer: A pilot study. Acta Radiol Open 6:2058460117729574, 2017. https://doi.org/10.1177/2058460117729574.
Xia, K., Yin, H., Qian, P., Jiang, Y., and Wang, S., Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access 7:96349–96358, 2019.
Xia, K., Yin, H., and Zhang, Y., Deep semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow algorithm. J. Medical Systems 43:2:1–2:12, 2018. https://doi.org/10.1007/s10916-018-1116-1.
Wibmer, A., Hricak, H., Gondo, T., Matsumoto, K., Veeraraghavan, H., Fehr, D. et al., Haralick texture analysis of prostate MRI: Utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25:2840–2850, 2015. https://doi.org/10.1007/s00330-015-3701-8.
Ytre-Hauge, S., Dybvik, J. A., Lundervold, A., Salvesen, Ø. O., Krakstad, C., Fasmer, K. E. et al., Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer. J Magn Reson Imaging 48:1637–1647, 2018. https://doi.org/10.1002/jmri.26184.
Ueno, Y., Forghani, B., Forghani, R., Dohan, A., Zeng, X. Z., Chamming's, F., Arseneau, J., Fu, L., Gilbert, L., Gallix, B., and Reinhold, C., Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-a preliminary analysis. Radiology 284:748–757, 2017. https://doi.org/10.1148/radiol.2017161950.
Kyriazi, S., Collins, D. J., Messiou, C., Pennert, K., Davidson, R. L., Giles, S. L., Kaye, S. B., and Desouza, N. M., Metastatic ovarian and primary peritoneal cancer: Assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology 261:182–192, 2011. https://doi.org/10.1148/radiol.11110577.
Choi, M. H., Oh, S. N., Rha, S. E., Choi, J. I., Lee, S. H., Jang, H. S., Kim, J. G., Grimm, R., and Son, Y., Diffusion-weighted imaging: Apparent diffusion coefficient histogram analysis for detecting pathologic complete response to chemoradiotherapy in locally advanced rectal cancer. J Magn Reson Imaging 44:212–220, 2016. https://doi.org/10.1002/jmri.25117.
Meng, Y., Zhang, C., Zou, S., Zhao, X., Xu, K., Zhang, H., and Zhou, C., MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget 9:11999–12008, 2017. https://doi.org/10.18632/oncotarget.23813.
Edge SB, Byrd DR, Compton CC (2010) American Joint Committee on Cancer. AJCC cancer staging manual. 7th ed. Springer, New York
Bosman, F. T., Carneiro, F., and Hruban, R. H., WHO classification of tumors of the digestive system. Geneva: World Health Organization, 2010.
DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L., Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44:837–845, 1988.
Kim, J. H., Ko, E. S., Lim, Y., Lee, K. S., Han, B. K., Ko, E. Y., Hahn, S. Y., and Nam, S. J., Breast Cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology 282:665–675, 2017. https://doi.org/10.1148/radiol.2016160261.
Caruso, D., Zerunian, M., Ciolina, M., de Santis, D., Rengo, M., Soomro, M. H. et al., Haralick's texture features for the prediction of response to therapy in colorectal cancer: A preliminary study. Radiol Med 123:161–167, 2018. https://doi.org/10.1007/s11547-017-0833-8.
Duvauferrier, R., Bezy, J., Bertaud, V., Toussaint, G., Morelli, J., and Lasbleiz, J., Texture analysis software: Integration with a radiological workstation. Stud Health Technol Inform 180:1030–1034, 2012.
Liu, L., Liu, Y., Xu, L., Li, Z., Lv, H., Dong, N., Li, W., Yang, Z., Wang, Z., and Jin, E., Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer. J Magn Reson Imaging 45:1798–1808, 2017. https://doi.org/10.1002/jmri.25460.
Li, W., Jiang, Z., Guan, Y., Chen, Y., Huang, X., Liu, S., He, J., Zhou, Z., and Ge, Y., Whole-lesion apparent diffusion coefficient first- and second-order texture features for the characterization of rectal Cancer pathological factors. J Comput Assist Tomogr 42:642–647, 2018. https://doi.org/10.1097/RCT.0000000000000731.
Song, J. H., Kim, S. H., Lee, J. H., Cho, H. M., Kim, D. Y., Kim, T. H. et al., Significance of histologic tumor grade in rectal cancer treated with preoperative chemoradiotherapy followed by curative surgery: A multi-institutional retrospective study. Radiother Oncol 118:387–392, 2016. https://doi.org/10.1016/j.radonc.2015.11.028.
Zhu, L., Pan, Z., Ma, Q., Yang, W., Shi, H., Fu, C., Yan, X., Du, L., Yan, F., and Zhang, H., Diffusion kurtosis imaging study of rectal adenocarcinoma associated with Histopathologic prognostic factors: Preliminary findings. Radiology 284:66–76, 2017. https://doi.org/10.1148/radiol.2016160094.
Rozenberg, R., Thornhill, R. E., Flood, T. A., Hakim, S. W., Lim, C., and Schieda, N., Whole-tumor quantitative apparent diffusion coefficient histogram and texture analysis to predict Gleason score upgrading in intermediate-risk 3 + 4 = 7 prostate Cancer. AJR Am J Roentgenol 206:775–782, 2016. https://doi.org/10.2214/AJR.15.15462.
Meng, J., Zhu, L., Zhu, L., Xie, L., Wang, H., Liu, S. et al., Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget 8:92442–92453, 2017. https://doi.org/10.18632/oncotarget.21374.
Kaur, H., Choi, H., You, Y. N., Rauch, G. M., Jensen, C. T., Hou, P., Chang, G. J., Skibber, J. M., and Ernst, R. D., MR imaging for preoperative evaluation of primary rectal cancer: Practical considerations. Radiographics 32:389–409, 2012. https://doi.org/10.1148/rg.322115122.
Akashi, M., Nakahusa, Y., Yakabe, T., Egashira, Y., Koga, Y., Sumi, K., Noshiro, H., Irie, H., Tokunaga, O., and Miyazaki, K., Assessment of aggressiveness of rectal cancer using 3-T MRI: Correlation between the apparent diffusion coefficient as a potential imaging biomarker and histologic prognostic factors. Acta Radiol 55:524–531, 2013. https://doi.org/10.1177/0284185113503154.
Sun, Y., Tong, T., Cai, S., Bi, R., Xin, C., and Gu, Y., Apparent diffusion coefficient (ADC) value: A potential imaging biomarker that reflects the biological features of rectal cancer. PLoS One 9:e109371, 2014. https://doi.org/10.1371/journal.pone.0109371.
Cui, Y., Yang, X., Du, X., Zhuo, Z., Xin, L., and Cheng, X., Whole-tumor diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors. Eur Radiol 28:1485–1494, 2018. https://doi.org/10.1007/s00330-017-5094-3.
Merkel, S., Mansmann, U., Siassi, M., Papadopoulos, T., Hohenberger, W., and Hermanek, P., The prognostic inhomogeneity in pT3 rectal carcinomas. Int J Colorectal Dis 16:298–304, 2001.
Cho, S. H., Kim, S. H., Bae, J. H., Jang, Y. J., Kim, H. J., Lee, D., Park, J. S., and Society of North America (RSNA), Prognostic stratification by extramural depth of tumor invasion of primary rectal cancer based on the Radiological Society of North America proposal. AJR Am J Roentgenol 202:1238–1244, 2014. https://doi.org/10.2214/AJR.13.11311.
Becker, A. S., Wagner, M. W., Wurnig, M. C., and Boss, A., Diffusion-weighted imaging of the abdomen: Impact of b-values on texture analysis features. NMR Biomed 30:e3669, 2017. https://doi.org/10.1002/nbm.3669.
Funding
This study was funded by Jiangsu Provincial Medical Youth Talent (QNRC2016212), Suzhou Clinical Special Disease Diagnosis and Treatment Program (LCZX201823), Suzhou GuSu Medical Talent Project (GSWS2019077) and Science and Technology Bureau of Changshu (CS201624).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Author Zhihua Lu has received research grants from Jiangsu Provincial Medical Youth Talent, Suzhou Clinical Special Disease Diagnosis and Treatment Program, Suzhou GuSu Medical Talent Project and Science and Technology Bureau of Changshu. Author Jianlong Jiang has received research grant from Suzhou Clinical Special Disease Diagnosis and Treatment Program. All authors have no relevant conflicts of interest including specific financial interests relevant to the subject of our manuscript.
Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of Changshu Hospital Affiliated to Soochow University. Requirements for written informed consent were waived due to the retrospective nature of the study.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the Topical Collection on Image & Signal Processing
Zhihua Lu and Lei Wang are co-first authors.
Rights and permissions
About this article
Cite this article
Lu, Z., Wang, L., Xia, K. et al. Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps. J Med Syst 43, 331 (2019). https://doi.org/10.1007/s10916-019-1464-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-019-1464-5