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Journal of Medical Systems

, 43:331 | Cite as

Prediction of Clinical Pathologic Prognostic Factors for Rectal Adenocarcinoma: Volumetric Texture Analysis Based on Apparent Diffusion Coefficient Maps

  • Zhihua LuEmail author
  • Lei Wang
  • Kaijian Xia
  • Heng Jiang
  • Xiaoyan Weng
  • Jianlong Jiang
  • Mei Wu
Image & Signal Processing
  • 10 Downloads
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care

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.

Keywords

Diffusion-weighted imaging Apparent diffusion coefficient Rectal cancer Texture analysis 

Notes

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).

Compliance with Ethical Standards

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.

References

  1. 1.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    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.PubMedGoogle Scholar
  3. 3.
    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.CrossRefPubMedGoogle Scholar
  4. 4.
    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.CrossRefPubMedGoogle Scholar
  5. 5.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    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.CrossRefPubMedGoogle Scholar
  7. 7.
    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.CrossRefGoogle Scholar
  8. 8.
    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.CrossRefPubMedGoogle Scholar
  9. 9.
    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.CrossRefPubMedGoogle Scholar
  10. 10.
    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.CrossRefPubMedGoogle Scholar
  11. 11.
    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.CrossRefPubMedGoogle Scholar
  12. 12.
    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.CrossRefGoogle Scholar
  13. 13.
    Just, N., Improving tumor heterogeneity MRI assessment with histograms. Br J Cancer 111:2205–2213, 2014.  https://doi.org/10.1038/bjc.2014.512.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    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.CrossRefGoogle Scholar
  15. 15.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    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.CrossRefGoogle Scholar
  17. 17.
    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.CrossRefGoogle Scholar
  18. 18.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    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.CrossRefPubMedGoogle Scholar
  20. 20.
    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.CrossRefPubMedGoogle Scholar
  21. 21.
    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.CrossRefPubMedGoogle Scholar
  22. 22.
    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.CrossRefPubMedGoogle Scholar
  23. 23.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Edge SB, Byrd DR, Compton CC (2010) American Joint Committee on Cancer. AJCC cancer staging manual. 7th ed. Springer, New YorkGoogle Scholar
  25. 25.
    Bosman, F. T., Carneiro, F., and Hruban, R. H., WHO classification of tumors of the digestive system. Geneva: World Health Organization, 2010.Google Scholar
  26. 26.
    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.CrossRefGoogle Scholar
  27. 27.
    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.CrossRefPubMedGoogle Scholar
  28. 28.
    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.CrossRefPubMedGoogle Scholar
  29. 29.
    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.PubMedGoogle Scholar
  30. 30.
    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.CrossRefPubMedGoogle Scholar
  31. 31.
    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.CrossRefPubMedGoogle Scholar
  32. 32.
    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.CrossRefPubMedGoogle Scholar
  33. 33.
    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.CrossRefPubMedGoogle Scholar
  34. 34.
    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.CrossRefPubMedGoogle Scholar
  35. 35.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    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.CrossRefPubMedGoogle Scholar
  37. 37.
    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.CrossRefPubMedGoogle Scholar
  38. 38.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    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.CrossRefPubMedGoogle Scholar
  40. 40.
    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.CrossRefGoogle Scholar
  41. 41.
    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.CrossRefPubMedGoogle Scholar
  42. 42.
    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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Zhihua Lu
    • 1
    Email author
  • Lei Wang
    • 1
  • Kaijian Xia
    • 1
  • Heng Jiang
    • 1
  • Xiaoyan Weng
    • 1
  • Jianlong Jiang
    • 1
  • Mei Wu
    • 1
  1. 1.First People’s Hospital of Changshu CityChangshu Hospital Affiliated to Soochow UniversityChangshuChina

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