Skip to main content

Advertisement

SpringerLink
Log in

Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC.

Methods

In this retrospective study, 215 patients (average age: 57 years (18–87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set.

Results

Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively.

Conclusion

FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions.

Key Points

• Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer.

• Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

FD:

Fractal dimension

LARC:

Locally advanced rectal cancer

MPP:

Mean positive pixel

nCRT:

Neo-adjuvant chemoradiation

pCR:

Pathological complete response

SD:

Standard deviation

SSF:

Spatial scaling filter

References

  1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019 (US statistics). CA Cancer J Clin 69(1):7–34

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics, 2021. CA Cancer J Clin 71(1):7–33

    Article  PubMed  Google Scholar 

  3. Folkesson J, Birgisson H, Pahlman L, Cedermark B, Glimelius B, Gunnarsson U (2005) Swedish rectal cancer trial: long lasting benefits from radiotherapy on survival and local recurrence rate. J Clin Oncol 23(24):5644–5650

    Article  PubMed  Google Scholar 

  4. Schmoll HJ, Van Cutsem E, Stein A et al (2012) Esmo consensus guidelines for management of patients with colon and rectal cancer. A personalized approach to clinical decision making. Ann Oncol 23(10):2479–2516

    Article  CAS  PubMed  Google Scholar 

  5. Sauer R, Liersch T, Merkel S et al (2012) Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years. J Clin Oncol 30(16):1926–1933

    CAS  PubMed  Google Scholar 

  6. Maas M, Nelemans P, Valentini V et al (2010) Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. Lancet Oncol 11(9):835–844

    Article  PubMed  Google Scholar 

  7. Renehan AG, Malcomson L, Emsley R et al (2016) Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (the OnCoRe project): a propensity-score matched cohort analysis. Lancet Oncol 17(2):174–183

    Article  PubMed  Google Scholar 

  8. Davnall F, Yip CSP, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3(6):573–589

    Article  PubMed  PubMed Central  Google Scholar 

  9. Gerlinger M (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 65(4):883–892

    Article  Google Scholar 

  10. Turner NC, Reis-Filho JS (2012) Genetic heterogeneity and cancer drug resistance. Lancet Oncol 13(4):e178–e185

    Article  PubMed  Google Scholar 

  11. Miles KA, Ganeshan B, Hayball MP (2013) CT texture analysis using the filtration-histogram method: what do the measurements mean? Cancer Imaging 13(3):400–406

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hayano K, Tian F, Kambadakone AR et al (2015) Texture analysis of non-contrast-enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma”. J Comput Assist Tomogr 39(4):607–612

    Article  PubMed  PubMed Central  Google Scholar 

  13. Noda Y, Goshima S, Tsuji Y et al (2019) Prognostic evaluation of pancreatic ductal adenocarcinoma: associations between molecular biomarkers and CT imaging findings. Pancreatology 19(2):331–339

    Article  CAS  PubMed  Google Scholar 

  14. Ganeshan B, Skogen K, Pressney I, Coutroubis D, Miles K (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67(2):157–164

    Article  CAS  PubMed  Google Scholar 

  15. Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22(4):796–802

    Article  PubMed  Google Scholar 

  16. Yin JD, Song LR, Lu HC, Zheng X (2020) Prediction of different stages of rectal cancer: texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps. World J Gastroenterol 26(17):2082–2096

    Article  PubMed  PubMed Central  Google Scholar 

  17. Nardone V, Reginelli Alfonso, Scala Fernando et al (2019) Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation. Gastroenterol Res Pract. https://doi.org/10.1155/2019/8505798

    Article  PubMed  PubMed Central  Google Scholar 

  18. Shu Z, Fang Songhua, Ye Qin et al (2019) Prediction of efficacy of neoadjuvant chemoradiotherapy for rectal cancer: the value of texture analysis of magnetic resonance images. Abdom Radiol (NY). https://doi.org/10.1007/s00261-019-01971-y

    Article  PubMed  Google Scholar 

  19. Chee CG, Kim YH, Lee KH et al (2017) 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 12(8):1–12

    Article  Google Scholar 

  20. Vandendorpe B, Durot C, Lebellec L et al (2019) Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer. Radiother Oncol 135:153–160

    Article  PubMed  Google Scholar 

  21. Mandelbrot BB (1982) Fractal geometry. W.H.Freeman and Company New York

  22. Kurata Y, Hayano K, Ohira G, Narushima K, Aoyagi T, Matsubara H (2018) Fractal analysis of contrast-enhanced CT images for preoperative prediction of malignant potential of gastrointestinal stromal tumor. Abdom Radiol (NY) 43(10):2659–2664

    Article  Google Scholar 

  23. Hayano K, Lee SH, Yoshida H, Zhu AX, Sahani DV (2014) Fractal analysis of CT perfusion images for evaluation of antiangiogenic treatment and survival in hepatocellular carcinoma. Acad Radiol 21(5):654–660

    Article  PubMed  Google Scholar 

  24. Hayano K, Yoshida H, Zhu AX, Sahani DV (2014) Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib. Dig Dis Sci 59(8):1996–2003

    Article  CAS  PubMed  Google Scholar 

  25. Abramyuk A, Wolf G, Shakirin G et al (2010) Preliminary assessment of dynamic contrast-enhanced CT implementation in pretreatment FDG-PET/CT for outcome prediction in head and neck tumors. Acta Radiol 51(7):793–799

    Article  PubMed  Google Scholar 

  26. Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13(1):140–149

    Article  PubMed  PubMed Central  Google Scholar 

  27. Nelson DA, White E, Tan T-T, Rabson AB, Anderson D, Degenhardt K (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18(17):2095–2107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Russnes HG, Hicks J, Borresen-Dale A-L, Navin N (2011) Insight into the heterogeneity of breast cancer through next-generation sequencing. J Clin Inves 121(10):3810

    Article  CAS  Google Scholar 

  29. Teicher BA (1995) Physiologic mechanisms of therapeutic resistance. Blood flow and hypoxia. Hematol Oncol Clin North Am 9(2):475–506

    Article  CAS  PubMed  Google Scholar 

  30. Sloothaak DA, Geijsen DE, van Leersum NJ et al (2013) Optimal time interval between neoadjuvant chemoradiotherapy and surgery for rectal cancer. Br J Surg 100(7):933–939

    Article  CAS  PubMed  Google Scholar 

  31. Grumann MM, Noack EM, Hoffmann IA, Schlag PM (2001) Comparison of quality of life in patients undergoing abdominoperineal extirpation or anterior resection for rectal cancer. Ann Surg 233(2):149–156

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kamran SC, Lennerz J, Margolis C et al (2019) Integrative molecular characterization of resistance to neoadjuvant chemoradiation in rectal cancer. Clin Cancer Res 25(18):5561–5571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. García-Figueiras R, Baleato-González S, Padhani AR et al (2018) Advanced imaging techniques in evaluation of colorectal cancer. Radiographics 38(3):740–765

    Article  PubMed  Google Scholar 

  34. Miles KA, Ganeshan B, Griffiths MR, Young RCD, Chatwin CR (2009) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250(2):444–452

    Article  PubMed  Google Scholar 

  35. Win T, Miles KA, Janes SM et al (2013) Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 19(13):3591–3599

    Article  CAS  PubMed  Google Scholar 

  36. Dohan A, Gallix B, Guiu B et al (2020) Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab. Gut 69(3):531–539

    Article  CAS  PubMed  Google Scholar 

  37. Yasaka K, Akai Hiroyuki, Mackin Dennis et al (2017) Precision of quantitative computed tomography texture analysis using image filtering: a phantom study for scanner variability. Medicine (Baltimore) 96(21):e6993

    Article  Google Scholar 

  38. Chen S, Ganeshan B, Fraioli F (2016) Reproducibility of CT Texture parameters by leveraging publicly available patient imaging datasets. In Radiological Society of North America, Chicago

    Google Scholar 

  39. Ng F, Kozarski R, Ganeshan B, Goh V (2013) Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol 82(2):342–348

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, Boston, MA, USA

    Toru Tochigi, Anushri Parakh, Yoshifumi Noda & Avinash Kambadakone

  2. Department of Frontier Surgery, Chiba University Graduate School of Medicine, Chiba, Japan

    Toru Tochigi

  3. Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA

    Sophia C. Kamran, Jennifer Y. Wo & Theodore S. Hong

  4. Department of Radiology, Gifu University, Gifu, Japan

    Yoshifumi Noda

  5. University College London, London, UK

    Balaji Ganeshan

  6. Department of Medical Oncology, Massachusetts General Hospital, Boston, MA, USA

    Lawrence S. Blaszkowsky, David P. Ryan & Jill N. Allen

  7. Department of Surgery, Massachusetts General Hospital, Boston, MA, USA

    David L. Berger

  8. Abdominal Imaging, Harvard Medical School, Massachusetts General Hospital, 55 Fruit Street, White 270, Boston, MA, 02114, USA

    Avinash Kambadakone

Authors
  1. Toru Tochigi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sophia C. Kamran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anushri Parakh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoshifumi Noda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Balaji Ganeshan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lawrence S. Blaszkowsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David P. Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jill N. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David L. Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jennifer Y. Wo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Theodore S. Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Avinash Kambadakone
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Avinash Kambadakone.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Avinash Kambadakone.

Conflict of interest

Avinash Kambadakone: Research Grants (GE Healthcare, Philips Healthcare and PanCAN), Advisory Board (Bayer).

All other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 18 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tochigi, T., Kamran, S.C., Parakh, A. et al. Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis. Eur Radiol 32, 2426–2436 (2022). https://doi.org/10.1007/s00330-021-08303-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-021-08303-z

Keywords

Search

Navigation