Skip to main content

Advertisement

SpringerLink
Log in

Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI

  • Original Article
  • Published:
Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC.

Materials and methods

Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal–Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1–3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA).

Results

Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model.

Conclusions

ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.

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
Fig. 6

Similar content being viewed by others

Abbreviations

AJCC:

American joint committee on cancer

ANN:

Artificial neural network

AUC:

Area under the curve

DCA:

Decision curve analysis

ICC:

Intra-class correlation coefficient

LARC:

Locally advanced rectal cancers

ML:

Machine learning

MRI:

Magnetic resonance imaging

nCRT:

Neoadjuvant chemoradiotherapy

pCR:

Pathological complete response

RF:

Random forest

SD:

Standard deviation

T2W-MRI:

T2-weighted MRI

TME:

Total mesorectal excision

TNM:

Tumor, node, metastasis

TRG:

Tumor regression grade

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.

    Article  Google Scholar 

  2. Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RGS, Barzi A, et al. Colorectal cancer statistics, 2017. CA Cancer J Clin. 2017;67:177–93.

    Article  Google Scholar 

  3. Sauer R, Becker H, Hohenberger W, Rödel C, Wittekind C, Fietkau R, et al. Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med. 2004;351:1731–40.

    Article  CAS  Google Scholar 

  4. Renehan AG, Malcomson L, Emsley R, Gollins S, Maw A, Myint AS, et al. 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. 2016;17:174–83.

    Article  Google Scholar 

  5. Hupkens BJP, Martens MH, Stoot JH, Berbee M, Melenhorst J, Beets-Tan RG, et al. Quality of life in rectal cancer patients after chemoradiation: watch-and-wait policy versus standard resection—a matched-controlled study. Dis Colon Rectum. 2017;60:1032–40.

    Article  Google Scholar 

  6. Habr-Gama A, Perez RO, Nadalin W, Sabbaga J, Ribeiro U, Silva Sousa AH, et al. Operative versus nonoperative treatment for stage 0 distal rectal cancer following chemoradiation therapy: long-term results. Ann Surg. 2004;240:711–7.

    Article  Google Scholar 

  7. van der Valk MJM, Hilling DE, Bastiaannet E, Meershoek-Klein Kranenbarg E, Beets GL, Figueiredo NL, et al. Long-term outcomes of clinical complete responders after neoadjuvant treatment for rectal cancer in the International Watch & Wait Database (IWWD): an international multicentre registry study. Lancet Lond Engl. 2018;391:2537–45.

    Article  Google Scholar 

  8. Sclafani F, Brown G, Cunningham D, Wotherspoon A, Mendes LST, Balyasnikova S, et al. Comparison between MRI and pathology in the assessment of tumour regression grade in rectal cancer. Br J Cancer. 2017;117:1478–85.

    Article  Google Scholar 

  9. Curvo-Semedo L, Lambregts DMJ, Maas M, Thywissen T, Mehsen RT, Lammering G, et al. Rectal cancer: assessment of complete response to preoperative combined radiation therapy with chemotherapy–conventional MR volumetry versus diffusion-weighted MR imaging. Radiology. 2011;260:734–43.

    Article  Google Scholar 

  10. Ha HI, Kim AY, Yu CS, Park SH, Ha HK. Locally advanced rectal cancer: diffusion-weighted MR tumour volumetry and the apparent diffusion coefficient for evaluating complete remission after preoperative chemoradiation therapy. Eur Radiol. 2013;23:3345–53.

    Article  Google Scholar 

  11. Intven M, Reerink O, Philippens MEP. Dynamic contrast enhanced MR imaging for rectal cancer response assessment after neo-adjuvant chemoradiation. J Magn Reson Imaging JMRI. 2015;41:1646–53.

    Article  Google Scholar 

  12. Nougaret S, Vargas HA, Lakhman Y, Sudre R, Do RKG, Bibeau F, 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;280:446–54.

    Article  Google Scholar 

  13. Kim SH, Lee JM, Hong SH, Kim GH, Lee JY, Han JK, et al. Locally advanced rectal cancer: added value of diffusion-weighted MR imaging in the evaluation of tumor response to neoadjuvant chemo- and radiation therapy. Radiology. 2009;253:116–25.

    Article  Google Scholar 

  14. Cai P-Q, Wu Y-P, An X, Qiu X, Kong L-H, Liu G-C, et al. Simple measurements on diffusion-weighted MR imaging for assessment of complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2014;24:2962–70.

    Article  Google Scholar 

  15. Hötker AM, Tarlinton L, Mazaheri Y, Woo KM, Gönen M, Saltz LB, et al. Multiparametric MRI in the assessment of response of rectal cancer to neoadjuvant chemoradiotherapy: a comparison of morphological, volumetric and functional MRI parameters. Eur Radiol. 2016;26:4303–12.

    Article  Google Scholar 

  16. Martens MH, Subhani S, Heijnen LA, Lambregts DMJ, Buijsen J, Maas M, et al. Can perfusion MRI predict response to preoperative treatment in rectal cancer? Radiother Oncol J Eur Soc Ther Radiol Oncol. 2015;114:218–23.

    Article  Google Scholar 

  17. Chen Y-G, Chen M-Q, Guo Y-Y, Li S-C, Wu J-X, Xu B-H. Apparent diffusion coefficient predicts pathology complete response of rectal cancer treated with neoadjuvant chemoradiotherapy. PLoS ONE. 2016;11: e0153944.

    Article  Google Scholar 

  18. Patel UB, Taylor F, Blomqvist L, George C, Evans H, Tekkis P, et al. Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience. J Clin Oncol Off J Am Soc Clin Oncol. 2011;29:3753–60.

    Article  Google Scholar 

  19. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures. They are data. Radiology. 2016;278:563–77.

    Article  Google Scholar 

  20. Koçak B, Durmaz EŞ, Ateş E, Kılıçkesmez Ö. Radiomics with artificial intelligence: a practical guide for beginners. Diagn Interv Radiol Ank Turk. 2019;25:485–95.

    Article  Google Scholar 

  21. Ryan R, Gibbons D, Hyland JMP, Treanor D, White A, Mulcahy HE, et al. Pathological response following long-course neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Histopathology. 2005;47:141–6.

    Article  CAS  Google Scholar 

  22. Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22:81–91.

    Article  CAS  Google Scholar 

  23. Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017;44:1050–62.

    Article  CAS  Google Scholar 

  24. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.

    Article  Google Scholar 

  25. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

    Article  Google Scholar 

  26. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–63.

    Article  Google Scholar 

  27. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak Int J Soc Med Decis Mak. 2006;26:565–74.

    Article  Google Scholar 

  28. Song Y, Zhang J, Zhang Y-D, Hou Y, Yan X, Wang Y, et al. FeAture Explorer (FAE): a tool for developing and comparing radiomics models. PLoS ONE. 2020;15: e0237587.

    Article  CAS  Google Scholar 

  29. Van Rossum G, Drake Jr FL. Python reference manual. Centrum voor Wiskunde en Informatica Amsterdam; 1995.

  30. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org/

  31. Nie K, Shi L, Chen Q, Hu X, Jabbour SK, Yue N, et al. Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res Off J Am Assoc Cancer Res. 2016;22:5256–64.

    Article  Google Scholar 

  32. Antunes JT, Ofshteyn A, Bera K, Wang EY, Brady JT, Willis JE, et al. Radiomic features of primary rectal cancers on baseline t2-weighted MRI are associated with pathologic complete response to neoadjuvant chemoradiation: a multisite study. J Magn Reson Imaging JMRI. 2020;52:1531–41.

    Article  Google Scholar 

  33. Wang J, Chen J, Zhou R, Gao Y, Li J. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients. BMC Cancer. 2022;22:420.

    Article  Google Scholar 

  34. Petkovska I, Tixier F, Ortiz EJ, Golia Pernicka JS, Paroder V, Bates DD, et al. Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy. Abdom Radiol N Y. 2020;45:3608–17.

    Article  Google Scholar 

  35. Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C, et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med (Torino). 2018;123:286–95.

    Article  Google Scholar 

  36. Meng Y, Zhang C, Zou S, Zhao X, Xu K, Zhang H, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer. Oncotarget. 2018;9:11999–2008.

    Article  Google Scholar 

  37. Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287:833–43.

    Article  Google Scholar 

  38. De Cecco CN, Ganeshan B, Ciolina M, Rengo M, Meinel FG, Musio D, 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;50:239–45.

    Article  Google Scholar 

  39. Zhang Z, Jiang X, Zhang R, Yu T, Liu S, Luo Y. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer. Diagn Interv Radiol Ank Turk. 2021;27:308–14.

    Article  Google Scholar 

  40. van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, et al. Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol N Y. 2020;45:632–43.

    Article  Google Scholar 

  41. Tang X, Jiang W, Li H, Xie F, Dong A, Liu L, et al. Predicting poor response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer: Model constructed using pre-treatment MRI features of structured report template. Radiother Oncol J Eur Soc Ther Radiol Oncol. 2020;148:97–106.

    Article  CAS  Google Scholar 

  42. Shaish H, Aukerman A, Vanguri R, Spinelli A, Armenta P, Jambawalikar S, et al. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study. Eur Radiol. 2020;30:6263–73.

    Article  CAS  Google Scholar 

Download references

Author information

Author notes

  1. Aytul Hande Yardimci and Burak Kocak contributed equally.

Authors and Affiliations

  1. Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey

    Aytul Hande Yardimci, Burak Kocak & Ozgur Kilickesmez

  2. Department of Radiology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey

    Ipek Sel & Ceyda Turan Bektas

  3. Department of Radiology, University of Health Sciences, Dr. Sami Ulus Maternity and Children Research and Training Hospital, Ankara, Turkey

    Hasan Bulut

  4. Department of Pathology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey

    Merve Cin

  5. Department of Pathology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey

    Nevra Dursun

  6. Department of General Surgery, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Istanbul, Turkey

    Hasan Bektas

  7. Department of Radiation Oncology, University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, Turkey

    Ozlem Mermut

  8. Faculty of Health Sciences, Istanbul Gelisim University, Istanbul, Turkey

    Veysi Hakan Yardimci

Authors
  1. Aytul Hande Yardimci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Burak Kocak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ipek Sel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hasan Bulut
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ceyda Turan Bektas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Merve Cin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nevra Dursun
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hasan Bektas
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ozlem Mermut
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Veysi Hakan Yardimci
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ozgur Kilickesmez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Burak Kocak.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yardimci, A.H., Kocak, B., Sel, I. et al. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI. Jpn J Radiol 41, 71–82 (2023). https://doi.org/10.1007/s11604-022-01325-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11604-022-01325-7

Keywords

Search

Navigation