Abdominal Radiology

, Volume 44, Issue 9, pp 2978–2987 | Cite as

Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

  • Lijuan Wan
  • Chongda Zhang
  • Qing Zhao
  • Yankai Meng
  • Shuangmei Zou
  • Yang Yang
  • Yuan Liu
  • Jun Jiang
  • Feng Ye
  • Han Ouyang
  • Xinming Zhao
  • Hongmei ZhangEmail author
Special Section: Rectal Cancer



The aim of this study was to build an appropriate diagnostic model for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), by combining magnetic resonance imaging (MRI) parameters with clinical factors.


Eighty-four patients with LARC who underwent MR examination before and after nCRT were enrolled in this study. MRI parameters including cylindrical approximated tumor volume (CATV) and relative signal intensity of tumor (rT2wSI) were measured; corresponding reduction rates (RR) were calculated; and MR tumor regression grade (mrTRG) and other conventional MRI parameters were assessed. Logistic regression with lasso regularization was performed and the appropriate prediction model for pCR was built up. An external cohort of thirty-six patients was used as the validation group for testing the model. Receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performance.


In the development and the validation group, 17 patients (20.2%) and 11 patients (30.6%), respectively, achieved pCR. Two CATV-related parameters (CATVpost, which is the CATV measured after nCRT and CATVRR), one rT2wSI-related parameter (rT2wSIRR), and mrTRG were the most important parameters for predicting pCR and were retained in the diagnostic model. In the development group, the area under the receiver-operating characteristic curve (AUC) for predicting pCR is 0.88 [95% confidence interval (CI) 0.78–0.97, p < 0.001], with a sensitivity of 82.4% and a specificity of 83.6%. In the validation group, the AUC is 0.84 (95% CI 0.70–0.98, p = 0.001), with a sensitivity of 81.8% and a specificity of 76.0%.


A diagnostic model including CATVpost, CATVRR, rT2wSIRR, and mrTRG was useful for predicting pCR after nCRT in patients with LARC and may be used as an effective organ-preservation strategy.


Rectal neoplasms Magnetic resonance imaging Magnetic resonance tumor regression grading Neoadjuvant chemoradiotherapy Pathological complete response 



Apparent diffusion coefficient


Circumferential percentage


Cylindrical approximated tumor volume


The reduction rate of cylindrical approximated tumor volume


Distance from tumor to anal verge


Extramural venous invasion


Locally advanced rectal cancer


Mesorectal fascia


Magnetic resonance tumor regression grading


Neoadjuvant chemoradiotherapy


Pathological complete response


Relative signal intensity of tumor


The reduction rate of relative signal intensity of tumor


Total mesorectal excision


Tumor position



The authors acknowledge Sainan Cheng and Shunan Che for their assistance with statistical analyses.

Conflict of interest

All authors (Lijuan Wan, Chongda Zhang, Qing Zhao, Yankai Meng, Shuangmei Zou, Yang Yang, Yuan Liu, Jun Jiang, Feng Ye, Han Ouyang, Xinming Zhao, and Hongmei Zhang) have no conflicts of interest to be disclosed related to this article.

Author contributions

Study concepts/study design, data acquisition or data analysis/interpretation, all authors; quality control of date and algorithms, Jun Jiang, Feng Ye, Han Ouyang, Hongmei Zhang; statistical analysis, Lijuan Wan, Chongda Zhang, Hongmei Zhang. drafting the article or revising it critically for important intellectual content, all author; final approval of the version to be submitted, all author.

Funding information

This research is supported by the Special scientific research projects of Beijing science and technology project [Grant Number Z16110000051610]; Beijing hope marathon special fund [Grant Number LC2016A05]; Peking Union Medical College Youth Fund, the Fundamental Research Funds for the Central Universities [Grant Number 3332018078]; Beijing Hope Run Special Fund of the Cancer Foundation of China [Grant Number LC2017B18].


  1. 1.
    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. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 30(16):1926–1933. CrossRefGoogle Scholar
  2. 2.
    Maas M, Nelemans PJ, 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. The Lancet Oncology 11(9):835–844. CrossRefGoogle Scholar
  3. 3.
    Hotker AM, Tarlinton L, Mazaheri Y, et al. (2016) Multiparametric MRI in the assessment of response of rectal cancer to neoadjuvant chemoradiotherapy: A comparison of morphological, volumetric and functional MRI parameters. European Radiology 26(12):4303–4312. CrossRefGoogle Scholar
  4. 4.
    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. The Lancet Oncology 17(2):174–183. CrossRefGoogle Scholar
  5. 5.
    Sartori CA, Sartori A, Vigna S, Occhipinti R, Baiocchi GL (2011) Urinary and sexual disorders after laparoscopic TME for rectal cancer in males. Journal of gastrointestinal surgery: official journal of the Society for Surgery of the Alimentary Tract 15(4):637–643. CrossRefGoogle Scholar
  6. 6.
    Braendengen M, Tveit KM, Bruheim K, et al. (2011) Late patient-reported toxicity after preoperative radiotherapy or chemoradiotherapy in nonresectable rectal cancer: results from a randomized Phase III study. International journal of radiation oncology, biology, physics 81(4):1017–1024. CrossRefGoogle Scholar
  7. 7.
    Tarallo N, Angeretti MG, Bracchi E, et al. (2018) Magnetic resonance imaging in locally advanced rectal cancer: quantitative evaluation of the complete response to neoadjuvant therapy. Polish journal of radiology 83:e600–e609. CrossRefGoogle Scholar
  8. 8.
    Kluza E, Rozeboom ED, Maas M, et al. (2013) T2 weighted signal intensity evolution may predict pathological complete response after treatment for rectal cancer. European radiology 23(1):253–261. CrossRefGoogle Scholar
  9. 9.
    Enkhbaatar NE, Inoue S, Yamamuro H, et al. (2018) MR Imaging with Apparent Diffusion Coefficient Histogram Analysis: Evaluation of Locally Advanced Rectal Cancer after Chemotherapy and Radiation Therapy. Radiology 288(1):129–137. CrossRefGoogle Scholar
  10. 10.
    Battersby NJ, Dattani M, Rao S, et al. (2017) A rectal cancer feasibility study with an embedded phase III trial design assessing magnetic resonance tumour regression grade (mrTRG) as a novel biomarker to stratify management by good and poor response to chemoradiotherapy (TRIGGER): study protocol for a randomised controlled trial. Trials 18(1):394. CrossRefGoogle Scholar
  11. 11.
    Lambregts DMJ, Boellaard TN, Beets-Tan RGH (2019) Response evaluation after neoadjuvant treatment for rectal cancer using modern MR imaging: a pictorial review. Insights into imaging 10(1):15. CrossRefGoogle Scholar
  12. 12.
    Kim YH, Kim DY, Kim TH, et al. (2005) Usefulness of magnetic resonance volumetric evaluation in predicting response to preoperative concurrent chemoradiotherapy in patients with resectable rectal cancer. International journal of radiation oncology, biology, physics 62(3):761–768. CrossRefGoogle Scholar
  13. 13.
    Bernier L, Balyasnikova S, Tait D, Brown G (2018) Watch-and-Wait as a Therapeutic Strategy in Rectal Cancer. Current colorectal cancer reports 14(2):37–55. CrossRefGoogle Scholar
  14. 14.
    Sclafani F, Brown G, Cunningham D, et al. (2017) Comparison between MRI and pathology in the assessment of tumour regression grade in rectal cancer. Br J Cancer 117(10):1478–1485. CrossRefGoogle Scholar
  15. 15.
    Park SH, Lim JS, Lee J, et al. (2017) Rectal Mucinous Adenocarcinoma: MR Imaging Assessment of Response to Concurrent Chemotherapy and Radiation Therapy-A Hypothesis-generating Study. Radiology 285(1):124–133. CrossRefGoogle Scholar
  16. 16.
    Quirke P, Durdey P, Dixon MF, Williams NS (1986) Local recurrence of rectal adenocarcinoma due to inadequate surgical resection. Histopathological study of lateral tumour spread and surgical excision. Lancet (London, England) 2 (8514):996-999Google Scholar
  17. 17.
    Smith NJ, Barbachano Y, Norman AR, et al. (2008) Prognostic significance of magnetic resonance imaging-detected extramural vascular invasion in rectal cancer. The British journal of surgery 95(2):229–236. CrossRefGoogle Scholar
  18. 18.
    Patel UB, Taylor F, Blomqvist L, et al. (2011) Magnetic resonance imaging-detected tumor response for locally advanced rectal cancer predicts survival outcomes: MERCURY experience. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 29(28):3753–3760. CrossRefGoogle Scholar
  19. 19.
    Neri E, Guidi E, Pancrazi F, et al. (2015) MRI tumor volume reduction rate vs tumor regression grade in the pre-operative re-staging of locally advanced rectal cancer after chemo-radiotherapy. European journal of radiology 84(12):2438–2443. CrossRefGoogle Scholar
  20. 20.
    Yeo SG, Kim DY, Park JW, et al. (2012) Tumor volume reduction rate after preoperative chemoradiotherapy as a prognostic factor in locally advanced rectal cancer. International journal of radiation oncology, biology, physics 82(2):e193–199. CrossRefGoogle Scholar
  21. 21.
    Seierstad T, Hole KH, Groholt KK, et al. (2015) MRI volumetry for prediction of tumour response to neoadjuvant chemotherapy followed by chemoradiotherapy in locally advanced rectal cancer. The British journal of radiology 88(1051):20150097. CrossRefGoogle Scholar
  22. 22.
    Siddiqui MR, Gormly KL, Bhoday J, et al. (2016) Interobserver agreement of radiologists assessing the response of rectal cancers to preoperative chemoradiation using the MRI tumour regression grading (mrTRG). Clinical radiology 71(9):854–862. CrossRefGoogle Scholar
  23. 23.
    Fayaz MS, Demian GA, Fathallah WM, et al. (2016) Significance of Magnetic Resonance Imaging-Assessed Tumor Response for Locally Advanced Rectal Cancer Treated With Preoperative Long-Course Chemoradiation. Journal of global oncology 2(4):216–221. CrossRefGoogle Scholar
  24. 24.
    Yu SK, Tait D, Chau I, Brown G (2013) MRI predictive factors for tumor response in rectal cancer following neoadjuvant chemoradiation therapy–implications for induction chemotherapy? International journal of radiation oncology, biology, physics 87(3):505–511. CrossRefGoogle Scholar
  25. 25.
    Garland ML, Vather R, Bunkley N, Pearse M, Bissett IP (2014) Clinical tumour size and nodal status predict pathologic complete response following neoadjuvant chemoradiotherapy for rectal cancer. International journal of colorectal disease 29(3):301–307. CrossRefGoogle Scholar
  26. 26.
    Wallin U, Rothenberger D, Lowry A, Luepker R, Mellgren A (2013) CEA - a predictor for pathologic complete response after neoadjuvant therapy for rectal cancer. Diseases of the colon and rectum 56(7):859–868. CrossRefGoogle Scholar
  27. 27.
    Huh JW, Kim HR, Kim YJ (2013) Clinical prediction of pathological complete response after preoperative chemoradiotherapy for rectal cancer. Diseases of the colon and rectum 56(6):698–703. CrossRefGoogle Scholar
  28. 28.
    Armstrong D, Raissouni S, Price Hiller J, et al. (2015) Predictors of Pathologic Complete Response After Neoadjuvant Treatment for Rectal Cancer: A Multicenter Study. Clinical colorectal cancer 14(4):291–295. CrossRefGoogle Scholar
  29. 29.
    Yang KL, Yang SH, Liang WY, et al. (2013) Carcinoembryonic antigen (CEA) level, CEA ratio, and treatment outcome of rectal cancer patients receiving pre-operative chemoradiation and surgery. Radiation oncology (London, England) 8:43. CrossRefGoogle Scholar
  30. 30.
    Kim S, Han K, Seo N, et al. (2018) T2-weighted signal intensity-selected volumetry for prediction of pathological complete response after preoperative chemoradiotherapy in locally advanced rectal cancer. European radiology 28(12):5231–5240. CrossRefGoogle Scholar
  31. 31.
    Liu Z, Zhang XY, Shi YJ, et al. (2017) Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clinical cancer research: an official journal of the American Association for Cancer Research 23(23):7253–7262. CrossRefGoogle Scholar
  32. 32.
    Saito G, Sadahiro S, Ogimi T, et al. (2018) Relations of Changes in Serum Carcinoembryonic Antigen Levels before and after Neoadjuvant Chemoradiotherapy and after Surgery to Histologic Response and Outcomes in Patients with Locally Advanced Rectal Cancer. Oncology 94(3):167–175. CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Lijuan Wan
    • 1
  • Chongda Zhang
    • 1
  • Qing Zhao
    • 1
  • Yankai Meng
    • 1
  • Shuangmei Zou
    • 2
  • Yang Yang
    • 1
  • Yuan Liu
    • 1
  • Jun Jiang
    • 1
  • Feng Ye
    • 1
  • Han Ouyang
    • 1
  • Xinming Zhao
    • 1
  • Hongmei Zhang
    • 1
    Email author
  1. 1.Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
  2. 2.Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Personalised recommendations