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MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer

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

Abstract

Objectives

To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer.

Methods

This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis.

Results

Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963–0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model.

Conclusions

Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies.

Key Points

• T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer.

• The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM.

• Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.

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Abbreviations

AUC:

Area under the curve

CA199:

Carbohydrate antigen 199

CEA:

Carcinoembryonic antigen

DKI:

Diffusion kurtosis imaging

DWI:

Diffusion-weighted imaging

GLCM:

Gray-level co-occurrence matrix

GLRLM:

Gray-level run-length matrix

ICC:

Intraclass correlation coefficient

LN:

Lymph node

NPV:

Negative predictive value

PPV:

Positive predictive value

ROC:

Receiver operating characteristic curve

SDM:

Synchronous distant metastasis

VOI:

Volume of interest

References

  1. Bosset JF, Collette L, Calais G et al (2006) Chemotherapy with preoperative radiotherapy in rectal cancer. N Engl J Med 355:1114–1123

    Article  CAS  PubMed  Google Scholar 

  2. Ho-Pun-Cheung A, Assenat E, Bascoul-Mollevi C et al (2011) EGFR and HER3 mRNA expression levels predict distant metastases in locally advanced rectal cancer. Int J Cancer 128:2938–2946

    Article  CAS  PubMed  Google Scholar 

  3. Lee WS, Yun SH, Chun HK et al (2008) Clinical outcomes of hepatic resection and radiofrequency ablation in patients with solitary colorectal liver metastasis. J Clin Gastroenterol 42:945–949

    Article  PubMed  Google Scholar 

  4. Butte JM, Gonen M, Ding P et al (2012) Patterns of failure in patients with early onset (synchronous) resectable liver metastases from rectal cancer. Cancer 118:5414–5423

    Article  PubMed  Google Scholar 

  5. Fossum CC, Alabbad JY, Romak LB et al (2017) The role of neoadjuvant radiotherapy for locally-advanced rectal cancer with resectable synchronous metastasis. J Gastrointest Oncol 8:650–658

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gaitanidis A, Alevizakos M, Tsaroucha A, Tsalikidis C, Pitiakoudis M (2018) Predictive nomograms for synchronous distant metastasis in rectal cancer. J Gastrointest Surg 22:1268–1276

    Article  PubMed  Google Scholar 

  7. Hur H, Ko YT, Min BS et al (2009) Comparative study of resection and radiofrequency ablation in the treatment of solitary colorectal liver metastases. Am J Surg 197:728–736

    Article  PubMed  Google Scholar 

  8. Kanas GP, Taylor A, Primrose JN et al (2012) Survival after liver resection in metastatic colorectal cancer: review and meta-analysis of prognostic factors. Clin Epidemiol 4:283–301

    PubMed  PubMed Central  Google Scholar 

  9. Sohn B, Lim JS, Kim H et al (2015) MRI-detected extramural vascular invasion is an independent prognostic factor for synchronous metastasis in patients with rectal cancer. Eur Radiol 25:1347–1355

    Article  PubMed  Google Scholar 

  10. Sun Y, Lin H, Lu X et al (2017) A nomogram to predict distant metastasis after neoadjuvant chemoradiotherapy and radical surgery in patients with locally advanced rectal cancer. J Surg Oncol 115:462–469

    Article  CAS  PubMed  Google Scholar 

  11. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  PubMed  Google Scholar 

  12. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  PubMed  Google Scholar 

  15. Ginsburg SB, Algohary A, Pahwa S et al (2017) Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging 46:184–193

    Article  PubMed  Google Scholar 

  16. Corino VDA, Montin E, Messina A et al (2018) Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions. J Magn Reson Imaging 47:829–840

    Article  PubMed  Google Scholar 

  17. Tian Q, Yan LF, Zhang X et al (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26010

  18. Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7:46349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778

    Article  PubMed  Google Scholar 

  20. Algohary A, Viswanath S, Shiradkar R et al (2018) Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: preliminary findings. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25983

  21. Ding J, Xing Z, Jiang Z et al (2018) CT-based radiomic model predicts high grade of clear cell renal cell carcinoma. Eur J Radiol 103:51–56

    Article  PubMed  Google Scholar 

  22. Hou Z, Li S, Ren W, Liu J, Yan J, Wan S (2018) Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 10:2256–2267

    Article  PubMed  PubMed Central  Google Scholar 

  23. Meng Y, Zhang Y, Dong D et al (2018) Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25968

  24. Park H, Lim Y, Ko ES et al (2018) Radiomics signature on magnetic resonance imaging: association with disease-free survival in patients with invasive breast cancer. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-17-3783

  25. Sun Y, Hu P, Wang J et al (2018) Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: preliminary findings. J Magn Reson Imaging. https://doi.org/10.1002/jmri.25969

  26. 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. Clin Cancer Res 23:7253–7262

    Article  CAS  PubMed  Google Scholar 

  27. Horvat N, Veeraraghavan H, Khan M et al (2018) MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 287:833–843

    Article  PubMed  Google Scholar 

  28. Nie K, Shi L, Chen Q et al (2016) Rectal cancer: assessment of neoadjuvant chemoradiation outcome based on radiomics of multiparametric MRI. Clin Cancer Res 22:5256–5264

    Article  PubMed  Google Scholar 

  29. Jhaveri KS, Hosseini-Nik H (2015) MRI of rectal cancer: an overview and update on recent advances. AJR Am J Roentgenol 205:W42–W55

    Article  PubMed  Google Scholar 

  30. Kim YC, Kim JK, Kim MJ, Lee JH, Kim YB, Shin SJ (2016) Feasibility of mesorectal vascular invasion in predicting early distant metastasis in patients with stage T3 rectal cancer based on rectal MRI. Eur Radiol 26:297–305

    Article  PubMed  Google Scholar 

  31. Liu H, Cui Y, Shen W et al (2016) Pretreatment magnetic resonance imaging of regional lymph nodes with carcinoembryonic antigen in prediction of synchronous distant metastasis in patients with rectal cancer. Oncotarget 7:27199–27207

    PubMed  PubMed Central  Google Scholar 

  32. Yu J, Huang DY, Li Y, Dai X, Shi HB (2016) Correlation of standard diffusion-weighted imaging and diffusion kurtosis imaging with distant metastases of rectal carcinoma. J Magn Reson Imaging 44:221–229

    Article  PubMed  Google Scholar 

  33. Zhu L, Pan Z, Ma Q et al (2016) Diffusion kurtosis imaging study of rectal adenocarcinoma associated with histopathologic prognostic factors: preliminary findings. Radiology 284:66–76

    Article  PubMed  Google Scholar 

  34. Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164

    Article  PubMed  Google Scholar 

  35. Taylor FG, Quirke P, Heald RJ et al (2014) Preoperative magnetic resonance imaging assessment of circumferential resection margin predicts disease-free survival and local recurrence: 5-year follow-up results of the MERCURY study. J Clin Oncol 32:34–43

    Article  PubMed  Google Scholar 

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Funding

This study has received funding from the National Key Research and Development Program of China (No. 2017YFC0109003) and Special Research Program of Shanghai Municipal Commission of Heath and Family Planning on medical intelligence (No. 2018ZHYL0108).

Author information

Author notes

  1. Huanhuan Liu and Caiyuan Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, No. 1665 Kongjiang Road, Shanghai, 200092, China

    Huanhuan Liu, Caiyuan Zhang, Lijun Wang, Ran Luo, Jinning Li, Hui Zheng, Qiufeng Yin, Zhongyang Zhang & Dengbin Wang

  2. GE Healthcare, Pudong New Town, No.1, Huatuo Road, Shanghai, 210000, China

    Shaofeng Duan & Xin Li

Authors
  1. Huanhuan Liu
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  10. Xin Li
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Corresponding author

Correspondence to Dengbin Wang.

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Guarantor

The scientific guarantor of this publication is Dengbin Wang, MD, PhD, the chief of department of radiology, Xinhua hospital affiliated to Shanghai Jiao Tong University School of Medicine.

Conflict of interest

The 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

Shaofeng Duan kindly provided statistical advice for the manuscript.

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

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Liu, H., Zhang, C., Wang, L. et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol 29, 4418–4426 (2019). https://doi.org/10.1007/s00330-018-5802-7

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  • DOI: https://doi.org/10.1007/s00330-018-5802-7

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