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MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study

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

Abstract

Objective

To build T2WI-based multiregional radiomics for predicting tumor deposit (TD) and prognosis in patients with resectable rectal cancer.

Materials and methods

A total of 208 patients with pathologically confirmed rectal cancer from two hospitals were prospectively enrolled. Intra- and peritumoral features were extracted separately from T2WI images and the least absolute shrinkage and selection operator was used to screen the most valuable radiomics features. Clinical-radiomics nomogram was developed by radiomics signatures and the most predictive clinical parameters. Prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis.

Results

For TD, the area under the receiver operating characteristic curve (AUC) for intratumoral radiomics model was 0.956, 0.823, and 0.860 in the training cohort, test cohort, and external validation cohort, respectively. AUC for the peritumoral radiomics model was 0.929, 0.906, and 0.773 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for combined intra- and peritumoral radiomics model was 0.976, 0.918, and 0.874 in the training cohort, test cohort, and external validation cohort, respectively. The AUC for clinical-radiomics nomogram was 0.989, 0.777, and 0.870 in the training cohort, test cohort, and external validation cohort, respectively. The prognostic model constructed by combining intra- and peritumoral radiomics signature score (radscore)–based TD and MRI-reported lymph nodes metastasis (LNM) indicated good performance for predicting 3-year RFS, with AUC of 0.824, 0.865, and 0.738 in the training cohort, test cohort and external validation cohort, respectively.

Conclusion

Combined intra- and peritumoral radiomics model showed good performance for predicting TD. Combining intra- and peritumoral radscore-based TD and MRI-reported LNM indicated the recurrence risk.

Clinical relevance statement

Combined intra- and peritumoral radiomics model could help accurately predict tumor deposits. Combining this predictive model-based tumor deposits with MRI-reported lymph node metastasis was associated with relapse risk of rectal cancer after surgery.

Key Points

• Combined intra- and peritumoral radiomics model provided better diagnostic performance than that of intratumoral and peritumoral radiomics model alone for predicting TD in rectal cancer.

• The predictive performance of the clinical-radiomics nomogram was not improved compared with the combined intra- and peritumoral radiomics model for predicting TD.

• The prognostic model constructed by combining intra- and peritumoral radscore-based TD and MRI-reported LNM showed good performance for assessing 3-year RFS.

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Abbreviations

AUC:

Areas under the receiver operating characteristic curve

CA199:

Carbohydrate antigen 199

CEA:

Carcinoembryonic antigen

CI:

Confidence interval

DCA:

Decision curve analysis

EMVI:

Extramural vascular invasion

ICC:

Intraclass correlation coefficient

LASSO:

Least absolute shrinkage selection operator

LNM:

Lymph node metastasis

MRF:

Mesorectal fascia

OR:

Odds ratio

Radscore:

Radiomics signature score

RFS:

Recurrence-free survival

ROC:

Receiver operating characteristic

T2WI:

T2-weighted imaging

TD:

Tumor deposits

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249

    Article  PubMed  Google Scholar 

  2. Nagtegaal ID, Knijn N, Hugen N et al (2017) Tumor deosits in colorectal cancer: improving the value of modern staging-a systematic review and meta-analysis. J Clin Oncol 35:1119–1127

    Article  PubMed  Google Scholar 

  3. Bouquot M, Creavin B, Goasguen N et al (2018) Prognostic value and characteristics of N1c colorectal cancer. Colorectal Dis 20(9):O248-o255

    Article  CAS  PubMed  Google Scholar 

  4. Liu FQ, Zhao J, Li C et al (2019) The unique prognostic characteristics of tumor deposits in colorectal cancer patients. Ann Transl Med 7(23):769

    Article  PubMed  PubMed Central  Google Scholar 

  5. Benoit O, Svrcek M, Creavin B et al (2020) Prognostic value of tumor deposits in rectal cancer: a monocentric series of 505 patients. J Surg Oncol 122(7):1481–1489

    Article  PubMed  Google Scholar 

  6. Glynne-Jones R, Wyrwicz L, Tiret E et al (2018) Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 29(Suppl 4):iv263

    Article  CAS  PubMed  Google Scholar 

  7. Diagnosis, Treatment Guidelines For Colorectal Cancer Working Group C (2019) Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for colorectal cancer 2018 (English version). Chin J Cancer Res 31(1):117–134

    Article  Google Scholar 

  8. Lambregts DMJ, Bogveradze N, Blomqvist LK et al (2022) Curret controversies in TNM for the radiological staging of rectal cancer and how to deal with them: results of a global online survey and multidisciplinary expert consensus. Eur Radiol 32(7):4991–5003

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lord AC, Moran B, Abulafi M et al (2020) Can extranodal tumour deposits be diagnosed on MRI? Protocol for a multicentre clinical trial (the COMET trial). BMJ Open 10(10):e033395

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yang YS, Feng F, Qiu YJ, Zheng GH, Ge YQ, Wang YT (2021) High-resolution MRI-based radiomics analysis to predict lymph node metastasis and tumor deposits respectively in rectal cancer. Abdom Radiol (NY) 46(3):873–884

    Article  PubMed  Google Scholar 

  11. Shin J, Seo N, Baek SE et al (2022) MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology 303(2):351–358

    Article  PubMed  Google Scholar 

  12. Yuan Y, Lu H, Ma X et al (2022) Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer? Abdom Radiol (NY) 47(5):1741–1749

    Article  PubMed  Google Scholar 

  13. Yuan Y, Chen XL, Li ZL et al (2022) The application of apparent diffusion coefficients derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer. Eur Radiol 32(8):5106–5118

    Article  CAS  PubMed  Google Scholar 

  14. Hu Y, Xie C, Yang H et al (2020) Assessment of intratumoral and peritumoral computed tomography radiomics for predicting pathological complete response to neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. JAMA Netw Open 3:e2015927

    Article  PubMed  PubMed Central  Google Scholar 

  15. Orel VE, Ashykhmin A, Golovko T, Rykhalskyi O, Orel VB (2021) Texture analysis of tumor and peritumoral tissues based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography hybrid imaging in patients with rectal cancer. J Comput Assist Tomogr 45:820–828

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Jin YM, Li M, Zhao YL et al (2021) Computed tomography-based radiomics for preoperative prediction of tumor deposits in rectal cancer. Front Oncol 11:710248

    Article  PubMed  PubMed Central  Google Scholar 

  18. Shin J, Seo N, Baek SE et al (2022) MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy. Radiology 303(2):351–358

    Article  PubMed  Google Scholar 

  19. Amin MB, Greene FL, Edge SB et al (2017) The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin 67(2):93–9

    Article  PubMed  Google Scholar 

  20. Beets-Tan RGH, Lambregts DMJ, Maas M et al (2018) Magnetic resonance imaging for clinical management of rectal cancer: Updated recommendations from the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus meeting. Eur Radiol 28(4):1465–1475

    Article  PubMed  Google Scholar 

  21. Jiang Y, Wang H, Wu J et al (2020) Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 31(6):760–768

    Article  CAS  PubMed  Google Scholar 

  22. Orlhac F, Lecler A, Savatovski J et al (2021) How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Eur Radiol 31:2272–2280

    Article  PubMed  Google Scholar 

  23. Pizzi AD, Chiarelli AM, Chiacchiaretta P et al (2021) MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep 11(1):5379

    Article  Google Scholar 

  24. Chen LD, Li W, Xian MF et al (2020) Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network-based US radiomics model. Eur Radiol 30(4):1969–1979

    Article  PubMed  Google Scholar 

  25. Puppa G, Maisonneuve P, Sonzogni A et al (2007) Pathological assessment of pericolonic tumor deposits in advanced colonic carcinoma: relevance to prognosis and tumor staging. Mod Pathol 20(8):843–855

    Article  PubMed  Google Scholar 

  26. Yamano T, Semba S, Noda M et al (2015) Prognostic significance of classified extramural tumor deposits and extracapsular lymph node invasion in T3–4 colorectal cancer: a retrospective single-center study. BMC Cancer 15:859

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang X, Zhao X, Li Q et al (2019) Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol 29:6049–6058

    Article  PubMed  Google Scholar 

  28. Ding J, Chen S, Serrano SM et al (2022) Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer. Acad Radiol 29(Suppl 1):S223–S228

    Article  PubMed  Google Scholar 

  29. Shi JX, Dong Y, Jiang WY et al (2022) MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: a multi-center study. Magn Reson Imaging 88:1–8

    Article  PubMed  Google Scholar 

  30. Jayaprakasam VS, Paroder V, Gibbs P et al (2022) MRI radiomics features of mesorectal fat can predict response to neoadjuvant chemoradiation therapy and tumor recurrence in patients with locally advanced rectal cancer. Eur Radiol 32(2):971–980

    Article  CAS  PubMed  Google Scholar 

  31. Yu Y, Tan Y, Xie C et al (2020) Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer. JAMA Netw Open 3(12):e2028086

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study has received funding from the Sichuan Science and Technology Program (grant number, 2020YFH0166) and the Key Research Project of Sichuan Province (grant number, 2022YFS0249).

Author information

Author notes

  1. Ming-hui Pang and Hong Pu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China

    Hang Li & Hong Pu

  2. Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, 610000, China

    Xiao-li Chen

  3. GE Healthcare, Shanghai, China

    Huan Liu

  4. Department of Pathology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China

    Yi-Sha Liu

  5. Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China

    Zhen-lin Li

  6. Department of Gastrointestinal Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, 32# Second Section of First Ring Road, Qingyang District, Chengdu, 610070, Sichuan, China

    Ming-hui Pang

Authors
  1. Hang Li
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Corresponding author

Correspondence to Hong Pu.

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Guarantor

The scientific guarantor of this publication is Hong Pu.

Conflict of interest

H. Liu is a statistician from GE Healthcare and controls of the study data.

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

Hang Li and Huan Liu kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all patients in this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic or prognostic study

• performed at two institutions

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Li, H., Chen, Xl., Liu, H. et al. MRI-based multiregional radiomics for preoperative prediction of tumor deposit and prognosis in resectable rectal cancer: a bicenter study. Eur Radiol 33, 7561–7572 (2023). https://doi.org/10.1007/s00330-023-09723-9

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  • DOI: https://doi.org/10.1007/s00330-023-09723-9

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