Skip to main content

Advertisement

SpringerLink
Log in

The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer

  • Hollow Organ GI
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To build and validate a magnetic resonance imaging-based radiomics model to preoperatively evaluate tumor budding (TB) in locally advanced rectal cancer (LARC).

Methods

Pathologically confirmed LARC cases submitted to preoperative rectal MRI in two distinct hospitals were enrolled in this retrospective study and assigned to cohort 1 (training set, n = 77; test set, n = 51) and cohort 2 (validation set, n = 96). Radiomics features were obtained from multiple sequences, comprising high-resolution T2, contrast-enhanced T1, and diffusion-weighted imaging (T2WI, CE-T1WI, and DWI, respectively). The least absolute shrinkage and selection operator (LASSO) was utilized to select the optimal features from T2WI, CE-T1WI, DWI, and the combination of multi-sequences, respectively. A support vector machine (SVM) classifier was utilized to construct various radiomics models for discriminating the TB grades. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were carried out to determine the diagnostic value.

Results

Five optimal features associated with TB grade were determined from combined multi-sequence data. Accordingly, a radiomics model based on combined multi-sequences had an area under the curve of 0.796, with an accuracy of 81.2% in the validation set, showing a better performance in comparison with other models in both cohorts (p < 0.05). DCA exhibited a clinical benefit for this radiomics model.

Conclusion

The novel MRI-based radiomics model combining multiple sequences is an effective and non-invasive approach for evaluating TB grade preoperatively 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

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. [1] Nagtegaal ID, Odze RD, Klimstra D et al. (2020) The 2019 WHO classification of tumours of the digestive system. Histopathology 76:182-188. https://doi.org/10.1111/his.13975

    Article  PubMed  Google Scholar 

  2. [2] Oronsky B, Reid T, Larson C, Knox SJ (2020) Locally advanced rectal cancer: The past, present, and future. Semin Oncol 47:85-92. https://doi.org/10.1053/j.seminoncol.2020.02.001

    Article  PubMed  Google Scholar 

  3. [3] Benson AB, Venook AP, Al-Hawary MM et al. (2018) Rectal Cancer, Version 2.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 16:874-901. https://doi.org/10.6004/jnccn.2018.0061

    Article  PubMed  Google Scholar 

  4. [4] Dawson H, Galuppini F, Träger P et al. (2019) Validation of the International Tumor Budding Consensus Conference 2016 recommendations on tumor budding in stage I-IV colorectal cancer. Hum Pathol 85:145-151. https://doi.org/10.1016/j.humpath.2018.10.023

    Article  PubMed  Google Scholar 

  5. [5] Rogers AC, Winter DC, Heeney A et al. (2016) Systematic review and meta-analysis of the impact of tumour budding in colorectal cancer. Br J Cancer 115:831-840. https://doi.org/10.1038/bjc.2016.274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. [6] Ueno H, Hase K, Hashiguchi Y et al. (2014) Novel risk factors for lymph node metastasis in early invasive colorectal cancer: a multi-institution pathology review. J Gastroenterol 49:1314-1323. https://doi.org/10.1007/s00535-013-0881-3

    Article  PubMed  Google Scholar 

  7. [7] Petrelli F, Pezzica E, Cabiddu M et al. (2015) Tumour Budding and Survival in Stage II Colorectal Cancer: a Systematic Review and Pooled Analysis. J Gastrointest Cancer 46:212-218. https://doi.org/10.1007/s12029-015-9716-1

    Article  CAS  PubMed  Google Scholar 

  8. [8] Nakamura T, Mitomi H, Kanazawa H, Ohkura Y, Watanabe M (2008) Tumor budding as an index to identify high-risk patients with stage II colon cancer. Dis Colon Rectum 51:568-572. https://doi.org/10.1007/s10350-008-9192-9

    Article  PubMed  Google Scholar 

  9. [9] van Wyk HC, Roseweir A, Alexander P et al. (2019) The Relationship between tumor budding, tumor microenvironment, and survival in patients with primary operable colorectal cancer. Ann Surg Oncol 26:4397-4404. https://doi.org/10.1245/s10434-019-07931-6

    Article  PubMed  PubMed Central  Google Scholar 

  10. [10] Lino-Silva LS, Salcedo-Hernández RA, Gamboa-Domínguez A (2018) Tumour budding in rectal cancer. A comprehensive review. Contemp Oncol (Pozn) 22:61-74. https://doi.org/10.5114/wo.2018.77043

    Article  CAS  Google Scholar 

  11. [11] 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. https://doi.org/10.1016/j.ejca.2011.11.036

    Article  PubMed  PubMed Central  Google Scholar 

  12. [12] Kumar V, Gu Y, Basu S et al. (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234-1248. https://doi.org/10.1016/j.mri.2012.06.010

    Article  PubMed  PubMed Central  Google Scholar 

  13. [13] Aerts HJ, Velazquez ER, Leijenaar RT et al. (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006

    Article  CAS  PubMed  Google Scholar 

  14. [14] Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563-577. https://doi.org/10.1148/radiol.2015151169

    Article  PubMed  Google Scholar 

  15. [15] Chen F, Ma X, Li S et al. (2020) MRI-Based Radiomics of Rectal Cancer: Assessment of the Local Recurrence at the Site of Anastomosis. Acad Radiol https://doi.org/10.1016/j.acra.2020.09.024

    Article  PubMed  PubMed Central  Google Scholar 

  16. [16] Ma X, Shen F, Jia Y, Xia Y, Li Q, Lu J (2019) MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. BMC Med Imaging 19:86. https://doi.org/10.1186/s12880-019-0392-7

    Article  PubMed  PubMed Central  Google Scholar 

  17. [17] Liu M, Ma X, Shen F, Xia Y, Jia Y, Lu J (2020) MRI-based radiomics nomogram to predict synchronous liver metastasis in primary rectal cancer patients. Cancer Med 9:5155-5163. https://doi.org/10.1002/cam4.3185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. [18] Zhou X, Yi Y, Liu Z et al. (2020) Radiomics-Based Preoperative Prediction of Lymph Node Status Following Neoadjuvant Therapy in Locally Advanced Rectal Cancer. Front Oncol 10:604. https://doi.org/10.3389/fonc.2020.00604

    Article  PubMed  PubMed Central  Google Scholar 

  19. [19] Yu X, Song W, Guo D et al. (2020) Preoperative Prediction of Extramural Venous Invasion in Rectal Cancer: Comparison of the Diagnostic Efficacy of Radiomics Models and Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Front Oncol 10:459. https://doi.org/10.3389/fonc.2020.00459

    Article  PubMed  PubMed Central  Google Scholar 

  20. [20] Zhang Y, He K, Guo Y et al. (2020) A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer. Front Oncol 10:457. https://doi.org/10.3389/fonc.2020.00457

    Article  PubMed  PubMed Central  Google Scholar 

  21. [21] Park H, Kim KA, Jung JH, Rhie J, Choi SY (2020) MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer. Eur Radiol 30:4201-4211. https://doi.org/10.1007/s00330-020-06835-4

    Article  PubMed  Google Scholar 

  22. [22] 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. https://doi.org/10.1158/1078-0432.ccr-17-1038

    Article  CAS  PubMed  Google Scholar 

  23. [23] 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. https://doi.org/10.1158/1078-0432.ccr-15-2997

    Article  PubMed  Google Scholar 

  24. [24] Cui Y, Yang X, Shi Z et al. (2019) Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol 29:1211-1220. https://doi.org/10.1007/s00330-018-5683-9

    Article  PubMed  Google Scholar 

  25. [25] Zhang XY, Wang L, Zhu HT et al. (2020) Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI. Radiology 296:56-64. https://doi.org/10.1148/radiol.2020190936

    Article  PubMed  Google Scholar 

  26. [26] Li Y, Liu W, Pei Q et al. (2019) Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer. Cancer Med 8:7244-7252. https://doi.org/10.1002/cam4.2636

    Article  PubMed  PubMed Central  Google Scholar 

  27. [27] 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 67:93-99. https://doi.org/10.3322/caac.21388

    Article  PubMed  Google Scholar 

  28. [28] Lugli A, Kirsch R, Ajioka Y et al. (2017) Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016. Mod Pathol 30:1299-1311. https://doi.org/10.1038/modpathol.2017.46

    Article  PubMed  Google Scholar 

  29. [29] Choi HJ, Park KJ, Shin JS, Roh MS, Kwon HC, Lee HS (2007) Tumor budding as a prognostic marker in stage-III rectal carcinoma. Int J Colorectal Dis 22:863-868. https://doi.org/10.1007/s00384-006-0249-8

    Article  PubMed  Google Scholar 

  30. [30] Guzińska-Ustymowicz K (2005) The role of tumour budding at the front of invasion and recurrence of rectal carcinoma. Anticancer Res 25:1269-1272.

    PubMed  Google Scholar 

  31. [31] Syk E, Lenander C, Nilsson PJ, Rubio CA, Glimelius B (2011) Tumour budding correlates with local recurrence of rectal cancer. Colorectal Dis 13:255-262. https://doi.org/10.1111/j.1463-1318.2009.02119.x

    Article  CAS  PubMed  Google Scholar 

  32. [32] Okuyama T, Nakamura T, Yamaguchi M (2003) Budding is useful to select high-risk patients in stage II well-differentiated or moderately differentiated colon adenocarcinoma. Dis Colon Rectum 46:1400-1406. https://doi.org/10.1007/s10350-004-6757-0

    Article  PubMed  Google Scholar 

  33. [33] Price WN, 2nd, Cohen IG (2019) Privacy in the age of medical big data. Nat Med 25:37-43. https://doi.org/10.1038/s41591-018-0272-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. [34] De Smedt L, Palmans S, Sagaert X (2016) Tumour budding in colorectal cancer: what do we know and what can we do? Virchows Arch 468:397-408. https://doi.org/10.1007/s00428-015-1886-5

    Article  PubMed  Google Scholar 

  35. [35] Koelzer VH, Zlobec I, Lugli A (2016) Tumor budding in colorectal cancer--ready for diagnostic practice? Hum Pathol 47:4-19. https://doi.org/10.1016/j.humpath.2015.08.007

    Article  PubMed  Google Scholar 

  36. [36] Koelzer VH, Zlobec I, Berger MD et al. (2015) Tumor budding in colorectal cancer revisited: results of a multicenter interobserver study. Virchows Arch 466:485-493. https://doi.org/10.1007/s00428-015-1740-9

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank our funders for coming up with the main ideas and designing the studies.

Funding

The present study was supported by the Project of the Action Plan of Major Diseases Prevention and Treatment (2017ZX01001-S12) and the Special Project of Integrated Traditional Chinese and Western Medicine in General Hospitals of Shanghai (ZHYY-ZXYJHZX-201901). They provide the main ideas and design the studies.

Author information

Author notes

  1. Zhihui Li, Fangying Chen and Shaoting Zhang have contributed equally to this work as co-first authors.

  2. Fu Shen and Yong Lu have contributed equally to the work.

Authors and Affiliations

  1. Department of Radiology, Ruijin Hospital Luwan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Zhihui Li

  2. Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, China

    Fangying Chen, Shaoting Zhang, Xiaolu Ma, Fu Shen & Chengwei Shao

  3. Huiying Medical Technology Co., Ltd, Beijing, China

    Yuwei Xia

  4. Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Yong Lu

Authors
  1. Zhihui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaoting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaolu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuwei Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fu Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chengwei Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZL performed segmentation and is a major contributor in writing the manuscript. FC performed segmentation and analyzed and interpreted the patient data regarding radiomics features. SZ analyzed and interpreted the patient data regarding radiomics features. XM acquired the data. YX performed statistical analysis. FS conceived the project. YL conceived the project. CS conceived the project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fu Shen.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

The present study was approved by the Biomedicine Ethics Review Committees of Ruijin Hospital and Changhai Hospital.

Consent to participate

Informed consent was waived.

Consent for publication

Not applicable.

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.

261_2021_3311_MOESM1_ESM.docx

Supplementary file1—Supplemental Table 1. Detailed parameters applied for T2WI, T1WI, and DWI and used for radiomics model building. (DOCX 18 kb)

Supplementary file2—Supplemental Table 2. Selected radiomics features for different sequences. (DOCX 17 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Chen, F., Zhang, S. et al. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer. Abdom Radiol 47, 56–65 (2022). https://doi.org/10.1007/s00261-021-03311-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-021-03311-5

Keywords

Search

Navigation