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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

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate whether pretreatment MRI-based radiomics of locally advanced rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict pathologic complete response (pCR), neoadjuvant rectal (NAR) score, and tumor regression grade (TRG).

Methods

One hundred thirty-two consecutive patients with LARC who underwent neoadjuvant chemoradiation and total mesorectal excision (TME) were retrospectively collected from 2 centers in the USA and Italy. The primary tumor and surrounding MC were segmented on the best available T2-weighted sequence (axial, coronal, or sagittal). Three thousand one hundred ninety radiomic features were extracted using a python package. The most salient radiomic features as well as MRI parameter and clinical-based features were selected using recursive feature elimination. A logistic regression classifier was built to distinguish between any 2 binned categories in the considered endpoints: pCR, NAR, and TRG. Repeated k-fold validation was performed and AUCs calculated.

Results

There were 24, 87, and 21 T4, T3, and T2 LARCs, respectively (median age 63 years, 32 to 86). For NAR and TRG, the best classification performance was obtained using both the tumor and MC segmentations. The AUCs for classifying NAR 0 versus 2, pCR, and TRG 0/1 versus 2/3 were 0.66 (95% CI, 0.60–0.71), 0.80 (95% CI, 0.74–0.85), and 0.80 (95% CI, 0.77–0.82), respectively.

Conclusion

Radiomics of pretreatment MRIs can predict pCR, TRG, and NAR score in patients with LARC undergoing neoadjuvant treatment and TME with moderate accuracy despite extremely heterogenous image data. Both the tumor and MC contain important prognostic information.

Key Points

• Machine learning of rectal cancer on images from the pretreatment MRI can predict important patient outcomes with moderate accuracy.

• The tumor and the tissue around it both contain important prognostic information.

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Abbreviations

AJCC:

American Joint Commission on Cancer

CEA:

Carcinoembryonic antigen

CRM:

Circumferential resection margin

cT:

Clinical T stage

DFS:

Disease-free survival

LARC:

Locally advanced rectal cancer

MC:

Mesorectal compartment

mrTRG:

MRI tumor regression grade

NAR:

Neoadjuvant rectal (score)

OS:

Overall survival

pCR:

Pathologic complete response

TME:

Total mesorectal excision

TRG:

Tumor regression grade

ypN:

Posttreatment pathologic N stage

ypT:

Posttreatment pathologic T stage

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10016, USA

    Hiram Shaish, Sachin Jambawalikar, Jasnit Makkar, Stuart Bentley-Hibbert & Lawrence Schwartz

  2. Department of Pathology, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10016, USA

    Andrew Aukerman, Rami Vanguri, Armando Del Portillo & Kevin L Gardner

  3. Department of Biomedical Sciences, Humanitas University, Via Manzoni, 113 20089, Rozzano, Milano, Italy

    Antonino Spinelli & Margarita Kirienko

  4. Division Colon and Rectal Surgery Unit, Humanitas Clinical and Research Center – IRCCS –, Via Manzoni, 56 20089, Rozzano, Milano, Italy

    Antonino Spinelli & Lara Monti

  5. New York, NY, USA

    Paul Armenta

  6. Department of Surgery, Columbia University Medical Center, 630 West 168th Street, New York, NY, 10016, USA

    Ravi Kiran & Deborah Keller

  7. Division of Radiology, Humanitas Clinical and Research Center, Via Manzoni, 56 20089, Rozzano, Milano, Italy

    Christiana Bonifacio

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  1. Hiram Shaish
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Corresponding author

Correspondence to Hiram Shaish.

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The scientific guarantor of this publication is Hiram Shaish, M.D.

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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

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• Retrospective

• diagnostic or prognostic study

• multicenter study

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Shaish, H., Aukerman, A., Vanguri, R. 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 30, 6263–6273 (2020). https://doi.org/10.1007/s00330-020-06968-6

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