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Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics

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Abstract

Purpose

To assess the role of pretreatment multiparametric (mp)MRI-based radiomic features in predicting pathologic complete response (pCR) of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiation therapy (nCRT).

Methods

This was a retrospective dual-center study including 98 patients (M/F 77/21, mean age 60 years) with LARC who underwent pretreatment mpMRI followed by nCRT and total mesorectal excision or watch and wait. Fifty-eight patients from institution 1 constituted the training set and 40 from institution 2 the validation set. Manual segmentation using volumes of interest was performed on T1WI pre-/post-contrast, T2WI and diffusion-weighted imaging (DWI) sequences. Demographic information and serum carcinoembryonic antigen (CEA) levels were collected. Shape, 1st and 2nd order radiomic features were extracted and entered in models based on principal component analysis used to predict pCR. The best model was obtained using a k-fold cross-validation method on the training set, and AUC, sensitivity and specificity for prediction of pCR were calculated on the validation set.

Results

Stage distribution was T3 (n = 79) or T4 (n = 19). Overall, 16 (16.3%) patients achieved pCR. Demographics, MRI TNM stage, and CEA were not predictive of pCR (p range 0.59–0.96), while several radiomic models achieved high diagnostic performance for prediction of pCR (in the validation set), with AUCs ranging from 0.7 to 0.9, with the best model based on high b-value DWI demonstrating AUC of 0.9 [95% confidence intervals: 0.67, 1], sensitivity of 100% [100%, 100%], and specificity of 81% [66%, 96%].

Conclusion

Radiomic models obtained from pre-treatment MRI show good to excellent performance for the prediction of pCR in patients with LARC, superior to clinical parameters and CEA. A larger study is needed for confirmation of these results.

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Data availability

Codes used to obtain radiomic models are available upon request.

Abbreviations

DWI:

Diffusion-weighted imaging

LARC:

Locally advanced rectal cancer

MLP:

Multilayer perceptron

mpMRI:

Multiparametric magnetic resonance imaging

nCRT:

Neoadjuvant chemoradiation therapy

PCA:

Principal component analysis

pCR:

Pathologic complete response

T1WI:

T1-weighted imaging

T2WI:

T2-weighted imaging

TRG:

Tumor regression grade

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Acknowledgments

The authors thank Joanne Chin, MFA, ELS, for editing the manuscript.

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Authors and Affiliations

  1. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA

    Maria El Homsi & Bachir Taouli

  2. Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York av, New York, USA

    Maria El Homsi

  3. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Octavia Bane, Valentin Fauveau, Stefanie Hectors, Naik Vietti Violi, Jordan Cuevas, Guillermo Carbonell & Bachir Taouli

  4. Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland

    Naik Vietti Violi

  5. Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Patricia Sylla

  6. Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Huai-Bin Ko

  7. Department of Pathology, Columbia University Medical Center, New York, NY, USA

    Huai-Bin Ko

  8. Department of Radiology, Virgen de la Arrixaca University Clinical Hospital, University of Murcia, Murcia, Spain

    Guillermo Carbonell

  9. Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Anthony Nehlsen

  10. Department of Epidemiology & Biostatistics, Columbia University Medical Center, New York, NY, USA

    Rami Vanguri

  11. Department of Radiology, Case Western University, Cleveland, OH, USA

    Satish Viswanath

  12. Department of Radiology, Columbia University Medical Center, New York, NY, USA

    Sachin Jambawalikar & Hiram Shaish

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  1. Maria El Homsi
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El Homsi, M., Bane, O., Fauveau, V. et al. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics. Abdom Radiol 49, 791–800 (2024). https://doi.org/10.1007/s00261-023-04128-0

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