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Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer

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Abstract

Purpose

To investigate whether pretreatment magnetic resonance (MR)-based radiomics nomogram can individualize prediction of perineural invasion (PNI) status in rectal cancer (RC).

Material and methods

A total of 122 RC patients with pathologically confirmed were classified as training cohort (n = 87) and test cohort (n = 35). 180 radiomics features were extracted from all lesions based on oblique axial T2WI TSE images. The dimensionality reduction and feature selection in training cohort were realized by the maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression model. A predictive model combining radiomics features and clinical risk factors (pathological N stage, pathological LVI status) was established by multivariate logistic regression analysis. The performance of the model was assessed based on its receiver operating characteristic (ROC) curve, nomogram, and calibration.

Results

The developed radiomics nomogram that integrated the radiomics signature and clinical risk factors could provide discrimination in the training and test cohorts. The accuracy and the area under the curve (AUC) for assessing PNI status were 0.82, 0.86, respectively, in the training cohort, while they were 0.71 and 0.85 in the test cohort. The goodness-of-fit of the nomogram was evaluated using the Hosmer–Lemeshow test (p = 0.52 in training cohort and p = 0.24 in test cohort). Decision curve analysis (DCA) showed that the radiomics nomogram was clinically useful.

Conclusion

The developed radiomics nomogram might be helpful in the individualized assessment PNI status in patients with RC. This stratification of RC patients according to their PNI status may provide the basis for individualized adjuvant therapy, especially for stage II patients.

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Abbreviations

AUC:

Area under curve

CI:

Confidence interval

RC:

Rectal cancer

LVI:

Lymphovascular invasion

PNI:

Perineural invasion

MR:

Magnetic resonance

RLM:

Run-length matrix

GLCM:

Gray-level co-occurrence matrix

GLZSM:

Parameters and Gray-Level Size Zone Matrix

PACS:

Picture archiving and communication system

ROC:

Receiver operating characteristic

AJCC:

American Joint Commission Cancer

mRMR:

Maximum relevance minimum redundancy

LASSO:

Least absolute shrinkage and selection operator

DCA:

Decision curve analysis

NCCN:

National comprehensive cancer network

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Authors and Affiliations

  1. Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China

    Jiayou Chen, Ying Chen, Dechun Zheng, Xiang Zheng & Jiang Liao

  2. GE Healthcare (China), Hangzhou, China

    Peipei Pang

  3. Department of Pathology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, 350014, Fujian, China

    Hejun Zhang

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  1. Jiayou Chen
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  2. Ying Chen
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  3. Dechun Zheng
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Correspondence to Jiayou Chen.

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Chen, J., Chen, Y., Zheng, D. et al. Pretreatment MR-based radiomics nomogram as potential imaging biomarker for individualized assessment of perineural invasion status in rectal cancer. Abdom Radiol 46, 847–857 (2021). https://doi.org/10.1007/s00261-020-02710-4

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