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Multicenter Study
. 2022 Sep;47(9):3251-3263.
doi: 10.1007/s00261-022-03620-3. Epub 2022 Aug 12.

Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study

Qiaoling Chen #  1 , Yanfen Cui #  2 , Ting Xue  1 , Hui Peng  1 , Manman Li  1 , Xinghua Zhu  3 , Shaofeng Duan  4 , Hongmei Gu #  5 , Feng Feng #  6
Affiliations
Multicenter Study

Computed tomography-based radiomics nomogram for the preoperative prediction of perineural invasion in colorectal cancer: a multicentre study

Qiaoling Chen et al. Abdom Radiol (NY). 2022 Sep.

Abstract

Purpose: To develop and validate a computed tomography (CT) radiomics nomogram from multicentre datasets for preoperative prediction of perineural invasion (PNI) in colorectal cancer.

Methods: A total of 299 patients with histologically confirmed colorectal cancer from three hospitals were enrolled in this retrospective study. Radiomic features were extracted from the whole tumour volume. The least absolute shrinkage and selection operator logistic regression was applied for feature selection and radiomics signature construction. Finally, a radiomics nomogram combining the radiomics score and clinical predictors was established. The receiver operating characteristic curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomics nomogram in the training cohort, internal validation and external validation cohorts.

Results: Twelve radiomics features extracted from the whole tumour volume were used to construct the radiomics model. The area under the curve (AUC) values of the radiomics model in the training cohort, internal validation cohort, external validation cohort 1, and external validation cohort 2 were 0.82 (0.75-0.90), 0.77 (0.62-0.92), 0.71 (0.56-0.85), and 0.73 (0.60-0.85), respectively. The nomogram, which combined the radiomics score with T category and N category by CT, yielded better performance in the training cohort (AUC = 0.88), internal validation cohort (AUC = 0.80), external validation cohort 1 (AUC = 0.75), and external validation cohort 2 (AUC = 0.76). DCA confirmed the clinical utility of the nomogram.

Conclusions: The CT-based radiomics nomogram has the potential to accurately predict PNI in patients with colorectal cancer.

Keywords: Colorectal cancer; Computed tomography; Nomogram; Perineural invasion; Radiomics.

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