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A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography - PubMed

  • ️Mon Jan 01 2024

A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography

Tairan Guo et al. BMC Cancer. 2024.

Abstract

Background: Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge.

Method: Pre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA).

Result: The total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively.

Conclusion: The study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.

Keywords: Colon cancer; PNI; Predictive model; Radiomics.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1

Workflow of the study

Fig. 2
Fig. 2

The process of model based on radiomics construction

Fig. 3
Fig. 3

LASSO regression analysis for selection of radiomics features. A λ called penalty factor was gotten after tenfold cross-validation. λ minimum and λ 1-SE were selected to sign the dotted vertical line in the plot. B LASSO coefficient profile for predicting PNI in stage II-III colon cancer

Fig. 4
Fig. 4

Data of LASSO-SVM radiomics model. A ROC of LASSO-SVM radiomics model of development cohort and validation cohort, respectively. B Radio-score in stage II-III colon cancer with or without PNI. C Radio-score in high or low risk of PNI in stage II-III colon cancer

Fig. 5
Fig. 5

ROC and decision curve of radiomics-clinical and radiomics models in development and validation cohort. ROC of development cohort (A) and validation cohort (B) of 2 models. Decision curve analysis of development cohort (C) and validation cohort (D) of 2 models

Fig. 6
Fig. 6

Nomogram of the radiomics-clinical model

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