Abstract
Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC).Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns.Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26–14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05–35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14–13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22–22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69–0.80) in the primary cohort and 0.82 (0.77–0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways.Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.
Highlights
Colorectal cancer (CRC) is one of the most common cancers and ranks as the third cause of cancer-related mortality worldwide (Siegel et al, 2020)
We aimed to develop and validate a radiomicsbased nomogram to predict the postoperative outcome of colorectal cancer patients
The results showed that the area under curve (AUC) values of Rad-score incorporating the TNM stage reached 0.734 and 0.86 in the primary and validation cohort, respectively, outperforming the result of using the TNM stage alone (Figure 4)
Summary
Colorectal cancer (CRC) is one of the most common cancers and ranks as the third cause of cancer-related mortality worldwide (Siegel et al, 2020). The emergence of gene expressionbased molecular biomarkers has brought hope for the precision treatment of colorectal cancer in the past decade, but the high cost and long detection time limited its clinical application. The medical images, which were routinely detected in clinical practice, have emerged to be promising biomarkers for cancer treatment and management. By transforming medical images into high-dimensional quantitative feature data, radiomics have been successfully used in some medical researches, such as tumor genetic analysis, lesions qualitative, curative effect evaluation and prognosis prediction (Kumar et al, 2012; Lambin et al, 2017; Limkin et al, 2017). Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. We developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC)
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