A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer

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.

A clinical‐radiomics model incorporating T2‐weighted and diffusion‐weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer

“…Liu et al first presents a radiomic nomogram in CRC that incorporating the radiomic signature, CT‐reported lymph node status, and clinical risk factors, which can be conveniently used to facilitate the individualized preoperative prediction of lymph node metastasis 15 . Subsequently, several radiomic studies based on CT evaluation have been shown to predict microsatellite instability and potential prognosis in CRC 43–46 . Recently, a small number of MRI‐based radiomic studies have been reported that analysis of multiple image phases' features could potentially be used for response prediction in CRC 47…”

“…15 Subsequently, several radiomic studies based on CT evaluation have been shown to predict microsatellite instability and potential prognosis in CRC. [43][44][45][46] Recently, a small number of MRI-based radiomic studies have been reported that analysis of multiple image phases' features could potentially be used for response prediction in CRC. 47 In the absence of biological rationale, the black box properties of "omics" methods seriously hinder its widespread use and make verification particularly difficult.…”

Repeatability of Radiomics Studies in Colorectal Cancer: A Systematic Review

“…Of the 31 CT-based imaging studies, except for 5 [25,[45][46][47][48] that did not provide scanner parameters, the other studies neither used consistent scanning parameters nor assessed the impact of scanner differences on feature repeatability. Therefore, it could not be ruled out that scanner differences do not affect the results of these studies.…”

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study

“…This technique uses intensity distribution (texture analysis) of pixel or voxel gray levels and pixel/voxel inter-connections within a region or volume of interest (e.g., tumor, lymph node) to extract these variables. In patients with cancer, first-order histogram variables (e.g., tumor shape, heterogeneity, uniformity) and second-order texture variables (e.g., Gray Level Co-occurrence Matrix [GLCM], Gray Level Dependence Matrix [GLDM]) can be used to characterize tumors [ 10 – 12 ] and have been correlated with tumor aggressiveness [ 13 ] and prognosis [ 14 ]. Machine learning models study pre-input samples with known labels (known as training data) and identify patterns from which they learn a general rule that maps inputs to outputs [ 15 ].…”