Published online Sep 7, 2020. doi: 10.3748/wjg.v26.i33.5008
Peer-review started: May 27, 2020
First decision: June 4, 2020
Revised: June 16, 2020
Accepted: August 13, 2020
Article in press: August 13, 2020
Published online: September 7, 2020
Rectal cancer (RC) patient stratification by different factors may yield variable results. Therefore, more efficient prognostic biomarkers are needed for improved risk stratification, personalized treatment, and prognostication of RC patients.
In up to 70% of patients with RC, surgical removal of the primary tumor is successful. However, local recurrence and distant metastases are commonly detected in approximately 30% of RC patients, often within 3 years after surgery. Therefore, it is necessary to identify patients who may be at a higher risk of developing adverse outcomes post-surgery. In these patients, alternative and adjunctive therapies, such as chemotherapy, radiotherapy, or other targeted therapies, may be needed to minimize the risk of developing distant metastases.
To build a novel clinicoradiologic model for predicting the presence of distant metastases and 3-year overall survival (OS) rates in RC patients.
This was a retrospective analysis of 148 patients (76 males and 72 females) with RC treated with curative resection, without neoadjuvant or postoperative chemoradiotherapy, between October 2012 and December 2015. These patients were allocated to a training or validation set, with a ratio of 7:3. Radiomic features were extracted from portal venous phase computed tomography images of RC. The least absolute shrinkage and selection operator regression analysis was used for feature selection. Multivariate logistic regression analysis was used to develop the Rad-score and the combined model. Receiver operating characteristic curves were constructed to evaluate the diagnostic performance of the models for predicting distant metastasis of RC. The association of the combined model with 3-year OS was investigated by Kaplan-Meier survival analysis.
A total of 51 (34.5%) patients had distant metastases, while 26 (17.6%) patients died, and 122 (82.4%) patients lived at least 3 years post-surgery. The values of both the Rad-score and the combined model were significantly different between the distant metastasis group and the non-metastasis group (0.46 ± 0.21 vs 0.32 ± 0.24 for the Rad-score, 0.60 ± 0.23 vs 0.28 ± 0.26 for the combined model; P < 0.001 for both models). Predictors contained in the combined model included the Rad-score, pathological N-stage, and T-stage. The combined model showed good discrimination, with an area under the curve of 0.842 and 0.802 for the training set and validation set, respectively. For the survival analysis, the combined model was associated with an improved OS in the whole cohort and the respective subgroups.
This study presents a novel model, visualized in a nomogram, that can be used to facilitate individualized prediction of distant metastasis and 3-year OS in patients with RC.
Radiomics may change the practice of medicine, particularly for patients with RC. However, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design, and the lack of robust multicenter validation set. Therefore, prospective multicenter studies of a larger size are needed to externally validate our proposed model in the future.