Abstract
At the time of diagnosis, approximately 15%‐20% of patients with rectal cancer (RC) presented synchronous liver metastasis (SLM), which is the most common cause of death in patients with RC. Therefore, preoperative, noninvasive, and accurate prediction of SLM is crucial for personalized treatment strategies. Recently, radiomics has been considered as an advanced image analysis method to evaluate the neoplastic heterogeneity with respect to diagnosis of the tumor and prediction of prognosis. In this study, a total of 1409 radiomics features were extracted for each volume of interest (VOI) from high‐resolution T2WI images of the primary RC. Subsequently, five optimal radiomics features were selected based on the training set using the least absolute shrinkage and selection operator (LASSO) method to construct the radiomics signature. In addition, radiomics signature combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19‐9 (CA19‐9) was included in the multifactor logistic regression to construct the nomogram model. It showed an optimal predictive performance in the validation set as compared to that in the radiomics model. The favorable calibration of the radiomics nomogram showed a nonsignificant Hosmer‐Lemeshow test statistic (P > .05). The decision curve analysis (DCA) showed that the radiomics nomogram is clinically superior to the radiomics model. Therefore, the nomogram amalgamating the radiomics signature and clinical risk factors serve as an effective quantitative approach to predict the SLM of primary RC.
Highlights
Rectal cancer (RC) is one of the most commonly diagnosed malignant tumors worldwide
At the time of or before the diagnosis of the primary tumor, approximately 15%-20% of the patients were detected with liver metastases (LM), which is termed as synchronous liver metastasis (SLM).[1,2]
Our results demonstrated that the radiomics nomogram provided predictive information for SLM in the primary RC
Summary
Rectal cancer (RC) is one of the most commonly diagnosed malignant tumors worldwide. At the time of or before the diagnosis of the primary tumor, approximately 15%-20% of the patients were detected with liver metastases (LM), which is termed as synchronous liver metastasis (SLM).[1,2] SLM is the most common cause of death in patients with RC,[3] and the prognosis of patients with untreated SLM is poor.[4]. The preoperative prediction of RC patients with a high risk of SLM is essential for treatment strategies. Previous studies demonstrated the feasibility of clinicopathological features for evaluating the potential risk of SLM in RC patients.[8,9]. Since the radiomics analysis of images provides comprehensive quantification information than that by a physician, the quantitative and objective descriptions of neoplastic heterogeneity could serve as alternatives in clinical studies. The quantitative imaging traits are subjected to a selection procedure, for feature analysis, which supports the decision-making[10-12] with respect to the cancer stage and the prediction of prognosis.[13-15]. Another study demonstrated that the texture analysis of the features extracted from liver CT images predicted the different prognosis of colorectal cancer patients.[16]. Some studies demonstrated that radiomics model predicts distant metastasis in different primary tumors.[17-22]. The present study aimed to investigate the predictive performance of radiomics nomogram for the diagnosis of SLM in RC patients
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