Abstract
ObjectiveTo establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.MethodsThe clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves.ResultsThe radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801.ConclusionThe radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.
Highlights
Rectal cancer is one of the major causes of cancer-related mortality in the world, with a local recurrence rate of up to 30% related to the surgical technique [1]
Several studies have shown that magnetic resonance imaging (MRI) has medium to high sensitivity and specificity in detecting extramural venous invasion (EMVI) compared with pathological evaluation [7,8,9]
It should be noted that MRI may not be able to correctly identify the invasion of small extramural and intramural vessels, which leads to the low sensitivity of conventional MRI in the evaluation of EMVI [10, 11]
Summary
Rectal cancer is one of the major causes of cancer-related mortality in the world, with a local recurrence rate of up to 30% related to the surgical technique [1]. Local recurrence and metastasis are the main causes of death in patients with rectal cancer, and there is much evidence that extramural venous invasion (EMVI) is an independent predictor of local tumor recurrence, ectopic nodules, distant metastasis and overall mortality [2,3,4]. Due to the advantages of high spatial resolution, magnetic resonance imaging (MRI) is an excellent imaging method to detect the adverse prognostic factors of rectal cancer, and it is a promising and repeatable technique for the identification of EMVI. Several studies have shown that MRI has medium to high sensitivity and specificity in detecting EMVI compared with pathological evaluation [7,8,9]. The sensitivity of CT for identifying EMVI was low due to its low resolution in soft tissue [16]
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