Assessment of MRI-Based Radiomics in Preoperative T Staging of Rectal Cancer: Comparison between Minimum and Maximum Delineation Methods

The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T s...

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Bibliographic Details
Published in:BioMed research international 2021, Vol.2021, p.1-9
Main Authors: Lu, Haidi, Yuan, Yuan, Zhou, Zhen, Ma, Xiaolu, Shen, Fu, Xia, Yuwei, Lu, Jianping
Format: Article
Language:English
Subjects:
Algorithms
Cancer
Colorectal cancer
Correlation coefficient
Correlation coefficients
Decision analysis
Delineation
Diagnosis
Feature extraction
Lesions
Magnetic resonance imaging
Methods
Patients
Prediction models
Preoperative care
Radiomics
Rectum
Segmentation
Software
Stability
Statistical analysis
Support vector machines
Tumor staging
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Summary:The manual delineation of the lesion is mainly used as a conventional segmentation method, but it is subjective and has poor stability and repeatability. The purpose of this study is to validate the effect of a radiomics model based on MRI derived from two delineation methods in the preoperative T staging of patients with rectal cancer (RC). A total of 454 consecutive patients with pathologically confirmed RC who underwent preoperative MRI between January 2018 and December 2019 were retrospectively analyzed. RC patients were grouped according to whether the muscularis propria was penetrated. Two radiologists segmented lesions, respectively, by minimum delineation (Method 1) and maximum delineation (Method 2), after which radiomics features were extracted. Inter- and intraclass correlation coefficient (ICC) of all features was evaluated. After feature reduction, the support vector machine (SVM) was trained to build a prediction model. The diagnostic performances of models were determined by receiver operating characteristic (ROC) curves. Then, the areas under the curve (AUCs) were compared by the DeLong test. Decision curve analysis (DCA) was performed to evaluate clinical benefit. Finally, 317 patients were assessed, including 152 cases in the training set and 165 cases in the validation set. Moreover, 1288/1409 (91.4%) features of Method 1 and 1273/1409 (90.3%) features of Method 2 had good robustness (P
ISSN:2314-6133
2314-6141
DOI:10.1155/2021/5566885