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Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis
- Source :
- Academic Radiology. 26:1495-1504
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Rationale and Objectives To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. Materials and Methods This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method. Five-fold cross-validation and two machine learning algorithms (support vector machine [SVM]; logistic regression [LR]) were utilized for predictive model constructing. The diagnostic performance of the models was evaluated by receiver operating characteristic curves with indicators of accuracy, sensitivity, specificity and area under the curve, and compared by DeLong test. Results Five, 8, and 22 optimal features were selected from 1029 T2WI, 1029 VP, and 2058 combining features, respectively. Four-group models were constructed using the five T2WI features (ModelT2), the 8 VP features (ModelVP), the combined 13 optimal features (Modelcombined), and the 22 optimal features selected from 2058 features (Modeloptimal). In ModelVP, the LR was superior to the SVM algorithm (P = 0.0303). The Modeloptimal using LR algorithm showed the best prediction performance (P = 0.0019–0.0081) with accuracy, sensitivity, specificity, and area under the curve of 0.80, 0.83, 0.76, and 0.87, respectively. Conclusion Radiomics models based on baseline rectal magnetic resonance imaging has high potential for MLM prediction, especially the Modeloptimal using LR algorithm. Moreover, except for ModelVP, the LR was not superior to the SVM algorithm for model construction.
- Subjects :
- Male
Support Vector Machine
Computer science
Colorectal cancer
Feature selection
Adenocarcinoma
Logistic regression
Machine learning
computer.software_genre
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
medicine
Humans
Radiology, Nuclear Medicine and imaging
Sensitivity (control systems)
Neoplasm Metastasis
Retrospective Studies
medicine.diagnostic_test
Receiver operating characteristic
Rectal Neoplasms
business.industry
Liver Neoplasms
Rectum
Magnetic resonance imaging
Middle Aged
medicine.disease
Magnetic Resonance Imaging
Support vector machine
ROC Curve
Feature (computer vision)
030220 oncology & carcinogenesis
Female
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 10766332
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Academic Radiology
- Accession number :
- edsair.doi.dedup.....37a5c8f3374822d9714dc5679e71b925