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Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma
- Source :
- BMC Cancer, Vol 21, Iss 1, Pp 1-13 (2021), BMC Cancer
- Publication Year :
- 2021
- Publisher :
- BMC, 2021.
-
Abstract
- Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.
- Subjects :
- Adult
Male
Cancer Research
Lymphatic metastasis
Feature selection
Adenocarcinoma
Machine learning
computer.software_genre
Sensitivity and Specificity
Extrahepatic cholangiocarcinoma
Cholangiocarcinoma
Predictive Value of Tests
Genetics
Cell differentiation
Medicine
Effective diffusion coefficient
Humans
RC254-282
Aged
Retrospective Studies
Aged, 80 and over
Radiomics
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Magnetic resonance imaging
Mutual information
Middle Aged
Magnetic Resonance Imaging
Statistical classification
Oncology
Bile Duct Neoplasms
ROC Curve
Area Under Curve
Female
Artificial intelligence
Neoplasm Grading
business
Precision and recall
computer
Algorithms
Diffusion MRI
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 14712407
- Volume :
- 21
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- BMC Cancer
- Accession number :
- edsair.doi.dedup.....975fb9f8052eee0fc8953619dd59d72e