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Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer
- Source :
- Cancers, Volume 13, Issue 6, Cancers, Vol 13, Iss 1406, p 1406 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10−3 mm2/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10−2), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p &lt<br />0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.
- Subjects :
- Cancer Research
medicine.medical_treatment
diffusion-weighted imaging
Machine learning
computer.software_genre
lcsh:RC254-282
Article
Standard deviation
magnetic resonance
03 medical and health sciences
0302 clinical medicine
medicine
Effective diffusion coefficient
Cutoff
metastasis
030219 obstetrics & reproductive medicine
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Endometrial cancer
computer-aided
Magnetic resonance imaging
lymph node
medicine.disease
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
machine learning
Oncology
radiomics
030220 oncology & carcinogenesis
endometrial cancer
Lymphadenectomy
Artificial intelligence
business
computer
Diffusion MRI
Subjects
Details
- Language :
- English
- ISSN :
- 20726694
- Database :
- OpenAIRE
- Journal :
- Cancers
- Accession number :
- edsair.doi.dedup.....cbbfb3b9836c88978dab1c8999d46b89
- Full Text :
- https://doi.org/10.3390/cancers13061406