An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer

This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed....

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Bibliographic Details
Published in:Journal of clinical medicine 2021-12, Vol.11 (1), p.229
Main Authors: Song, Heekyoung, Bak, Seongeun, Kim, Imhyeon, Woo, Jae Yeon, Cho, Eui Jin, Choi, Youn Jin, Rha, Sung Eun, Oh, Shin Ah, Youn, Seo Yeon, Lee, Sung Jong
Format: Article
Language:English
Subjects:
Chemotherapy
Clinical medicine
Endometrial cancer
Machine learning
Magnetic resonance imaging
Morphology
Ovarian cancer
Patients
Statistical analysis
Tumors
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Summary:This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADC ) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADC of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADC and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADC , disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm11010229