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

Abstract

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 (ADCmean) 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 ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean 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, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

“…More connection with the clinical setting is observed. In particular, the comparison of the CAD model to either assessment of scans by clinicians such as radiologists, sonographists or gynecologists or to commonly used models in ultrasound (RMI or LR1-2) is now included [ 33 , 35 – 38 , 48 , 51 , 60 , 61 , 68 ].…”

Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma

“…The study of Liu et al demonstrated that combining tumor morphology and enhancement degree from CECT/MRI imaging achieved a sensitivity of 61.36% [11]. Other studies indicated that MRI functional parameters had the sensitivity 76.0%-85.0% in identifying EOC subtypes [12,13]. Notably, despite high AUC 0.823-0.970 for CECT/MRI radiomics analysis in EOC subtype evaluation [14][15][16][17][18], satisfying results in addressing methodological quality challenges remain elusive [19].…”