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.
Highlights
Ovarian cancer is the eighth most common cancer in women, with 313,959 new diagnoses worldwide in 2020 [1]
This study demonstrated that the ADCmean values of the solid components were significantly correlated with the histological types of epithelial ovarian cancer (EOC), for High-grade serous ovarian cancer (HGSOC) and nonHGSOCs, and for the five histological types of EOC (HGSOC, endometrioid carcinoma (EC), cell carcinoma (CCC), mucinous carcinoma (MC), low-grade serous ovarian carcinoma (LGSOC))
Zhang et al found that an increased mean apparent diffusion coefficient (ADC) value was more likely to suggest type I EOC (odds ratio (OR) = 16.80, p < 0.01); the study was limited by selection bias, as patients with borderline epithelial tumours comprised 51% of the study patients with type I ovarian cancer [24]
Summary
Ovarian cancer is the eighth most common cancer in women, with 313,959 new diagnoses worldwide in 2020 [1]. High-grade serous ovarian cancer (HGSOC) accounts for up to 80% of epithelial ovarian carcinomas (EOCs) [1,2,3]. HGSOCs are found at more advanced stages with multiple metastases and are characterised by P53 mutations with frequent genomic instability due to defects in the pathways contributing to DNA repair [4,5]. In non-HGSOC, mucinous carcinoma cancer is frequently diagnosed at an early stage [6]. Mucinous carcinoma is more difficult to surgically excise in advanced ovarian cancer than other histologic types of epithelial ovarian cancer [7,8]. Clearcell and mucinous carcinomas are characterised by resistance to carboplatin and paclitaxel chemotherapy [9,10,11]. The preoperative prediction of histological types could be useful when counselling patients regarding fertility-sparing surgery, extent of cytoreductive surgery, decision to undergo neoadjuvant chemotherapy, and survival outcomes
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