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

We investigated (1) pregnancy and neonatal outcomes in women with and without disabilities, (2) time trends in deliveries, and (3) risks of pregnancy and neonatal complications among women with various disability types and severity. This was a nationwide population-based study merging the database of the Korea National Health Insurance claims, National Health Screening Program for Infants and Children, and Disability Registration System to compare perinatal outcomes in women with and without disabilities. Pregnancy and neonatal outcomes were analyzed during 2007 and 2015, as were time trends of deliveries. Multivariate logistic regression was used to evaluate risk of perinatal outcomes among women with various disability types and severities. Women with disabilities showed higher rates of cesarean section (aOR, 1.73; 95% CI, 1.69–1.77), hypertensive disorders (aOR, 1.74; 95% CI, 1.63–1.86), placenta abruption (aOR, 1.27; 95% CI, 1.12–1.45), placenta previa (aOR, 1.14; 95% CI, 1.05–1.24), stillbirths (aOR, 1.30; 95% CI, 1.17–1.45), preterm births (aOR, 1.67; 95% CI, 1.57–1.78), and LBW (aOR, 1.87; 95% CI, 1.78–1.97) than those without disabilities. From 2007 to 2015, although delivery rate in women with disabilities decreased steeply compared with that in women without disabilities, the rate of cesarean section increased in women with disabilities. Women with intellectual disability and those with vision impairment had the highest number of perinatal complications among women with various types of disabilities. Women with disability had more adverse pregnancy and neonatal outcomes than those without disabilities. Specific disability types & severities are more vulnerable to specific perinatal complications.

Despite the progress in diagnostics and therapeutics, epithelial ovarian cancer (EOC) remains a fatal disease. Using shallow whole-genome sequencing of plasma cell-free DNA (cfDNA), we investigated biomarkers that could detect EOC and predict survival. Plasma cfDNA from 40 EOC patients and 20 healthy subjects were analyzed by shallow whole-genome sequencing (WGS) to identify copy number variations (CNVs) and determine the Z-scores of genes. In addition, we also calculated the genome-wide scores (Gi scores) to quantify chromosomal instability. We found that the Gi scores could distinguish EOC patients from healthy subjects and identify various EOC histological subtypes (e.g., high-grade serous carcinoma). In addition, we characterized EOC CNVs and demonstrated a relationship between RAB25 amplification (alone or with CA125), and disease-free survival and overall survival. This study identified RAB25 amplification as a predictor of EOC patient survival. Moreover, we showed that Gi scores could detect EOC. These data demonstrated that cfDNA, detected by shallow WGS, represented a potential tool for diagnosing EOC and predicting its prognosis.

While ovarian cancer screening is not recommended in the general population, attention has shifted to screening women with elevated hereditary risks. Although germline BRCA 1/2 pathogenic variants account for 40% of inherited ovarian cancer risk and family history (FH) remains important, known germline variants alone do not fully explain familial ovarian cancer risk. Whole-exome sequencing (WES) was performed on blood samples taken from 231 individuals, including 39 patients with high-grade serous ovarian cancer (HGSOC) and 192 healthy controls (HCs) stratified by FH. We analyzed pathogenic or likely pathogenic (P/LP) germline variants in cancer-related genes and assessed their association with family cancer history. Additionally, we performed somatic variant comparisons using 1:4 propensity score matching and analyzed clonal hematopoiesis of indeterminate potential (CHIP)-related somatic variants. P/LP germline variants were detected in 56.4% of HGSOC patients, 49.4% of controls with FH, and 33.3% without. The HGSOC group and controls with FH exhibited similar P/LP germline mutation patterns in ovarian cancer-related genes. From CHIP analysis, somatic CHIP mutations were detected in 6.3% of the HGSOC group and 8.5% in HCs. Our findings demonstrate genomic overlap between ovarian cancer patients and FH-positive individuals. Therefore, germline variant screening could be considered to facilitate early diagnosis.