Endometrial cysts (ECs) are thought to be the origin of endometriosis-associated ovarian cancer (EAOC). A hypothesis that the oxidative stress of iron in cysts causes “malignant transformation of ECs” has been proposed, but this has not been verified. Several population-based studies showed that endometriosis was a risk factor but did not reflect the “malignant transformation of ECs”. A review showed that most patients were diagnosed with EAOC early in monitoring following detection of ECs, and that these cases might have been cancer from the start. Epidemiologically, EAOC was reduced by hysterectomy rather than by cystectomy of ECs. Gene mutation analyses identified oncogenic mutations in endometriosis and normal endometrium and revealed that the same mutations were present at different endometriotic lesions. It was also shown that most of the gene mutations found in endometriosis occurred in normal endometrium. Taking together, EAOC might be caused by eutopic endometrial glandular epithelial cells with oncogenic mutations that have undergone menstrual blood reflux and engrafted in the ovary, rather than by low-risk ECs acquiring oncogenic mutations and becoming malignant. This review discusses the mechanisms of EAOC development and targeted therapy based on genetic variation in EAOC with a focus on eutopic endometrium.

MRI plays an essential role in patients before treatment for uterine mesenchymal malignancies. Although MRI includes methods such as diffusion-weighted imaging and dynamic contrast-enhanced MRI, the differentiation between uterine myoma and sarcoma always becomes problematic. The present paper discusses important findings to ensure that sarcomas are not overlooked in magnetic resonance (MR) images, and we describe the update in the differentiation between uterine leiomyoma and sarcoma with recent reports.

Background and Objectives: Maternal brain tumors diagnosed during pregnancy are very rare, and their clinical course remains incompletely understood. We recently experienced a case of a brain tumor diagnosed at 30 weeks of gestation, and the treatment was initiated after delivery at 32 weeks of gestation. In this study, we reviewed case reports of brain tumors diagnosed during pregnancy, focusing on whether the brain tumor was treated during pregnancy or after termination of pregnancy and on the timing of therapeutic intervention. Materials and Methods: We searched PubMed and Ichushi-Web for articles published after January 2000 that reported cases of maternal brain tumors diagnosed during pregnancy. The patients were divided into two groups according to whether the tumor was treated during pregnancy (Group A) or after termination of pregnancy (Group B). Results: In total, 42 patients were included in the study (13 (31%) in Group A and 29 (69%) in Group B). The most common symptoms before diagnosis were those caused by increased intracranial pressure (57.1%). The diagnosis was made at 18 ± 6 weeks of gestation in Group A and 26 ± 9 weeks of gestation in Group B (p = 0.007). In all cases diagnosed after 34 weeks of gestation, termination of pregnancy was followed by treatment. Treatment was initiated within two weeks of diagnosis in 50% of patients in Group A and 30% in Group B. Conclusions: When severe symptoms caused by increased intracranial pressure last for several weeks, imaging tests should be considered. Termination of pregnancy is a good option for a brain tumor diagnosed after 34 weeks of gestation, while comprehensive treatment decisions should be made based on the severity of symptoms and the course of pregnancy in other cases.

Recent studies have reported cancer-associated mutations in normal endometrium. Mutations in eutopic endometrium may lead to endometriosis and endometriosis-associated ovarian cancer. We investigated PIK3CA mutations (PIK3CAm) for three hotspots (E542K, E545K, H1047R) in eutopic endometrium in patients with ovarian cancer and endometriosis from formalin-fixed paraffin-embedded specimens by laser-capture microdissection and droplet digital PCR. The presence of PIK3CAm in eutopic endometrial glands with mutant allele frequency ≥ 15% were as follows: ovarian clear cell carcinoma (OCCC) with PIK3CAm in tumors, 20/300 hotspots in 11/14 cases; OCCC without PIK3CAm, 42/78 hotspots in 11/12 cases; high-grade serous ovarian carcinoma, 8/45 hotspots in 3/5 cases; and endometriotic cysts, 5/63 hotspots in 5/6 cases. These rates were more frequent than in noncancer nonendometriosis controls (7/309 hotspots in 5/17 cases). In OCCC without PIK3CAm, 7/12 (58%) cases showed multiple hotspot mutations in the same eutopic endometrial glands. In 3/54 (5.6%) cases, PIK3CAm was found in eutopic endometrial stroma. Multisampling of the OCCC tumors with PIK3CAm showed intratumor heterogeneity in three of eight cases. In two cases, PIK3CAm was detected in the stromal component of the tumor. Homogenous PIK3CAm in the epithelial component of the tumor matched the mutation in eutopic endometrial glands in only one case. Eutopic endometrial glands in ovarian cancer and endometriosis show high frequency of PIK3CAm that is not consistent with tumors, and multiple hotspot mutations are often found in the same glands. While the mutations identified in eutopic endometrium may not be driver mutations in the patient's cancer, these are still driver mutations but this specific clone has not undergone the requisite steps for the development of cancer.

Colposcopy is an essential examination tool to identify cervical intraepithelial neoplasia (CIN), a precancerous lesion of the uterine cervix, and to sample its tissues for histological examination. In colposcopy, gynecologists visually identify the lesion highlighted by applying an acetic acid solution to the cervix using a magnifying glass. This paper proposes a deep learning method to aid the colposcopic diagnosis of CIN by segmenting lesions. In this method, to segment the lesion effectively, the colposcopic images taken before acetic acid solution application were input to the deep learning network, U-Net, for lesion segmentation with the images taken following acetic acid solution application. We conducted experiments using 30 actual colposcopic images of acetowhite epithelium, one of the representative types of CIN. As a result, it was confirmed that accuracy, precision, and F1 scores, which were 0.894, 0.837, and 0.834, respectively, were significantly better when images taken before and after acetic acid solution application were used than when only images taken after acetic acid solution application were used (0.882, 0.823, and 0.823, respectively). This result indicates that the image taken before acetic acid solution application is helpful for accurately segmenting the CIN in deep learning.