To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 $$\pm$$± 0.25, 0.76 $$\pm$$± 0.18, and 0.60 $$\pm$$± 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists’ assessment.

A predictive model for Corona Virus Disease 2019

A growing body of research has established a correlation between tumors and persistent chronic inflammatory infiltration. As a primary instigator of inflammation, the majority of microbiomes naturally residing within our bodies engage in a mutually beneficial symbiotic relationship. Nevertheless, alterations in the microbiome's composition or breaches in the normal barrier function can disrupt the internal environment's homeostasis, potentially leading to the development and progression of various diseases, including tumors. The investigation of tumor-related microbiomes has contributed to a deeper understanding of their role in tumorigenesis. This review offers a comprehensive overview of the microbiome alterations and the associated inflammatory changes in ovarian cancer. It may aid in advancing research to elucidate the mechanisms underlying the ovarian cancer-associated microbiome, providing potential theoretical support for the future development of microbiome-targeted antitumor therapies and early screening through convenient methods.

Background: Epithelial ovarian cancer (EOC) is insensitive to immunotherapy due to its poor immunogenicity; thus, suitable biomarkers need to be identified for better prognostic stratification and individualized treatment. CD47 is a novel immunotherapy target; however, its impact on EOC prognosis is controversial and correlation with genetic features is unclear. The aim of this study was to investigate the prognostic significance of CD47 and its correlations with biological behaviors and genetic features of EOC. Methods: Immunohistochemistry (IHC) and next-generation sequencing (NGS) were performed to examine expressions of CD47, PD-L1, and genomic mutations in the tissue samples of 75 EOC patients. Various clinicopathologic and genomic features were then evaluated to determine their correlation with CD47 expression. Kaplan–Meier analysis and Cox regression analysis were used to identify independent prognostic factors. Risk score modeling was then established, and the predictive capacity of this model was further confirmed by nomogram analysis. Results: CD47 was mainly expressed in the tumor cell membrane and cytoplasm, and the rate of high CD47 expression was 63.7%. CD47 expression was associated with various clinicopathological factors, including FIGO stage, CA125 and HE4 value, presence of multidisciplinary surgeries, presence and volume of ascites, lymph-node metastasis, Ki-67 index and platinum-resistant, as well as genetic characteristics like BRCA mutation, HRD status, and TP53 mutation in EOC. Patients with high CD47 expression showed worse prognosis than the low-expression group. Cox regression analysis demonstrated that CA125, CD47, and BRCA mutation were independent factors for EOC prognosis. Patients were then categorized into high-risk and low-risk subgroups based on the risk score of the aforementioned independent factors, and the prognosis of the high-risk group was worse than those of the low-risk group. The nomogram showed adequate discrimination with a concordance index of 0.777 (95% CI, 0.732–0.822). The calibration curve showed good consistency. Conclusion: CD47 correlated with various malignant biology and genetic characteristics of EOC and may play pivotal and multifaceted roles in the tumor microenvironment of EOC Finally, we constructed a reliable prediction model centered on CD47 and integrated CA125 and BRCA to better guide high-risk population management.

Highly differentiated follicular carcinoma (HDFCO) is a rare form of struma-derived thyroid-type carcinoma in ovary, defined as ovarian struma spreading beyond ovary but consisting of benign thyroid tissues. No more than 30 cases of HDFCO have been reported since it was first recognized in 2008. The clinicopathologic and molecular features of HDFCO remain unclear up till now. A 38-year-old, para 1 gravida 5 woman has a long history of recurrent right ovarian cysts. Histological evaluation showed the tumor progressed from ovarian mature cystic teratoma (OMCT) to highly differentiated follicular carcinoma (HDFCO) during three relapses. Whole-exome sequencing revealed the germline FGFR4 Gly388Arg polymorphism. Repeated operations were performed to remove lesions for the first two relapses. On the third recurrence, the patient received radical surgery with subsequent thyroidectomy and radioactive iodine ablation. No evidence of disease was observed by February 2022 (8 months). The germline FGFR4 Gly388Arg polymorphism may accelerate the malignant transformation of HDFCO, probably by working as a second hit in the developing spectrum.