We examined whether automated visual evaluation (AVE), a deep learning computer application for cervical cancer screening, can be used on cervix images taken by a contemporary smartphone camera. A large number of cervix images acquired by the commercial MobileODT EVA system were filtered for acceptable visual quality and then 7587 filtered images from 3221 women were annotated by a group of gynecologic oncologists (so the gold standard is an expert impression, not histopathology). We tested and analyzed on multiple random splits of the images using two deep learning, object detection networks. For all the receiver operating characteristics curves, the area under the curve values for the discrimination of the most likely precancer cases from least likely cases (most likely controls) were above 0.90. These results showed that AVE can classify cervix images with confidence scores that are strongly associated with expert evaluations of severity for the same images. The results on a small subset of images that have histopathologic diagnoses further supported the capability of AVE for predicting cervical precancer. We examined the associations of AVE severity score with gynecologic oncologist impression at all regions where we had a sufficient number of cases and controls, and the influence of a woman's age. The method was found generally resilient to regional variation in the appearance of the cervix. This work suggests that using AVE on smartphones could be a useful adjunct to health‐worker visual assessment with acetic acid, a cervical cancer screening method commonly used in low‐ and middle‐resource settings.
Women with stage I endometrioid endometrial cancer with synchronous stage I endometrioid ovarian cancer have a survival outcome similar to those with stage I endometrioid endometrial cancer without synchronous ovarian cancer.
The cytoskeletal interacting protein Septin 9 (SEPT9), a member of the septin gene family, has been proposed to have oncogenic functions. It is a known hot spot of retroviral tagging insertion and a fusion partner of both de novo and therapy-induced mixed lineage leukemia (MLL). Of all septins, SEPT9 holds the strongest link to cancer, especially breast cancer. Murine models of breast cancer frequently exhibit Sept9 amplification in the form of double minute chromosomes, and about 20% of human breast cancer display genomic amplification and protein over expression at the SEPT9 locus. Yet, a clear mechanism by which SEPT9 elicits tumor-promoting functions is lacking.
To obtain unbiased insights on molecular signatures of SEPT9 upregulation in breast tumors, we overexpressed several of its isoforms in breast cancer cell lines. Global transcriptomic profiling supports a role of SEPT9 in invasion. Functional studies reveal that SEPT9 upregulation is sufficient to increase degradation of the extracellular matrix, while SEPT9 downregulation inhibits this process. The degradation pattern is peripheral and associated with focal adhesions (FA), where it is coupled with increased expression of matrix metalloproteinases. SEPT9 overexpression induces MMP upregulation in human tumors and in culture models and promotes MMP3 secretion to the media at FAs. Downregulation of SEPT9 or chemical inhibition of septin filament assembly impairs recruitment of MMP3 to FAs. Our results indicate that SEPT9 promotes upregulation and both trafficking and secretion of MMPs near FAs, thus enhancing migration and invasion of breast cancer cells.
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