Catalyzing early ovarian cancer detection: Platelet RNA-based precision screening.

Early detection of ovarian cancer is crucial for successful treatment, yet most cases are diagnosed at advanced stages due to a lack of effective screening. Recent advancements in RNA technology from platelets aid in early tumor detection. Here, we proposed our two-step method for assessing the existence of pelvic mass either located at ovaries or uterus with more than 99% specificity by utilizing exon-exon junction features with a sampling invariant normalization technique; then next our model finds the malignancy of detected mass with more than 99% negative predictive value for ovarian cancer to practically assist clinicians' further investigation via combined features of exon-exon junctions, and hematology parameters. We diverged from traditional methods by employing intron-spanning reads (ISR) counts rather than gene expression levels to use splice junctions as features in our models. If integrated with current screening methods, our algorithm holds promise for identifying ovarian or endometrial cancer in its early stages.