AI Article Synopsis
- Ovarian cancer is deadly but rare, highlighting the need for better screening methods and early detection.
- A study developed a serum-based biomarker panel and algorithm to accurately detect ovarian cancer, achieving an impressive area under the curve (AUC) of 0.98.
- The CPBA Ov algorithm demonstrated 100% sensitivity and 83.7% specificity in a small sample of patients, outperforming existing detection methods like CA-125 and the Risk of Ovarian Malignancy Algorithm.
Article Abstract
Ovarian cancer is often fatal and incidence in the general population is low, underscoring the necessity (and the challenges) for advancements in screening and early detection. The goal of this study was to design a serum-based biomarker panel and corresponding multivariate algorithm that can be used to accurately detect ovarian cancer. A combinatorial protein biomarker assay (CPBA) that uses CA125, HE4, and 3 tumor-associated autoantibodies resulted in an area under the curve of 0.98. The CPBA Ov algorithm was trained using subjects who were suspected to have gynecological cancer and were scheduled for surgery. As a surgical rule-out test, the clinical performance achieves 100% sensitivity and 83.7% specificity. Although sample size (n = 60) is a limiting factor, the CPBA Ov algorithm performed better than either CA-125 alone or the Risk of Ovarian Malignancy Algorithm.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8842374 | PMC |
| http://dx.doi.org/10.1177/1179299X18756646 | DOI Listing |
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