Endometrial cancer: predictive lymph node metastasis model

A study by Berg and others at the University of Berg in Norway demonstrated that data-driven predictive models that add protein markers to clinical information about endometrial cancer (EEC) have significant potential for improving diagnosis in patients with preoperative lymph node metastasis (LNM).
(Br J Cancer. 2020 doi:10.1038/s41416-020-0745-6.) In the EEC, current clinical algorithms are unable to accurately predict patients accompanying LNM, which leads to undertreated and overtreated patients.
the study aims to develop a model that combines protein data with clinical information to identify patients who need more aggressive surgery, including lymph node cleaning.
protein expression spectrum was produced in 399 patients using an inverse protein array.
used training sets to build three broad linear models, including protein and clinical information (model 1), magnetic resonance imaging information (model 2) and protein-only information (model 3), and tested it in a separate validation set.
the test using gene expression data from the tumor.
predicted LNM with a curved area of 0.72-0.89 and cell cycle protein D1;
high levels of fiber-linked proteins and cell cycle protein D1 were associated with poor survival rates (P-0.018) and were associated with tumor immersion markers.
of FN1 and CCND1 messenger RNA was associated with cancer attacks and mesothyst esolyses.
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