The GALAD scoring algorithm based on AFP, AFP-L3, and DCP significantly improves detection of BCLC early stage hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the leading causes of death in cirrhotic patients worldwide. The detection rate for early stage HCC remains low despite screening programs. Thus, the majority of HCC cases are detected at advanced tumor stages with limited treatment options. To facilitate earlier diagnosis, this study aims to validate the added benefit of the combination of AFP, the novel biomarkers AFP-L3, DCP, and an associated novel diagnostic algorithm called GALAD. Material and methods: Between 2007 and 2008 and from 2010 to 2012, 285 patients newly diagnosed with HCC and 402 control patients suffering from chronic liver disease were enrolled. AFP, AFP-L3, and DCP were measured using the µTASWako i30 automated immunoanalyzer. The diagnostic performance of biomarkers was measured as single parameters and in a logistic regression model. Furthermore, a diagnostic algorithm (GALAD) based on gender, age, and the biomarkers mentioned above was validated. Results: AFP, AFP-L3, and DCP showed comparable sensitivities and specifities for HCC detection. The combination of all biomarkers had the highest sensitivity with decreased specificity. In contrast, utilization of the biomarker-based GALAD score resulted in a superior specificity of 93.3 % and sensitivity of 85.6 %. In the scenario of BCLC 0/A stage HCC, the GALAD algorithm provided the highest overall AUROC with 0.9242, which was superior to any other marker combination. Conclusions: We could demonstrate in our cohort the superior detection of early stage HCC with the combined use of the respective biomarkers and in particular GALAD even in AFP-negative tumors.

Predictive modeling of the outcomes of chemotherapy-induced (febrile) neutropenia prophylaxis with biosimilar filgrastim (MONITOR-GCSF study).

BACKGROUND: Risk models of chemotherapy-induced (CIN) and febrile neutropenia (FN) have to date focused on determinants measured at the start of chemotherapy. We extended this static approach with a dynamic approach of CIN/FN risk modeling at the start of each cycle. DESIGN: We applied predictive modeling using multivariate logistic regression to identify determinants of CIN/FN episodes and related hospitalizations and chemotherapy disturbances (CIN/FN consequences) in analyses at the patient ('ever' during the whole period of chemotherapy) and cycle-level (during a given chemotherapy cycle). Statistical dependence of cycle data being 'nested' under patients was managed using generalized estimation equations. Predictive performance of each model was evaluated using bootstrapped c concordance statistics. RESULTS: Static patient-level risk models of 'ever' experiencing CIN/FN adverse events and consequences during a planned chemotherapy regimen included predictors related to history, risk factors, and prophylaxis initiation and intensity. Dynamic cycle-level risk models of experiencing CIN/FN adverse events and consequences in an upcoming cycle included predictors related to history, risk factors, and prophylaxis initiation and intensity; as well as prophylaxis duration, CIN/FN in prior cycle, and treatment center characteristics. CONCLUSIONS: These 'real-world evidence' models provide clinicians with the ability to anticipate CIN/FN adverse events and their consequences at the start of a chemotherapy line (static models); and, innovatively, to assess risk of CIN/FN adverse events and their consequences at the start of each cycle (dynamic models). This enables individualized patient treatment and is consistent with the EORTC recommendation to re-appraise CIN/FN risk at the start of each cycle. Prophylaxis intensity (under-, correctly-, or over-prophylacted relative to current EORTC guidelines) is a major determinant. Under-prophylaxis is clinically unsafe. Over-prophylaxis of patients administered chemotherapy with intermediate or low myelotoxicity levels may be beneficial, both in patients with and without risk factors, and must be validated in future studies.