39 DEVELOPED HCC by EASL criteria The use of an external QR Code is strictly forbidden The GALAD model as a tool for HCC surveillance: a re-analysis of a prospective study Philip J. Johnson1, Emily de Groot2, Hiroyuki Yamada3, Sarah Berhane1 1. Molecular & Clinical Cancer Medicine, University of Liverpool, & Clatterbridge Cancer Centre, Liverpool, United Kingdom. 2. University of St. Andrews, St. Andrews, United Kingdom. 3. Wako Life Sciences, Inc., Mountain View, CA, United States INTRODUCTION PATIENTS & METHODS 2 CONCLUSION The serum-based tool (“GALAD”), for HCC diagnosis, is based on Gender, Age and 3 serological biomarkers, AFP, AFP-L3 and DCP1. The model was based on rigorous statistical analysis of a prospectively collected cohort. It performs well irrespective of aetiology and tumour size (AUROC typically .85 - .95) and has been extensively and internationally validated in more than 6000 patients2. A prospective evaluation would support the model for use in clinical practice. Between 2000 and 2004, before GALAD was developed, a prospective study of these three biomarkers was undertaken in North America but they were considered independently, not in a combinatory model such as GALAD3. The GALAD score is validated in the US setting. The GALAD score is already significantly raised among those destined to develop HCC at the start of the study, when there is no radiological evidence of HCC and rises steadily thereafter. The GALAD score may offer an approach to risk stratification in the HCC surveillance setting. Limitations This study was not specifically designed to prospectively evaluate the GALAD score The GALAD score was built on established HCC cases, a separate model may be needed for ‘pre-radiographic diagnosis’. To visualize changes in biomarker levels over time (in observations taken at irregular time-points), we fitted a curve for each patient using fractional polynomial (FP) regression with GALAD, (or other variable of interest) as the dependent variable and “time to HCC diagnosis/last sample” as the explanatory variable. This allows values at various time points, up to two years prior to HCC diagnosis, to be interpolated and an aggregate curve thereby generated by calculating median value for all patients at that time point. 95% confidence intervals were generated by the bootstrap method. Table 1: Baseline characteristics of the patients in each group (same groups as those underlined and in italics and yellow from the flow chart) Variable No HCC (n=330) Developed HCCs (n=39) Established HCCs (n=54) Overall (n=423) Gender (% male) 71.87, n=327 79.49, n=39 88.68, n=53 74.70, n=419 Age (years) 52 (47, 55), n=326 53 (49, 56), n=39 55 (51, 62), n=53 52 (48, 57), n=418 AFP (ng/ml) 5.8 (3.4, 12.3), n=328 13.2 (7.9, 37.3), n=39 35.9 (7.4, 217.2), n=54 6.9 (3.7, 19.4), n=421 L3 (%) 6.9 (0.5, 9.2), n=328 10.9 (7.3, 19.7), n=39 8.75 (4.6, 16.2), n=54 7.3 (0.5, 9.7), n=421 DCP (ng/ml) 0.44 (0.23, 1.12), n=329 2.17 (0.43, 6.87), n=36 5.93 (1.69, 18.19), n=51 0.54 (0.27, 2.75), n=416 GALAD -2.38 (-3.53, -0.84), n=323 -0.44 (-1.37, 0.92), n=36 1.69 (-0.60, 4.06), n=50 -1.75 (-3.19, -0.20), n=409 Median (and interquartile range) presented for continuous variables. Percentages for categorical variables. RESULTS Taking the entire group of HCCs from the study (established HCC and developed HCC) the AUROC of the GALAD score was 0.86, significantly higher than the conventional marker of AFP (AUROC=0.79), p<0.01) (Figure 1). On entry into the study, the GALAD score among those destined to develop HCC was already higher than those not developing HCC, median of -0.44 vs -2.38 (Table 1) and AUROC of 0.79 (Figure 2). It rose steadily thereafter until diagnosis whereas the no HCC group remained low and did not change significantly (Figure 3). The figures for positive and negative predictive values suggests that using the cut-off of -0.63, surveillance can be risk stratified. Those above -0.63 having a 2 year risk of HCC of >20%, those below, 5% (Table 2). AIM ACKNOWLEDGEMENTS To re-analyse a North American HCC surveillance dataset, and thereby: Validate the GALAD model in a North American series. Examine the extent to which the application of the GALAD model may permit detection of HCC prior to its detection within a conventional radiologically based surveillance program. The authors wish to acknowledge the work of Sterling et al3 in terms of study design and data collection. REFERENCES 1.Johnson et al, Cancer Epidemiol Biomarkers Prev. 2014 Jan;23(1):144-53 2.Berhane et al., Clinical Gastroenterology and Hepatology 2016;14:875–886 3.Sterling RK et al., Clinical Gastroenterology and Hepatology 2009;7:104 –113 PATIENTS & METHODS 1 369 without HCC Followed prospectively up to 2 years 39 DEVELOPED HCC by EASL criteria (4.3% p.a.) Biomarkers every 3-6 months, Imaging 6-12 months 54 ESTABLISHED HCC 330 NO-HCC 423 Enrolled HCV, cirrhotic Log-rank tests: GALAD vs AFP, p=0.0039 GALAD vs AFP-L3, p=0.0001 GALAD vs DCP, p=0.0089 Flow chart3 DISCLOSURES Philip Johnson received an educational grant from Wako Life Sciences, Inc. Figure 1: ROC curves comparing all HCCs vs no-HCC at diagnosis. Receiver operating characteristic (ROC) curve were plotted to assess GALAD discrimination between the non-HCCs and all HCCs at diagnosis, and between non-HCCs and HCC developers at first and last observation. Sensitivity, specificity, positive and negative predictive value for HCC development after passing GALAD cut-off of -0.63 (based on previous publication1) within one and two years were calculated. Figure 3: Serial changes in GALAD score, those developing HCC vs those not developing HCC CONTACT INFORMATION Table 2: Prediction of HCC development/detection within 1 and 2 years after crossing the GALAD boundary Cut-off Estimates For HCC detection within 1 years For HCC detection within 2 years -0.63 Sensitivity (95% Ci) 0.59 (0.39, 0.76) 0.69 (0.52, 0.82) Specificity (95% Ci) 0.71 (0.66, 0.76) 0.73 (0.68, 0.78) Positive Predictive Value (95% Ci) 0.15 (0.09, 0.23) 0.23 (0.16, 0.32) Negative Predictive Value (95% Ci) 0.95 (0.92, 0.97) Correspondence to: Philip Johnson Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool L69 3GA, UK E-mail: philip.johnson@liverpool.ac.uk Figure 2: ROC curves comparing HCC developers and non-HCCs at baseline (first) and diagnosis (last) for GALAD.