Purpose:　Circulating　tumor　DNA　(ctDNA)　provides　a　novel　approach　for　detecting　tumor　burden　and　predicting　clinical　outcomes　of　hepatocellular　carcinoma　(HCC).　Here,　we　performed　a　thorough　evaluation　of　HCC　circulating　genetic　features　and　further　fully　integrated　them　to　build　a　robust　strategy　for　HCC　monitoring　and　prognostic　outcome　assessment.　Experimental　Design:　We　performed　target　sequencing　and　low-coverage　whole-genome　sequencing　on　plasma　samples　collected　from　34　long-term　follow-up　patients　with　HCC　to　capture　tumor　somatic　SNVs　and　CNVs,　respectively.　Clinical　information　was　also　obtained　to　evaluate　the　prognostic　performance　of　ctDNA　comparing　with　clinically　applied　protein　biomarkers.　Results:　All　plasma　samples　before　surgery　showed　somatic　genetic　variations　resembling　corresponding　tumor　tissues.　During　follow-up,　SNVs　and　CNVs　dynamically　changed　correlating　to　patients＇　tumor　burden.　We　integrated　the　comprehensive　ctDNA　mutation　profiles　to　provide　a　robust　strategy　to　accurately　assess　patients＇　tumor　burden　with　high　consistence　comparing　with　imaging　results.　This　strategy　could　discover　tumor　occurrence　in　advance　of　imaging　for　an　average　of　4.6　months,　and　showed　superior　performance　than　serum　biomarkers　AFP,　AFP-L3%,　and　Des-Gamma-Carboxy　Prothrombin　(DCP).　Furthermore,　our　strategy　could　precisely　detect　minimal　residual　disease　(MRD)　in　advance　and　predict　patients＇　prognostic　outcomes　for　both　relapse-free　survival　(P　=　0.001)　and　overall　survival　(P　=　0.001);　further　combining　ctDNA　with　DCP　could　increase　the　sensitivity　for　MRD　detection.　Conclusions:　We　demonstrated　that　plasma　CNV　and　SNV　levels　dynamically　correlated　with　patients＇　tumor　burden　in　HCC.　Our　strategy　of　comprehensive　mutation　profile　integration　could　accurately　and　better　evaluate　patients＇　prognostic　risk　and　detect　tumor　occurrence　in　advance　than　traditional　strategies.