Ultrasound　envelope　statistics　imaging,　including　ultrasound　Nakagami　imaging,　homodyned-K　imaging,　and　information　entropy　imaging,　is　an　important　group　of　quantitative　ultrasound　techniques　for　characterizing　tissue　scatterer　distribution　patterns,　such　as　scatterer　concentrations　and　arrangements.　In　this　study,　we　proposed　a　machine　learning　approach　to　integrate　the　strength　of　multimodality　quantitative　ultrasound　envelope　statistics　imaging　techniques　and　applied　it　to　detecting　microwave　ablation　induced　thermal　lesions　in　porcine　liver　ex　vivo.　The　quantitative　ultrasound　parameters　included　were　homodyned-K　α　which　is　a　scatterer　clustering　parameter　related　to　the　effective　scatterer　number　per　resolution　cell,　Nakagami　m　which　is　a　shape　parameter　of　the　envelope　probability　density　function,　and　Shannon　entropy　which　is　a　measure　of　signal　uncertainty　or　complexity.　Specifically,　the　homodyned-K　log10(α),　Nakagami-m,　and　horizontally　normalized　Shannon　entropy　parameters　were　combined　as　input　features　to　train　a　support　vector　machine　(SVM)　model　to　classify　thermal　lesions　with　higher　scatterer　concentrations　from　normal　tissues　with　lower　scatterer　concentrations.　Through　heterogeneous　phantom　simulations　based　on　Field　II,　the　proposed　SVM　model　showed　a　classification　accuracy　above　0.90;　the　area　accuracy　and　Dice　score　of　higher-scatterer-concentration　zone　identification　exceeded　83%　and　0.86,　respectively,　with　the　Hausdorff　distance　＜26.　Microwave　ablation　experiments　of　porcine　liver　ex　vivo　at　60–80　W,　1–3　min　showed　that　the　SVM　model　achieved　a　classification　accuracy　of　0.85;　compared　with　single　log10(α),m,　or　hNSE　parametric　imaging,　the　SVM　model　achieved　the　highest　area　accuracy　(89.1%)　and　Dice　score　(0.77)　as　well　as　the　smallest　Hausdorff　distance　(46.38)　of　coagulation　zone　identification.　We　concluded　that　the　proposed　multimodality　quantitative　ultrasound　envelope　statistics　imaging　based　SVM　approach　can　enhance　the　capability　to　characterize　tissue　scatterer　distribution　patterns　and　has　the　potential　to　detect　the　thermal　lesions　induced　by　microwave　ablation.　©　2024　The　Author(s)