Background　and　Objective:　Computer-aided　diagnosis　(CAD)　systems　promote　accurate　diagnosis　and　re-duce　the　burden　of　radiologists.　A　CAD　system　for　lung　cancer　diagnosis　includes　nodule　candidate　de-tection　and　nodule　malignancy　evaluation.　Recently,　deep　learning-based　pulmonary　nodule　detection　has　reached　satisfactory　performance　ready　for　clinical　application.　However,　deep　learning-based　nodule　ma-lignancy　evaluation　depends　on　heuristic　inference　from　low-dose　computed　tomography　(LDCT)　volume　to　malignant　probability,　and　lacks　clinical　cognition.　Methods:　In　this　paper,　we　propose　a　joint　radiology　analysis　and　malignancy　evaluation　network　called　R2MNet　to　evaluate　pulmonary　nodule　malignancy　via　the　analysis　of　radiological　characteristics.　Radiological　features　are　extracted　as　channel　descriptor　to　highlight　specific　regions　of　the　input　volume　that　are　critical　for　nodule　malignancy　evaluation.　In　addition,　for　model　explanations,　we　propose　channel-dependent　activation　mapping　(CDAM)　to　visualize　features　and　shed　light　on　the　decision　process　of　deep　neural　networks　(DNNs).　Results:　Experimental　results　on　the　lung　image　database　consortium　image　collection　(LIDC-IDRI)　dataset　demonstrate　that　the　proposed　method　achieved　an　area　under　curve　(AUC)　of　96　.　27%　and　97　.　52%　on　nodule　radiology　analysis　and　nodule　malignancy　evaluation,　respectively.　In　addition,　explanations　of　CDAM　features　proved　that　the　shape　and　density　of　nodule　regions　are　two　critical　factors　that　influence　a　nodule　to　be　inferred　as　malignant.　This　process　conforms　to　the　diagnosis　cognition　of　experienced　radiologists.　Conclusion:　The　network　inference　process　conforms　to　the　diagnostic　procedure　of　radiologists　and　increases　the　confidence　of　evaluation　results　by　incorporating　radiology　analysis　with　nodule　malignancy　evaluation.　Besides,　model　interpretation　with　CDAM　features　shed　light　on　the　focus　regions　of　DNNs　during　the　estimation　of　nodule　malignancy　probabilities.　(c)　2021　Elsevier　B.V.　All　rights　reserved.