PurposeAvailable　research　indicates　that　the　mammalian　target　of　rapamycin　complex　1　(mTORC1)　signaling　pathway　is　significantly　correlated　with　lung　cancer　brain　metastasis　(BM).　This　study　established　a　clinical　predictive　model　for　assessing　the　risk　of　BM　based　on　the　mTORC1-related　single　nucleotide　polymorphisms　(SNPs).MethodsIn　this　single-center　retrospective　study,　395　patients　with　non-small　cell　lung　cancer　were　included.　Clinical,　pathological,　imaging,　and　mTORC1-related　single　nucleotide　polymorphism　data　were　collected.　Lasso　regression　was　used　to　identify　variables　related　to　the　risk　of　BM　in　lung　cancer,　and　a　nomogram　was　constructed.　Internal　validation　was　performed　using　1,000　bootstrap　samples.　We　plotted　the　receiver　operating　characteristic　(ROC)　curve　and　calculated　the　area　under　the　curve　(AUC).　The　calibration　of　the　model　was　assessed　using　calibration　curves　and　the　Hosmer-Lemeshow　goodness-of-fit　test,　and　decision　curve　analysis　(DCA)　was　plotted　to　evaluate　the　net　clinical　benefit.ResultsThe　nomogram＇s　predictive　factors　included　lung　cancer　histology,　clinical　N　stage,　CEA,　neutrophil　to　lymphocyte　ratio　(NLR),　lymphocyte　to　monocyte　ratio　(LMR),　RPTOR:　rs1062935,　and　RPTOR:　rs3751934.　The　AUC　of　the　model　in　the　training　set　and　internal　validation　were　0.849　and　0.801,　respectively.　The　calibration　curves　and　Hosmer-Lemeshow　test　both　indicated　a　good　fit.ConclusionThe　nomogram　has　practicality　and　efficacy　in　predicting　the　high　risk　of　BM　in　lung　cancer　patients,　confirming　that　single　nucleotide　polymorphisms　in　the　mTORC1　pathway　genes　may　be　good　predictors　in　clinical　prediction　models.