Platinum-based　neoadjuvant　chemotherapy　(NACT)　followed　by　radical　hysterectomy　has　been　proposed　as　an　alternative　treatment　approach　for　cervical　cancer　(CC)　in　stage　Ib2-IIb,　who　had　a　strong　desire　to　be　treated　with　surgery.　Our　study　aims　to　develop　a　model　based　on　multimodal　MRI　by　using　radiomics　and　deep　learning　to　predict　the　treatment　response　in　CC　patients　treated　with　neoadjuvant　chemoradiotherapy　(NACRT).　From　August　2009　to　June　2013,　CC　patients　in　stage　Ib2-IIb　(FIGO　2008)　who　received　NACRT　at　Fujian　Cancer　Hospital　were　enrolled　in　our　study.　Clinical　information,　contrast-enhanced　T1-weighted　imaging　(CE-T1WI),　and　T2-weighted　imaging　(T2WI)　data　were　respectively　collected.　Radiomic　features　and　deep　abstract　features　were　extracted　from　the　images　using　radiomics　and　deep　learning　models,　respectively.　Then,　ElasticNet　and　SVM-RFE　were　employed　for　feature　selection　to　construct　four　single-sequence　feature　sets.　Early　fusion　of　two　multi-sequence　feature　sets　and　one　hybrid　feature　set　were　performed,　followed　by　classification　prediction　using　four　machine　learning　classifiers.　Subsequently,　the　performance　of　the　models　in　predicting　the　response　to　NACRT　was　evaluated　by　separating　patients　into　training　and　validation　sets.　Additionally,　overall　survival　(OS)　and　disease-free　survival　(DFS)　were　assessed　using　Kaplan-Meier　survival　curves.　Among　the　four　machine　learning　models,　SVM　exhibited　the　best　predictive　performance　(AUC=0.86).　Among　the　seven　feature　sets,　the　hybrid　feature　set　achieved　the　highest　values　for　AUC　(0.86),　ACC　(0.75),　Recall　(0.75),　Precision　(0.81),　and　F1-score　(0.75)　in　the　validation　set,　outperforming　other　feature　sets.　Furthermore,　the　predicted　outcomes　of　the　model　were　closely　associated　with　patient　OS　and　DFS　(p　=　0.0044;　p　=　0.0039).　A　model　based　on　MRI　images　with　features　from　multiple　sequences　and　different　methods　could　precisely　predict　the　response　to　NACRT　in　CC　patients.　This　model　could　assist　clinicians　in　devising　personalized　treatment　plans　and　predicting　patient　survival　outcomes.　©　The　Author(s)　2024.