ArticleWorld journal of radiology2026
Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting
Article in World journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCervical cancer remains one of the leading causes of cancer-related mortality among women worldwide. Although californium-252 (
aimTo develop and validate a predictive model that integrates multiparametric magnetic resonance imaging (MRI) habitat atlas, radiomics, and deep learning features for the non-invasive and accurate prediction of treatment efficacy in cervical cancer patients receiving
methodsA total of 100 cervical cancer patients who underwent the aforementioned treatment at our institution from January 2020 to June 2025 were retrospectively enrolled. All patients underwent pretreatment multi-sequence MRI scans [T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced (DCE)-MRI, and diffusion weighted imaging (DWI)]. Based on DCE-MRI and DWI parameter maps, a
resultsHabitat analysis successfully identified three types of functional subregions, whose distribution was significantly associated with tumor heterogeneity. The fusion model (Model-Fusion) achieved an area under curve (AUC) of 0.892 (95% confidence interval: 0.821-0.963) and an accuracy of 0.867 in the validation set, significantly outperforming the model based solely on clinical features (AUC = 0.712), the whole-tumor radiomics model (AUC = 0.783), and the radiomics combined with deep learning model (AUC = 0.835) (all
conclusionThis study integrates MRI habitat atlas analysis with deep learning and radiomics. The constructed multimodal fusion model can non-invasively and accurately predict the efficacy of
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