Development and validation of a multimodal MRI habitat-based deep learning fusion model for predicting <sup>252</sup>Cf neutron therapy response in cervical cancer.
Authors
Affiliations (5)
Affiliations (5)
- Radiology Imaging Center, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar 161002, Heilongjiang Province, China.
- Department of Magnetic Resonance Imaging, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar 161002, Heilongjiang Province, China.
- Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar 161002, Heilongjiang Province, China.
- Department of Radiation Oncology, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar 161002, Heilongjiang Province, China.
- Department of Magnetic Resonance Imaging, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar 161002, Heilongjiang Province, China. [email protected].
Abstract
Cervical cancer remains one of the leading causes of cancer-related mortality among women worldwide. Although californium-252 (<sup>252</sup>Cf) neutron brachytherapy has demonstrated favorable therapeutic efficacy for locally advanced cervical cancer, substantial inter-patient heterogeneity in treatment response still exists. Therefore, developing a reliable non-invasive predictive model before treatment is of considerable clinical importance. To 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 <sup>252</sup>Cf neutron intracavitary brachytherapy combined with external beam radiotherapy. A 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 <i>k</i>-means clustering algorithm was employed to delineate the tumor habitat atlas, partitioning the tumor into three functional subregions (H1: High vascularity/high cellularity; H2: Low vascularity/high cellularity; H3: Low vascularity/low cellularity). Radiomics features were extracted from both the whole tumor region and each habitat subregion, while deep learning features were extracted using a pre-trained ResNet50 network. The aforementioned features, along with clinical-dosimetric parameters, were fused. Following feature selection <i>via</i> the least absolute shrinkage and selection operator, a fusion prediction model was constructed using a support vector machine. The dataset was split into a training set (<i>n</i> = 70) and a validation set (<i>n</i> = 30) at a 7:3 ratio for model evaluation. Habitat 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 <i>P</i> < 0.05). Decision curve analysis demonstrated that the fusion model provided the highest clinical net benefit. The 3-year overall survival rate was significantly higher in the model-predicted high-benefit group (92.5%) compared to the low-benefit group (71.4%, <i>P</i> = 0.003). This 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 <sup>252</sup>Cf neutron therapy for cervical cancer, providing a promising approach for individualized precision radiotherapy decision-making.