Diagnostics
CSFINet-Based Multimodal MRI Image Segmentation and Survival Prediction
Par Jia-Lien Hsu et Pei-Yu Yu.
Contenu en anglais
Résumé
Background
Brain tumors are highly lethal malignant neoplasms, for which accurate tumor segmentation and survival-day prediction are critical to treatment planning and prognostic assessment. This study establishes an exploratory two-stage analytical framework spanning multimodal MRI tumor segmentation and survival-day prediction.
Methods
The study is based on the BraTS 2020 multimodal MRI dataset, employing four imaging modalities—T1, T1ce, T2, and FLAIR—with patient age and extent-of-resection status as candidate clinical features. The 235 cases with survival labels were split at the patient level (188 training, 47 test; single 80:20 split). In the first stage, CSFINet is used for brain tumor segmentation; in the second stage, the resulting binary whole-tumor mask, age, and resection status serve as inputs for survival-day prediction. The survival models are trained on the ground-truth whole-tumor masks but evaluated on the masks predicted by CSFINet, and the survival training runs were not seeded. Four models—image-only, image with age, image with resection status, and image with both—are constructed for ablation experiments, and Gradient SHAP is introduced for interpretability analysis.