CSFINet-Based Multimodal MRI Image Segmentation and Survival Prediction

Abstract

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. Results: CSFINet achieves a Mean Dice, Mean Precision, and Mean Recall of 0.7849±0.1937, 0.8739±0.1978, and 0.7480±0.1936, compared with 0.5084, 0.8676, and 0.3822 for a U-Net baseline trained in this study under an identical configuration. The gains are in Dice and recall; the precision difference (+0.0063, 95% CI −0.0601 to 0.1086) is not distinguishable from zero. This comparison is internal to the present study; published BraTS 2020 results were obtained under different evaluation protocols and are not directly comparable. The model combining imaging features with age yields the lowest error, achieving a mean absolute error of 251.69 days (95% CI [186.56, 336.79])—lower than the 279.41 days obtained by the image-only model—whereas incorporating resection status does not lead to further improvement. Conclusions: CSFINet improves whole-tumor segmentation relative to this identically configured U-Net baseline—not relative to the published state of the art—and the inclusion of age was associated with a lower survival-day prediction error in this study. The survival findings are exploratory and cohort-specific: an MAE of 252 days is roughly 57% of the reported median survival of glioblastoma and is not clinically actionable, and the web interface is a research prototype. This study establishes an integrated analytical pipeline spanning tumor segmentation, survival-day prediction, and result visualization, serving as a preliminary reference for brain tumor image analysis and prognostic assessment. These are exploratory, single-cohort associations obtained from BraTS 2020; they do not establish a causal relationship between the clinical variables and survival, and they have not been externally validated.