{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Information Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jia-Lien Hsu",
      "orcid": "0000-0002-4818-3198"
    },
    {
      "affs": [
        0
      ],
      "name": "Pei-Yu Yu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/77b8t88dw5e77.bib",
  "cited_by": 0,
  "doi": "10.3390/diagnostics16193210",
  "fulltext_url": "https://europepmc.org/articles/PMC13664131",
  "id": "77b8t88dw5e77",
  "issns": [
    "2075-4418"
  ],
  "landing_url": "https://europepmc.org/article/MED/42882365",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2808|Brain Tumor Detection and Classification"
    ]
  ],
  "openalex": "W7215906621",
  "pdf_url": "https://europepmc.org/articles/PMC13664131?pdf=render",
  "pmcid": "PMC13664131",
  "pmid": "42882365",
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/77b8t88dw5e77.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42882365",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42882365"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2808|Brain Tumor Detection and Classification",
      "subfield": "neurology"
    }
  ],
  "title": "CSFINet-Based Multimodal MRI Image Segmentation and Survival Prediction.",
  "title_text": "CSFINet-Based Multimodal MRI Image Segmentation and Survival Prediction.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:00Z",
  "url": "https://research.draconitic.com/paper/77b8t88dw5e77",
  "venue": "Diagnostics (Basel, Switzerland)",
  "versions": []
}