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    "headline": "One Slide Model Took On Diagnosis, Grade and Prognosis",
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    "dek": "A multicenter renal-tumor system combined tissue detection, subtype classification, nuclear grading and survival stratification.",
    "summary": "A multicenter renal-tumor system combined tissue detection, subtype classification, nuclear grading and survival stratification.",
    "body_text": "The retrospective study used 11,135 whole-slide images from 7,033 patients across four medical centers and two public cohorts. A framework built on Prov-GigaPath representations reported area-under-the-curve values of 0.956 to 0.998 for nine major renal-tumor subtypes across validation cohorts, 0.867 for nuclear-grade prediction, and a slide-derived risk score that independently stratified overall survival. The breadth and external validation are notable, but prospective workflow studies are still needed to measure clinical utility and effects on pathologist performance.",
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    "tags": [
      "medical AI",
      "digital pathology",
      "renal tumors"
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      "title": "BMC Medicine: full-stack AI for renal tumor pathology",
      "publisher": "BMC",
      "url": "https://doi.org/10.1186/s12916-026-05110-5",
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    "title": "One Slide Model Took On Diagnosis, Grade and Prognosis",
    "publisher": "The Machine Press",
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