{
  "id": 18278486,
  "title": "Clinical-Anchored Incremental Survival Learning: A Stability-Constrained Framework for Molecularly Enhanced Survival Prediction in Lung Adenocarcinoma",
  "url": "https://urgent.news/2026/11/05/clinical-anchored-incremental-survival-learning-a-stability",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-05T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.04.763606v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Accurate survival prediction in lung adenocarcinoma (LUAD) remains challenging because high-dimensional molecular data can introduce overfitting and unstable feature selection. We developed Clinical-Anchored Incremental Survival Learning (CAISL), a framework that integrates a sparse molecular risk correction with a clinical baseline to improve prognostic discrimination while constraining model…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}