{
  "id": 7699,
  "url": "https://doi.org/10.1371/journal.pone.0137036",
  "title": "Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks",
  "summary": "Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a \"radiomics\" approach whereby a large amount of quantitative feat",
  "authors": "Petros-Pavlos Ypsilantis, Musib Siddique, Hyon-Mok Sohn, Andrew Davies, Gary Cook, Vicky Goh",
  "category": "research",
  "topics": "healthcare,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2015-09-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:33:27.485Z",
  "source_slug": "openalex",
  "source_name": "OpenAlex",
  "source_homepage": "https://openalex.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7699",
  "original_url": "https://doi.org/10.1371/journal.pone.0137036",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}