{
  "abstract": "Prostate cancer, one of the most common cancers affecting men's health worldwide, ranks second among cancer-related deaths. Therefore, it represents a critical health issue, and early diagnosis is crucial. Several imaging and analytical methods are used to diagnose this condition. This study focuses on research conducted between 2021 and 2025 that analyzes, diagnoses, and grades prostate cancer based on histopathological images using machine learning and deep learning algorithms. In this context, the reviewed studies also include an analysis of findings related to preprocessing and feature extraction methods that may aid in prostate cancer detection, as well as the classifiers used. According to the findings of the reviewed studies, artificial intelligence-supported prostate cancer detection systems demonstrate high clinical performance and significantly improve the diagnostic accuracy of pathologists by enabling rapid diagnosis. The presented research findings indicate a need for comprehensive studies focusing on prostate cancer using histopathological images. The integration of artificial intelligence is transforming prostate cancer pathology from a traditional and subjective process into a data-driven and standardized framework. This systematic analysis of histopathological images will make significant contributions to future prostate cancer research, help achieve more reliable results in clinical decision-making processes, and lay the groundwork for personalized medicine.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Engineering, Erciyes University, Kayseri, Türkiye.",
    "Department of Histology and Embryology, Faculty of Medicine, Erciyes University, Kayseri, Türkiye.",
    "Department of Biomedical Engineering, İnönü University, Malatya, Türkiye."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fatma Latifoğlu"
    },
    {
      "affs": [
        1
      ],
      "name": "Arzu Hanım Yay"
    },
    {
      "affs": [
        2
      ],
      "name": "Fırat Orhan Bulucu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fnrbyvv5nam9j.bib",
  "cited_by": 0,
  "doi": "10.14744/cpr.2026.49499",
  "fulltext_url": "https://europepmc.org/articles/PMC13715661",
  "id": "fnrbyvv5nam9j",
  "issns": [
    "2980-2156"
  ],
  "landing_url": "https://europepmc.org/article/MED/42953790",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Systematic Review"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7220956515",
  "pdf_url": "https://europepmc.org/articles/PMC13715661?pdf=render",
  "pmcid": "PMC13715661",
  "pmid": "42953790",
  "published": "2026-10-08",
  "publisher": "Erciyes University Faculty of Medicine",
  "ris": "https://research.draconitic.com/paper/fnrbyvv5nam9j.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:41Z",
      "native_id": "MED:42953790",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42953790"
    },
    {
      "first_seen": "2026-11-01T12:22:53Z",
      "native_id": "PMC13715661",
      "source": "pmc",
      "url": "https://doi.org/10.14744/cpr.2026.49499"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "Histopathological Imaging and Artificial Intelligence for Cancer Diagnosis and Gleason Grading: A Brief Review of Prostate Cancer.",
  "title_text": "Histopathological Imaging and Artificial Intelligence for Cancer Diagnosis and Gleason Grading: A Brief Review of Prostate Cancer.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:22:53Z",
  "url": "https://research.draconitic.com/paper/fnrbyvv5nam9j",
  "venue": "Journal of clinical practice and research",
  "versions": []
}