Histopathological Imaging and Artificial Intelligence for Cancer Diagnosis and Gleason Grading: A Brief Review of Prostate Cancer.
- 1Department of Biomedical Engineering, Erciyes University, Kayseri, Türkiye.
- 2Department of Histology and Embryology, Faculty of Medicine, Erciyes University, Kayseri, Türkiye.
- 3Department of Biomedical Engineering, İnönü University, Malatya, Türkiye.
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.
Related
- Artificial Intelligence in Oncologic Imaging: Deep Learning, Radiomics, and Clinical Integration for Precision Cancer Diagnosis
- Differential diagnosis of acid-fast mycobacteria utilizing an artificial intelligence-based approach in histopathological analysis
- Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data.
- Advances in artificial intelligence for brain imaging and computer-aided diagnosis of neurological diseases: a systematic literature review.
- Artificial intelligence-based prostate cancer risk stratification via large-scale ultrasound images paired with histopathology
BibTeX
@article{latifoglu2026histopathological,
title = {{Histopathological Imaging and Artificial Intelligence for Cancer Diagnosis and Gleason Grading: A Brief Review of Prostate Cancer.}},
author = {Fatma Latifoğlu and Arzu Hanım Yay and Fırat Orhan Bulucu},
journal = {Journal of clinical practice and research},
year = {2026},
month = oct,
doi = {10.14744/cpr.2026.49499},
url = {https://europepmc.org/article/MED/42953790},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Histopathological Imaging and Artificial Intelligence for Cancer Diagnosis and Gleason Grading: A Brief Review of Prostate Cancer.
AU - Fatma Latifoğlu
AU - Arzu Hanım Yay
AU - Fırat Orhan Bulucu
PY - 2026
DA - 2026/10/08
JO - Journal of clinical practice and research
DO - 10.14744/cpr.2026.49499
UR - https://europepmc.org/article/MED/42953790
AB - 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.
SN - 2980-2156
ER -