ArticleDiagnostics (Basel, Switzerland)2022
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 32 citations in OpenAlex.
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.Journal of imaging informatics in medicine · 2026Article
- The current landscape of artificial intelligence in computational histopathology for cancer diagnosis.Discover oncology · 2025Review
- Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).Quantitative imaging in medicine and surgery · 2024Article
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis.Cancers · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.BMC bioinformatics · 2023Review
- Endoscopic Image Classification Based on Explainable Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis".Diagnostics (Basel, Switzerland) · 2023Article
- Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer.BMC cancer · 2023Article
- Transfer Learning for Adenocarcinoma Classifications in the Transurethral Resection of Prostate Whole-Slide Images.Cancers · 2022Article
- Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- Weakly Supervised Learning for Poorly Differentiated Adenocarcinoma Classification in GastricEndoscopic Submucosal Dissection Whole Slide Images.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.
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