ArticleScientific reports2017
Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
66 citing papers in PubMed, 3 syntheses or guidelines pooled it, 197 citations in OpenAlex.
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- A deep learning approach based on YOLO v11 for automatic detection of jaw cysts.BMC oral health · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.Radiology. Artificial intelligence · 2025Article
- Computer-aided diagnosis based on 3D deep convolutional neural network system using novel 3D magnetic resonance imaging sequences for high-grade prostate cancer.Current urology · 2025Article
- Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.Scientific reports · 2025Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?Biomedicines · 2025Review
- Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks.Journal of imaging · 2025Article
- Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.Radiology. Artificial intelligence · 2025Article
- Factors affecting medical artificial intelligence (AI) readiness among medical students: taking stock and looking forward.BMC medical education · 2025Article
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Convoluted Neural Network for Detection of Clinically Significant Prostate Cancer onNuclear medicine and molecular imaging · 2024Article
- Article
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
- Detection and Segmentation of Radiolucent Lesions in the Lower Jaw on Panoramic Radiographs Using Deep Neural Networks.Medicina (Kaunas, Lithuania) · 2023Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.