ArticleIEEE transactions on medical imaging2018
Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Artificial intelligence-based prostate cancer risk stratification via large-scale ultrasound images paired with histopathology.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence in assisted localization of fetal ultrasound standard planes: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial intelligence for prostate cancer detection and risk stratification using transrectal ultrasound: a narrative review.Gland surgery · 2026Review
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- Critical review of patient outcome study in head and neck cancer radiotherapy.Meta-radiology · 2025Article
- Developments in Ultrasound-Based Imaging for Prostate Cancer Detection.The Prostate · 2025Review
- Article
- Tumor-Agnostic Therapies in Practice: Challenges, Innovations, and Future Perspectives.Cancers · 2025Review
- The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography.Journal of imaging · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- Evaluation of Deep Learning Model Architectures for Point-of-Care Ultrasound Diagnostics.Bioengineering (Basel, Switzerland) · 2024Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.Medical & biological engineering & computing · 2023Article
- Ultrasound Volume Reconstruction From Freehand Scans Without Tracking.IEEE transactions on bio-medical engineering · 2023Article
- Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.Journal of biomedical physics & engineering · 2023Article
- A deep look into radiomics.La Radiologia medica · 2021Review
- Functional Brain Imaging Reliably Predicts Bimanual Motor Skill Performance in a Standardized Surgical Task.IEEE transactions on bio-medical engineering · 2021Article
- Current status of deep learning applications in abdominal ultrasonography.Ultrasonography (Seoul, Korea) · 2021Article
- Articles That Use Artificial Intelligence for Ultrasound: A Reader's Guide.Frontiers in oncology · 2021Review
- Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study.International journal of computer assisted radiology and surgery · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.