SynthesisJournal of medical systems2020
Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.
Synthesis in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- The promise of artificial intelligence-assisted radiotherapy for prostate cancer in Morocco: a transformational opportunity.Frontiers in medicine · 2025Article
- Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study.Neuroradiology · 2024Article
- The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review.Diagnostics (Basel, Switzerland) · 2024Review
- A geometry and dose-volume based performance monitoring of artificial intelligence models in radiotherapy treatment planning for prostate cancer.Physics and imaging in radiation oncology · 2023Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.Insights into imaging · 2023Article
- Beyond diagnosis: is there a role for radiomics in prostate cancer management?European radiology experimental · 2023Review
- A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images.Frontiers in oncology · 2023Article
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
- A bi-directional deep learning architecture for lung nodule semantic segmentation.The Visual computer · 2022Article
- Article
- Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.Diagnostics (Basel, Switzerland) · 2022Review
- Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory.BMC medical ethics · 2021Article
- Comparison of atlas-based auto-segmentation accuracy for radiotherapy in prostate cancer.Physics and imaging in radiation oncology · 2021Article
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice.
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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.