ReviewDiagnostics (Basel, Switzerland)2021
Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses 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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep learning in CT image segmentation of cervical cancer: a systematic review and meta-analysis.Radiation oncology (London, England) · 2022Pooled it
- Clinical evaluation of novel deep learning-based auto-segmentation software: Utility and potential pitfalls.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Preoperative dual-ROI mpMRI radiomics integrating intratumoral and periprostatic adipose tissue features with clinical factors for predicting positive surgical margins after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- A novel interpretable machine learning framework integrating clinicopathological and radiomic features for early recurrence prediction in mass-forming intrahepatic cholangiocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients.Frontiers in oncology · 2026Article
- Multi-class transformer-based segmentation of pancreatic ductal adenocarcinoma and surrounding structures in CT imaging: a multi-center evaluation.Abdominal radiology (New York) · 2026Article
- Evaluation of Model Performance and Clinical Usefulness in Automated Rectal Segmentation in CT for Prostate and Cervical Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Automatic segmentation of male pelvic floor soft tissue structures for anatomical simulation and morphological assessment in lower rectal cancer surgery.Techniques in coloproctology · 2025Article
- Review
- Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.Journal of applied clinical medical physics · 2025Article
- Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers.Advances in radiation oncology · 2025Article
- Volume change of the prostate during moderately hypo-fractionated radiotherapy assessed by artificial intelligence.Physics and imaging in radiation oncology · 2025Article
- Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025Review
- Clinical evaluation of atlas-based auto-segmentation for contouring pelvic CTVs in the treatment of anal cancer with FDG-PET-positive lymph node involvement.Frontiers in oncology · 2025Article
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications.Physics and imaging in radiation oncology · 2024Article
- 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
- Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.Advances in radiation oncology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
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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.