ReviewJournal of applied clinical medical physics2025
Machine learning in image-based outcome prediction after radiotherapy: A review.
Review in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Machine learning decision tree models for multiclass classification of prognosis in patient undergoing palliative radiotherapy for bone metastases.Journal of applied clinical medical physics · 2026Article
- Evaluation of the reliability of markerless tumor tracking with single-energy and dual-energy imaging using machine learning.Journal of applied clinical medical physics · 2026Article
- Spatial Dosimetric-Based Prediction of Long-Term Urinary Toxicity After Permanent Prostate Brachytherapy.Cancers · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- AI-enabled precision prediction and proactive management of cutaneous toxicities in cancer immunoradiotherapy (ICI+RT).Frontiers in oncology · 2026Review
- A transformer-based multi-modal fusion framework for laryngeal lesion classification using contact endoscopy.Frontiers in surgery · 2026Article
- Cancer Pain: Radiotherapy as a Double-Edged Sword.International journal of molecular sciences · 2025Review
- Uncertainties in outcome modelling in radiation oncology.Physics and imaging in radiation oncology · 2025Review
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Attention-based Vision Transformer Enables Early Detection of Radiotherapy-Induced Toxicity in Magnetic Resonance Images of a Preclinical Model.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.
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.