ArticleMedical physics2018
Machine learning and modeling: Data, validation, communication challenges.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- Review
- Benchmarking of radiobiological NTCP models in head and neck radiotherapy using independent computational pipelines: an institutional validation study with machine learning augmentation.Ecancermedicalscience · 2026Article
- Consistency evaluation and performance optimization of deep learning-based auto-contouring for nasopharyngeal carcinoma.Scientific reports · 2025Article
- Layer by Layer: Assessing AI Diagnostic Accuracy With Incremental Case Information in Neuroradiology.Cureus · 2025Article
- Quality Management for Radiation Oncology In-House Software Products.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.Radiation oncology (London, England) · 2024Article
- Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance.Journal of applied clinical medical physics · 2023Article
- A convolutional neural network model for EPID-based non-transit dosimetry.Journal of applied clinical medical physics · 2023Article
- Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients.JCO clinical cancer informatics · 2023Article
- Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy.Journal of applied clinical medical physics · 2022Article
- Machine Learning-Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal.JMIR medical informatics · 2022Review
- Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review.JMIR research protocols · 2022Article
- Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy.Radiation oncology (London, England) · 2022Article
- Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer.Frontiers in oncology · 2022Article
- The role of machine learning in HIV risk prediction.Frontiers in reproductive health · 2022Article
- Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.Medical physics · 2021Article
- Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.Molecular oncology · 2021Review
- Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.International journal of radiation oncology, biology, physics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day-to-day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results.
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.