ArticleMedical physics2022
The markerless lung target tracking AAPM Grand Challenge (MATCH) results.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 31 citations in OpenAlex.
- 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
- Assessing the technical capability of a room- and a gantry-mounted kV imaging device for intra-fractional fluoroscopy during stereotactic lung radiotherapy at a C-arm linear accelerator.Physics and imaging in radiation oncology · 2026Article
- Feasibility of markerless real-time tumor tracking volumetric modulated arc therapy using diaphragm-based respiratory offset vectors.Medical physics · 2025Article
- A dynamic anthropomorphic phantom for end-to-end testing in image- and surface-guided adaptive radiotherapy.Medical physics · 2025Article
- Application of respiratory motion management technology for patients with lung cancer treated with stereotactic body radiotherapy (Review).Oncology letters · 2025Review
- Use of a deep learning neural network to generate bone suppressed images for markerless lung tumor tracking.Medical physics · 2025Article
- Feasibility of a real-time dual energy markerless monitoring of lung tumors using a clinical room-mounted stereoscopic and monoscopic x-ray imaging system.Medical physics · 2025Article
- Explainable AI for raising confidence in deep learning-based tumor tracking models.Medical physics · 2025Article
- Evaluation of dose delivery accuracy of the lung with the respiratory technique of the Radixact Synchrony real-time tumor tracking system.Technical innovations & patient support in radiation oncology · 2025Article
- Evaluation of novel detector readout modes for MV scatter reduction on kV images.Medical physics · 2025Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review.Radiation oncology (London, England) · 2024Review
- Digital twins for chronic lung diseases.European respiratory review : an official journal of the European Respiratory Society · 2024Review
- Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.Medical physics · 2024Article
- Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets.Journal of applied clinical medical physics · 2024Article
- Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.Medical physics · 2023Article
- Biomedical advances and future prospects of high-precision three-dimensional radiotherapy and four-dimensional radiotherapy.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2023Article
- Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images.Journal of applied clinical medical physics · 2023Article
- Effect of scattered megavoltage x-rays on markerless tumor tracking using dual energy kilovoltage imaging.Journal of applied clinical medical physics · 2023Article
- Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.Journal of applied clinical medical physics · 2023Article
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 at 8 institutions in 4 countries.
Funding
Abstract
purposeLung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study.
methodsMATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth.
resultsA total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C-RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%.
conclusionsA common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub-millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.
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.