ArticleMedical physics2025
Explainable AI for raising confidence in deep learning-based tumor tracking models.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- 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
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Explainable AI for raising confidence in deep learning-based tumor tracking models.Medical physics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecently, tumor position monitoring using fluoroscopic images acquired during volumetric modulated arc therapy (VMAT) delivery has become available in a research setting. Accurate tracking during stereotactic body radiotherapy (SBRT), using VMAT, for lung tumors can help to ensure that the tumor is only irradiated when it is inside the planning target volume. PURPOSE: Traditionally, template matching is used to determine the tumor position, but with low tracking rates. A deep learning-based approach has the potential to improve this, but as deep learning is considered a "black box," it would be desirable to know when to trust the predictions made by the model.
methodsWe investigate the reliability and effectiveness of four explainable AI (XAI) methods (Guided Backpropagation (GBP), Layer-wise Relevance Propagation (LRP), DeepLIFT and PatternAttribtuion) to highlight the most relevant features for a deep learning-based 2D markerless lung tumor tracking model. The experiments are conducted on two phantoms and six clinical patients with small lung tumors (0.23-2.93
resultsOur findings suggest that out of the four selected XAI methods, only GBP and DeepLIFT demonstrate a reliable and consistent behavior across all patients and phantoms; LRP shows good performance in the phantom setting but has lower qualitative results on the clinical data.
conclusionsBased on our results, we argue that GBP and DeepLIFT can be used out-of-the-box to explain deep learning-based tracking models for SBRT using VMAT. Further investigation is needed to develop a robust measure of the model's reliability in clinical practice during treatment delivery.
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.