Evidence map›Paper›PMID 42834946›Full record

ArticlePhysics and imaging in radiation oncology2026

AI-based detection of temporal changes in MR-Linac images acquired during routine prostate radiotherapy.

Seungbin Park, Peilin Wang, Joshua Paik, Ryan Pennell, Francesca Khani, Himanshu Nagar, Timothy McClure, Emily S Weg, Mert R Sabuncu, Daniel Margolis and 1 more

Abstract read
In one paragraph

Article in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Seungbin ParkWeill Cornell Medicine, United States of America.
Peilin WangWeill Cornell Medicine, United States of America.
Joshua PaikWeill Cornell Medicine, United States of America.
Ryan PennellWeill Cornell Medicine, United States of America.
Francesca KhaniWeill Cornell Medicine, United States of America.
Himanshu NagarMemorial Sloan Kettering Cancer Center, United States of America.
Timothy McClureWeill Cornell Medicine, United States of America.
Emily S WegWeill Cornell Medicine, United States of America.
Mert R SabuncuWeill Cornell Medicine, United States of America.
Daniel MargolisWeill Cornell Medicine, United States of America.
Heejong KimWeill Cornell Medicine, United States of America.

Funding

Deep Learning Algorithms for FreeSurferR01AG064027 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI FISCHL, BRUCE · 2020 to 2024
$3.4M
Diagnosing the undiagnosable: studies of Alzheimer disease mimics and confounders via "neuropathometry" of dissection photos with 3D scanningR01AG070988 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI IGLESIAS GONZALEZ, JUAN EUGENIO · 2021 to 2025
$2.7M
Advanced machine learning algorithms that integrate genomewide, longitudinal MRI and demographic data to predict future cognitive decline toward dementiaR01AG053949 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI SABUNCU, MERT RORY · 2017 to 2021
$2.4M
Robust and Interpretable Multimodal Machine Learning Models for Diagnosis and Prognosis of Prostate CancerK25CA283145 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI Heejong Kim · 2024 to 2026
$463k
NCI NIH HHS K25 CA283145NIA NIH HHS R01 AG053949NIA NIH HHS R01 AG064027NIA NIH HHS R01 AG070988
6 · The paper itself

Abstract

Background and purpose: To investigate whether an AI-based method can detect subtle inter-fraction changes in MR-Linac images acquired during radiotherapy and explore the broader potential of MR-Linac imaging. Material and methods: This retrospective study included longitudinal 0.35T MR-Linac images from 761 patients. To identify temporal changes, we employed a deep learning model using temporal ordering via pairwise comparison, previously shown effective for longitudinal imaging studies. The model was trained using first-to-last fraction pairs ( Results: The Conclusions: These findings demonstrate the feasibility of detecting subtle inter-fractional changes over short periods (two days on average) in routine MR-Linac images during prostate radiotherapy and contribute to the development of imaging biomarkers for treatment response.

Indexed as

Adaptive radiotherapyArtificial intelligenceDeep learningLongitudinal image analysisMR-LinacProstate cancerRadiation therapy

Identifiers

PMID42834946
PMCPMC13634774

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.