Evidence map›Paper›PMID 42151998›Full record

ArticleBMC medicine2026

Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.

Daria Laslo, Julia Wolleb, Maria Monzon, Raimund Kottke, Selma Sirin, Timothy Müller, Dror Suhami, Franziska Vogt, Aashim Bhatia, Deep B Gandhi and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

24 authors.

Daria LasloD-HEST, ETH Zurich, Zurich, Switzerland.
Julia WollebDepartment of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
Maria MonzonD-HEST, ETH Zurich, Zurich, Switzerland.
Raimund KottkeDepartment of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland.
Selma SirinDepartment of Diagnostic Imaging, University Children's Hospital Zurich, Zurich, Switzerland.
Timothy MüllerNeurosurgery, University Children's Hospital Zurich, Zurich, Switzerland.
Dror SuhamiCenter for Intelligent Imaging, Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, USA.
Franziska VogtD-HEST, ETH Zurich, Zurich, Switzerland.
Aashim BhatiaThe Center for Data Driven Discovery of Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, USA.
Deep B GandhiThe Center for Data Driven Discovery of Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, USA.
Anahita Fathi KazerooniThe Center for Data Driven Discovery of Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, USA.
Ariana M FamiliarThe Center for Data Driven Discovery of Biomedicine, The Children's Hospital of Philadelphia, Philadelphia, USA.
Thien NguyenDivision of Pediatric Oncology, Stanford University, Stanford, USA.
Zhifan JiangSheikh Zayed Institute, Children's National Hospital, Washington, DC, USA.
Abhijeet ParidaSheikh Zayed Institute, Children's National Hospital, Washington, DC, USA.
Nicolas U GerberDepartment of Pediatrics, University Children's Hospital Zurich, Zurich, Switzerland.
Ana Guerreiro StücklinDepartment of Pediatrics, University Children's Hospital Zurich, Zurich, Switzerland.
Ali NabavizadehUniversity of Pennsylvania, Philadelphia, USA.
Javad NazarianDepartment of Pediatrics, University Children's Hospital Zurich, Zurich, Switzerland.
Marius George LinguraruSheikh Zayed Institute, Children's National Hospital, Washington, DC, USA.
Andreas M RauscheckerCenter for Intelligent Imaging, Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, USA.
Sabine MüllerNeurosurgery, University Children's Hospital Zurich, Zurich, Switzerland.
Catherine Jutzeler *D-HEST, ETH Zurich, Zurich, Switzerland.
Sarah Brüningk *Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland. sarah.brueningk@unibe.ch.

Funding

Botnar Research Centre for Child Health, University of Basel PEP-2021-1008Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung TMSGI3_225913
6 · The paper itself

Abstract

backgroundMagnetic resonance imaging (MRI) is a cornerstone of non-invasive diagnosis and response monitoring in neuro-oncology, and predictions of spatial tumor progression conditioned on the patients' anatomy are increasingly important. We present a proof-of-principle of personalized spatial tumor progression on MRI through generative AI, focusing on pediatric Diffuse Midline Glioma (DMG).

methodsWe employed guided Denoising Diffusion Implicit Models (DDIM) to model anatomical tumor growth in pediatric DMGs on MRI. Multiparametric scans from adult (n = 1,251) and pediatric (n = 144) patients from the BraTS23 challenge were used to train a slice-based framework, conditioned on baseline scans and a target tumor size. Repeated image generations produce probabilistic tumor growth maps highlighting likely regions of progression. The realism of the generated MRIs was evaluated quantitatively and qualitatively through expert assessment. Spatial growth predictions were validated against an independent dataset of longitudinal MRI scans from a multi-institutional pre-radiotherapy DMG dataset (n = 178 paired slices).

resultsWe generated anatomically coherent, patient-specific T2-FLAIR (fluid-attenuated inversion recovery) MRI axial slices. Quantitative measures and expert evaluations confirmed the high quality of the generated images, which trained radiologists were unable to reliably distinguish from real scans (accuracy 0.53 ± 0.03). While radiomic features analyses showed good agreement (83% non-significant features) between synthetic and real images, a classifier detected subtle pixel-wise differences (accuracy of 0.69). Tumor growth probability maps aligned well with true tumor growth observed in follow-up imaging, obtaining a mean continuous DICE score of 0.79 ± 0.13.

conclusionsWe present guided DDIMs as a predictive tool for spatial tumor growth, illustrated for the progression of DMGs, that demonstrates potential for its integration in personalized radiotherapy planning. Our comprehensive image quality analysis highlights the importance of carefully evaluating synthetic data and its integration in research and clinical workflows.

Indexed as

Brain NeoplasmsGliomaMagnetic Resonance ImagingAdolescentChildChild, PreschoolDisease ProgressionFemaleGenerative Artificial IntelligenceHumansInfantMaleProof of Concept StudyAnatomical tumor growthBrain tumorDenoising Diffusion ModelsDMGGenerative AI

Identifiers

PMID42151998
PMCPMC13352721

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Registered trials

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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.