Evidence map›Paper›PMID 41176552›Full record

ArticleEuropean radiology2026

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

Ricarda von Krüchten, Michael Barrow, Lisa Adams, Shashi Bhushan Singh, Zahra Shokri Varniab, Vidyani Suryadevara, Prinska Ghimire, Allison Pribnow, Jing Qi, Dylan Applin and 3 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 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

5 · Who and what money

Authors and funding

13 authors.

Ricarda von KrüchtenDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Michael BarrowDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Lisa AdamsDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Shashi Bhushan SinghDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Zahra Shokri VarniabDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Vidyani SuryadevaraDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Prinska GhimireDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Allison PribnowDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
Jing QiDepartment of Radiology, Children's Wisconsin, The Medical College of Wisconsin, Milwaukee, WI, USA.
Dylan ApplinDepartment of Radiology, Children's Wisconsin, The Medical College of Wisconsin, Milwaukee, WI, USA.
Yashas Ullas LokeshaDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Kerem NernekliDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
Heike E Daldrup-LinkDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA. heiked@stanford.edu.ORCID http://orcid.org/0000-0002-4929-819X

Funding

Advanced Imaging Tools to Assess Cancer Therapeutics in Pediatric PatientsR01CA269231 · NCI · STANFORD UNIVERSITY · PI Heike Elizabeth Daldrup-Link · 2022 to 2026
$3.5M
NCI NIH HHS R01 CA269231NCI NIH HHS R01CA269231
6 · The paper itself

Abstract

objectivesTo assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes and predict response to induction chemotherapy using histopathology as the reference standard. MATERIALS AND

methodsThis retrospective, multicentre study included magnetic resonance imaging (MRI) scans from osteosarcoma patients acquired between January 2006 and July 2024. A 3D U-Net CNN segmented tumours and calculated volumes at baseline and post-chemotherapy. A second CNN predicted treatment response based on MRI-derived tumour volume changes using histopathologic necrosis (≥ 90%) as the reference standard. Both models were trained on 162 scans from 81 patients (Centre A) and validated on 40 scans from 20 patients (10 per centre) with Centre B as the external test set. Human readers measured 3D tumour diameters and volumes, compared with CNN-derived volumes using Spearman's correlation, Bland-Altman plots, and Dice coefficients. Prediction performance was assessed using accuracy, sensitivity, and specificity, with significance determined by agreement metrics.

resultsPatients from Centre A had a mean age of 15 ± 5 years (52 males), and from Centre B a mean age of 13 ± 0 years (8 males). CNN- and human-derived tumour volumes showed strong correlation (Centre A: r = 0.98, Centre B: r = 0.95; p < 0.001). Dice coefficients were 0.86 (Centre A) and 0.81 (Centre B), with median Hausdorff distances of 15.0 mm and 14.2 mm. The response prediction model classified 16/20 cases (80% accuracy) with 90% sensitivity and 70% specificity.

conclusionCNN-derived tumour volume measurements were comparable to human assessments. CNN-based volume changes predicted histopathologic response to chemotherapy in paediatric osteosarcoma. KEY POINTS: Question Accurate, noninvasive assessment of treatment response in paediatric osteosarcoma is limited by its reliance on manual tumour measurements and post-surgical histopathology. Findings Automated deep learning accurately measured tumour volumes on MRI and predicted chemotherapy response with 80% accuracy, 90% sensitivity, and 70% specificity. Clinical relevance Automated deep learning enables accurate tumour volume assessment and prediction of chemotherapy response in paediatric osteosarcoma, offering a noninvasive tool to support and refine patient management.

Indexed as

Bone NeoplasmsDeep LearningMagnetic Resonance ImagingOsteosarcomaAdolescentChildFemaleHumansImage Interpretation, Computer-AssistedMaleRetrospective StudiesSensitivity and SpecificityTreatment OutcomeTumor BurdenConvolutional neural networksMagnetic resonance imagingPaediatric osteosarcomaTherapy response predictionTumour volume segmentation

Identifiers

PMID41176552
PMCPMC12694974

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.