aiqbench analysis
This is a medical-imaging research paper where the core contribution is a learning-based model that adds clinical text, dose, and prior-registration history to deformable image registration. The reported gains are large on internal and external cohorts, but the clinical evidence rests on a blinded review of only 20 longitudinal cases and a single controlled dose benchmark, so generalization is still unproven. For AI readers, the takeaway is the design choice of conditioning registration on patient-specific trajectories and domain context rather than treating each image pair independently.
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy
arXiv:2610.18926v1 Announce Type: cross Abstract: Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images. Existing registration methods treat each image pair independently, without using radiotherapy-specific context or the patient's preceding anatomical trajectory. We developed Clinical-context-Aware and Patient-adaptive Deformable Image Registration (CAP-DIR), integrating source contours, dose, clinical text and prior-registration features. We evaluated 16,361 CT examinations from 3,473 patients across four institutional cohorts and five public benchmarks. CAP-DIR increased internal-test organ-at-risk-region normalized cross-correlation from 84.76% to 96.92% relative to the strongest learning-based comparator, with low deformation folding, and remained superior across seven external cohorts. In a blinded review of 20 longitudinal cases, clinically acceptable contours increased from 45.0% to 82.5% for clinical target volumes and from 22.5% to 62.5% for organs at risk, while estimated editing time decreased. In a controlled benchmark, CAP-DIR reduced global dose-mapping error by 34.5% relative to the strongest comparator. These findings show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
Authors:Caiwen Jiang, Songlin Zhao, Yuzhen Ding, Jia Mi, Samir H. Patel, Alexandra Bennett, Keldon K. Lin, Terence T. Sio, Jonathan B. Ashman, Lisa A. McGee, Jean-Claude M. Rwigema, William G. Rule, Sameer R. Keole, Sujay A. Vora, William W. Wong, Nathan Y. Yu, Michele Y. Halyard, Steven E. Schild, Safia K. Ahmed, Daniel J. Ma, Robert L. Foote, Michelle A. Neben Wittich, Thomas J. Whitaker, Haibo Lin, Dinggang Shen, Wei Liu
Abstract:Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images. Existing registration methods treat each image pair independently, without using radiotherapy-specific context or the patient's preceding anatomical trajectory. We developed Clinical-context-Aware and Patient-adaptive Deformable Image Registration (CAP-DIR), integrating source contours, dose, clinical text and prior-registration features. We evaluated 16,361 CT examinations from 3,473 patients across four institutional cohorts and five public benchmarks. CAP-DIR increased internal-test organ-at-risk-region normalized cross-correlation from 84.76% to 96.92% relative to the strongest learning-based comparator, with low deformation folding, and remained superior across seven external cohorts. In a blinded review of 20 longitudinal cases, clinically acceptable contours increased from 45.0% to 82.5% for clinical target volumes and from 22.5% to 62.5% for organs at risk, while estimated editing time decreased. In a controlled benchmark, CAP-DIR reduced global dose-mapping error by 34.5% relative to the strongest comparator. These findings show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
27 pages, 7 figures and 6 tables. The submission includes Supplementary Information
Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
arXiv:2610.18926 [eess.IV]
(or arXiv:2610.18926v1 [eess.IV] for this version)
https://doi.org/10.48550/arXiv.2610.18926 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 278 words
Related coverage
Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration
arXiv · Sep 16, 2026
Transformer-Guided Graph Attention for Direct Cardiac Mesh Reconstruction: A Structural Digital Twin Framework
arXiv · Jun 12, 2026
Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction
arXiv · Jul 23, 2026
Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
arXiv · Jun 17, 2026