ArticlePhysics and imaging in radiation oncology2025
Rectal-RadioSAM: Large model-assisted multi-parametric magnetic resonance imaging pipeline for predicting response to neoadjuvant chemoradiotherapy in rectal cancer without human intervention.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Models Using Magnetic Resonance Imaging to Predict Response to Chemoradiotherapy in Rectal Cancer: A Systematic Review.ANZ journal of surgery · 2026Pooled it
- Multiparametric MRI-based multi-channel deep learning model for accurate preoperative prediction of perineural invasion in lymph node-negative rectal cancer.Abdominal radiology (New York) · 2026Article
- Article
- Adaptive ensemble learning for prostate cancer classification on multi-modal MRI: reducing unnecessary biopsies.BMC medical imaging · 2026Article
- Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis.Scientific reports · 2026Article
- Artificial intelligence and computational prediction models for risk stratification, treatment response, and outcomes in colorectal cancer: a narrative review.Frontiers in oncology · 2026Review
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Authors and funding
8 authors.
Funding
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Abstract
Background and purpose: Accurate evaluation of response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer is critical for guiding clinical decision-making. This study developed and validated a large model-assisted automated prediction tool to assess response to nCRT in locally advanced rectal cancer (LARC), focusing on segmentation and radiomic feature extraction. Material and methods: A retrospective analysis included 378 LARC patients (756 cases: baseline and post-nCRT MRI). MRI protocols comprised T2-weighted imaging (T2WI) and logarithmic diffusion-weighted imaging (DWI, b = 1000 s/mm Results: In the independent testing set comprising 112 LARC patients, the large segmentation models achieved mean (± std) Dice coefficients of 0.74 (± 0.09), 0.66 (± 0.13), 0.67 (± 0.15), and 0.46 (± 0.15) for pre-nCRT T2WI, post-nCRT T2WI, pre-nCRT DWI (log[S(1000)]), and post-nCRT DWI (log[S(1000)]) images, respectively. Meanwhile, First-Order and Shape radiomic features exhibited significant correlations between the large model-assisted segmentations and manual delineations ( Conclusion: The large model-assisted multi-parametric MRI pipeline demonstrated robust performance in predicting pCR for rectal cancer, enabling fully automated radiological assessment without human intervention.
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