ArticleRadiation oncology (London, England)2023
Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Article in Radiation oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.International journal of colorectal disease · 2025Pooled it
- Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects.Abdominal radiology (New York) · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Article
- MRI-based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.Precision radiation oncology · 2026Review
- MR-DELTAnet: A Longitudinal MRI-Transformer Model Predicting Pathological Complete Response and Revealing Immune Microenvironment via scRNA-seq in Locally Advanced Rectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- MRI's evolving role in rectal cancer in the era of personalized medicine.Abdominal radiology (New York) · 2026Review
- Research progress in multimodal radiomics of rectal cancer tumors and peritumoral regions in MRI.Abdominal radiology (New York) · 2025Review
- Review
- Preoperative prediction of malignant transformation in sinonasal inverted papilloma: a novel MRI-based deep learning approach.European radiology · 2025Article
- Attention mechanism-based multi-parametric MRI ensemble model for predicting tumor budding grade in rectal cancer patients.Abdominal radiology (New York) · 2025Article
- Article
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
- Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: a multicenter study.Journal of cancer research and clinical oncology · 2024Observational
- Article
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Authors and funding
9 authors at 5 institutions in 1 country.
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Abstract
backgroundAccurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC.
methodsThis retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital.
resultsThe deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism.
conclusionsThe self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.
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