Sep 2026· Journal of imaging informatics in medicine· 0 citations· 25 references
Medicine
TL;DR
The findings underscore the potential of the proposed multimodal deep learning framework to provide accessible, accurate, and generalizable recurrence risk prediction using routinely available clinical data, potentially supporting more informed treatment decisions and personalized post-treatment monitoring in real-world clinical practice.
This paper presents a systematic review of deep learning approaches for predicting breast cancer recurrence from multimodal Magnetic Resonance Imaging and clinicopathological data, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.
Ahlam Ait Yahia, Ichrak Khoulqi, N. Idrissi· EPJ Web of Conferences· 0 citations
The proposed UMPM-Net is a comprehensive, explainable and robust multimodal learning framework that is capable of accurate, interpretable, and clinically relevant prognostication for colorectal cancer.
K. Muthuchamy, S. K. Piramu Preethika· Journal of Computer Science· 0 citations
A novel multimodal deep learning approach that combines pre-treatment dynamic contrast-enhanced MRI and clinical data for predicting pCR before chemotherapy, and is the first to integrate attention-based multiple instance learning technique for slice aggregation and a self-supervised contrastive objective to align imag...
Manu Goyal, Tanmay Shukla, Saeed Hassanpour· Journal of imaging informati...· 0 citations
The deep learning-based multi-cancer disease-free survival model performed robustly across cancer subtypes, with the nomogram improving risk stratification, particularly for stage I malignancies, complementing existing staging.
Si-Teng Chen, En-Cheng Zhang, Fu-Kang Sun et al.· Cancers· 0 citations
The role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology is focused on.
Shruthi Suresh, A. Parvathy, Megha Raj et al.· Frontiers in Digital Health· 0 citations
Deep learning models based on 2·5D DCE-MRI, especially Transformer models that achieve global feature fusion through self-attention mechanisms, can effectively and non-invasively predict Ki-67 expression status in breast cancer, surpassing traditional methods.
Yi-Ying Cao, Mi Lin, Yanshan Ouyang et al.· Cancer Imaging· 0 citations
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