ArticlePrecision clinical medicine2024
Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.
Article in Precision clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence-based multimodal integration of ultrasound and digital breast tomosynthesis for breast-level risk classification.Precision clinical medicine · 2026Article
- A Sensor-Oriented Multimodal Medical Data Acquisition and Modeling Framework for Tumor Grading and Treatment Response Analysis.Sensors (Basel, Switzerland) · 2026Article
- Domain adaptation, self-supervision, and generative augmentation enhance GNNs for breast cancer prediction.Scientific reports · 2026Article
- Design and evaluation of a remote damage control surgery real-time guidance system based on HoloLens 2 in low-speed network environments.Scientific reports · 2026Article
- Predictive value of peripheral blood cell-free DNA breast cancer gene mutation profiling for postoperative pathological malignancy in BI-RADS 4 breast nodules.Frontiers in genetics · 2026Article
- Prediction of neoadjuvant therapy response in breast cancer based on interpretable artificial intelligence.International journal of surgery (London, England) · 2026Article
- Racial disparities in breast cancer subtypes, recurrence, and survival: a longitudinal cohort analysis.Frontiers in oncology · 2026Article
- Histological Hyperspectral Breast Cancer Recurrence Database (HistologyHSI-BC Recurrence).Scientific data · 2025Article
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study.Precision clinical medicine · 2024Article
- Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis.Frontiers in medicine · 2024Review
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Authors and funding
12 authors.
Funding
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Abstract
Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort ( Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.
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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 OpenQuestion graph.