Enterprise AI Analysis
Research on the developments of artificial intelligence in radiomics for oncology over the past decade: a bibliometric and visualized analysis
This study provides a comprehensive bibliometric analysis of the integration of AI and radiomics in oncology over the past decade. It highlights a rapid increase in publications, with China and the USA as leading contributors. Key insights include the dominance of machine learning and deep learning in feature extraction and analysis, their application in various cancer types for diagnosis, prognosis, and treatment prediction, and the emerging challenges in data standardization and ethical considerations. The research underscores AI's potential to revolutionize personalized cancer management.
Executive Impact: Key Metrics
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Total Publications Analyzed
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Top H-Index (USA)
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Avg. Annual Growth (2020-2021)
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Top Co-Cited Author (Lambin P)
Deep Analysis & Enterprise Applications
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The past decade has seen a meteoric rise in the integration of Artificial Intelligence with radiomics in oncology. Publications surged by 63.75% between 2020 and 2021 alone, reflecting AI's increasing popularity and application. This growth is largely driven by advancements in machine learning (ML) and deep learning (DL), which offer enhanced capabilities for managing large, unstructured medical imaging datasets. These technologies are pivotal for tasks like segmentation, registration, and lesion detection, moving beyond traditional visual evaluations to uncover more nuanced radiological abnormalities and underlying pathobiology. The escalating complexity of AI applications necessitates continuous innovation to maintain relevance and competitiveness, positioning the field for substantial further acceleration.
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Of total articles published in the last decade were released in the last 5 years
Enterprise Process Flow
Identify relevant literature from Web of Science (WoS)
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Filter by language (English) and document type (original/review)
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Download paper metadata (title, keywords, abstract, authors, institution, bibliographic record)
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De-duplicate retrieved documents using CiteSpace V
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Analyze and visualize data using Bibliometrix R, VOSviewer, and CiteSpace
| Feature | US Research Landscape | Chinese Research Landscape |
|---|---|---|
| Publication Count | 850 articles (20.6% of total) | 2087 articles (50.57% of total) |
| H-Index | 78 (highest among top countries) | 70 |
| Total Link Strength (TLS) | 897 (highest, indicating strong connections) | 411 (subpar, suggesting less international collaboration) |
| Average Citations | 39.0059 | 14.5223 (subpar performance) |
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AI-Driven Enhancements in Radiomics for Oncology
The integration of AI with radiomics is revolutionizing oncology by offering advanced capabilities in various aspects of cancer management. Pre-trained deep learning models, such as ResNet50, based on CNNs, significantly improve the efficacy of feature extraction from medical images, automatically discerning complex patterns and texture features with greater efficiency and accuracy than conventional methods. Multimodal image segmentation models like VISTA3D enable automatic 3D super voxel feature extraction and fusion of data from different modalities (CT, MRI), enhancing diagnostic accuracy and reliability. The TransUNet hybrid architecture further refines image analysis by capturing long-range dependencies.
AI also plays a critical role in predicting cancer risk and treatment response. Narrow-task models localize lesions and assess malignancy risk in lung and breast cancer, often outperforming expert diagnosticians. These algorithms process raw pixel data directly through deep-learning CNNs, trained on radiologist-labeled ground truth. While current models show remarkable performance in AUC, sensitivity, and specificity, the focus is shifting towards optimizing existing data streams (genomics, imaging, health records) to enhance predictive accuracy, facilitating personalized treatment. Early and precise tumor classification is crucial for timely intervention, and AI-radiomics combinations promise non-invasive diagnosis and characterization of liver cancers, prediction of microvascular invasion (MVI) in HCC, and improved recurrence-free survival.
In treatment planning, AI significantly contributes to radiotherapy by automating the segmentation of target regions and organs at risk (OARs), reducing time and interobserver variability. Models like RefineNet-based 2D and 3D automated segmentation and 3D U-Net have achieved high DSCs for various organs, indicating significant promise for adaptive radiotherapy. AI can also predict tumor response to neoadjuvant chemotherapy with high accuracy, facilitating early identification of patients likely to benefit. Multimodal data fusion, integrating radiomics with genomics and pathology, provides a more complete understanding of cancer, identifying novel phenotypes and predicting mutation statuses (EGFR, KRAF, NRAS, BRAF).
Key Outcome: AI and radiomics collectively empower more accurate diagnostics, personalized treatment plans, and improved prognostic assessments, significantly enhancing patient outcomes and healthcare efficiency by providing non-invasive biomarkers, real-time tumor status information, and clinical decision support.
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Your AI Implementation Roadmap
A strategic overview of the typical phases involved in integrating AI solutions into your enterprise, ensuring a smooth and successful transition.
Phase 1: Discovery & Strategy
Initial assessment of current workflows, data infrastructure, and identifying key opportunities for AI integration in radiomics. Defining clear objectives and success metrics for personalized oncology.
Phase 2: Data Preparation & Model Development
Standardizing imaging data, ensuring quality, and developing or fine-tuning AI models (e.g., deep learning networks) for specific oncology applications like tumor segmentation, diagnosis, and prognosis prediction.
Phase 3: Integration & Pilot Deployment
Integrating AI solutions into existing clinical systems and workflows. Conducting pilot programs in a controlled environment to test performance, usability, and gather initial feedback.
Phase 4: Scaling & Optimization
Expanding AI deployment across relevant departments. Continuous monitoring, evaluation, and iterative refinement of models and processes to maximize efficiency and clinical impact in cancer care.
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