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市场调查报告书
商品编码
1785230
放射组学市场 - 全球产业规模、份额、趋势、机会及预测,依模式、影像类型、技术、应用、地区及竞争细分,2020-2030 年预测Radiomics Market - Global Industry Size, Share, Trends, Opportunity & Forecast, Segmented By Modality, By Image Type, By Technology, By Application, By Region & Competition, 2020-2030F |
2024 年放射组学市场价值为 153.5 亿美元,预计到 2030 年将达到 306.4 亿美元,复合年增长率为 12.17%。全球放射组学市场正在经历显着成长,这得益于精准医疗的日益普及以及高级分析技术在医学影像中的整合。关键市场驱动因素包括癌症和心血管疾病等慢性病盛行率的上升、对非侵入性诊断工具的需求不断增长以及成像和人工智慧 (AI) 的技术进步。根据 2022 年 GLOBOCAN,全球有近 2,000 万例新发癌症病例和 970 万例癌症死亡病例。肺癌是最常见的癌症,占所有病例的 12.4%,也是癌症死亡的主要原因,占癌症相关死亡的 18.7%。报告预测,到2050年,每年新增癌症病例将达到3,500万例,比2022年增加77%,凸显了加强全球癌症控制措施的迫切需求。放射组学在从标准医学影像中提取定量特征方面发挥关键作用,有助于深入了解疾病的特征、预后和治疗反应。
市场概览 | |
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预测期 | 2026-2030 |
2024年市场规模 | 153.5亿美元 |
2030年市场规模 | 306.4亿美元 |
2025-2030 年复合年增长率 | 12.17% |
成长最快的领域 | 磁振造影(MRI) |
最大的市场 | 北美洲 |
然而,市场面临一些挑战,包括缺乏标准化的放射组学资料收集和分析协议、成像平台之间的互通性有限,以及对资料隐私和法规遵循的担忧。此外,放射组学工作流程的复杂性以及对多学科专业知识的需求,可能会阻碍其更广泛的临床应用。
塑造市场的新兴趋势包括将放射组学整合到临床决策支援系统中,加强影像软体供应商与人工智慧解决方案提供者之间的合作,以及将放射组学生物标记纳入临床试验,用于药物开发和个人化治疗策略。学术和研究机构也正在将放射组学的应用范围从肿瘤学扩展到神经病学、心臟病学和发炎性疾病,从而促进创新。
COVID-19 的影响最初扰乱了影像学程序和临床工作流程;然而,这场疫情最终凸显了远端诊断、巨量资料分析和非接触式筛检工具的价值。这一转变加速了人们对放射组学的兴趣,使其成为虚拟医疗技术和人工智慧辅助诊断的关键推动因素,并使其成为现代数据驱动型医疗保健系统发展的基石。
个人化和精准医疗需求不断成长
缺乏标准化
人工智慧与机器学习的融合
Radiomics market was valued at USD 15.35 Billion in 2024 and is expected to reach USD 30.64 Billion by 2030 with a CAGR of 12.17%. The global radiomics market is witnessing significant growth, fueled by the increasing adoption of precision medicine and the integration of advanced analytics in medical imaging. Key market drivers include the rising prevalence of chronic diseases such as cancer and cardiovascular conditions, growing demand for non-invasive diagnostic tools, and technological advancements in imaging and artificial intelligence (AI). According to the 2022 GLOBOCAN, there were nearly 20 million new cancer cases and 9.7 million cancer deaths worldwide. Lung cancer was the most frequently diagnosed, accounting for 12.4% of all cases, and was also the leading cause of cancer death, responsible for 18.7% of cancer-related deaths. The report projects that by 2050, annual new cancer cases will reach 35 million, a 77% increase from 2022, highlighting the urgent need for enhanced global cancer control measures. Radiomics plays a pivotal role in extracting quantitative features from standard medical images, offering deeper insights into disease characterization, prognosis, and treatment response.
Market Overview | |
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Forecast Period | 2026-2030 |
Market Size 2024 | USD 15.35 Billion |
Market Size 2030 | USD 30.64 Billion |
CAGR 2025-2030 | 12.17% |
Fastest Growing Segment | Magnetic Resonance Imaging (MRI) |
Largest Market | North America |
However, the market faces certain challenges, including the lack of standardized protocols for radiomic data acquisition and analysis, limited interoperability between imaging platforms, and concerns regarding data privacy and regulatory compliance. Additionally, the complexity of radiomics workflows and the need for multidisciplinary expertise can hinder broader clinical adoption.
Emerging trends shaping the market include the integration of radiomics into clinical decision support systems, increasing collaboration between imaging software vendors and AI solution providers, and the incorporation of radiomic biomarkers in clinical trials for drug development and personalized treatment strategies. Academic and research institutions are also contributing to innovation by expanding the applications of radiomics beyond oncology to neurology, cardiology, and inflammatory diseases.
The impact of COVID-19 initially disrupted imaging procedures and clinical workflows; however, the pandemic ultimately underscored the value of remote diagnostics, big data analytics, and non-contact screening tools. This shift accelerated interest in radiomics as a key enabler of virtual health technologies and AI-assisted diagnostics, positioning it as a cornerstone in the evolution of modern, data-driven healthcare systems.
Key Market Drivers
Rising Demand for Personalized and Precision Medicine
The rising demand for personalized and precision medicine is a significant driver fueling the growth of the global radiomics market. As cancer and other complex diseases continue to impact millions worldwide, nearly 10 million cancer-related deaths occurred in 2020 alone, healthcare systems are shifting toward more individualized, data-driven treatment strategies. Personalized medicine tailors' medical treatment to the individual characteristics of each patient, and radiomics plays a critical role in enabling this transformation by extracting vast amounts of quantitative data from medical images such as CT, MRI, and PET scans.
These imaging biomarkers provide deep insights into tumor phenotype, tissue heterogeneity, and disease progression, which are often undetectable by the human eye. This capability is vital in oncology, where radiomics helps identify tumor subtypes, predict therapeutic responses, and monitor treatment outcomes in a non-invasive manner. The advent of targeted therapies and immunotherapies has made such precise tools indispensable for effective patient stratification.
Radiomics also complements traditional diagnostics by offering additional layers of information, while its integration with genomic and clinical data-termed radiogenomics-enables a more comprehensive understanding of disease biology. Furthermore, pharmaceutical companies increasingly use radiomics in clinical trials to optimize patient selection and improve drug efficacy. As global healthcare moves toward precision and value-based care, the demand for radiomics as a tool for personalized medicine is expected to surge significantly.
Key Market Challenges
Lack of Standardization
One of the most significant challenges hindering the growth of the global radiomics market is the lack of standardization across various stages of the radiomics workflow. Radiomics involves the extraction of quantitative features from medical images, and for these features to be clinically meaningful and reproducible, consistent imaging protocols are essential. However, there is currently a high degree of variability in how imaging data is acquired, processed, and analyzed across different healthcare institutions, scanner types, software platforms, and even operators.
Differences in image acquisition parameters-such as slice thickness, contrast usage, resolution, and scanning protocols-can significantly alter the radiomic features extracted, even when analyzing the same patient or pathology. This inconsistency creates challenges in comparing data across studies or validating radiomic models at scale. As a result, findings that appear promising in research setting often fail to translate into real-world clinical practice, limiting the trust of healthcare professionals in adopting radiomics-based tools.
The lack of universally accepted guidelines or standards for image preprocessing, feature selection, and model validation further exacerbates the issue. This fragmented landscape hinders the development of regulatory-compliant, scalable radiomics solutions that can be used confidently in multi-institutional trials or integrated into electronic health records (EHRs).
Key Market Trends
Integration of Artificial Intelligence and Machine Learning
The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies is revolutionizing the global radiomics market by significantly enhancing the accuracy, efficiency, and clinical utility of radiomic analysis. Radiomics involves extracting vast amounts of quantitative data from medical images, which can be complex and time-consuming to analyze manually. AI and ML algorithms automate and optimize this process, enabling faster and more precise identification of patterns and features that may be imperceptible to the human eye.
AI-powered radiomics platforms utilize advanced machine learning models to analyze imaging data, segment regions of interest, and extract relevant features with high consistency. These models can learn from large datasets, improving their predictive performance over time. By integrating AI, radiomics shifts from a primarily descriptive approach to a predictive and prognostic tool, aiding clinicians in making informed decisions regarding diagnosis, treatment planning, and patient monitoring.
Moreover, AI facilitates the integration of multimodal data, combining imaging, genomic, clinical, and pathological information, to deliver a holistic view of a patient's condition. This fusion is critical for advancing precision medicine, allowing healthcare providers to tailor interventions based on comprehensive insights.
The adoption of AI and ML also addresses several operational challenges by automating routine tasks, reducing inter-operator variability, and accelerating turnaround times. This makes radiomics more scalable and accessible in busy clinical environments.
As AI continues to evolve, ongoing research focuses on enhancing algorithm transparency, interpretability, and regulatory compliance to build trust among clinicians and patients. Overall, the synergy between AI, ML, and radiomics is a pivotal trend driving innovation and adoption in the healthcare industry, unlocking new possibilities for personalized, data-driven care.
In this report, the Global Radiomics Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:
Company Profiles: Detailed analysis of the major companies presents in the Global Radiomics Market.
Global Radiomics market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report: