![]() |
市場調查報告書
商品編碼
1785230
放射組學市場 - 全球產業規模、佔有率、趨勢、機會及預測,依模式、影像類型、技術、應用、地區及競爭細分,2020-2030 年預測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 年放射組學市場價值為 153.5 億美元,預計到 2030 年將達到 306.4 億美元,複合年成長率為 12.17%。全球放射組學市場正在經歷顯著成長,這得益於精準醫療的日益普及以及高級分析技術在醫學影像中的整合。關鍵市場促進因素包括癌症和心血管疾病等慢性病盛行率的上升、對非侵入性診斷工具的需求不斷成長以及成像和人工智慧 (AI) 的技術進步。根據 2022 年 GLOBOCAN,全球有近 2,000 萬例新發癌症病例和 970 萬例癌症死亡病例。肺癌是最常見的癌症,佔所有病例的 12.4%,也是癌症死亡的主要原因,佔癌症相關死亡的 18.7%。報告預測,到2050年,每年新增癌症病例將達到3,500萬例,比2022年增加77%,凸顯了加強全球癌症控制措施的迫切需求。放射組學在從標準醫學影像中提取定量特徵方面發揮關鍵作用,有助於深入了解疾病的特徵、預後和治療反應。
市場概覽 | |
---|---|
預測期 | 2026-2030 |
2024年市場規模 | 153.5億美元 |
2030年市場規模 | 306.4億美元 |
2025-2030 年複合年成長率 | 12.17% |
成長最快的領域 | 磁振造影(MRI) |
最大的市場 | 北美洲 |
然而,市場面臨一些挑戰,包括缺乏標準化的放射組學資料收集和分析協議、成像平台之間的互通性有限,以及對資料隱私和法規遵循的擔憂。此外,放射組學工作流程的複雜性以及對多學科專業知識的需求,可能會阻礙其更廣泛的臨床應用。
塑造市場的新興趨勢包括將放射組學整合到臨床決策支援系統中,加強影像軟體供應商與人工智慧解決方案提供者之間的合作,以及將放射組學生物標記納入臨床試驗,用於藥物開發和個人化治療策略。學術和研究機構也正在將放射組學的應用範圍從腫瘤學擴展到神經病學、心臟病學和發炎性疾病,從而促進創新。
COVID-19 的影響最初擾亂了影像學程序和臨床工作流程;然而,這場疫情最終凸顯了遠端診斷、巨量資料分析和非接觸式篩檢工具的價值。這一轉變加速了人們對放射組學的興趣,使其成為虛擬醫療技術和人工智慧輔助診斷的關鍵推動因素,並使其成為現代數據驅動型醫療保健系統發展的基石。
個人化和精準醫療需求不斷成長
缺乏標準化
人工智慧與機器學習的融合
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 | |
---|---|
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: