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市場調査レポート
商品コード
1785230
ラジオミクス市場- 世界の産業規模、シェア、動向、機会、予測、モダリティ別、画像タイプ別、技術別、用途別、地域別、競合市場別、2020~2030年Radiomics Market - Global Industry Size, Share, Trends, Opportunity & Forecast, Segmented By Modality, By Image Type, By Technology, By Application, By Region & Competition, 2020-2030F |
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カスタマイズ可能
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ラジオミクス市場- 世界の産業規模、シェア、動向、機会、予測、モダリティ別、画像タイプ別、技術別、用途別、地域別、競合市場別、2020~2030年 |
出版日: 2025年08月06日
発行: TechSci Research
ページ情報: 英文 188 Pages
納期: 2~3営業日
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ラジオミクス市場は2024年に153億5,000万米ドルと評価され、2030年にはCAGR 12.17%で306億4,000万米ドルに達すると予測されています。
世界のラジオミクス市場は、精密医療の採用増加や医療画像における先進的分析の統合に後押しされ、大きな成長を遂げています。主要市場促進要因には、がんや心血管疾患などの慢性疾患の有病率の上昇、非侵襲的診断ツールに対する需要の高まり、イメージングや人工知能(AI)の技術進歩などがあります。2022年のGLOBOCANによると、世界では約2,000万人の新たながん患者が発生し、970万人が死亡しました。肺がんは最も多く診断され、全症例の12.4%を占め、がん関連死の18.7%を占めるがん死亡の主要原因でもあります。報告書では、2050年までに新たに発生するがん患者は3,500万人に達し、2022年から77%増加すると予測しており、世界のがん対策の強化が急務であることを強調しています。ラジオミクスは、標準的な医療用画像から定量的な特徴を抽出する上で極めて重要な役割を果たし、疾患の特徴、予後、治療効果に関する深い洞察を記載しています。
市場概要 | |
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予測期間 | 2026~2030年 |
市場規模:2024年 | 153億5,000万米ドル |
市場規模:2030年 | 306億4,000万米ドル |
CAGR:2025~2030年 | 12.17% |
急成長セグメント | 磁気共鳴画像法(MRI) |
最大市場 | 北米 |
しかし、同市場は、ラジオミックデータの取得と分析のための標準化されたプロトコルの欠如、イメージングプラットフォーム間の限られた相互運用性、データのプライバシーと規制遵守に関する懸念などの課題に直面しています。さらに、ラジオミクスワークフローの複雑さや多職種による専門知識の必要性が、より広範な臨床導入の妨げとなる可能性があります。
市場を形成する市場の動向としては、臨床意思決定支援システムへのラジオミクスの統合、画像処理ソフトウェアベンダーとAIソリューションプロバイダ間の協力関係の強化、薬剤開発や個別化治療戦略のための臨床検査へのラジオミックバイオマーカーの組み込みなどが挙げられます。また、学術・研究機関も、腫瘍学だけでなく神経学、循環器学、炎症性疾患へとラジオミクスの応用を拡大し、イノベーションに貢献しています。
COVID-19の影響は当初、画像診断の手順や臨床ワークフローを混乱させたが、パンデミックは最終的に遠隔診断、ビッグデータ分析、非接触型スクリーニングツールの価値を浮き彫りにしました。この変化は、バーチャルヘルス技術とAI支援診断の重要なイネーブラーとしてのラジオミクスへの関心を加速させ、最新のデータ駆動型ヘルスケアシステムの進化における礎石として位置づけた。
個別化医療と精密医療に対する需要の高まり
標準化の欠如
人工知能と機械学習の統合
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: