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體外診斷人工智能診斷用人工智能

市場調查報告書

商品編碼

2126433

放射基因體學市場－策略分析與預測（2026-2035）

Radiogenomics Market - Strategic Insights and Forecasts (2026-2035)

出版日期: 2026年08月12日 | 出版商:

Knowledge Sourcing Intelligence | 英文 171 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

預計放射基因組學市場將從 2026 年的 25.6 億美元成長到 2035 年的 88.7 億美元，複合年成長率為 14.8%。

全球放射基因組學市場正經歷快速成長，這主要得益於先進醫學影像、基因測序和人工智慧的融合。放射基因組學是一個新興的交叉學科領域，它整合了放射學、基因組學、生物資訊學和人工智慧，旨在提升多個治療領域疾病的特徵分析。該市場主要聚焦於腫瘤學領域，影像衍生的生物標記在補充基因組分析、支持診斷、預後、治療選擇以及疾病長期監測方面發揮日益重要的作用。隨著醫療系統採用精準醫療模式，對影像和分子資訊的整合解讀而非單一診斷評估的需求不斷成長。傳統的診斷流程存在臨床資訊不足的問題，因為組織切片檢查只能提供局部分子訊息，而影像只能捕捉腫瘤整體的異質性，無法揭示基因組突變。醫療機構正在投資放射組學軟體、雲端分析、機器學習和次世代定序平台，以增強影像和基因組資料集的整合。隨著放射基因組學對個人化醫療決策和生物標記主導的醫療保健的影響日益加深，監管機構正在加強對人工智慧驅動的診斷軟體、患者資料管治和臨床檢驗的監管。

市場促進因素

精準腫瘤學的廣泛應用

在精準腫瘤學中，全面的腫瘤特徵分析至關重要，因為治療方案的選擇越來越依賴對分子突變和影像表現型的理解。隨著臨床醫生將放射基因組學分析整合到腫瘤診療流程中，以識別與基因組突變和治療反應相關的影像生物標記物，這種需求日益成長。傳統的基於切片檢查的診斷方法在評估疾病的長期進展方面存在局限性，因為重複的組織取樣具有侵入性，並且可能無法充分反映腫瘤的異質性。醫療機構正在擴展其人工智慧（AI）平台、放射組學軟體和基因測序能力，以改善多學科臨床決策並支援個人化治療策略。放射基因組學透過在整個患者照護中實現全面、非侵入性的評估，增強了精準腫瘤學的水平。

人工智慧在醫學影像領域應用進展

人工智慧透過賦能機器學習演算法，使其超越人類解讀，識別與複雜基因組特徵相關的影像特徵，進而增強放射基因組學。隨著醫療機構採用人工智慧驅動的放射組學平台來提高腫瘤和神經系統疾病的診斷準確性和預測分析能力，對人工智慧的需求日益成長。人工影像解讀有分析局限性，因為傳統的放射學評估常常會遺漏一些細微的影像生物標記。科技公司正在開發深度學習模型、自動化特徵提取平台和基於雲端的影像分析技術，以支援基因組預測並提高結果的可重複性。人工智慧透過實現對多維臨床資料集的可擴展分析，增強了放射基因組學。

加大基因組醫學的投資

基因組醫學為放射基因組學提供了分子基礎，因為影像生物標記需要與檢驗的基因組進行關聯才能產生具有臨床意義的見解。隨著各國政府、研究機構和醫療服務提供者擴大基因測序計畫以支持精準醫療舉措，對放射基因組學的需求日益成長。僅進行獨立的基因組分析在臨床上效率較低，因為分子層面的觀察需要補充表現型資訊才能改善治療決策。醫療機構正在整合定序技術、生物資訊學和放射影像分析，以支援個人化醫療並提高解讀的準確性。放射基因組學透過將結構成像與基因組資訊結合，增強了疾病特徵的表徵並擴大了其臨床價值。

定量成像生物標記的擴展

定量影像生物標記能夠改善疾病的客觀評估，因為標準化的影像指標使得跨醫療系統進行可重複的臨床評估成為可能。隨著製藥公司和學術機構將放射組學整合到生物標記發現和臨床研究計畫中，對定量影像生物標記的需求日益成長。傳統的定性影像解讀由於主觀評估而存在差異，導致觀察者和機構之間的一致性降低。影像軟體開發商正在擴展自動特徵提取、影像標準化和高級計算分析功能，從而改善生物標記的檢驗並提高臨床可靠性。定量影像診斷透過提供可測量的表現型指標來增強放射基因組學，這些指標是對分子診斷的補充。

市場限制因素

標準化影像擷取方案和放射組學檢驗框架的匱乏降低了不同醫療機構間結果的可重複性。影像檔案、基因組資料庫和臨床資訊系統整合的挑戰增加了實施的複雜性和營運成本。人工智慧（AI）在臨床決策支援和影像生物標記檢驗方面存在的監管不確定性延長了商業化進程。

按顯像模式
- 磁振造影（MRI）
- 電腦斷層掃描（CT）
- 正子斷層掃描（PET）
- 正子斷層掃描（PET）/電腦斷層掃描（CT）
- 其他影像診斷方法
透過基因組技術
- 次世代定序（NGS）
- 全基因測序
- 全EXOME定序
- 轉錄組學
- 多組體學平台
透過使用
- 瘤
- 神經病學
- 心血管疾病
- 罕見疾病和遺傳疾病
- 其他用途
最終用戶
- 醫院
- 學術研究機構
- 診斷影像中心
- 製藥和生物技術公司
- CRO（委外研發機構）

第10章：放射基因體學市場：區域分析

北美洲
歐洲
亞太地區
南美洲
中東和非洲

第11章放射基因體學市場：國家分析

美國
加拿大
德國
英國
法國
義大利
西班牙
荷蘭
中國
日本
韓國
印度
澳洲
巴西
沙烏地阿拉伯

第12章競爭格局

市佔率分析
策略趨勢
企業合併、商業夥伴關係和合作
新產品發布

第13章：公司簡介

Siemens Healthineers AG
GE HealthCare Technologies Inc.
Koninklijke Philips NV
Canon Medical Systems Corporation
Fujifilm Holdings Corporation
Illumina, Inc.
F. Hoffmann-La Roche Ltd.
Thermo Fisher Scientific Inc.
QIAGEN NV
SOPHiA GENETICS SA
Tempus AI, Inc.
Agilent Technologies, Inc.
ConcertAI
Ibex Medical Analytics Ltd.
Paige AI, Inc.

第14章：放射基因體學市場：商業預測分析

第15章投資與資金籌措分析

創業投資趨勢
政府資金
研發投資

第16章：未來展望

主要成長機遇
未來產業趨勢

簡介目錄

Product Code: KSI-009151

The Radiogenomics Market is forecast to grow at a CAGR of 14.8%, reaching USD 8.87 billion in 2035 from USD 2.56 billion in 2026.

The global radiogenomics market is experiencing rapid growth, driven by the convergence of advanced medical imaging, genomic sequencing, and artificial intelligence. Radiogenomics represents an emerging multidisciplinary field that combines radiology, genomics, bioinformatics, and artificial intelligence to improve disease characterization across multiple therapeutic areas. The market primarily focuses on oncology, where imaging-derived biomarkers increasingly complement genomic profiling to support diagnosis, prognosis, treatment selection, and longitudinal disease monitoring. Demand is expanding because healthcare systems are adopting precision medicine models that require integrated interpretation of imaging and molecular information rather than isolated diagnostic assessments. Conventional diagnostic pathways limit clinical insight because tissue biopsies provide localized molecular information while imaging captures whole-tumor heterogeneity without revealing genomic alterations. Healthcare organizations are investing in radiomics software, cloud-based analytics, machine learning, and next-generation sequencing platforms that strengthen integration between imaging and genomic datasets. Regulatory agencies are strengthening oversight for artificial intelligence-enabled diagnostic software, patient data governance, and clinical validation because radiogenomics increasingly influences personalized treatment decisions and biomarker-driven healthcare.

Market Drivers

Growing Adoption of Precision Oncology

Precision oncology requires comprehensive tumor characterization because treatment selection increasingly depends on understanding both molecular alterations and imaging phenotypes. Demand is increasing as clinicians integrate radiogenomic analysis into oncology workflows to identify imaging biomarkers associated with genomic mutations and therapeutic response. Conventional biopsy-based diagnostics limit longitudinal disease assessment because repeated tissue sampling is invasive and may not fully represent tumor heterogeneity. Healthcare institutions are expanding artificial intelligence platforms, radiomics software, and genomic sequencing capabilities that improve multidisciplinary clinical decision-making while supporting personalized treatment strategies. Radiogenomics strengthens precision oncology by enabling comprehensive, non-invasive evaluation throughout the patient care pathway.

Increasing Integration of Artificial Intelligence in Medical Imaging

Artificial intelligence enhances radiogenomics because machine learning algorithms identify imaging features that correlate with complex genomic signatures beyond human interpretation. Demand is increasing as healthcare providers adopt AI-enabled radiomics platforms to improve diagnostic accuracy and predictive analytics across oncology and neurological disorders. Manual image interpretation creates analytical limitations because subtle imaging biomarkers frequently remain undetected within conventional radiological assessment. Technology companies are developing deep learning models, automated feature extraction platforms, and cloud-based image analytics that improve reproducibility while supporting genomic prediction. Artificial intelligence strengthens radiogenomics by enabling scalable analysis of multidimensional clinical datasets.

Rising Investments in Genomic Medicine

Genomic medicine provides the molecular foundation for radiogenomics because imaging biomarkers require validated genomic correlations to generate clinically meaningful insights. Demand is increasing as governments, research organizations, and healthcare providers expand genomic sequencing programs supporting precision healthcare initiatives. Independent genomic analysis reduces clinical efficiency because molecular findings require complementary phenotypic information to improve therapeutic decision-making. Healthcare organizations are integrating sequencing technologies, bioinformatics, and radiological analytics that improve interpretation while supporting personalized medicine. Radiogenomics expands clinical value by combining structural imaging with genomic intelligence to improve disease characterization.

Expansion of Quantitative Imaging Biomarkers

Quantitative imaging biomarkers improve objective disease assessment because standardized imaging metrics support reproducible clinical evaluation across healthcare systems. Demand is increasing as pharmaceutical companies and academic institutions incorporate radiomics into biomarker discovery and clinical research programs. Conventional qualitative imaging interpretation introduces variability because subjective assessment reduces consistency across observers and institutions. Imaging software developers are expanding automated feature extraction, image standardization, and advanced computational analytics that improve biomarker validation while strengthening clinical confidence. Quantitative imaging strengthens radiogenomics by providing measurable phenotypic indicators that complement molecular diagnostics.

Market Restraints

Limited availability of standardized imaging acquisition protocols and radiomics validation frameworks reduces reproducibility across healthcare institutions. Integration challenges between imaging archives, genomic databases, and clinical information systems increase implementation complexity and operational costs. Regulatory uncertainty surrounding artificial intelligence-based clinical decision support and imaging biomarker validation extends commercialization timelines.

Technology and Segment Insights

By Imaging Modality

Magnetic Resonance Imaging (MRI) represents the leading imaging modality because it provides superior soft tissue contrast and multiparametric imaging that enables detailed characterization of tumor biology. Demand is increasing as oncology centers utilize MRI-derived radiomic features to predict molecular subtypes, therapeutic response, and disease progression. Healthcare providers are integrating advanced MRI techniques, artificial intelligence, and radiomics analytics with genomic sequencing platforms that improve prediction of molecular biomarkers. Computed Tomography (CT), Positron Emission Tomography (PET), and PET/CT are also significant modalities, each offering unique quantitative biomarkers.

By Genomic Technology

Next-Generation Sequencing (NGS) represents the largest genomic technology segment because comprehensive genomic profiling forms the molecular foundation of radiogenomic analysis. Demand is increasing as healthcare providers utilize NGS to identify genomic alterations that correlate with imaging biomarkers and therapeutic response. Whole genome sequencing, whole exome sequencing, transcriptomics, and multi-omics platforms represent additional critical technologies.

By Application

Oncology dominates the radiogenomics market because cancer management increasingly depends on combining imaging biomarkers with genomic profiling to personalize diagnosis, treatment selection, and disease monitoring. Demand is increasing as clinicians integrate radiomics, AI, and molecular diagnostics to predict tumor aggressiveness and therapeutic response. Neurology, cardiovascular diseases, and rare and genetic disorders represent growing application areas.

Competitive and Strategic Outlook

The competitive landscape features major medical imaging and genomic technology companies. GE HealthCare Technologies Inc. differentiates itself through its broad portfolio of diagnostic imaging systems and AI-enabled software. Koninklijke Philips N.V. maintains a strong position through integrated imaging, digital pathology, and healthcare informatics. Canon Medical Systems Corporation focuses on high-performance imaging technologies. Fujifilm Holdings Corporation combines medical imaging, digital pathology, and AI. Illumina, Inc. provides the genomic foundation through its next-generation sequencing technologies. F. Hoffmann-La Roche Ltd. integrates molecular diagnostics and pharmaceutical expertise. Thermo Fisher Scientific Inc. offers a broad portfolio of genomic technologies and bioinformatics.
Strategic developments include increasing integration of AI and cloud-based analytics, expansion of multi-omics platforms, and growth of liquid biopsy integration. Companies are forming partnerships to enhance interoperability, clinical validation, and healthcare data integration. Product launches focus on AI-enabled radiomics platforms, integrated diagnostic solutions, and precision medicine applications. Companies that successfully combine imaging excellence, genomic expertise, and scalable digital infrastructure are expected to lead the market.

Conclusion

The radiogenomics market is poised for substantial growth, driven by the adoption of precision oncology, AI integration, and genomic medicine expansion. The evolution from isolated imaging and genomic analysis toward integrated, AI-powered diagnostic platforms is reshaping precision healthcare. Companies that successfully deliver validated, interoperable, and clinically actionable radiogenomic solutions are expected to lead the market. Ongoing technological innovation, regulatory support, and expanding digital health infrastructure will further accelerate adoption throughout the forecast period.

Key Benefits of this Report

Insightful Analysis: Detailed market insights across regions, customer segments, policies, socio-economic factors, consumer preferences, and industry verticals.
Competitive Landscape: Understand strategic moves by key players to identify optimal market entry approaches.
Market Drivers and Future Trends: Assess major growth forces and emerging developments shaping the market.
Actionable Recommendations: Support strategic decisions to unlock new revenue streams.
Caters to a Wide Audience: Suitable for startups, research institutions, consultants, SMEs, and large enterprises.

What Businesses Use Our Reports For

Industry and market insights, opportunity assessment, product demand forecasting, market entry strategy, geographical expansion, capital investment decisions, regulatory analysis, new product development, and competitive intelligence.

Report Coverage

Historical data from 2021 to 2024, Base year 2025, and Forecast years from 2026 to 2035
Growth opportunities, challenges, supply chain outlook, regulatory framework, and trend analysis
Competitive positioning, strategies, and market share evaluation, and trade analysis
Revenue growth and forecast assessment across segments and regions
Company profiling including strategies, products, financials, and key developments

1. Executive Summary

1.1 Market Snapshot
1.2 Key Findings
1.3 Analyst Insights
1.4 Strategic Recommendations

2. Research Methodology

2.1 Research Design
2.2 Data Collection Methodology
2.3 Market Size Estimation
2.4 Forecasting Model
2.5 Assumptions & Limitations

3. Radiogenomics Market Overview, Size & Forecast

3.1 Market Definition & Scope
3.2 Industry Overview
3.3 Industry Evolution
3.4 Key Market Trends
3.5 Historical Market Size Analysis (2021-2025)
3.6 Market Forecast (2026-2035)
3.7 Precision Medicine Landscape
3.8 Oncology Biomarker Landscape
3.9 Imaging Biomarkers and Genomic Correlation
3.10 Clinical Adoption Landscape
3.11 Patient Journey Analysis in Precision Oncology

4. Market Dynamics

4.1 Market Drivers
4.2 Market Restraints
4.3 Market Opportunities
4.4 Market Challenges

5. Industry Landscape

5.1 Industry Value Chain Analysis
5.2 Pricing Analysis
5.3 Reimbursement Landscape

6. Innovation Landscape

6.1 Artificial Intelligence in Radiogenomics
6.2 Radiomics and Quantitative Imaging Advances
6.3 Multi-Omics Integration
6.4 Digital Pathology Integration
6.5 Cloud-Based Image Analytics
6.6 Machine Learning & Deep Learning Applications
6.7 Emerging Imaging Biomarkers
6.8 Product Innovation
6.9 Clinical Trial Analysis
6.10 Technology Roadmap

7. Regulatory Landscape

7.1 Regulatory Framework
7.2 Approval Pathways
7.3 Compliance Requirements

8. Radiogenomics Market Landscape Analysis

8.1 Analysis by Imaging Modality
8.2 Analysis by Genomic Technology
8.3 Analysis by Biomarker Type
8.4 Analysis by Clinical Application
8.5 Analysis by Artificial Intelligence Integration

9. Radiogenomics Market Segment Analysis (2021-2035)

9.1 By Imaging Modality
- 9.1.1 Magnetic Resonance Imaging (MRI)
- 9.1.2 Computed Tomography (CT)
- 9.1.3 Positron Emission Tomography (PET)
- 9.1.4 Positron Emission Tomography-Computed Tomography (PET/CT)
- 9.1.5 Other Imaging Modalities
9.2 By Genomic Technology
- 9.2.1 Next-Generation Sequencing (NGS)
- 9.2.2 Whole Genome Sequencing
- 9.2.3 Whole Exome Sequencing
- 9.2.4 Transcriptomics
- 9.2.5 Multi-Omics Platforms
9.3 By Application
- 9.3.1 Oncology
- 9.3.2 Neurology
- 9.3.3 Cardiovascular Diseases
- 9.3.4 Rare & Genetic Disorders
- 9.3.5 Other Applications
9.4 By End User
- 9.4.1 Hospitals
- 9.4.2 Academic & Research Institutes
- 9.4.3 Diagnostic Imaging Centers
- 9.4.4 Pharmaceutical & Biotechnology Companies
- 9.4.5 Contract Research Organizations (CROs)