Quantitative Multimodal Characterization of Biological Tissue
Scientific challenge

Biological tissues contain structural and biochemical information across multiple spatial scales. No individual imaging modality provides a complete description of this complexity.
TETRA will therefore promote complementary use of imaging and sensing techniques capable of characterizing different manifestations of tissue structure and composition.
These may include:
·      Raman spectroscopy;
·      fluorescence microscopy;
·      brightfield microscopy;
·      quantitative optical imaging;
·      polarization-sensitive imaging;
·      structured-light approaches;
·      nonlinear optical techniques;
·      microwave and electromagnetic sensing;
·      other relevant biophotonic modalities.

TETRA priorities

The Action will seek to:
1.        harmonize sample preparation and experimental procedures where feasible;
2.        identify quantitative parameters obtainable from different imaging modalities;
3.        improve reproducibility of imaging and spectral analysis;
4.        investigate complementarity between structural, optical and biochemical measurements;
5.        establish links between experimental measurements and mathematical representations of biological structure;
6.        generate datasets suitable for computational modelling and AI.

Expected outcome

Quantitative Multimodal Characterization of Biological Tissue
Scientific challenge

Biological tissues contain structural and biochemical information across multiple spatial scales. No individual imaging modality provides a complete description of this complexity.
TETRA will therefore promote complementary use of imaging and sensing techniques capable of characterizing different manifestations of tissue structure and composition.
These may include:
·      Raman spectroscopy;
·      fluorescence microscopy;
·      brightfield microscopy;
·      quantitative optical imaging;
·      polarization-sensitive imaging;
·      structured-light approaches;
·      nonlinear optical techniques;
·      microwave and electromagnetic sensing;
·      other relevant biophotonic modalities.

TETRA priorities

The Action will seek to:
1.        harmonize sample preparation and experimental procedures where feasible;
2.        identify quantitative parameters obtainable from different imaging modalities;
3.        improve reproducibility of imaging and spectral analysis;
4.        investigate complementarity between structural, optical and biochemical measurements;
5.        establish links between experimental measurements and mathematical representations of biological structure;
6.        generate datasets suitable for computational modelling and AI.

Expected outcome

6. Research Priority 3

Mathematical Representation of Highly Disordered Biological Structures

Biological tissue is characterized by heterogeneity, anisotropy and structural disorder.
Classical homogeneous material descriptions cannot fully capture these characteristics. TETRA therefore investigates advanced mathematical approaches capable of linking microscopic structural organization with effective macroscopic properties.
Work already performed within the Action includes structural sums, generalized Eisenstein–Rayleigh sums, Schwarz alternating methods and constructive homogenization approaches.

Future research priorities

Expected outcome

A set of mathematically defined structural and effective-property descriptors that can be tested as candidate biomarkers of normal and abnormal tissue.

7. Research Priority 4

Quantitative Material Biomarkers

A central scientific hypothesis of Metamaterial Medicine is that disease-associated modifications in biological organization may produce measurable changes in effective physical properties.
Cancer progression can involve alterations in:
·      cellular density;
·      nuclear morphology;
·      extracellular matrix architecture;
·      vascular structure;
·      biochemical composition;
·      tissue organization;
·      optical scattering;
·      anisotropy and polarization behaviour.
The TETRA consortium should therefore systematically investigate whether such changes can be expressed through quantitative material descriptors.

Candidate biomarker families

Potential biomarkers may include:
Optical biomarkers
·      refractive-index-related parameters;
·      scattering coefficients;
·      absorption characteristics;
·      polarization retardance;
·      depolarization;
·      anisotropy.
Electromagnetic biomarkers
·      effective permittivity;
·      conductivity;
·      frequency-dependent response.
Structural biomarkers
·      degree of disorder;
·      spatial correlation;
·      structural sums;
·      orientation distributions;
·      morphological heterogeneity.
Biochemical biomarkers
·      Raman spectral signatures;
·      molecular composition indicators.

Expected outcome

Identification of candidate quantitative biomarkers capable of complementing conventional morphological and molecular markers.

8. Research Priority 5

Physics-Informed Artificial Intelligence

Artificial intelligence is increasingly important for biomedical imaging and digital pathology. However, purely data-driven algorithms can be difficult to interpret and may be sensitive to acquisition conditions, dataset bias and domain shifts.
TETRA provides an opportunity to investigate a complementary strategy in which experimentally measured and mathematically derived physical parameters are incorporated into computational models.
The proposed progression is:
Raw image / measurement
→ preprocessing
→ physical and structural property extraction
→ quantitative biomarkers
→ physics-informed AI
→ classification or decision support

Research priorities

The consortium should investigate:
·      integration of physical parameters into machine-learning features;
·      hybrid physical/data-driven models;
·      explainable AI approaches;
·      uncertainty quantification;
·      robustness across imaging conditions;
·      feature stability across datasets and institutions;
·      comparison between raw-image AI and physics-informed AI;
·      identification of interpretable biomarkers contributing to algorithmic decisions.

Expected outcome

More interpretable and potentially more transferable computational approaches for biological-tissue classification.

9. Research Priority 6

Multimodal Data Integration

A major opportunity for TETRA lies in combining complementary information from different experimental and computational modalities.
Rather than treating Raman spectroscopy, fluorescence imaging, polarimetry, structural imaging, electromagnetic sensing and mathematical modelling as independent approaches, future TETRA activities should investigate how these modalities describe different aspects of the same biological material system.

Proposed framework

Morphological informationOptical / electromagnetic properties
Biochemical information
Structural descriptors
Biological interpretation
↓
Integrated quantitative tissue representation

Priority research questions

·      Which modalities provide complementary rather than redundant information?
·      Which combinations provide the highest discrimination between tissue states?
·      Can features be integrated across different spatial scales?
·      Can physical models provide a common framework for multimodal integration?
·      Can multimodal biomarkers improve robustness of classification algorithms?

Expected outcome

Integrated tissue representations combining complementary physical, biological and computational information.

10. Research Priority 7

Digital Pathology and Cancer Recognition

Digital Pathology and Cancer RecognitionA major translational objective of TETRA is to investigate whether the metamaterial-formalism approach can contribute to automated cancer recognition.
The Action’s collaboration with the Institut Germans Trias i Pujol provides an important connection to clinically relevant expertise and tissue-analysis workflows.
The translational pathway should progress through clearly defined stages:
Dataset preparation
→ Image and signal preprocessing
→ Quantitative descriptor extraction
→ Algorithm development
→ Preliminary classification
→ Validation using appropriate tissue datasets
→ Comparison with accepted reference methods
→ Clinical feasibility assessment
TETRA should avoid premature claims of clinical implementation and instead focus on generating robust scientific evidence supporting future translation.

Expected outcome

Validated proof-of-concept computational methodologies capable of demonstrating whether quantitative material descriptors can contribute to cancer classification.

11. Research Priority 8

Clinical Validation and Gold-Standard Comparison

Quantitative biomarkers and classification algorithms are scientifically useful only if they can eventually be related to accepted biological or clinical reference information.
TETRA should therefore strengthen collaboration with:
·      pathologists;
·      oncologists;
·      biomedical scientists;
·      clinical imaging specialists;
·      cancer research centres;
      digital pathology experts.
Where suitable data and ethical approvals are available, quantitative TETRA readouts should be compared with established reference approaches such as histopathology and other accepted clinical or laboratory methods.

Key considerations

Validation activities should examine:
·      sensitivity;
·      specificity;
·      reproducibility;
·      robustness;
·      inter-sample variability;
·      inter-institutional variability;
·      uncertainty;
·      biological relevance of identified features.

Expected outcome

A scientifically rigorous evidence base defining both the capabilities and limitations of the TETRA methodology.

12. Research Priority 9

Regulatory, Ethical and Responsible AI Considerations

Translation of computational cancer-recognition methodologies requires consideration of regulatory and ethical requirements from an early stage.
WG4 should coordinate assessment of issues including:
·      protection of patient and biological data;
·      responsible use of artificial intelligence;
·      algorithmic transparency;
·      bias and representativeness;
·      explainability;
·      uncertainty reporting;
·      reproducibility;
·      clinical validation requirements;
·      cybersecurity where applicable;
·      potential medical-device classification;
·      pathways towards future regulatory acceptance.
Stakeholder dialogue should involve clinical experts, regulatory specialists, public-health experts and technology developers.

Expected outcome

A preliminary regulatory and ethical framework supporting the Month-48 TETRA analysis of translational considerations.

13. Research Priority 10

Live-Cell Imaging, Dynamic Biomarkers and Molecular Profiling

An important future direction identified by the TETRA community is the transition from static tissue characterization towards dynamic cellular characterization.
Live-cell imaging can provide information about:
·      cellular morphology;
·      migration;
·      proliferation;
·      intracellular dynamics;
·      metabolic behaviour;
·      responses to treatment.
Combining these observations with transcriptomic or other molecular profiling could enable connections between measurable physical cell behaviour and underlying molecular states.
This direction may expand Metamaterial Medicine from static effective-property characterization towards dynamic and molecularly informed material biomarkers.

Expected outcome

A future research pathway connecting biophotonic measurements, dynamic cell behaviour and molecular information for quantitative biomarker discovery.

14. Cross-Working-Group Integration

The success of the Research Agenda depends on strong integration of the five Working Groups.

WG1 – Experimental Visualization

Primary contribution:
·      imaging methodologies;
·      tissue visualization;
·      Raman and optical measurements;
·      data acquisition;
·      experimental standardization.

WG2 – Mathematical Modelling

Primary contribution:
·      homogenization;
·      structural sums;
·      effective-property modelling;
·      representation of disorder and anisotropy;
·      computational descriptors.

WG3 – Biological Characterization

Primary contribution:
·      biological interpretation;
·      tissue and cellular properties;
·      connection between measurable features and biological processes;
·      validation of biologically meaningful parameters.

WG4 – Translation towards Cancer Detection

Primary contribution:
·      digital pathology;
·      algorithm development and validation;
·      clinical engagement;
·      stakeholder interaction;
·      regulatory and translational assessment.

WG5 – Communication, Outreach and Exploitation

Primary contribution:
·      dissemination;
·      stakeholder communication;
·      visibility of scientific results;
·      promotion of shared resources;
·      sustainability and exploitation.

Integrated TETRA workflow

WG3 biological question
→ WG1 experimental measurement
→ WG2 quantitative mathematical representation
→ WG1/WG2/WG3 biomarker identification
→ WG4 computational classification and validation
→ WG5 dissemination, stakeholder engagement and sustainability

15. Priority Activities for Months 25–48

The consortium identifies the following priority activities for the remaining period of the Action:
1.        increase integration of WG1–WG4 through common datasets and research questions;
2.        expand and improve documentation of the biological tissue image repository;
3.        apply mathematical descriptors to experimentally derived biological structures;
4.        identify candidate quantitative material biomarkers;
5.        develop and assess physics-informed AI approaches;
6.        strengthen multimodal imaging and data integration;
7.        continue development and validation of cancer-recognition algorithms;
8.        increase collaboration with clinical partners;
9.        establish structured interaction with regulatory and public-health stakeholders;
10.  deliver interdisciplinary Training Schools, workshops and STSMs;
11.  increase Early Career Researcher leadership and scientific visibility;
12.  develop follow-on collaborative funding proposals;
13.  consolidate the conceptual framework of Metamaterial Medicine.

16. Indicative Scientific Outputs

The implementation of this Research Agenda is expected to generate:
·      harmonized experimental protocols;
·      expanded open biological imaging resources;
·      mathematical descriptors of biological disorder and anisotropy;
·      candidate quantitative tissue biomarkers;
·      multimodal tissue-analysis pipelines;
·      physics-informed machine-learning models;
·      cancer-classification algorithms;
·      comparative validation studies;
·      collaborative publications;
·      interdisciplinary Training Schools and workshops;
·      regulatory and ethical guidance;
·      follow-on research proposals;
·      long-term international collaborations.

17. Indicators of Progress

Progress towards the Research Agenda should be monitored using measurable indicators where possible, including:
·      number of cross-WG research activities;
·      number of shared datasets;
·      number of tissue types and imaging modalities represented in the repository;
·      number of publications involving participants from multiple Working Groups and countries;
·      number of STSMs linking complementary disciplines;
·      number of Early Career Researchers participating in leadership or scientific outputs;
·      number of clinical and regulatory stakeholders engaged;
·      number of joint proposals submitted;
·      external funding obtained through Action-enabled collaborations;
·      completion of Month-36 and Month-48 deliverables.

18. Sustainability Beyond COST

The TETRA Research Agenda is intended to extend beyond the formal end of COST Action CA23125.
Long-term sustainability should be supported through:
·      continued maintenance of the TETRA scientific network;
·      maintenance and expansion of open-access research resources;
·      collaborative publications;
·      joint European and international research proposals;
·      continued clinical and stakeholder collaborations;
·      recurring workshops or thematic conference sessions;
·      researcher exchanges;
·      collaborative supervision of Early Career Researchers;
·      development of a broader international community around Metamaterial Medicine.
The scientific concept itself can provide a unifying identity for continued collaboration after the COST funding period.

19. Long-Term Research Roadmap

Phase I – Foundation

Current Action period

Imaging → data curation → mathematical modelling → candidate biomarkers

Phase II – Integration

Multimodal measurements → effective material properties → physics-informed AI

Phase III – Validation

Cancer datasets → computational classification → biological and clinical comparison

Phase IV – Translation

Robust biomarkers → explainable diagnostic models → regulatory assessment

Phase V – Metamaterial Medicine

Patient-specific quantitative tissue characterization supporting precision diagnostics and future clinical decision support

20. Conclusion

The first 24 months of TETRA have created the scientific network and methodological foundation required to explore Metamaterial Medicine as an emerging interdisciplinary field.
The next phase should focus on integration.
Experimental imaging alone, mathematical modelling alone, biological characterization alone and artificial intelligence alone cannot fully address the complexity of cancerous tissue. The distinctive opportunity created by TETRA is the ability to connect these disciplines within a common framework.
The TETRA Metamaterial Medicine Research Agenda therefore establishes the following strategic progression:Measure the tissue.
Describe its material properties.
Model its structural complexity.
Identify quantitative biomarkers.
Integrate these biomarkers with physics-informed AI.
Validate them against biological and clinical evidence.
Translate the resulting knowledge towards future cancer-recognition technologies.
Through this approach, TETRA aims to establish Metamaterial Medicine as a durable interdisciplinary research direction connecting physics, photonics, mathematics, biology, artificial intelligence and medicine.