Next generation imaging and image-guided diagnosis and therapy for cancer

• Patients benefit from improved diagnostic and therapeutic procedures and innovations better adapted to their individual health condition, while meeting the needs of the healthcare system.
• Contributing to the development of high-quality tools, high-quality data, advanced patient imaging and image-guided technologies and processes for improved early diagnosis, prognosis, staging, intervention planning, therapy and management of cancer and long term follow up.
• Including next-generation imaging technologies and image-guided solutions as part of combined cancer therapies (e.g., theranostics, chemotherapy, targeted therapy including immunotherapy, radiotherapy and/or surgery) through seamless integration of tools, data and algorithms into the care pathways.
• Enabling the development of improved artificial intelligence (AI) and machine learning (ML) validation and evaluation methodologies for imaging and image guided diagnosis and image-guided therapy for cancer.
• Better informed decision-making at different levels of the healthcare system that will in turn contribute to a better allocation of resources towards cost-effective innovations.
• Contributing to the objectives of Europe's Beating Cancer Plan and to the Horizon Europe Mission on Cancer and the initiatives in the Digital Europe Programmes.

The proposals are expected to focus on image-based cancer diagnosis, prognosis, treatment planning and therapy. Project results must contribute to all of these expected outputs and outcomes:

• Expanded use of cancer patient imaging data sources, with improved data quality, annotation and computability, contributing to solutions that automatically link images to clinical data to improve diagnostic, staging, predictive and therapeutic tools for clinicians, including image-guided tools.
• Robust evaluation and validation frameworks for AI/ML-based algorithms applied to cancer patient images, to improve image-guided diagnosis, prediction of therapy outcome, planning and therapy of cancer patients.
• Healthcare professionals across Europe get access to advanced, easy-to-use solutions for minimally invasive interventions, guided by medical imaging for monitoring disease progression or treatment response, in combination with biomarkers and other relevant data.
• Improved image-driven planning and predictive tools that enable healthcare providers to facilitate diagnosis, treatment, and follow-up to improve patient outcomes.
• Novel, continuously self-learning, trustworthy, explainable AI/ML-enabled image guided diagnosis, therapy planning, and interventional systems used in clinics/hospitals and possible related benchmarks.
• Demonstrated added-value for end-users such as patients and carers, healthcare professionals, national health systems, and healthcare providers in using next generation imaging and image-guided diagnosis and therapy solutions for cancer.
• Enable seamless and successful further development of the concepts and solutions developed, leading to integrated products and services delivering proven benefits to patients, carers, healthcare systems and society as a whole.

The specific challenge to be solved by this call topic is to provide early evidence of improved cancer patient care when using next-generation imaging technologies and image-guided solutions as part of combined cancer therapies. An optimised image-based care path from early diagnosis and screening to treatment and follow-up is essential to improve the outcome of cancer patients and help optimise clinical workflows and cancer patients' journey.

Innovative solutions in cancer diagnosis, therapy planning, interventions and outcomes can be achieved by pooling, linking, and using existing cancer patient imaging and other relevant data for the development of robust AI/ML-based algorithms and enhancing of image-guided tools in clinical settings. A key point underpinning the use of AI and ML in the fight against cancer is access to high quality data. Furthermore, there are limited recognised validation and performance evaluation frameworks for AI/ML-based diagnostic algorithms.

Within the framework of the European Cancer Imaging Initiative¹ and building on the results of other relevant research projects, the proposal should enable secure, General Data Protection Regulation (EU GDPR) compliant and interoperable access to cancer imaging data sources for the purpose of developing and/or enhancing new innovative features of AI/ML-enabled tools used for diagnosis, prognosis, therapy planning, intervention, and follow up. Proposals should also focus on understanding challenges and propose sustainable solutions to close gaps in algorithm validation and algorithm evaluation in the context of developing AI/ML-based tools for cancer diagnosis and outcome prediction.

The proposal should aim to improve AI/ML-enabled imaging and image guided solutions in order to assist and guide clinicians during diagnosis, staging, patient monitoring, therapy planning, intervention and follow-up. Where appropriate, proposals should demonstrate novel ways to interact with the imaging data. The driving principle must be improving and enhancing image-based diagnosis and therapy, e.g. through automated image interpretation and segmentation, quantitative disease assessment, intuitive treatment planning and smart guidance both during treatment itself and in post-treatment monitoring of response to therapy, to enable more efficient patient-centric diagnosis/therapies/interventions and better patient outcomes.

The proposed research and innovation (R&I) activities should result in simplified clinical workflows, for instance through enhanced or complementary robotic-assisted procedures, thus resulting in more precise therapeutic and interventional procedures for patients, reduced workload on staff, a reduction in therapy planning and intervention time, and shorter recovery times/hospital stays.

^{1 This initiative is part of the Europe’s Beating Cancer Plan: https://eur-lex.europa.eu/legal-content/en/TXT/?uri=COM:2021:44:FIN}