Newsletter

The fourteenth issue of the IDEA4RC newsletter

Hello,

As IDEA4RC reaches the end of its four-year journey, we spoke with project coordinator Annalisa Trama, head of the Epidemiology Unit at the National Cancer Institute of Milan and responsible for the EURACAN registry, the registry of the European Reference Network for rare adult solid cancers. We asked her what the consortium has learned, how her own perspective has changed, and what comes next for rare cancer data in Europe.

By subscribing to the newsletter, you will be receiving updates on the project’s advancements. If you want to invite your friends to subscribe, send them this link.

Looking back after four years, what would you keep and what would you change in IDEA4RC’s approach to making rare cancer data reusable for research?

The objectives of IDEA4RC remain as relevant today as they were four years ago. Our experience over this period have reinforced my conviction that an infrastructure like the one we have developed is needed. Working with data, and with large volumes of data, remains an urgent need, even though there’s now a widespread narrative that we’re ready to ‘unlock the data’. What I would change is the amount of support provided to clinical centres and, as a consequence, my expectations about how quickly such an infrastructure could be built.

Clinical centres differ greatly in their technical capacity and in their culture around data, and the information they hold is extremely heterogeneous, particularly when you want to reuse it for research. IDEA4RC set out to use Natural Language Processing (NLP) to extract high quality data from clinical records, but clinical records themselves have intrinsic limitations.

They are written by doctors, often under considerable time pressure, and some details about the status of the disease may be omitted because they are not essential for deciding the best treatment plan, or because clinicians can retrieve them later from other documents if needed.

Collaborating with expert centers introduces an additional layer of complexity regarding data quality. Expert centres often see a patient only during one phase of the disease. A sarcoma patient, for example, may come to our institute for surgery and then continue treatment elsewhere, so we may know very little about what happens afterwards. Conversely, patients with head and neck cancer may arrive at an expert centre after a second or third recurrence, several years after their initial treatment elsewhere.

NLP can help us retrieve information that is contained in clinical notes, pathology reports and radiology reports, but it cannot recover information that was never recorded in the first place.

Does this mean that improving data reuse also requires changing how data are collected in the first place?

Absolutely. We need to work both downstream, by developing better tools to extract and harmonise existing information, and upstream, by improving the quality of data when they are first recorded.

But this does not mean asking clinicians to completely change the way they work. Technology should adapt to clinical practice and support it. Doctors cannot be expected to complete an electronic health record as if they were filling in a research database with hundreds of predefined fields. They need to be able to write clinical notes in natural language.

Within IDEA4RC, together with our partner CliniNote, we explored a different approach: supporting clinicians as they write, for example by suggesting more standardised terminology or prompting them to specify information that may not be essential for immediate patient care but could be important for future research. If a clinician writes that a tumour has increased or decreased in size, for instance, the system could prompt them to record the change according to a standard scale or to provide additional details regarding the magnitude of the change.

Could the IDEA4RC approach also serve as a blueprint beyond rare cancers?

I believe so. Because rare cancers are a subset of all cancers, the overall approach, architecture, and technological components developed in IDEA4RC can be adapted and extended to more common cancer types. A critical part of this adaptability is having a flexible yet standardised way to represent cancer data across different types. This need led us to develop the European Cancer Common Data Model (ECCDM), which provides a shared core framework for cancer information that supports both clinical care and research. We are already exploring how the ECCDM can be validated and expanded for use in common cancers such as breast cancer, making the approach scalable beyond rare cancers.

The work on ECCDM, led by HL7 within IDEA4RC, has attracted interest from major EU Cancer Mission projects like CANDLE and UNCAN-Connect, which see its potential as a foundation for wider cancer data initiatives.

Starting with rare cancers made particular sense for a European project because individual centres rarely see enough patients on their own, making collaboration essential. We leveraged the expertise and networks of EURACAN, the European Reference Network for rare adult solid cancers, to build this infrastructure. The growing interest from other projects demonstrates that much of this work can be generalized across cancer types.

The methodology and infrastructure we developed could even extend beyond oncology to other disease areas, though different data models would be required for those fields.

Interdisciplinarity has become a buzzword in research, but IDEA4RC had to put it into practice. How did the reality of interdisciplinary collaboration compare with your expectations?

Interdisciplinary collaboration is essential for progress, yet it remains complex and challenging to achieve. While individual experts need to be open to ongoing dialogue and learning across disciplines, no single person can change the culture alone. A broader cultural shift is necessary, one that influences organizational structures, education, and ways of working.

Technical experts and clinicians often work in separate spheres, with clinicians sometimes delegating data tasks due to time constraints or tradition. Changing this mindset towards shared responsibility is crucial for effective collaboration.

This cultural shift should begin early in education, supported by universities, scientific societies, and regulatory bodies, promoting autonomy and innovation. Rather than training all-round experts, the focus should be on developing professionals skilled at working across disciplines.

IDEA4RC succeeded in fostering genuine interdisciplinary collaboration because all partners came to appreciate its importance.

Read the full interview here.