=Paper=
{{Paper
|id=Vol-3415/paper-2
|storemode=property
|title=Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered
collaborations
|pdfUrl=https://ceur-ws.org/Vol-3415/paper-2.pdf
|volume=Vol-3415
|dblpUrl=https://dblp.org/rec/conf/swat4ls/GouthamchandCHW23
}}
==Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered
collaborations==
https://ceur-ws.org/Vol-3415/paper-2.pdf
Privacy-Preserving Dashboard for F.A.I.R Head and
Neck Cancer data supporting multi-centered
collaborations
Varsha Gouthamchand1,2,∗ , Ananya Choudhury1,2 , Frank Hoebers1 ,
Frederik Wesseling1 , Mattea Welch3 , Sejin Kim3 , Benjamin Haibe-Kains3 ,
Joanna Kazmierska4 , Andre Dekker1,2 , Johan van Soest5,1 and Leonard Wee1,2
1
Dept of Radiation Oncology (Maastro), GROW School of Oncology and Reproduction, Maastricht University Medical
Centre+, Maastricht, The Netherlands
2
Clinical Data Science, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
3
Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
4
Dept of Radiation Oncology, Greater Poland Cancer Centre II, Poznan, Poland
5
Brightlands Institute for Smart Society, Faculty of Science and Engineering, Maastricht University, Heerlen, The
Netherlands
Abstract
Research in modern healthcare requires vast volumes of data from various healthcare centers across
the globe. It is not always feasible to centralize clinical data without compromising privacy. A tool
addressing these issues and facilitating reuse of clinical data is the need of the hour. The Federated
Learning approach, governed in a set of agreements such as the Personal Health Train (PHT) manages
to tackle these concerns by distributing models to the data centers instead of the traditional approach
of centralizing datasets. One of the prerequisites of PHT is using semantically interoperable datasets
for the models to be able to find them. FAIR (Findable, Accessible, Interoperable, Reusable) principles
help in building interoperable and reusable data by adding knowledge representation and providing
descriptive metadata. However, the process of making data FAIR is not always easy and straight-forward.
Our main objective is to disentangle this process by using domain and technical expertise and get data
prepared for federated learning. This paper introduces applications that are easily deployable as Docker
containers, which will automate parts of the aforementioned process and significantly simplify the
task of creating FAIR clinical data. Our method bypasses the need for clinical researchers to have a
high degree of technical skills. We demonstrate the FAIR-ification process by applying it to five Head
and Neck cancer datasets (four public and one private). The PHT paradigm is explored by building a
distributed visualization dashboard from the aggregated summaries of the FAIR-ified datasets. Using the
PHT infrastructure for exchanging only statistical summaries or model coefficients allows researchers to
explore data from multiple centers without breaching privacy.
Keywords
FAIR, Knowledge graphs, Linked Data, Semantic Web, Ontologies, SPARQL, RDF, Federated Learning,
SWAT4HCLS 2023: The 14th International Conference on Semantic Web Applications and Tools for Health Care and Life
Sciences
∗
Corresponding author.
Envelope-Open varsha.gouthamchand@maastro.nl (V. Gouthamchand)
Orcid 0000-0002-4756-2866 (V. Gouthamchand); 0000-0001-9847-8165 (A. Choudhury); 0000-0002-4317-9181
(F. Hoebers); 0000-0002-5887-9826 (M. Welch); 0000-0002-0422-7996 (A. Dekker); 0000-0003-2548-0330 (J. v. Soest);
0000-0003-1612-9055 (L. Wee)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
1. Background
Real-world clinical data is defined as data relating to an individual person’s health status and/or
the delivery of healthcare to a population that is routinely collected from a variety of sources.
This is increasingly being used as evidence of treatment effectiveness as well as to guide clinical
decision-making through the development of predictive prognostic models [2].
Re-use of real-world clinical data at scale presents two challenges. First is a lack of syntactic
interoperability, i.e., technical differences due to database organization and divergence of
human languages. A more flexible alternative is an “open world” approach focusing on semantic
interoperability where the data can be queried and retrieved by independent external researchers
[13] without having to know details in advance, about database structure or native coding schema
of the data. Second is that clinical data will be usually horizontally partitioned; healthcare
institutions each own similar sets of data fields but exclusively on their own human subjects.
Due to the highly sensitive nature of patient medical data, great care must be taken if shared.
Concerns over patient confidentiality and data controllership implies that it is not always an
attractive option to aggregate all individual-level data into a few centralized repositories.
A federated learning paradigm attempts to address some of the privacy concerns. A privacy-
by-design paradigm, e.g., Personal Health Train (PHT) [14], exchanges only aggregated statistical
information. A necessary consequence of using the PHT is that the data must first be made
semantically interoperable for algorithms to analyze the data remotely and autonomously.
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles were developed to
maximize the value of digital assets, including real world clinical data, and it further emphasizes
making data interoperable for machine processors and not only for humans [20]. It is important
to emphasize that FAIR data does not imply open data, and open data itself may not be FAIR.
The purpose of FAIR is that a given community, e.g., researchers and cancer clinicians, can
achieve a high degree of interoperability and reusability on each other’s data [8].
The Linked Data [3] concept elegantly captures some of the needs of FAIR by assigning
machine-readable unique resource identifiers (URIs) to data elements, as well as capturing the
relationships between them. This attribute of linked data can be exploited to integrate disparate
pools of data, even if they comprise different domains, e.g., clinical examinations and image-
based biomarkers extracted from radiology scans. Independent but linked databases readable by
machines over Hypertext Transfer Protocol (HTTP) makes up a worldwide “Semantic Web” of
FAIR data. Semantic web standards define a set of essential tools such as the Resource Descriptor
Framework (RDF) [10] and a SPARQL Protocol and RDF Query Language (SPARQL) [12], for
storing data and querying data, respectively. RDF represents data using a series of statements
known as “triples”, i.e., subject-predicate-object.
This work defines a partly automated procedure to make structured real-world patient
data more FAIR according to Semantic Web standards. Specialist competencies are leveraged
collaboratively at the right place, i.e., clinicians and data creators will focus on annotation using
rich descriptions; domain experts and ontologists can encode knowledge representation with
data graph construction, and data scientists can instantiate these annotations and integrate data
from disparate sources using a singular SPARQL query. Privacy-preserving federated learning
is used to generate a visualization of aggregated cohort statistics across five private and public
datasets in head-and-neck cancer, without revealing individual-level data.
2. Implementation
A schematic illustration of the general workflow to convert structured raw data to FAIR semantic
web data is provided in Fig 1. Our tooling consists of three principal parts, (1) a graphical user
interface (GUI) to select data for serialization as RDF triples and for the data owner to attach
some descriptive information, (2) a collaborative annotation step to attach one or more relevant
domain ontologies and hence define a fit-for-purpose graph data structure, and (3) a means to
query data across multiple FAIR data graphs via a single federated SPARQL query. We packaged
these tools as Docker containers to make them platform-independent and easier to deploy;
these are made open access (see Availability section).
2.1. Structured data conversion to FAIR data graph
The first step takes existing structured data and processes either comma-separated values (CSV)
or relational databases (any generic SQL format) into RDF. This component is provided with
a GUI running locally where (non-technical) data owners simply browse and choose their
dataset for processing. Triplifier [11] is a Java-based resource (integrated with the GUI) that
automatically serializes a table into RDF as a Terse-Triple Language (TTL) file [16] and compiles
the schema of the ingested table as a database-specific Web Ontology Language (OWL) file [9].
The same GUI also requests data owners to add descriptions and metadata, such as data type of
each field (continuous, discrete, categorical ordinal, or patient identifier). The data owner is also
able to attach some preselected definitions to their data fields and/or include supplementary
information in free text comments. The data owner’s pre-annotations are directly written into
the aforementioned OWL schema file. The resulting TTL and OWL files are automatically saved
in a graph database (we included a free version of GraphDB in our Docker deployment).
2.2. Annotation of datasets with ontologies
The procedure was specifically designed to decouple the contents of the database (in the TTL)
from the dictionary/coding of the database (in the OWL), thus allowing an external collaborator
to work on semantic annotations using the OWL without needing to read the actual contents
of the data, which does contain person-specific and highly privacy-sensitive information. The
data owner may thus share the OWL file either publicly, or privately to a defined collaboration,
of researchers, domain experts, clinicians and other data owners. For each distinct dataset, a
semantically meaningful mapping of the database-specific entities onto a publicly accessible
do-main ontology (e.g., ROO and NCIT) will be made through consensus and correspondence
(e.g., through extended email discussions).
We provide Python scripts that add unique dataset specific annotations as a graph object
(“annotation.local”) directly into the local GraphDB. Importantly, if the use case changes (or
if a new research question emerges) such that an alternative annotation is required, this can
be easily implemented. Re-annotation of the data is always possible because it sits on top of
the original OWL and TTL without needing to edit or modify any of the original schema and
original contents. A SPARQL query external to the dataset then references the equivalencies in
the “annotation.local” in order to extract the correct entities and relations specific to the dataset.
2.3. Tagged release of software
The code for this project is made open access on Github (refer Availability section); instructions
for use have been provided in associated markdown documents. We have provided a Docker
installation containing a Jupyter notebook, GraphDB and triplifier. All the python scripts needed
are packaged into the distribution as Jupyter notebooks.
2.4. PHT infrastructure
An open source Vantage6 infrastructure [7] (v0.2.4) implementing the PHT method has been pre-
viously used to develop and validate a federated CPH model in anal cancer patients across three
countries [15]. Full details of Vantage6 are given in its accompanying technical documentation
[19].
2.5. Distributed dashboard aggregation of head and neck cancer data
A demonstration was previously made available as a preprint [4] and remains available from
our GitHub repository as the “MedRxiv” branch. To recapitulate briefly, we had created an
automated process where the clinical case-mix data in four different open access datasets on
The Cancer Imaging Archive (TCIA) [1] were serialized using triplifier, and each set of TTL and
OWL files were inserted into its respective GraphDB database. On top of each of these GraphDB
databases, Python scripts were executed which inserted local annotations on top of the TTL
and OWL files, utilizing class entities and predicates from the NCIT and ROO ontologies.
This work extends the previous by adding a hitherto unpublished private dataset (HN3). We
used the aforementioned GUI and triplifier to serialize the data and create the custom semantic
ontology annotation for its RDF graph. We placed each of these five datasets in geographically
dispersed Ubuntu virtual machines, with unique public IP addresses and network firewalls, but
all connected to a Vantage6 infra-structure (illustrated schematically in Fig 2). We distributed a
single SPARQL query through the PHT infrastructure to obtain aggregated cohort statistics
(e.g., mean, range, etc.) from the federated datasets, then presented these in two ways – (i) an
interactive Python visual dashboard built from PlotLy and Dash libraries, and (ii) a case-mix
summary data frame that could be downloaded from the Vantage6 aggregation server as a
Comma-Separated Values (CSV) file.
3. Results
For open data, the interested reader can get the original clinical data frame directly from TCIA.
An example fragment of TTL and OWL generated by triplifier is shown in Fig 3. Note that
(for ease of understanding) we have compressed the namespaces using standard semantic web
notation in the bottom-left corner of the figure.
A graphical representation of as-serialized TTL content for one subject is shown on the left
side of Fig 4. For argument’s sake, the original contents might not be syntactically usable to the
reader (e.g., the labels are in the Dutch language) and are not yet semantically interoperable. On
the right side of Fig 4, we show how dataset-specific annotations mapped to the ROO and NCIT
ontologies (including new descriptive predicates) render this data more FAIR. The inserted
annotations are strictly additive, i.e., it does not alter or over-write the as-serialized contents. For
simplicity of visualization, we masked some of the schema classes and predicates auto generated
by triplifier. One can readily look up the unique URIs - C25364, C28421 and C16576 in the NCIT
and find definitions for “patient identifier”, “sex” and “female”, respectively. Likewise, with
the ROO, the URIs P100061, P100018 and P100042 are resolved as predicates “has_identifier”,
“has_biological_sex” and “has_value”, respectively. Where needed, numerical values may be
supplemented by extra predicates and classes indicating the exact units of the measure, e.g., age
in years, and follow-up time as intervals of days, or months, or years. Additionally, if concepts
like dates (Date of Birth/Death) need to be re-formatted to an agreed style (e.g., DD/MM/YYYY),
an appropriate formatting task can be sent using the Vantage6 server to the data nodes.
A snapshot of an interactive visual dashboard is given as Fig 5, and we retrieved a case-mix
aggregated summary table directly from the Vantage6 server as a CSV file. The latter was then
reformatted and tidied to provide Table 1 in Supplementary material.
4. Discussion and Conclusion
We have produced a partly automated procedure that makes structured clinical data FAIR and
available for distributed applications. Semantic interoperability and data linking has been
achieved by local annotation with semantic ontologies, such that a single global SPARQL query
using those ontologies will correctly filter the data.
This illustrative use case was selected to address how common clinically relevant questions
may be addressed via privacy-preserving federated learning. In our distributed dashboard
approach, we envisage that partners in an established collaboration will be able to safely explore
and inter-compare each other’s private data repositories for suitable subsets of patients, without
violating patient privacy. If open datasets are also annotated and made FAIR in the abovemen-
tioned manner, then published via an accessible web address, they can also be efficiently queried
en masse with a single query in the above manner.
One of the most important and effective ways to make data FAIR is to assign a globally unique
and persistent identifier to both the data repository and its linked metadata. The four open
datasets here are unambiguously referenced using a Digital Object Identifier (DOI). Though
private dataset HN3 is not openly accessible, we do openly disseminate the readable description
of the dataset plus the schema (OWL) and its semantic ontology annotations (“annotation.local”)
in open Zenodo repository with its unique persistent DOI for the metadata.
Assuming that re-casting the data into a universal master schema is not already done, our
method proposes a flexible and adaptable means of applying semantic interoperability by means
of annotation with an open semantic ontologies. In the Linked Data paradigm, each data entity
as well as its relationship to other data entities is traceably and collectively mapped to a unique
and persistent identifier. Every instance of the same identifier must mean semantic equivalence,
entirely irrespective of the human-readable label, which is generally in the data owner’s own
language. The ontologies not only establish the terminology and definitions but also include
some knowledge representation that allows the possibility to apply machine-assisted logical
inferencing.
Figure 1: Making clinical data accessible as a FAIR graph database object
Availability
Project name: Flyover (tagged release: v1.0, preprint demonstration project branch: MedRxiv)
Project home page: https://github.com/MaastrichtU-CDS/projects_flyover_project
Zenodo Repository DOI: https://doi.org/10.5281/zenodo.7190551
Four public datasets were obtained from TCIA. RADIOMICS-HN1 [18] comprises clinical
data, volumetric CT and PET of 137 patients with laryngeal carcinoma and OPC treated by
RT alone or currently with either cisplatin or cetuximab. HNSCC contains clinical data and
contrast-enhanced CT scans of 627 oropharyngeal cancer (OPC) patients [5]. OPC-Radiomics
has clinical data and CT scans of 606 OPC subjects, treated by either radiotherapy or chemo-
radiotherapy between 2005 and 2010 [6]. HEAD-NECK-PET-CT [17] com-prised 298 subjects
with multiple subsites of HNC each with clinical descriptors, PET and planning CT, treated
between April 2006 and November 2014. The HN3 dataset is not publicly available at the present
time due to material that is potentially identifiable to an individual.
Figure 2: Schematic illustration of the Vantage6 infrastructure used in this work to show a likely clinical
use case
Figure 3: Example showing the expected output of triplifier processing. A hypothetical input table
is shown on the left. Namespace aliases are used to improve readability. A fragment of the serialized
database contents is shown top right in the TTL file, and a part of the database schema is shown with a
database-specific ontology in the OWL file at bottom right
Figure 4: Examples showing knowledge graph of a patient “0” with classes ID and biological sex. The
image on the left is from Triplifier after the data has been converted to RDF. On the right, is the image
after the annotation graph has been added. Double-sided green arrows with double crossing bars are
the predicate owl:equivalentClass
Figure 5: Distributed Dashboard
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Supplementary
Table 1
Patient Demographics Table from five data nodes
HN1 HNSCC OPC HEAD-NECK HN3
Sample size 137 492 606 298 165
Age in years
Mean 61.9 57.8 60.5 63.3 62.6
Range 44-83 28-87 33-89 18-90 29-84
Sex
Female 26 69 125 71 43
Male 111 423 481 227 122
Tumour stage
T1 35 92 103 39 14
T2 32 203 198 109 31
T3 24 117 183 94 68
T4 46 80 122 46 52
Tx - - - 10 -
Nodal stage
N0 60 45 101 59 48
N1 16 53 61 40 45
N2 58 378 397 180 54
N3 3 16 47 19 18
Nx - - - - -
Metastasis stage
M0 136 492 606 294 165
M1 1 0 - 0 0
Mx - - - 4 -
Overall stage (7th ed.)
I 24 3 11 4 -
II 11 16 38 27 -
III 23 67 85 61 -
IV‘ 79 406 472 204 -
Unspecified - - - 2 -
Tumour location
Nasopharynx - - - 28 -
Oropharynx 88 492 606 203 63
Hypopharynx - - - 13 31
Larynx 49 - - 45 64
Unknown - - - 9 -
HPV status
Positive 23 248 356 78 34
Negative 58 44 143 46 29
Unknown 56 200 107 174 102
Radiotherapy type
Radiotherapy 100 57 309 48 104
Chemoradiotherapy 37 435 297 250 61
Survival status
Censored 63 376 347 242 77
Deceased 74 116 259 56 88