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Oral Mouth Cancer Biography
Abstract—One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely
the cancer will relapse, clinical practice usually recommends
adjuvant treatments that have strong side-effects. A way to
optimize treatments is to predict the recurrence probability by
analysing a set of bio-markers. The NeoMark European project
has identified a set of preliminary bio-markers for the case of
oral cancer by collecting a large series of data from genomic,
imaging and clinical evidences. This heterogeneous set of data
needs a proper representation in order to be stored, computed
and communicated efficiently. Ontologies are often considered the
proper mean to integrate biomedical data, for their high level of
formality and for the need of interoperable, universally accepted,
models. This paper presents the NeoMark system and how an
ontology has been designed to integrate all its heterogeneous data.
The system has been validated in a pilot which data will populate
the ontology and will be made public for further research.
Index Terms—Cancer, Genetic expression, Computer aided
diagnosis, Biomedical image processing
I. INTRODUCTION
Cancer is the second cause of death in western countries.
Although current treatments can be effective, the main problem of cancer is its recurrence either locally or by distant
metastases, which are difficult to predict and prevent. The Oral
Squamous Cell Carcinoma (OSCC), focus of this research,
accounts for the 5% of all cancers, and has a rate of 25
to 50% of recurrence in 5 years, 90% of which within two
years from surgery [1]. In order to avoid relapses, adjuvant
chemo or radio-therapy treatments are usually administered
to all patients during follow-up, even in absence of disease
signs. These treatments are heavy and have strong side-effects
that may harm also patients who are in fact already completely
recovered. Knowing in advance which patients have the higher
risk of disease recurrence, would help to initiate adjuvant treatments only in a limited, high-risk subgroup. In addition, the
early identification of a neoplastic recurrence during follow-up
would allow starting an appropriate treatment in time.
D.Salvi, M.T. Arredondo and M.F. Cabrera are with Life Supporting Technologies, Universidad Politecnica
The most classical method to predict OSCC recurrence is
the TNM staging, which is based mainly on the dimensional
characteristics of the tumour and on the presence, number and
site of neck nodes metastasis. Unfortunately, its inadequacy
is today recognised because of the uncertain behaviour of
squamous cancer, which can be sometimes very aggressive
and others can metastasize slowly after surgery [2]. This
uncertainty in progression has led researchers to seek a
larger number of markers. Many clinical, histopathological,
radiological and genetic factors were studied, but none of the
different groups taken, distinctly provides clinically applicable
markers of tumour aggressiveness [3]. Given the multi-level
nature of cancer (genes, cells, tissues, organs) integration of
the different groups of data is required. Whereas different
reports are present in literature on data integration and creation
of standardized prognostic algorithms for bladder and breast
cancer, nothing is available for head and neck cancer. To cover
this lack, the NeoMark project was created.
NeoMark [4] is an European co-funded research project
which aimed at identifying the optimal set of patient-specific
and disease-specific bio-markers with a high predictive power
for the case of OSCC cancer.
The NeoMark strategy is designed to be integrated into
normal staging and follow-up protocols. Patients are assessed
before treatment and, at the time of remission, a wide range
of data is collected including clinical observations, radiologic
and genomic data. A set of relevant markers expressed only in
presence of the disease is then selected and relapse probability
is estimated. If the same set of bio-markers appears during
post-remission follow-up, it would show a high probability of
relapse, advising early intervention.
This strategy is supported by an Information and Communications Technology (ICT) based system which allows
physicians to:
administer patients
upload clinical data including histological information,
surgery evidence, and risk factors
analyse jointly multiple images from MR/CT scans
analyse gene expressions by means of micro-arrays and
a mobile PCR system
receive indicators of the probability of relapse for supporting the clinical decision during the follow-up
download anonymised data for further research and statistics
The following sections describe the details of the NeoMark
system, how an ontology for integrating all the data collected
in the system has been designed, some results of the pilot we
Oral Mouth Cancer Biography
Abstract—One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely
the cancer will relapse, clinical practice usually recommends
adjuvant treatments that have strong side-effects. A way to
optimize treatments is to predict the recurrence probability by
analysing a set of bio-markers. The NeoMark European project
has identified a set of preliminary bio-markers for the case of
oral cancer by collecting a large series of data from genomic,
imaging and clinical evidences. This heterogeneous set of data
needs a proper representation in order to be stored, computed
and communicated efficiently. Ontologies are often considered the
proper mean to integrate biomedical data, for their high level of
formality and for the need of interoperable, universally accepted,
models. This paper presents the NeoMark system and how an
ontology has been designed to integrate all its heterogeneous data.
The system has been validated in a pilot which data will populate
the ontology and will be made public for further research.
Index Terms—Cancer, Genetic expression, Computer aided
diagnosis, Biomedical image processing
I. INTRODUCTION
Cancer is the second cause of death in western countries.
Although current treatments can be effective, the main problem of cancer is its recurrence either locally or by distant
metastases, which are difficult to predict and prevent. The Oral
Squamous Cell Carcinoma (OSCC), focus of this research,
accounts for the 5% of all cancers, and has a rate of 25
to 50% of recurrence in 5 years, 90% of which within two
years from surgery [1]. In order to avoid relapses, adjuvant
chemo or radio-therapy treatments are usually administered
to all patients during follow-up, even in absence of disease
signs. These treatments are heavy and have strong side-effects
that may harm also patients who are in fact already completely
recovered. Knowing in advance which patients have the higher
risk of disease recurrence, would help to initiate adjuvant treatments only in a limited, high-risk subgroup. In addition, the
early identification of a neoplastic recurrence during follow-up
would allow starting an appropriate treatment in time.
D.Salvi, M.T. Arredondo and M.F. Cabrera are with Life Supporting Technologies, Universidad Politecnica
The most classical method to predict OSCC recurrence is
the TNM staging, which is based mainly on the dimensional
characteristics of the tumour and on the presence, number and
site of neck nodes metastasis. Unfortunately, its inadequacy
is today recognised because of the uncertain behaviour of
squamous cancer, which can be sometimes very aggressive
and others can metastasize slowly after surgery [2]. This
uncertainty in progression has led researchers to seek a
larger number of markers. Many clinical, histopathological,
radiological and genetic factors were studied, but none of the
different groups taken, distinctly provides clinically applicable
markers of tumour aggressiveness [3]. Given the multi-level
nature of cancer (genes, cells, tissues, organs) integration of
the different groups of data is required. Whereas different
reports are present in literature on data integration and creation
of standardized prognostic algorithms for bladder and breast
cancer, nothing is available for head and neck cancer. To cover
this lack, the NeoMark project was created.
NeoMark [4] is an European co-funded research project
which aimed at identifying the optimal set of patient-specific
and disease-specific bio-markers with a high predictive power
for the case of OSCC cancer.
The NeoMark strategy is designed to be integrated into
normal staging and follow-up protocols. Patients are assessed
before treatment and, at the time of remission, a wide range
of data is collected including clinical observations, radiologic
and genomic data. A set of relevant markers expressed only in
presence of the disease is then selected and relapse probability
is estimated. If the same set of bio-markers appears during
post-remission follow-up, it would show a high probability of
relapse, advising early intervention.
This strategy is supported by an Information and Communications Technology (ICT) based system which allows
physicians to:
administer patients
upload clinical data including histological information,
surgery evidence, and risk factors
analyse jointly multiple images from MR/CT scans
analyse gene expressions by means of micro-arrays and
a mobile PCR system
receive indicators of the probability of relapse for supporting the clinical decision during the follow-up
download anonymised data for further research and statistics
The following sections describe the details of the NeoMark
system, how an ontology for integrating all the data collected
in the system has been designed, some results of the pilot we
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