TY - JOUR
T1 - Identification of Key Elements in Prostate Cancer for Ontology Building via a Multidisciplinary Consensus Agreement
AU - Moreno, Amy
AU - Solanki, Abhishek A.
AU - Xu, Tianlin
AU - Lin, Ruitao
AU - Palta, Jatinder
AU - Daugherty, Emily
AU - Hong, David
AU - Hong, Julian
AU - Kamran, Sophia C.
AU - Katsoulakis, Evangelia
AU - Brock, Kristy
AU - Feng, Mary
AU - Fuller, Clifton
AU - Mayo, Charles
AU - Consortium, BDSC Prostate Cancer
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/6
Y1 - 2023/6
N2 - Background: Clinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research. Methods: A modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM). Results: The panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM. Conclusions: This expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).
AB - Background: Clinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research. Methods: A modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM). Results: The panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM. Conclusions: This expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).
KW - clinical guidelines
KW - informatics
KW - ontology
KW - prostate cancer
KW - treatment-related toxicities
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U2 - 10.3390/cancers15123121
DO - 10.3390/cancers15123121
M3 - Article
C2 - 37370731
AN - SCOPUS:85163980880
SN - 2072-6694
VL - 15
JO - Cancers
JF - Cancers
IS - 12
M1 - 3121
ER -