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Updated: Jun 11, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Establishing Diagnostic Cut-Off Scores for the Perceived Cognitive Impairment Subscale of the Functional Assessment
Ana F Oliveira1,2, Sofia A Marques-Fernandes1,3, Ana Bártolo4
1RISE-Health, Department of Education and Psychology, University of Aveiro, Aveiro, Portugal.
Objective:
Cancer-related cognitive impairment (CRCI) negatively impacts the quality of life of non-central nervous system (non-CNS) cancer survivors. Detecting these cognitive changes remains challenging due to the lack of standardized CRCI classification and detection criteria. This study aimed to establish cut-off scores for the perceived cognitive impairment (CogPCI) subscale of the European Portuguese version of the Functional Assessment of Cancer Therapy-Cognitive Function-Version 3 (FACT-Cog-v3).
Methods:
We analyzed data from 266 Portuguese-speaking non-CNS cancer survivors and performed a receiver operating characteristic (ROC) curve analysis to evaluate the diagnostic validity of the CogPCI subscale of the FACT-Cog-v3 to detect meaningful perceived CRCI.
Results:
The CogPCI subscale demonstrated good screening performance in identifying individuals with and without clinically relevant CRCI, as indicated by a moderate-to-high area under the curve (AUC) score of 0.87. A suggested optimal cut-off score of < 51 provided good sensitivity (81.2%) and specificity (78.9%) for CogPCI to differentiate between the two groups. Over half of the participants reported significant cognitive complaints, highlighting the urgent need for standardized cognitive assessments in oncology.
Conclusions:
This study contributes valuable insights into the early and fast detection and intervention of CRCI in Portugal. Using a brief, validated, self-report cognitive assessment tool can enhance the management of cognitive symptoms in non-CNS cancer survivors.
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