Clinical utility of the mood and anxiety symptom questionnaire in a chinese sample of patients with pancreatic cancer

Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.

Psycho-oncology assessment in Chinese populations: a systematic review of quality of life and psychosocial measures

This systematic review describes psychosocial and quality of life (QOL) measures used in psycho-oncology research with cancer patients and caregivers in China. Medline and PsycINFO databases were searched (1980-2014). Studies reviewed met the following criteria: English language; peer-reviewed; sampled Chinese cancer patients/caregivers; developed, validated or assessed psychometric properties of psychosocial or QOL outcome measures; and reported validation data. The review examined characteristics of measures and participants, translation and cultural adaptation processes and psychometric properties of the measures. Ninety five studies met review criteria. Common characteristics of studies reviewed were they: assessed primarily QOL measures, sampled patients with breast, colorectal, or head and neck cancer, and validated existing measures (>80%) originating in North America or Europe. Few studies reported difficulties translating measures. Regarding psychometric properties of the measures >50% of studies reported subscale reliabilities <α = 0.70, <50% reported test-retest reliability, and <30% reported divergent validity. Few reported sensitivity, specificity or responsiveness. Improved accuracy and transparency of reporting for translation, cultural adaptation and psychometric testing of psychosocial measures is needed. Developing support structures for translating and validating psychosocial measures would enable this and ensure Chinese psycho-oncology clinical practice and research keeps pace with international focus on patient reported outcome measures and data management.