Psychometric properties and measurement equivalence of the Multidimensional Fatigue Syndrome Inventory- Short Form (MFSI-SF) amongst breast cancer and lymphoma patients in Singapore.

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Psychometric properties and measurement equivalence of the Multidimensional Fatigue Syndrome Inventory- Short Form (MFSI-SF) amongst breast cancer and lymphoma patients in Singapore.

Health Qual Life Outcomes. 2018 Jan 19;16(1):20

Authors: Chan A, Lew C, Wang XJ, Ng T, Chae JW, Yeo HL, Shwe M, Gan YX

Abstract
BACKGROUND: Currently, several fatigue measurement instruments are available to evaluate and measure cancer-related fatigue. Amongst them, Multidimensional Fatigue Syndrome Inventory-Short Form (MFSI-SF) is a self-reported instrument and a multidimensional scale that aims to capture the global, somatic, affective, cognitive and behavioural symptoms of fatigue. This study examines the psychometric properties and measurement equivalence of the English and Chinese versions of MFSI-SF in breast cancer and lymphoma patients in Singapore.
METHODS: Patients were recruited from National Cancer Centre Singapore. Validity, reliability and responsiveness of MFSI-SF were evaluated in this study. Convergent validity was evaluated by correlating total and subscales of MFSI-SF to known related constructs in EORTC QLQ-C30. Known group validity was assessed based on patients' cancer stage, pain, insomnia and depression symptoms. Reliability was evaluated by Cronbach's α. Responsiveness analyses were performed with patients who have undergone at least one cycle of chemotherapy. Multiple regression was used to compare the total and subscale scores of MSFI-SF between the two language versions.
RESULTS: Data from 246 (160 English and 86 Chinese version) breast cancer and lymphoma patients were included in the study. Moderate to high correlations were observed between correlated MFSI-SF subscales and EORTC QLQ-C30 domains (|r| = 0.524 to 0.774) except for a poor correlation (r = 0.394) observed between MFSI-SF vigour subscale and EORTC QLQ-C30 role functioning subscale. Total MFSI-SF scores could differentiate between patients with higher depression, pain and insomnia status. Internal consistency of MFSI-SF was also high (α = 0.749 to 0.944). Moderate correlation was observed between change in total MFSI-SF score and change in fatigue symptom scale score and global QoL score on EORTC QLQ-C30 (|r| = 0.478 and 0.404 respectively). Poor correlations were observed between change in scores of hypothesised subscales (|r| = 0.202 to 0.361) except for a moderate correlation between change in MFSI-SF emotional fatigue score and change in EORTC QLQ-C30 emotional functioning domain score. Measurement equivalence was established for all subscales and total MFSI-SF score except for the emotional and vigour subscales.
CONCLUSIONS: This study supports the use of MFSI-SF as a reasonably valid scale with good internal consistency for measuring fatigue levels in the Singapore cancer population.

PMID: 29351803 [PubMed - in process]

Identification of the functional alteration signatures across different cancer types with support vector machine and feature analysis.

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Identification of the functional alteration signatures across different cancer types with support vector machine and feature analysis.

Biochim Biophys Acta. 2017 Dec 19;:

Authors: Wang S, Cai Y

Abstract
Cancers are regarded as malignant proliferations of tumor cells present in many tissues and organs, which can severely curtail the quality of human life. The potential of using plasma DNA for cancer detection has been widely recognized, leading to the need of mapping the tissue-of-origin through the identification of somatic mutations. With cutting-edge technologies, such as next-generation sequencing, numerous somatic mutations have been identified, and the mutation signatures have been uncovered across different cancer types. However, somatic mutations are not independent events in carcinogenesis but exert functional effects. In this study, we applied a pan-cancer analysis to five types of cancers: (I) breast cancer (BRCA), (II) colorectal adenocarcinoma (COADREAD), (III) head and neck squamous cell carcinoma (HNSC), (IV) kidney renal clear cell carcinoma (KIRC), and (V) ovarian cancer (OV). Based on the mutated genes of patients suffering from one of the aforementioned cancer types, patients they were encoded into a large number of numerical values based upon the enrichment theory of gene ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We analyzed these features with the Monte-Carlo Feature Selection (MCFS) method, followed by the incremental feature selection (IFS) method to identify functional alteration features that could be used to build the support vector machine (SVM)-based classifier for distinguishing the five types of cancers. Our results showed that the optimal classifier with the selected 344 features had the highest Matthews correlation coefficient value of 0.523. Sixteen decision rules produced by the MCFS method can yield an overall accuracy of 0.498 for the classification of the five cancer types. Further analysis indicated that some of these features and rules were supported by previous experiments. This study not only presents a new approach to mapping the tissue-of-origin for cancer detection but also unveils the specific functional alterations of each cancer type, providing insight into cancer-specific functional aberrations as potential therapeutic targets. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

PMID: 29277326 [PubMed - as supplied by publisher]