Purpose: This study aimed to investigate validity of a newly developed Morbidity Screening Tool (MST) to screen for fatigue, pain, swelling (lymphedema) and arm function after breast cancer treatment.
Methods: A cross-sectional study included women attending reviews after completing treatment (surgery, chemotherapy and radiotherapy), without recurrence, who could read English. They completed the MST and comparator questionnaires: Disability of the Arm, Shoulder and Hand questionnaire (DASH), Chronic Pain Grade Questionnaire (CPGQ), Lymphedema and Breast Cancer Questionnaire (LBCQ) and Functional Assessment of Cancer Therapy questionnaire with subscales for fatigue (FACT F) and breast cancer (FACT B + 4). Bilateral combined shoulder ranges of motion were compared (upward reach; hand behind back) and percentage upper limb volume difference (%LVD =/>10% diagnosed as lymphedema) measured with the vertical perometer (400T).
Results: 613 of 617 participants completed questionnaires (mean age 62.3 years, SD 10.0; mean time since treatment 63.0 months, SD 46.6) and 417 completed objective testing. Morbidity prevalence was estimated as 35.8%, 21.9%, 19.8% and 34.4% for fatigue, impaired upper limb function, lymphedema and pain respectively. Comparing those self-reporting the presence or absence of each type of morbidity, statistically significant differences in comparator variables supported validity of the MST. Statistically significant correlations resulted between MST scores focussing on impact of morbidity, and comparator variables that reflect function and quality of life.
Conclusion: Analysis supports the validity of all four short-forms of the MST as providing indications of both presence of morbidity and impacts on participants' lives. This may facilitate early and appropriate referral for intervention.
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http://dx.doi.org/10.1016/j.ejon.2013.10.006 | DOI Listing |
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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