Background: In the frame of a nationwide study of oropharyngeal carcinoma in the Netherlands (1986-1990), the current International Union Against Cancer 1992/American Joint Committee on Cancer 1988 staging system was evaluated with respect to patient distribution and prognostic value.
Methods: Data related to epidemiology, treatment and survival from 640 patients referred for primary treatment were analyzed. Staging was first evaluated in a proportional-hazard regression analysis controlled for these data. Next, all possible combinations of T, N, and M were tested in a stepwise backward elimination model until all remaining indicator variables had a P value of less than 0.05. New stages were defined, based on the coefficients of the remaining indicator variables.
Results: The revised stages revealed two advantages compared with the UICC 1992/AJCC 1988 version: a more balanced distribution of patients (31% in Stage I, 31% in Stage II, 18% in Stage III, 14% in Stage IV, and 5% unknown in the revised staging system versus 7% in Stage I, 17% in Stage II, 24% in Stage III, 50% in Stage IV, and 2% unknown in the UICC 1992/AJCC 1988 staging system), and an improved prognostic discrimination for the disease specific survival (5-year results in the revised staging were 67% in Stage I, 42% in Stage II, 28% in Stage III, and 11% in Stage IV, versus 68% in Stage I, 64% in Stage II, 44% in Stage III and 27% in Stage IV in UICC 1992/AJCC 1988).
Conclusion: Improvements in the current staging system in patient distribution in the stages in prognostic discrimination is feasible by regrouping the T, N, and M but without redefining the categories themselves.
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http://dx.doi.org/10.1002/1097-0142(19950601)75:11<2656::aid-cncr2820751103>3.0.co;2-r | DOI Listing |
Sci Adv
March 2025
Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA.
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies.
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March 2025
Cancer Ecosystems Program, Garvan Institute of Medical Research and The Kinghorn Cancer Centre, Darlinghurst, Sydney, New South Wales, Australia.
Pancreatic cancer (PC) is a highly metastatic malignancy. More than 80% of patients with PC present with advanced-stage disease, preventing potentially curative surgery. The neuropeptide Y (NPY) system, best known for its role in controlling energy homeostasis, has also been shown to promote tumorigenesis in a range of cancer types, but its role in PC has yet to be explored.
View Article and Find Full Text PDFPLoS Negl Trop Dis
March 2025
Center for Tropical and Emerging Global Diseases, University of Georgia, Athens, Georgia, United States of America.
Trypanosoma cruzi is a single-celled eukaryotic parasite responsible for Chagas disease, a major cause of morbidity and mortality in Central and South America. While the host-pathogen interactions of T. cruzi have been extensively studied in vertebrate models, investigations into its interactions within its insect host remain limited.
View Article and Find Full Text PDFJMIR Med Inform
March 2025
Center for General Practice at Aalborg University, Department of Clinical Medicine, Aalborg University, Selma Lagerløfs vej 249, Aalborg, 9260 Gistrup, Denmark, 45 29807944.
Background: Artificial intelligence (AI) has been deemed revolutionary in medicine; however, no AI tools have been implemented or validated in Danish general practice. General practice in Denmark has an excellent digitization system for developing and using AI. Nevertheless, there is a lack of involvement of general practitioners (GPs) in developing AI.
View Article and Find Full Text PDFElife
March 2025
Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.
Longitudinal neuroimaging studies offer valuable insight into brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully leverage the potential of longitudinal neuroimaging, we need methodologies that account for the complex interplay between population variation and individual dynamics.
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