Background: In patients with advanced melanoma the detection of BRAF mutations is considered mandatory before the initiation of an expensive treatment with BRAF/MEK inhibitors. Sometimes it is difficult to perform such an analysis if archival tumor tissue is not available and fresh tissue has to be collected.
Results: 514 of 1170 patients (44%) carried a BRAF mutation. All models revealed age and histological subtype of melanoma as the two major predictive variables. Accuracy ranged from 0.65-0.71, being best in the random forest model. Sensitivity ranged 0.76-0.84, again best in the random forest model. Specificity was low in all models ranging 0.51-0.55.
Methods: We collected the clinical data and mutational status of 1170 patients with advanced melanoma and established three different predictive models (binary logistic regression, classification and regression trees, and random forest) to forecast the BRAF status.
Conclusions: Up to date statistical models are not able to predict BRAF mutations in an acceptable accuracy. The analysis of the mutational status by sequencing or immunohistochemistry must still be considered as standard of care.
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http://dx.doi.org/10.18632/oncotarget.9143 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
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View Article and Find Full Text PDFSci Adv
January 2025
Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA.
Climate change threatens smallholder agriculture and food security in the Global South. While cropland expansion is often used to counter adverse climate effects despite ecological trade-offs, the benefits for diets and nutrition remain unclear. This study quantitatively examines relationships between climate anomalies, forest loss from cropland expansion, and dietary outcomes in Nigeria, Africa's most populous country.
View Article and Find Full Text PDFVet Med Sci
January 2025
Department of Microbiology, Faculty of Veterinary and Animal Science, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
Background: Brucellosis is a zoonotic disease caused by Brucella spp., affecting various animals and humans, leading to significant economic and public health impacts. Traditional diagnostic methods, mainly serological, often fail to detect seronegative carriers, which continue to spread the infection.
View Article and Find Full Text PDFCells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Department of Computer Science, School of Arts, Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK.
: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy.
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