Azer Rashidov, Husam Katib, Sarah K Alem, Faisal Al Harbi, Aminah Noor, Rosma Luna
The Epidemiology of Needlestick and Sharp Injuries Among Healthcare Workers in a Secondary Care Hospital in Saudi Arabia: A Retrospective Study
Cureus
cutaneous squamous cell carcinoma scc increasingly prevalent global health concern current diagnostic surgical methods reliable require considerable resources provide metabolomic insight matrixassisted laser desorptionionization mass spectrometry imaging maldimsi enables detailed spatially resolved metabolomic analysis tissue samples integrated machine learning maldimsi could yield detailed information pertaining metabolic alterations characteristic scc insights potential enhance scc diagnosis therapy improving patient outcomes tackling growing disease burden study employs maldimsi data labelled according histology train supervised machine learning model logistic regression recognition delineation scc model based data acquired discrete tumor sections n 25 mouse model scc achieved predictive accuracy 923 crossvalidation labelled data pathologist unacquainted dataset tasked evaluating predictive power model unlabelled regions agreed model prediction 99 tissue areas findings highlight potential value integrating maldimsi machine learning characterize delineate scc suggesting promising direction advancement mass spectrometry techniques clinical diagnosis scc related keratinocyte carcinomas