title: Statistical analysis of a lung cancer spectral histopathology (SHP) data set.
abstract: We report results on a statistical analysis of an infrared spectral dataset comprising a total of 388 lung biopsies from 374 patients. The method of correlating classical and spectral results and analyzing the resulting data has been referred to as spectral histopathology (SHP) in the past. Here, we show that standard bio-statistical procedures, such as strict separation of training and blinded test sets, result in a balanced accuracy of better than 95% for the distinction of normal, necrotic and cancerous tissues, and better than 90% balanced accuracy for the classification of small cell, squamous cell and adenocarcinomas. Preliminary results indicate that further sub-classification of adenocarcinomas should be feasible with similar accuracy once sufficiently large datasets have been collected.
authors: Department of Mathematics and Statistics and Program in Bioinformatics, Boston University, Boston, MA, USA.
authors: Xinying
authors: X
authors: Mu
authors: Mark
authors: M
authors: Kon
authors: Ayşegül
authors: A
authors: Ergin
authors: Stan
authors: S
authors: Remiszewski
authors: Ali
authors: A
authors: Akalin
authors: Clay M
authors: CM
authors: Thompson
authors: Max
authors: M
authors: Diem
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D013055:Spectrophotometry, Infrared