title: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features.
abstract: Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.
authors.affiliation: Biomedical Informatics Program, Stanford University, 1265 Welch Road, MSOB, X-215, MC 5479, Stanford 94305-5479, California, USA.|Department of Genetics, Stanford University, 300 Pasteur Dr, M-344, Stanford 94305-5120, California, USA.
authors.forename: Kun-Hsing
authors.identifier: 0000-0001-9892-8218
authors.initials: KH
authors.lastname: Yu
authors.affiliation: Department of Computer Science, Stanford University, 353 Serra Mall, Stanford 94305-9025, California, USA.
authors.forename: Ce
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Pathology, Stanford University, 300 Pasteur Dr, L235, Stanford 94305, California, USA.
authors.forename: Gerald J
authors.initials: GJ
authors.lastname: Berry
authors.affiliation: Biomedical Informatics Program, Stanford University, 1265 Welch Road, MSOB, X-215, MC 5479, Stanford 94305-5479, California, USA.
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
authors.affiliation: Department of Computer Science, Stanford University, 353 Serra Mall, Stanford 94305-9025, California, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Ré
authors.affiliation: Biomedical Informatics Program, Stanford University, 1265 Welch Road, MSOB, X-215, MC 5479, Stanford 94305-5479, California, USA.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
authors.affiliation: Department of Genetics, Stanford University, 300 Pasteur Dr, M-344, Stanford 94305-5120, California, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Snyder
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis*; D000368:Aged; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D053208:Kaplan-Meier Estimate; D008168:Lung / Q000473:pathology*; D008175:Lung Neoplasms / Q000175:diagnosis*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010338:Pathology, Clinical / Q000379:methods; D011379:Prognosis