Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Nature Communications

Volumn 7, Issue , 2016, Pages

  Yu, Kun Hsing ^a   Zhang, Ce ^b   Berry, Gerald J ^c   Altman, Russ B ^a   Ré, Christopher ^b   Rubin, Daniel L ^a   Snyder, Michael ^a  

^a STANFORD UNIVERSITY   (United States)

^b Stanford University   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER; CELLS AND CELL COMPONENTS; DATABASE; DISEASE PREVALENCE; HISTOPATHOLOGY; IMAGE ANALYSIS; MACHINE LEARNING; RESPIRATORY DISEASE;

AGED; ARTICLE; AUTOMATION; BAYESIAN LEARNING; CANCER PROGNOSIS; CANCER STAGING; CANCER SURVIVAL; CANCER SURVIVOR; FEMALE; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; KERNEL METHOD; LUNG ADENOCARCINOMA; MAJOR CLINICAL STUDY; MALE; QUANTITATIVE ANALYSIS; RANDOM FOREST; SQUAMOUS CELL LUNG CARCINOMA; SUPPORT VECTOR MACHINE; SURVIVAL PREDICTION; ADENOCARCINOMA; IMAGE PROCESSING; KAPLAN MEIER METHOD; LUNG; LUNG TUMOR; MACHINE LEARNING; MIDDLE AGED; PATHOLOGY; PROCEDURES; PROGNOSIS; SQUAMOUS CELL CARCINOMA;

ADENOCARCINOMA; AGED; CARCINOMA, SQUAMOUS CELL; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KAPLAN-MEIER ESTIMATE; LUNG; LUNG NEOPLASMS; MACHINE LEARNING; MALE; MIDDLE AGED; PATHOLOGY, CLINICAL; PROGNOSIS;

EID: 84982218412     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms12474     Document Type: Article

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