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Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis  Xin Luo, MD, Xiao Zang, BS, Lin Yang, MD, Junzhou Huang, PhD,

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Presentation on theme: "Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis  Xin Luo, MD, Xiao Zang, BS, Lin Yang, MD, Junzhou Huang, PhD,"— Presentation transcript:

1 Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis 
Xin Luo, MD, Xiao Zang, BS, Lin Yang, MD, Junzhou Huang, PhD, Faming Liang, PhD, Jaime Rodriguez-Canales, MD, Ignacio I. Wistuba, MD, Adi Gazdar, MD, Yang Xie, MD, PhD, Guanghua Xiao, PhD  Journal of Thoracic Oncology  Volume 12, Issue 3, Pages (March 2017) DOI: /j.jtho Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

2 Figure 1 Pathological image data processing and analyses pipeline. Image data obtained from The Cancer Genome Atlas lung cancer cohort contain slides of both nonmalignant lung and tumor. After initial filtering and normalization, morphological phenotypes of images were analyzed by using CellProfiler, and 943 morphological features of tissue texture, cells, nuclei, and neighboring architecture were extracted. These features were used to develop prediction models for patient survival, and cross-validations were implemented for model evaluation. Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

3 Figure 2 Associations between survival outcomes and morphological feature–defined risk groups in The Cancer Genome Atlas (TCGA) cohorts of patients with adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Kaplan-Meier survival curves for patients in the high-risk group (red) and low-risk group classified by morphological feature–based prediction models for both ADC (p = 0.009) (A) and SCC (p = ) (B). The features and prediction model were derived from the training data, and the survival analysis was performed in the testing data set. Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

4 Figure 3 Representative images for high- and low-risk patients with adenocarcinoma (ADC) and squamous cell carcinoma (SCC). The patients with ADC and SCC in the testing set were classified into high- and low-risk groups on the basis of prediction from extracted morphological features. Three representative images are shown for each risk group for patients with ADC (A) and SCC (B). Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

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