The presentation discusses a study on lung cancer, the most prevalent cancer worldwide, focusing on its histopathological classification and the prediction of survival based on fully automated microscopic pathology image features. Utilizing databases like The Cancer Genome Atlas and Stanford Tissue Microarray, the researchers developed an automated workflow to analyze whole slide pathology for various types of lung cancer, including adenocarcinoma and squamous cell carcinoma. Results showed promise in predicting prognosis and survival rates, highlighting the potential of machine learning in pathology.

1. Presented by:
Tsai, Mu-Hung, M.D.
Department of Radiation Oncology, National Cheng Kung University Hospital
2016/09/02

2. Lung Cancer
 Most prevalent cancer worldwide
 Histopathological classification
 Small cell carcinoma
 Non-small cell carcinoma
 Squamous cell carcinoma
 Adenocarcinoma
 Grade (Gr.1 – Gr.3)
 Low level of inter-observer agreement

3. Pathology
Squamous cell carcinoma Adenocarcinoma

4. Aim
 Fully automated microscopic pathology image features
 Predict lung cancer survival

5. Training
Validation
Methods & Materials
 The Cancer Genome
Atlas (TCGA)
 Stanford Tissue
Microarray (TMA)
Database
Cross-validate

6. Image: euthman @ Flickr

9. Results
Identifying Tumor

10. Adenocarcinoma vs. Normal Lung

11. SCC vs. Normal Lung

12. Adenocarcinoma vs. SCC (TCGA)

13. Adenocarcinoma vs. SCC (TMA)

14. Results
Predicting Survival

15. Predicting Adenocarcinoma Prognosis

16. Stage IB Grade 3 adenocarcinoma

17. Predicting SCC Prognosis

18. Discussion
 Automated workflow to analyze whole slide pathology
 Elastic net-Cox proportional hazards models
 Computationally efficient
 Handles right-censored survival data
 Performance not very sensitive to ML method
 Obtain large number of features!