Figure 4 : Quantitative image features predicted the survival outcomes of lung squamous cell carcinoma patients.

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

Figure 4

(a) Kaplan–Meier curves of lung squamous cell carcinoma patients stratified by tumour stage. Although patients with higher stages generally have worse outcomes, the trend was not statistically significant (left: TCGA data set, log-rank test P value=0.216; right: TMA data set, log-rank test P value=0.388). (b) Kaplan–Meier curves of stage I lung squamous cell carcinoma patients stratified by tumour grade. Tumour grade did not significantly correlate with survival. (left: TCGA data set, log-rank test P value=0.847; right: TMA data set, log-rank test P value=0.964). (c) Kaplan–Meier curves of lung squamous cell carcinoma patients stratified using quantitative image features. The image features predicted the survival outcomes. Elastic net-Cox proportional hazards model categorized patients into two prognostic groups, with a statistically significant difference in their survival in the TCGA test set (log-rank test P value=0.023). (d) The same classification workflow was validated in the TMA data set, with comparable prediction performance. (log-rank test P value=0.035). (e) Sample image of lung squamous cell carcinoma in a patient with long survival. This patient suffered from stage I, grade 1 lung squamous cell carcinoma, and survived more than 70 months after diagnosis. Our classifier correctly predicted the patient as a long survivor. (f) Sample image of squamous cell carcinoma in a patient with short survival. This patient suffered from stage I, grade 1 lung squamous cell carcinoma, and only survived 12.4 months after diagnosis. Our classifier correctly predicted the patient as a short survivor.