Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Abstract

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42-67.52, P < 0.001).