Non-small cell lung cancer (NSCLC) is the leading cause of cancer related deaths worldwide. The treatment of choice for early stage NSCLC is surgical resection followed by adjuvant chemotherapy for high risk patients. Currently, the decision to offer chemotherapy is primarily dependent on several clinical and visual radiographic factors as there is a lack of a biomarker which can accurately stratify and predict disease risk in these patients. Computer extracted image features from CT scans (radiomic) and (pathomic) from H&E tissue slides have already shown promising results in predicting recurrence free survival (RFS) in lung cancer patients. This paper presents new radiology-pathology fusion approach (RaPtomics) to combine radiomic and pathomic features for predicting recurrence in early stage NSCLC. Radiomic textural features (Gabor, Haralick, Law, Laplace and CoLlAGe) from within and outside lung nodules on CT scans and intranuclear pathology features (Shape, Cell Cluster Graph and Global Graph Features) were extracted from digitized whole slide H&E tissue images on an initial discovery set of 50 patients. The top most predictive radiomic and pathomic features were then combined and in conjunction with machine learning algorithms were used to predict classifier. The performance of the RaPtomic classifier was evaluated on a training set from the Cleveland Clinic (n=50) and independently validated on images from the publicly available cancer genome atlas (TCGA) dataset (n=43). The RaPtomic prognostic model using Linear Discriminant Analysis (LDA) classifier, in conjunction with two radiomic and two pathomic shape features, significantly predicted 5-year recurrence free survival (RFS) (AUC 0.78; p<0.005) as compared to radiomic (AUC 0.74; p<0.01) and pathomic (AUC 0.67; p<0.05) features alone.