Vascular network organization via hough transform (VaNgOGH): A novel radiomic biomarker for diagnosis and treatment response

As a “hallmark of cancer”, tumor-induced angiogenesis is one of the most important mechanisms of a tumor’s adaptation to changes in nutrient requirement. The angiogenic activity of certain tumors has been found to be predictive of a patient’s ultimate response to therapeutic intervention. This then begs the question if there are differences in vessel arrangement and corresponding convolutedness, between tumors that appear phenotypically similar, but respond differently to treatment. Even though textural radiomics and deep learning-based approaches have been shown to distinguish disease aggressiveness and assess therapeutic response, these descriptors do not specifically interpret differences in vessel characteristics. Moreover, most existing approaches have attempted to model disease characteristics just within tumor confines, or right outside, but do not consider explicit parenchymal vessel morphology. In this work, we introduce VaNgOGH (Vascular Network Organization via Hough transform), a new descriptor of architectural disorder of the tumor’s vascular network. We demonstrate the efficacy of VaNgOGH in two clinically challenging problems: (a) Predicting pathologically complete response (pCR) in breast cancer prior to treatment (BCa, N = 76) and (b) distinguishing benign nodules from malignant non-small cell lung cancer (LCa, N = 81). For both tasks, VaNgOGH had test area under the receiver operating characteristic curve (AUC_BCa =0.75, AUC_LCa =0.68) higher than, or comparable to, state of the art radiomic approaches (AUC_BCa =0.75, AUC_LCa =0.62) and convolutional neural networks (AUC_BCa =0.67, AUC_LCa =0.66). Interestingly, when a known radiomic signature was used in conjunction with VaNgOGH, AUC_BCa increased to 0.79.