Abstract

Purpose Precision cancer medicine is dependent on accurate prediction of disease and treatment outcome, requiring integration of clinical, imaging and interventional knowledge. User controlled pipelines are capable of feature integration with varied levels of human interaction. In this work we present two pipelines designed to combine clinical, radiomic (quantified imaging), and RTx-omic (quantified radiation therapy (RT) plan) information for prediction of locoregional failure (LRF) in head and neck cancer (HN and 2) a pipeline with minimal user input that utilizes deep learning convolutional neural networks to extract and combine CT imaging, RT dose and clinical features for model development. Results Clinical features with logistic regression in our highly user-driven pipeline had the highest precision recall area under the curve (PR-AUC) of 0.66 (0.33–0.93), where a PR-AUC = 0.11 is considered random. CONCLUSIONS: Our work demonstrates the potential to aggregate features from multiple specialties for conditional-outcome predictions using pipelines with varied levels of human interaction. Most importantly, our results provide insights into the importance of data curation and quality, as well as user, data and methodology bias awareness as it pertains to result interpretation in user controlled pipelines.

Abstract

In-service degradation of a pipe section is monitored in real time with torsional guided waves which can resolve 5% damage to the internal wall. We use a single permanently installed source–receiver pair as opposed to the current state-of-the-art sensor rings. There is no baseline subtraction requirement as a single reflection coefficient is derived by internal referencing of the time trace. Even with continuously operating pump vibration, there is enough acoustic signal for confident damage localization. Since pipelines wear out gradually in industrial installations, the acoustic footprint is similar to that previously determined in periodically damaged pipes. The reflective method can thus be applied successfully to monitor structural health in industrial pipelines during operation as opposed to the current state-of-the-art guided wave inspection approaches using near-weld reflection techniques along with disassembled and re-assembled sensor rings.