Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma.

OBJECTIVE: Radiomics pipelines have been developed to extract novel information from radiological images, which may help in phenotypic profiling of tumours that would correlate to prognosis. Here, we compared two publicly available pipelines for radiomics analyses on head and neck CT and MRI in nasopharynx cancer (NPC). METHODS AND MATERIALS: 100 biopsy-proven NPC cases stratified by T- and N-categories were enrolled in this study. Two radiomics pipeline, Moddicom (v. 0.51) and Pyradiomics (v. 2.1.2) were used to extract radiomics features of CT and MRI. Segmentation of primary gross tumour volume was performed using Velocity v. 4.0 by consensus agreement between three radiation oncologists. Intraclass correlation between common features of the two pipelines was analysed by Spearman's rank correlation. Unsupervised hierarchical clustering was used to determine association between radiomics features and clinical parameters. RESULTS: We observed a high proportion of correlated features in the CT data set, but not for MRI; 76.1% (51 of 67 common between Moddicom and Pyradiomics) of CT features and 28.6% (20 of 70 common) of MRI features were significantly correlated. Of these, 100% were shape-related for both CT and MRI, 100 and 23.5% were first-order-related, 61.9 and 19.0% were texture-related, respectively. This interpipeline heterogeneity affected the downstream clustering with known prognostic clinical parameters of cTN-status and GTVp. Nonetheless, shape features were the most reproducible predictors of clinical parameters among the different radiomics modules. CONCLUSION: Here, we highlighted significant heterogeneity between two publicly available radiomics pipelines that could affect the downstream association with prognostic clinical factors in NPC. ADVANCES IN KNOWLEDGE: The present study emphasized the broader importance of selecting stable radiomics features for disease phenotyping, and it is necessary prior to any investigation of multicentre imaging datasets to validate the stability of CT-related radiomics features for clinical prognostication.

Managing patients' wait time in specialist out-patient clinic using real-time data from existing queue management and ADT systems.

In major cancer centers, heavy patients load and multiple registration stations could cause significant wait time, and can be result in patient complains. Real-time patient journey data and visual display are useful tools in hospital patient queue management. This paper demonstrates how we capture patient queue data without deploying any tracing devices; and how to convert data into useful patient journey information to understand where interventions are likely to be most effective. During our system development, remarkable effort has been spent on resolving data discrepancy and balancing between accuracy and system performances. A web-based dashboard to display real-time information and a framework for data analysis were also developed to facilitate our clinics' operation. Result shows our system could eliminate more than 95% of data capturing errors and has improved patient wait time data accuracy since it was deployed.