Creating Prognostic Systems for Well-Differentiated Thyroid Cancer Using Machine Learning

Updates to staging models are needed to reflect a greater understanding of tumor behavior and clinical outcomes for well-differentiated thyroid carcinomas. We used a machine learning algorithm and disease-specific survival data of differentiated thyroid carcinoma from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute to integrate clinical factors to improve prognostic accuracy. The concordance statistic (C-index) was used to cut dendrograms resulting from the learning process to generate prognostic groups. We created one computational prognostic model (7 prognostic groups with C-index = 0.8583) based on tumor size (T), regional lymph nodes (N), status of distant metastasis (M), and age to mirror the contemporary American Joint Committee on Cancer (AJCC) staging system (C-index = 0.8387). We showed that adding histologic type (papillary and follicular) improved the survival prediction of the model. We also showed that 55 is the best cutoff of age in the model, consistent with the changes from the most recent 8th edition staging manual from AJCC. The demonstrated approach has the potential to create prognostic systems permitting data driven and real time analysis that can aid decision-making in patient management and prognostication

What information do shoppers share? The effect of personnel-, retailer-, and country-trust on willingness to share information

The relationship between consumers' privacy concerns and their willingness to disclose personal information to retailers is more complex than a simple negative one. The multi-faced context, within which privacy decisions take place, shapes and bounds this relationship. Drawing on privacy contextual integrity theory, we model the privacy decisions as influenced by individuals' multilevel trusting surroundings, which include trust in a retailer and in its personnel at the micro-level, and trust in a country at the macro-level. Based on 22,050 survey data across seven product categories in fourteen countries, our Bayesian multilevel modeling reveals that micro- and macro-level trust may promote consumers' disclosure intentions via three mechanisms: (1) micro-level trust positive effect on consumers' willingness to disclose their data; (2) micro-level trust effect by attenuating privacy concerns' negative influence on this willingness; and (3) the positive indirect effect of trust in the country on both the direct and indirect impacts of trust in a retailer and in its personnel. Interestingly, trust's direct effects are found in all the investigated types of information (i.e., identification, medical, financial, locational, demographic, lifestyle, and media usage data), whereas the indirect effects are found to vary across information types. Our post-hoc cluster analysis shows that different retail contexts can be classified into three clusters and help retailers understand whether they should invest in developing both trust in their retail company and in their personnel, or mainly on one of the two. By taking different types of trust and context effects into consideration, our findings help different retailers encourage customers to disclose their data with them. (C) 2020 New York University. Published by Elsevier Inc. All rights reserved