A Review of AI and Data Science Support for Cancer Management

Abstract

Introduction - Thanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ Data Science methods to monitor the health status and provide support to cancer patients managed at home. Objective - Our main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt. Methods - We designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient state from it and deliver coaching/behavior change interventions. Results - Starting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 110 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching. Conclusion - Development of modern decision support system for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality of life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

“… 113 The most frequent interventions through APPs are to improve the quality of life of patients, to relieve adverse emotions, such as anxiety and depression, and to improve health status. 6 of these demonstrated 107 , 108 , 109 , 110 , 111 , 112 , 113 that interventions via the app were effective and of great value, while helping to improve clinical workflow and also removing barriers and limitations to access to specialty care. 1 of the studies of patients with colon cancer 113 indicated that the intervention via the app was not statistically significant versus the offline approach.…”

“…In the studies with interventions via APP alone. The populations included melanoma 110 , breast cancer 111 , lung cancer 112 , gynecologic tumors 113 , prostate cancer 114 , colon cancer 115 , and cancers J o u r n a l P r e -p r o o f that did not distinguish between disease types 116 . The most frequent interventions through APPs are to improve the quality of life (QoL) of patients, to relieve adverse emotions, such as anxiety and depression, and to improve health status.…”

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

“…151 While traditional screening methods have advanced research in the field, AI and ML can accurately monitor the health statuses of cancer patients, which can support the management of patients. 152 In particular, early detection of cancer through the adoption of AI and ML stands as one of the innovations of the 21st century that can significantly help control the prevalence of cancer globally. 153…”

“…151 While traditional screening methods have advanced research in the field, AI and ML can accurately monitor the health statuses of cancer patients, which can support the management of patients. 152 In particular, early detection of cancer through the adoption of AI and ML stands as one of the innovations of the 21st century that can significantly help control the prevalence of cancer globally. 153 The mapping and visualizing analysis of the keywords related to AI and ML in cancer research provides in-depth WordCloud analysis of the top 100 keywords based on the frequency, thematic analysis, structure, and development status of the topics found on similarities into five distinguish clusters, systematically and intuitively reveal the subject structure and development status and make predictions subject in the fields.…”