From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

Reviewing the Landscape of Cancer Survivorship: Insights from Dr. Lidia Schapira's Programs and Beyond

The constantly escalating population of cancer survivors worldwide has prompted a focused exploration of their unique needs and experiences within the context of healthcare medicine. This review initiates its analysis inspired by Dr. Lidia Schapira's insightful keynote conference on the Survivorship 1.0 and Survivorship 2.0 Programs, shedding light on their implementation challenges and setting the stage for a comprehensive analysis of cancer survivorship initiatives. Within the transformed landscape of cancer care, patient-centric strategies embedded in cancer survivorship programs comprising vital elements such as connection, support, and education are presented. While placing cancer recurrence surveillance at the forefront, the review underlines concern regarding the potential oversight of the enduring impact on mental and physical health. Dr. Schapira's insights further extend into the exploration of mental health challenges faced by survivors, promoting an examination of diverse strategies to address these concerns. Furthermore, the discussion continues toward promising areas of research, notably Precision Medicine's role in de-escalating cancer therapies, and advocates for measures such as early cancer awareness and timely referrals to supportive services. Highlighting the significance of education, the role of online resources in enhancing the knowledge of healthcare practitioners and future generations in cancer care is then explored. The paper concludes by presenting some of the most prominent global current survivorship programs, identifying critical knowledge gaps in cancer care and projecting future developments aimed at delivering accurate and holistic care, improving the quality of life for survivors, and enhancing both mental and physical well-being. Drawing upon the insights from Dr. Schapira, this review lays the groundwork for a nuanced exploration of cancer survivorship and its multifaceted implications.

In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare

Artificial Intelligence and its subdomain, Machine Learning (ML), have shown the potential to make an unprecedented impact in healthcare. Federated Learning (FL) has been introduced to alleviate some of the limitations of ML, particularly the capability to train on larger datasets for improved performance, which is usually cumbersome for an inter-institutional collaboration due to existing patient protection laws and regulations. Moreover, FL may also play a crucial role in circumventing ML's exigent bias problem by accessing underrepresented groups' data spanning geographically distributed locations. In this paper, we have discussed three FL challenges, namely: privacy of the model exchange, ethical perspectives, and legal considerations. Lastly, we have proposed a model that could aide in assessing data contributions of a FL implementation. In light of the expediency and adaptability of using the Sørensen-Dice Coefficient over the more limited (e.g., horizontal FL) and computationally expensive Shapley Values, we sought to demonstrate a new paradigm that we hope, will become invaluable for sharing any profit and responsibilities that may accompany a FL endeavor.

Behavioral Research in Cancer Prevention and Control: Emerging Challenges and Opportunities

It is estimated that behaviors such as poor diet, alcohol consumption, tobacco use, sedentary behavior, and excessive ultraviolet exposure account for nearly one-half of all cancer morbidity and mortality. Accordingly, the behavioral, social, and communication sciences have been important contributors to cancer prevention and control research, with methodological advances and implementation science helping to produce optimally effective interventions. To sustain these contributions, it is vital to adapt to the contemporary context. Efforts must consider ancillary effects of the 2019 coronavirus disease pandemic, profound changes in the information environment and public understanding of and trust in science, renewed attention to structural racism and social determinants of health, and the rapidly increasing population of cancer survivors. Within this context, it is essential to accelerate reductions in tobacco use across all population subgroups; consider new models of energy balance (diet, physical activity, sedentary behavior); increase awareness of alcohol as a risk factor for cancer; and identify better communication practices in the context of cancer-related decisions such as screening and genetic testing. Successful integration of behavioral research and cancer prevention depends on working globally and seamlessly across disciplines, taking a multilevel approach where possible. Methodological and analytic approaches should be emphasized in research training programs and should use new and underused data sources and technologies. As the leadership core of the National Cancer Institute’s Behavioral Research Program, we reflect on these challenges and opportunities and consider implications for the next phase of behavioral research in cancer prevention and control.

Genomic Landscape of Liquid Biopsy for Hepatocellular Carcinoma Personalized Medicine

Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide. Advanced-stage HCC patients have poor survival rates and this requires the discovery of novel clear biomarkers for HCC early diagnosis and prognosis, identifying risk factors, distinguishing HCC from non-HCC liver diseases, and assessment of treatment response. Liquid biopsy has emerged as a novel minimally invasive approach to enable monitoring tumor progression, metastasis, and recurrence. Since the liquid biopsy analysis has relatively high specificity and low sensitivity in cancer early detection, there is a risk of bias. Next-generation sequencing (NGS) technologies provide accurate and comprehensive gene expression and mutational profiling of liquid biopsies including cell-free circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and genomic components of extracellular vesicles (EVs) including micro-RNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). Since HCC is a highly heterogeneous cancer, HCC patients can display various genomic, epigenomic, and transcriptomic patterns and exhibit varying sensitivity to treatment options. Identification of individual variabilities in genomic signatures in liquid biopsy has the potential to greatly enhance precision oncology capabilities. In this review, we highlight and critically discuss the latest progress in characterizing the genomic landscape of liquid biopsy, which can advance HCC personalized medicine.

Personality Traits and Cardiotoxicity Arising From Cancer Treatments: An Hypothesized Relationship

Thanks to the evolution in medical and pharmaceutical research, to date, the number of cancer treatments is increasingly on the rise. Despite this, several side effects related to cancer treatments can exacerbate patients’ physical and psychological conditions, such as cardiotoxicity. Over the years, researchers have explored the possible relationship between psychological variables and physical diseases. Even though some authors examined the relationship between personality and specific diseases, no scientific attention has been paid to the role of personality in the development of cardiotoxicity arising from cancer treatments. Yet this is an important objective, given that determining whether personality influences cardiac toxicity of anticancer treatments could inform the processes by which stable psychological factors influence health. This contribution summarizes and analyzes the available scientific evidence about the association between personality and main cardiotoxicity-related-diseases of anticancer therapies, including cancer and cardiovascular diseases, in order to sketch a hypothetical model of the relationship between personality traits and cardiotoxicity.

Cancer Informatics in 2019: Deep Learning Takes Center Stage

Objective : To summarize significant research contributions on cancer informatics published in 2019.

Methods : An extensive search using PubMed/Medline and manual review was conducted to identify the scientific contributions published in 2019 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of two best papers was conducted by the editorial committee of the Yearbook.

Results : The two selected best papers demonstrate the clinical utility of deep learning in two important cancer domains: radiology and pathology.

Conclusion : Cancer informatics is a broad and vigorous subfield of biomedical informatics. Applications of new and emerging computational technologies are especially notable in 2019.