Emerging Trends in Health Impact Assessment of Cancer: Innovations in Data Science and Patient-Centered Outcomes
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Survivorship and Quality of Life".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 8
Special Issue Editor
2. Department of Radiation Oncology, Dalhousie University, Halifax, NS B3H 4R2, Canada
3. Department of Community Health and Epidemiology, Dalhousie University, Halifax, NS B3H 4R2, Canada
Interests: oncology; cancer survivorship; prostate cancer; brain oncology; pancreatic cancer; urologic oncology; mental health; quality of life; psychosocial oncology; patient education and empowerment programs
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Special Issue Information
Dear Colleagues,
Health impact assessment (HIA) has become an essential component of cancer research, care planning, and health policy development. The integration of advanced data science methods and rigorous patient-centered outcome measures is transforming our ability to generate evidence that directly improves the lives of people affected by cancer.
We are pleased to invite you to contribute to this Special Issue of Cancers, which will showcase innovative approaches that drive meaningful change in oncology care delivery.
This Special Issue aims to highlight multidisciplinary research and cutting-edge methodologies that bridge the gap between discovery and implementation. We welcome contributions employing artificial intelligence, machine learning, real-world evidence, and big data analytics to improve prediction, personalize care, and enhance system efficiency. Studies using realist evaluations, implementation science, pragmatic and randomized controlled trials, and participatory approaches to co-design interventions with patients, caregivers, and communities are strongly encouraged. The goal is to gather work that not only advances scientific understanding but also demonstrates clear pathways to improving patient experience, reducing inequities, and informing more effective models of care.
In this Special Issue, original research articles, clinical trials, systematic reviews, and methodological papers are welcome. Research areas may include (but are not limited to) the following: predictive modeling, patient-reported outcomes, , realist and mixed-methods evaluations, implementation strategies, digital health innovations, and trials assessing the impact of novel interventions in cancer care.
We look forward to receiving your contributions.
Dr. Gabriela Ilie
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- health impact assessment
- cancer outcomes
- data science
- machine learning
- patient-reported outcomes
- realist evaluation
- implementation science
- clinical trials
- health equity
- digital health
- translational research
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