Undergraduate Course: Early detection and prevention of cancer (BIME10082)
Course Outline
| School | Edinburgh Medical School |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Cancer presents a major health, social and economic burden across the world. As ageing populations increase, the incidence of cancer is set to become greater. Simultaneously, as more sophisticated yet complex technologies for treating cancer continue to emerge, the financial and logistic burden for healthcare systems will increase. Also, many cancers are still diagnosed at a stage where treatment options extend life rather than invoke stable remission. On this background, early detection and prevention of cancer, as opposed to treatment of late-stage disease, presents opportunities for ameliorating all of the above issues. This course will deliver the fundamentals of early detection and prevention (EDP) of cancer.
Topics will include: cancer screening; testing; evaluation of risk; translational animal and human studies for generation of new approaches; and future roles for artificial intelligence-supported approaches in EDP. This course will examine its topical content via dovetailing molecular, genetic and clinical population biology perspectives. This course will be taught by University of Edinburgh clinicians and academics with substantial expertise cross-cutting these areas. Expertise will be drawn upon from across Schools.
This course will complement existing undergraduate teaching in cancer research focussed on specific cancers, e.g., Reproductive Cancers, or targeted therapeutic approaches to later stage cancer, e.g., Cancer Biology and Medicine. The content of this course is not covered by existing electives. Additionally, many of the topics under consideration will be relevant - in broad conceptual terms - to prevention and diagnosis of other age-related disease. |
| Course description |
This SCQF Level 10 taught course is designed to promote a comprehensive understanding of crucial aspects of early detection and prevention of cancer, with an emphasis on how fundamental discovery science informs clinical practice and vice-versa. The course will be delivered in-person with a combination of recorded lectures, small-group tutorials, a practical tutorial, and in-course assessments.
Course materials will cover:
1) population cancer screening ¿ including why we do this, including what makes a good screening test.
2) risk group stratification and how this can play into screening at an individual and familial level, including prophylaxis against cancers with an inheritable component. 3) preventative interventions and the biology underpinning these, e.g., lifestyle factors, chemoprevention, precision prevention, vaccination.
4) experimental and molecular approaches to understanding cancer risk that will inform future early detection and prevention strategies, spanning from artificial intelligence (AI)-mediated appraisal of medical histories through to animal and human tissue studies interrogating topics such as putative biomarkers for disease and what the role of ageing in risk of cancer can inform upon at a molecular level. Throughout the course, we will embed our explorations in a discussion of opportunities and barriers for successful implementation of EDP at societal and policy levels.
The two ICA are substantial pieces of work with multiple components contributing to their completion. Both assessments must be passed in order to pass the course. These assessments comprise:
1) a digital simulation of cancer screening, in which students will simulate optimisation of stratification and screening methods for a given cancer and population, maximising benefits while reducing harms in the context of a finite pool of resources. This simulation will be performed in peer groups, followed by individual completion of a short scientific report on the findings.
2) Working individually, the students will produce and orally present (poster presentation format) of an infographic on current and future improvements in screening and prevention (for a cancer type of the student¿s choice). The graphic will be refined with collaborative assistance from the Edinburgh Language Model generative AI tool in concert with the student¿s own parallel investigations. The AI process will be reflected upon by the students and this reflection will also be summatively assessed as part of the overall grading.
There is no synoptic exam.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2026/27, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 50,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
146 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
In-course assessment - 100%
Written 100% (ICA1 - 50% and ICA2 - 50%). Both assessments must be passed in order to pass the course.
ICA1 - Students will work in groups (2+ depending upon enrolment) to perform digital simulations of screening for cancer in a gamified format. They will be asked to present their findings to class and, after a moderated discussion, individually write a short scientific report on their own group's findings that will be assessed, with a focus on issues such as test sensitivity and specificity, and ethics of subpopulation selection for screening.
ICA2 - Students will construct an infographic poster describing current methods and potential future improvements in prevention (both screening and non-screening related; for a specific cancer type of the student's choice). This will be presented orally. This exercise is an individual student effort. |
| Feedback |
ICA1 Preparative feedback on results obtained
The results obtained by the groups performing the screening simulations will be presented to the class. A recorded discussion will then ensue and the facilitators shall ensure that the students highlight important facets of the data that help steer (at a high level) what students might discuss in their summatively assessed reports. Summation bullet points of the discussion will be provided to the class afterwards.
Written summative assessment
For the subsequent summative assessment, comprehensive written feedback will provided, individually, within 15 working days of the assessment deadline/final delivery date. Students are expected to use this feedback to inform their future performance and are encourage to seek additional clarification if required.
ICA2 Peer feedback
The infographic poster presentations will involve peer-to-peer feedback. A session will be provided where class will be split into two and take turns, along with a session facilitator, circulating around posters of the infographics and being presented to. Students will be encouraged to ask questions to clarify what they understand and don't understand. This will allow students to reflect on what works and doesn't work in terms of communicating the concepts in their infographic. Similarly, the teaching assistant facilitators will provide similar key pointer feedback in the form of responsive oral feedback, they key bullet points of which will also be recorded and provided as formative feedback.
Summative assessment components
One week later, two independent assessors will be presented to by the students and a mark agreed upon (80%). The mark will be predominantly for the quality of the infographic as presented, along with the oral presentation.
The students will also be requested to submit a record of the generative AI process used to refine their poster, which will be marked in accordance with whether best practice has been followed, including the extent to which the tool has been used appropriately and insightfully as a collaboration tool rather than to generate a pro-forma answer (20%).
The mark and comprehensive written feedback will be provided individually within 15 working days of the assessment deadline/final delivery date. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate knowledge of current and future aspects of early detection and prevention (theory and practices);
- apply understanding of acquired knowledge via projects involving critical evaluation of early detection and prevention approaches;
- identify and conceptualise potential problems and solutions in early detection and prevention;
- present information on a cancer-specific area of early detection/prevention at a professional level, including an assessed poster presentation (oral presentation of graphical content);
- use generative artificial intelligence in an appropriate manner to support and enhance investigations at this level.
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Reading List
Alberts, DS and Hess, LM, Fundamentals of Cancer Prevention, 4th edition (available via the University Springer subscription), 2019
Specific up-to-date suggested further readings will also be allocated each iteration of the course. |
Additional Information
| Graduate Attributes and Skills |
The graduate will acquire skills in critical thinking, independent learning, AI-assisted learning and production, communication, presentation, peer collaboration, data analysis and time management. |
| Keywords | Prevention; Cancer; Vaccines; Screening; Early Detection; Disease Risk; AI; Chemoprevention |
Contacts
| Course organiser | Dr Simon Wilkinson
Tel:
Email: S.Wilkinson@ed.ac.uk |
Course secretary | |
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