Outline

Overview

The Basic Oncology syllabus covers the requirements of trainee physician practice. The unit is focussed on diagnosis and investigation of common solid malignancies and the essentials of management of these conditions. The learning materials are linked to a library of clinical cases representing common and important malignancies.

Unit details and rules

Academic unit	Internal Medicine
Credit points	3
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	This unit of study assumes theoretical and practical knowledge at least at the level of a registered medical practitioner
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Preeti Choudhary, preeti.choudhary@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Multiple Choice Questions	40%	Formal exam period	1 hour	AI prohibited
Outcomes assessed:
Out-of-class quiz	Graded quizzes Multiple Choice Questions	20%	Multiple weeks	60 minutes per quiz	AI allowed
Outcomes assessed:
Presentation	Case Presentation Case presentation of clinical case.	12%	Week 06 Due date: 13 Sep 2026 at 23:59	5-7 minutes	AI allowed
Outcomes assessed:
Written work	Case Presentation AI Prompts Download and submit a copy of your Case Presentation AI prompts/ interaction	8%	Week 06 Due date: 13 Sep 2026 at 23:59	As per AI interaction download	AI allowed
Outcomes assessed:
Written work	Case report Report	20%	Week 08 Due date: 27 Sep 2026 at 23:59	1000 words	AI allowed
Outcomes assessed:

Assessment summary

Graded quiz: The quizzes are designed to help student gauge their progress in meeting the learning outcomes. Students may refer to learning materials when answering. There will be 3 quizzes, each with 10 multiple choice questions. There is one attempt only. The quizzes are open for a set period.
Case Presentation: Students will choose one of 3 cases provided. They will prepare a clear 5–7 minute narrated PowerPoint presentation (saved as MP4) covering Clinical presentation, Diagnostic approach and/or management plan as relevant. Students will be able to utilise generative Artificial Intelligence (AI) to assist in analysing or summarising one aspect of the chosen case and will reflect on how AI contributed to or augmented their clinical reasoning.
Case presentation AI interaction report: : Students will download and submit a copy of their Case Presentation AI prompts and AI interaction showing reflection and critical engagement with AI.
Case report: In general, about a third of the report should be allocated to the case history and two thirds to discussion of the case. The report should be based on a patient the student
has cared for, or seen, in the course of their clinical work. The format of the case report can be as a narrative or arranged as medical record documentation, including history of the
presenting illness, previous medical history, social history, examination, investigations and diagnosis.
Final exam: The final exam is a graded, fully invigilated exam conducted via Canvas with the help of an online proctor. This is known as "Live+ (Type A)" exam. You will be required to
schedule your exam later in the semester. The exam will contain 30 multiple-choice questions (single best answer). The exam is "closed book", you are not permitted any materials with you for this exam.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Module 1: fundamentals of cancer	Self-directed learning (5 hr)
Week 02	Module 1: fundamentals of cancer	Self-directed learning (5 hr)
Week 03	Module 2: Breast cancer and gynaecological malignancies	Self-directed learning (5 hr)
Week 04	Module 2: Breast cancer and gynaecological malignancies	Self-directed learning (5 hr)
Week 05	Module 2: Breast cancer and gynaecological malignancies	Self-directed learning (5 hr)
Week 06	Module 3: gastrointestinal malignancies	Self-directed learning (5 hr)
Week 07	Module 3: gastrointestinal malignancies	Self-directed learning (5 hr)
Week 08	Module 3: gastrointestinal malignancies	Self-directed learning (5 hr)
Week 09	Module 4: immunotherapy and its most relevant applications	Self-directed learning (5 hr)
Week 10	Module 4: immunotherapy and its most relevant applications	Self-directed learning (5 hr)
Week 11	Module 5: urological cancers	Self-directed learning (5 hr)
Week 12	Module 6: oncologic emergencies and symptom control	Self-directed learning (5 hr)
Week 13	Module 6: oncologic emergencies and symptom control	Self-directed learning (5 hr)

Attendance and class requirements

Required materials: The following equipment is required in order to take the final exam: a desktop computer or laptop (not tablet), a webcam and microphone, an internet connection speed of at least 2 Mbps download and 2 Mbps upload speed and a browser with pop-up blocker disabled (Chrome is recommended).

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 3 credit point unit, this equates to roughly 60-75 hours of student effort in total.

Required readings

The texts for this course are:

Harrison’s Principles of Internal Medicine 20th edition, McGraw-Hill 2018
Oxford Textbook of Medicine, 6th edition, Oxford Medicine Online 2020

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. recognise common patterns of presentation of cancer and cancer-related complications
LO2. diagnose and investigate patients presenting with these conditions
LO3. describe management options for patients presenting with these conditions
LO4. discuss diagnosis, management, and symptom control for oncology patients with colleagues.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

We value your feedback about any aspect of the unit of study and your experience as a student of Sydney Medical School. To help ensure our courses meet your needs and maintain a high standard, we welcome your feedback at any time, and we ask you to complete the Mid-Semester Evaluation and the unit of study Evaluation Survey at the end of the semester. Your ratings and comments are anonymous and specifying what you liked and didn’t like about any of the learning materials, assessment items, discussion forums, feedback etc will help us to target our improvement efforts. Please note that your participation in this unit of study permits de-identified information about your learning experience and interaction with learning resources to be used for the purpose of improving the student learning experience.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

INTM5010: Basic Oncology

Semester 2, 2026 [Online] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links