Guide-linked quiz · Medium Level
AI in Radiation Therapy Planning Quiz
Tests auto-contouring of targets and organs at risk, knowledge-based dose planning, Dice score limits, and the physicist review that stays in the loop.
Question 1 of 8
What does auto-contouring do in radiation therapy planning?
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Answers and explanations appear as you take the quiz.
- What does auto-contouring do in radiation therapy planning?
- Delivers the radiation beam
- Outlines tumors and organs at risk on CT or MRI scans
- Schedules patient appointments
- Calculates the patient's bill
- Which contouring task is currently most mature for AI?
- Organs at risk such as the spinal cord and parotid glands
- Clinical target volumes after surgery
- Deciding the radiation prescription
- Choosing whether to treat at all
- What does the planning target volume (PTV) add to the clinical target volume?
- The patient's healthy organs
- A second tumor
- A margin for setup errors and motion
- Nothing; they are the same
- How does knowledge-based planning such as RapidPlan work?
- It measures dose with a physical detector
- It lets patients design their own plans
- It removes the need for imaging
- It learns from prior approved plans to predict achievable dose-volume histograms
- Why is a high Dice score not enough to prove a contour is clinically acceptable?
- Dice ignores where errors occur, and a small error near a critical organ can matter a lot
- Dice only measures image brightness
- Dice scores are always 100 percent
- Dice measures dose, not shape
- Why does adaptive radiotherapy benefit strongly from AI?
- It removes the need for a linear accelerator
- Re-contouring and re-planning while the patient waits must happen within minutes
- It uses no imaging
- It only treats skin cancers
- Who approves contours and treatment plans before delivery?
- The AI vendor
- The hospital billing office
- Radiation oncologists, with medical physicists checking plans and running QA
- The patient alone
- What should a clinic do when an auto-contouring vendor releases a model update?
- Use it immediately without testing
- Stop using AI permanently
- Retrain it on one patient
- Re-validate it on local data before clinical use
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