Spring 2027

Advanced Radiation Treatment Planning

Advanced Radiation Treatment Planning covers the physics of clinical, computer-based radiotherapy planning, including dose algorithms, commissioning, contouring, beam placement, and forward/inverse planning methods like IMRT optimization.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2022: 3.57 GPA, 80.0% A/AB (n=15 letter grades); Fall 2024: 3.58 GPA, 85.0% A/AB (n=20 letter grades); Fall 2025: 3.63 GPA, 93.3% A/AB (n=15 letter grades).

What you’ll be able to do

  • Design simple and intermediate forward-based photon and electron external beam plans using beam arrangements/energy, wedges and blocks intelligently with regards to underlying physics

  • Create target and region at risk planning volumes, setup objectives for, and optimize, inverse planned intensity modulated plans.

  • Evaluate dose distributions using a variety of metrics.

  • Understand beam model commissioning process and limitations including data requirements and processing.

  • Understand dose algorithms used in radiation therapy (including but not limited to: convolution superposition, Monte Carlo, pencil beam.)

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MED PHYS/​B M E 566 and graduate/professional standing

MEDPHYS 772

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.

Meeting source records

Student experience

the class

Recent recorded grades — Fall 2022: 3.57 GPA, 80.0% A/AB (n=15 letter grades); Fall 2024: 3.58 GPA, 85.0% A/AB (n=20 letter grades); Fall 2025: 3.63 GPA, 93.3% A/AB (n=15 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Dose algorithms, measurement data, commissioning, contouring, volume definition, beam placement, modifiers, apertures, and plan evaluation
  • Forward-based and inverse planning, including IMRT optimization
  • Physics of clinical, computer-based radiotherapy planning

Skills

  • Designing forward-based photon and electron external beam plans
  • Creating and optimizing inverse planned intensity modulated plans
  • Evaluating dose distributions using various metrics
  • Understanding beam model commissioning processes and limitations
  • Understanding dose algorithms such as convolution superposition, Monte Carlo, and pencil beam

Grades

Latest available · Fall 2025— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Fall 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Selected offering source records

No offering records for the selected term.

Raw records
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews

No records available.

Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "08c8f7eebd7cf2eb0d82a8db3454829d2779d5418dd4ad08b500471f29e69ae7",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}