Considerations in Medical Problem Solving � What is already known by rrboy


									           Considerations in Medical Problem Solving

• What is already known?

• What is the form of the knowledge?

• How is the knowledge used?

• Does the knowledge consist of small independent chunks or is it large
  and complex?
A typical radiotherapy machine setup
                         Radiation therapy planning

 1. Gather clinical and physical data

 2. Decide general approach

 3. Select radiation type(s)

 4. Use computer simulation to configure radiation beams

 5. Verify feasibility

Repeat steps as necessary. . .
Defining the GTV, CTV and PTV
          Planning Target Volume Expansion Components

The Planning Target Volume allows for:

 • tumor motion (internal)

 • patient motion (external)

 • variation in treatment setup (from treatment to treatment)
               Determining the Planning Target Volume

Factors affecting target volume:

  • Tumor site (variable granularity)

  • Stage (primarily T-stage)

  • Cell type, e.g., squamous cell, adenocarcinoma, etc.

  • Patient immobilization - e.g. head mask, plaster shell, etc.
                 Planning Target Volume Tool (PTVT)

Takes a tumor volume and expands it into a planning target volume.

 1. Determine all the margin factors

 2. Combine in a root-mean-square sum

 3. Expand each (2-D) tumor contour radially from a “center of gravity”

 4. Add a contour at each “end” to expand in the z direction

 • Common LISP, CLOS

 • Margin factors represented by production rules

 • No user interaction - intended to be embedded in an RTP system
                       Example PTV Rules

(<- (setup-error ?x (0.8 0.8 0.8))   ;; (0.8,?,?) Verhey82,
    ;; approved SH 3/5/92
    (AND (within ?x head-and-neck)
 (immob-type none)))

(<- (pt-movement ?x (0.3 0.3 0.3)) ;; MAS/JMU-2/3/94
    (AND (within ?x head-and-neck)
 (immob-type none)))

(<- (setup-error ?x (0.5 0.5 0.5))   ;; SH 3/5/92
    (AND (within ?x head-and-neck)
 (immob-type mask)))

(<- (pt-movement ?x (0.1 0.1 0.1)) ;; MAS/JMU-2/3/94
    (AND (within ?x head-and-neck)
 (immob-type mask)))
The PTV Tool in Action

     Prism demo!
               Optimization of Radiotherapy Plans

• Manual interactive design

• Linear programming, simulated annealing (Rosen, et. al.)

• Inverse methods (Altschuler, Brahme, et. al)

• Heuristic search
              Automated Radiotherapy Plan Optimization


 1. Complex treatment designs can be found that are superior to typical
    clinical practice.

 2. An artificial intelligence program can find such treatment plans faster
    than human or numerical techniques.
Generate-and-test plan optimization

        Generate Prototype Plans

       Compute Dose Distribution

      Find high and low dose regions

      Generate           Compare with
   "repaired" plans       other plans

      Select                 Add to
   most promising          "evaluated"
       plans                 plan list
                          Prototype plans

• PO-LAT - parallel opposed fields, each compensating for the other, to
  give uniform dose

• WEDGED-PAIR - treats a corner rather than a central region

• WEDGED-OBLIQUE - like WEDGED-PAIR but avoids a critical struc-

• TRI-BEAM - treats a central target with obstacles
                    Treatment Plan Revision Rules

If: cold spot in deep area and hot spot in shallow area
     Then: switch to higher energy

If: cold spot just outside a beam edge
     Then: increase beam opening at that edge

If: organ overdosed by one beam

    1. reduce beam weight, increase others

    2. change gantry angle

    3. add block to that beam
              Coding Rules in Lisp

(<- (solution-p ?beam increase energy)
    (and (type-p ?beam beam)
         (penetrates-p ?beam shallow)))

(<- (penetrates-p ?beam shallow)
    (and (type-p ?beam beam)
         (type-p ?spot trouble-spot)
         (dose-p ?spot cold)
         (deepin-p ?spot ?beam)))

(<- (deepin-p ?spot ?beam)
    (and (type-p ?spot trouble-spot)
         (type-p ?beam beam)
         (within-p ?spot ?beam)
         (region-p ?spot deep)))
                Managing the search for good plans

• Objective Evaluation describes a plan’s deficiencies

• Subjective Evaluation compares a plan to other plans

• A Similarity Metric is used to associate a plan with a region or cluster
  of evaluated plans

• A Prioritizing Algorithm is used to select the most promising new plans
Objective evaluation:

  • is a list of trouble spots

  • used to generate revised (improved) plans

  • also used for subjective evaluation

Subjective evaluation

  • is a summary of pairwise comparision with other plans

  • uses numerical properties of trouble spots

  • is sometimes equivocal

  • is used to characterize regions of the problem space

A Similarity Metric:

  • associates a plan with a cluster of evaluated plans

  • is used to prune the search for good plans
                             How did it turn out?

• Implemented only in two dimensions

• Initial cases showed that pruning improved the search efficiency by a factor of two.

• References:
    – Paluszynski, W., Kalet, I. Design Optimization Using Dynamic Evaluation. Pro-
      ceedings of the Eleventh International Joint Conference on Artificial Intelligence,
      August 1989, Detroit, Michigan, American Association for Artificial Intelligence,
    – Paluszynski, W. Designing radiation therapy for cancer, an approach to knowl-
      edge based optimization. PhD thesis, University of Washington, 1990.
                  Medical Guidelines and Systems

Some history:

 • Rule based “expert systems” for medical diagnosis and therapy

 • Interoperability of rules - knowledge sharing strategies

 • Arden Syntax - a standard text based interchange format

 • GLIF - flowchart based model, superset of Arden

 • ASBRU - incorporates time and intention
                       Semantics of Guidelines

A guideline represents a single modular decision

 • Is there such a thing in medicine?

 • What input is needed (the “curley braces” problem)

 • What does it mean to execute a guideline?
                            Arden Syntax

• Medical Logic Modules (MLM) represented in ASCII text files

• Procedural in style, but “frame-based”

• Can “call” each other, but not usually written that way

• Medical systems introduce engineering complications

• Medical knowledge is not categorical (need probability and defaults)

• Much remains to be done. . .

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