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 Implementation: • 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 Hypothesis: 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- ture • 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 Then: 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, 1989. ´ – 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 Conclusions • Medical systems introduce engineering complications • Medical knowledge is not categorical (need probability and defaults) • Much remains to be done. . .
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