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Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment

Eduardo Cisternas Jiménez¹, Fang-Fang Yin^1,2,3

¹Medical Physics Graduate Program, Duke University, Durham, NC, United States.

Frontiers in Artificial Intelligence

|February 27, 2025

Summary

This summary is machine-generated.

This study introduces an AI system using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to automate Intensity-Modulated Radiation Therapy (IMRT) planning. The ANFIS system optimizes treatment parameters, significantly improving dose delivery accuracy and reducing healthy tissue exposure.

Keywords:

Adaptive Neuro-Fuzzy Inference System artificial intelligence in radiotherapy planning fuzzy inference system fuzzy set theory intensity-modulated radiation therapy treatment plan parameters treatment planning system

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Area of Science:

Medical Physics
Artificial Intelligence in Medicine
Radiation Oncology

Background:

Intensity-Modulated Radiation Therapy (IMRT) requires manual optimization of numerous treatment plan parameters (TPPs) through trial-and-error.
Achieving patient-specific dose distributions that balance target coverage and organ-at-risk (OAR) sparing is complex and time-consuming.
Automated prescription optimization is needed for scenarios with uncertain trade-offs and patient variability.

Purpose of the Study:

To develop and validate a proof-of-concept Artificial Intelligence (AI) system using Adaptive Neuro-Fuzzy Inference System (ANFIS) for automated IMRT prescription optimization.
To guide IMRT planning towards optimal, patient-specific prescriptions aligned with radiation oncologist objectives.
To enhance the accuracy and efficiency of IMRT planning by mimicking expert planner adjustments.

Main Methods:

An in-house ANFIS-AI system was developed, utilizing Prescription Dose (PD) constraints to guide optimization.
The system adjusted TPPs, represented as dose-volume constraints, informed by a Fuzzy Inference System (FIS) incorporating expert knowledge via "if-then" rules.
ANFIS adaptively fine-tuned FIS components (membership functions, rule strengths) to improve accuracy.

Main Results:

ANFIS consistently met dosimetric goals, outperforming conventional FIS.
Demonstrated a 0.7% improvement in mean dose conformity for the planning target volume (PTV) in a C-Shape phantom.
Achieved a 28% reduction in mean OAR dose in a C-Shape phantom and reduced mean rectal and bladder doses by 17.4% and 14.1% respectively in a prostate phantom.

Conclusions:

The ANFIS-AI system shows significant potential for efficient and accurate IMRT planning.
ANFIS effectively optimizes patient-specific prescriptions, improving target dose conformity and OAR sparing.
This AI approach offers a promising avenue for integration into clinical IMRT workflows.