Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning
Intensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution th...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1523390/full |
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| author | Eduardo Cisternas Jiménez Fang-Fang Yin Fang-Fang Yin Fang-Fang Yin |
| author_facet | Eduardo Cisternas Jiménez Fang-Fang Yin Fang-Fang Yin Fang-Fang Yin |
| author_sort | Eduardo Cisternas Jiménez |
| collection | DOAJ |
| description | Intensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution that adheres to a prescribed plan tailored to each patient. Developing an automated approach to optimize patient-specific prescriptions is valuable in scenarios where trade-off selection is uncertain and varies among patients. This study presents a proof-of-concept artificial intelligence (AI) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to guide IMRT planning and achieve optimal, patient-specific prescriptions in aligned with a radiation oncologist's treatment objectives. We developed an in-house ANFIS-AI system utilizing Prescription Dose (PD) constraints to guide the optimization process toward achievable prescriptions. Mimicking human planning behavior, the AI system adjusts TPPs, represented as dose-volume constraints, to meet the prescribed dose goals. This process is informed by a Fuzzy Inference System (FIS) that incorporates prior knowledge from experienced planners, captured through “if-then” rules based on routine planning adjustments. The innovative aspect of our research lies in employing ANFIS's adaptive network to fine-tune the FIS components (membership functions and rule strengths), thereby enhancing the accuracy of the system. Once calibrated, the AI system modifies TPPs for each patient, progressing through acceptable prescription levels, from restrictive to clinically allowable. The system evaluates dosimetric parameters and compares dose distributions, dose-volume histograms, and dosimetric statistics between the conventional FIS and ANFIS. Results demonstrate that ANFIS consistently met dosimetric goals, outperforming FIS with a 0.7% improvement in mean dose conformity for the planning target volume (PTV) and a 28% reduction in mean dose exposure for organs at risk (OARs) in a C-Shape phantom. In a mock prostate phantom, ANFIS reduced the mean dose by 17.4% for the rectum and by 14.1% for the bladder. These findings highlight ANFIS's potential for efficient, accurate IMRT planning and its integration into clinical workflows. |
| format | Article |
| id | doaj-art-861a930021a5448ab6293321fd9b5e1d |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-861a930021a5448ab6293321fd9b5e1d2025-02-12T07:25:32ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01810.3389/frai.2025.15233901523390Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planningEduardo Cisternas Jiménez0Fang-Fang Yin1Fang-Fang Yin2Fang-Fang Yin3Medical Physics Graduate Program, Duke University, Durham, NC, United StatesMedical Physics Graduate Program, Duke University, Durham, NC, United StatesDepartment of Radiation Oncology, Duke University Medical Center, Durham, NC, United StatesMedical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, ChinaIntensity-Modulated Radiation Therapy requires the manual adjustment to numerous treatment plan parameters (TPPs) through a trial-and-error process to deliver precise radiation doses to the target while minimizing exposure to surrounding healthy tissues. The goal is to achieve a dose distribution that adheres to a prescribed plan tailored to each patient. Developing an automated approach to optimize patient-specific prescriptions is valuable in scenarios where trade-off selection is uncertain and varies among patients. This study presents a proof-of-concept artificial intelligence (AI) system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to guide IMRT planning and achieve optimal, patient-specific prescriptions in aligned with a radiation oncologist's treatment objectives. We developed an in-house ANFIS-AI system utilizing Prescription Dose (PD) constraints to guide the optimization process toward achievable prescriptions. Mimicking human planning behavior, the AI system adjusts TPPs, represented as dose-volume constraints, to meet the prescribed dose goals. This process is informed by a Fuzzy Inference System (FIS) that incorporates prior knowledge from experienced planners, captured through “if-then” rules based on routine planning adjustments. The innovative aspect of our research lies in employing ANFIS's adaptive network to fine-tune the FIS components (membership functions and rule strengths), thereby enhancing the accuracy of the system. Once calibrated, the AI system modifies TPPs for each patient, progressing through acceptable prescription levels, from restrictive to clinically allowable. The system evaluates dosimetric parameters and compares dose distributions, dose-volume histograms, and dosimetric statistics between the conventional FIS and ANFIS. Results demonstrate that ANFIS consistently met dosimetric goals, outperforming FIS with a 0.7% improvement in mean dose conformity for the planning target volume (PTV) and a 28% reduction in mean dose exposure for organs at risk (OARs) in a C-Shape phantom. In a mock prostate phantom, ANFIS reduced the mean dose by 17.4% for the rectum and by 14.1% for the bladder. These findings highlight ANFIS's potential for efficient, accurate IMRT planning and its integration into clinical workflows.https://www.frontiersin.org/articles/10.3389/frai.2025.1523390/fulltreatment planning systemfuzzy set theoryfuzzy inference systemAdaptive Neuro-Fuzzy Inference Systemtreatment plan parametersartificial intelligence in radiotherapy planning |
| spellingShingle | Eduardo Cisternas Jiménez Fang-Fang Yin Fang-Fang Yin Fang-Fang Yin Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning Frontiers in Artificial Intelligence treatment planning system fuzzy set theory fuzzy inference system Adaptive Neuro-Fuzzy Inference System treatment plan parameters artificial intelligence in radiotherapy planning |
| title | Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning |
| title_full | Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning |
| title_fullStr | Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning |
| title_full_unstemmed | Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning |
| title_short | Adaptive Neuro-Fuzzy Inference System guided objective function parameter optimization for inverse treatment planning |
| title_sort | adaptive neuro fuzzy inference system guided objective function parameter optimization for inverse treatment planning |
| topic | treatment planning system fuzzy set theory fuzzy inference system Adaptive Neuro-Fuzzy Inference System treatment plan parameters artificial intelligence in radiotherapy planning |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1523390/full |
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