Knowledge-based adaptive planning quality assurance using dosimetric indicators for stereotactic adaptive radiotherapy for pancreatic cancer

Forghani, Farnoush; Ginn, John S.; Schiff, Joshua; Zhu, Tong; Marut, L.; Laugeman, Eric; Maraghechi, B.; Badiyan, Shahed N.; Samson, Pamela; Kim, Hyun; Robinson, Clifford G.; Hugo, Geoff; Henke, Lauren E.; Price, Alex

doi:10.1016/j.radonc.2023.109603

Cited by 4 publications

(2 citation statements)

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“…Previous studies have suggested that these challenges can be overcome by knowledge-based QA/QC models based on knowledge learned from previous treatment prescriptions, plan parameters, or EPID dosimetry. [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Essentially,assuming plans with the same treatment site, technique and modality share similar clinical "knowledge," machine learning based QA/QC methods can interpret statistical significance of plan parameters and apply learned knowledge in the process of checking new treatment plans. For instance, by interpreting statistical significance of plan parameters, a warning could be raised when inappropriate X-ray energy, monitor units per fractional dose (MU/cGy ratio), or total number of beams are used for a head and neck IMRT (intensity-modulated radiation therapy) plan, leading to inadequate dose coverage or inadequate normal tissue sparing.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, even all organ‐at‐risk constraints are met for a treatment plan, such a method may fail to identify an inappropriate planning technique, energy, or beam arrangement for a tumor type or location. Previous studies have suggested that these challenges can be overcome by knowledge‐based QA/QC models based on knowledge learned from previous treatment prescriptions, plan parameters, or EPID dosimetry 22–36 . Essentially, assuming plans with the same treatment site, technique and modality share similar clinical “knowledge,” machine learning based QA/QC methods can interpret statistical significance of plan parameters and apply learned knowledge in the process of checking new treatment plans.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a knowledge‐based decision support system for treatment plan auditing through automation

et al. 2023

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BackgroundIndependent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross‐campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members.PurposeA knowledge‐based automated anomaly‐detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed. This standardized and improved the efficiency of the assessment of our external beam radiotherapy (EBRT) treatment planning across all eight campuses of our institution.MethodsA total of 843 external beam radiotherapy plans for 721 lung patients from January 2020 to March 2021 were automatically acquired from our clinical treatment planning and management systems. From each plan, 44 parameters were automatically extracted and pre‐processed. A knowledge‐based anomaly detection algorithm, namely, “isolation forest” (iForest), was then applied to the plan dataset. An anomaly score was determined for each plan using recursive partitioning mechanism. Top 20 plans ranked with the highest anomaly scores for each treatment technique (2D/3D/IMRT/VMAT/SBRT) including auto‐populated parameters were used to guide the manual auditing process and validated by two plan auditors.ResultsThe two auditors verified that 75.6% plans with the highest iForest anomaly scores have similar concerning qualities that may lead to actionable recommendations for our planning procedures and staff training materials. The time to audit a chart was approximately 20.8 min on average when done manually and 14.0 min when done with the iForest guidance. Approximately 6.8 min were saved per chart with the iForest method. For our typical internal audit review of 250 charts annually, the total time savings are approximately 30 hr per year.ConclusioniForest effectively detects anomalous plans and strengthens our cross‐campus manual plan auditing procedure by adding decision support and further improve standardization. Due to the use of automation, this method was efficient and will be used to establish a standard plan auditing procedure, which could occur more frequently.

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