Data mining to aid beam angle selection for intensity-modulated radiation therapy

Price, Stuart; Golden, Bruce L.; Wasil, Edward; Zhang, Hao Helen

doi:10.1145/2649387.2649412

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Researchers have recently demonstrated the potential of data mining in selecting Intensity Modulated Radiation Therapy (IMRT) beam angles, external beam radiation therapy techniques, and predicting cancer risks. [ 6 7 8 ] Integrated research with data mining and predictive toxicity, modeling can be expanded to personalized patient treatment decision support in RO. [ 9 ]…”

Section: Introductionmentioning

confidence: 99%

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Chauhan

Pradhan

Munshi

et al. 2023

Journal of Medical Physics

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Background and Purpose: In recent years, data science approaches have entered health-care systems such as radiology, pathology, and radiation oncology. In our pilot study, we developed an automated data mining approach to extract data from a treatment planning system (TPS) with high speed, maximum accuracy, and little human interaction. We compared the amount of time required for manual data extraction versus the automated data mining technique. Materials and Methods: A Python programming script was created to extract specified parameters and features pertaining to patients and treatment (a total of 25 features) from TPS. We successfully implemented automation in data mining, utilizing the application programming interface environment provided by the external beam radiation therapy equipment provider for the whole group of patients who were accepted for treatment. Results: This in-house Python-based script extracted selected features for 427 patients in 0.28 ± 0.03 min with 100% accuracy at an astonishing rate of 0.04 s/plan. Comparatively, manual extraction of 25 parameters took an average of 4.5 ± 0.33 min/plan, along with associated transcriptional and transpositional errors and missing data information. This new approach turned out to be 6850 times faster than the conventional approach. Manual feature extraction time increased by a factor of nearly 2.5 if we doubled the number of features extracted, whereas for the Python script, it increased by a factor of just 1.15. Conclusion: We conclude that our in-house developed Python script can extract plan data from TPS at a far higher speed (>6000 times) and with the best possible accuracy compared to manual data extraction.

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Section: Introductionmentioning

confidence: 99%

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Chauhan

Pradhan

Munshi

et al. 2023

Journal of Medical Physics

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IMRT QA using machine learning: A multi‐institutional validation

Valdés

Chan

Lim

et al. 2017

J Applied Clin Med Phys

129

138

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PurposeTo validate a machine learning approach to Virtual intensity‐modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode‐array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode‐array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per‐beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under‐response in low‐dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.

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Data Analysis in Radiotherapy Treatments

Anacleto

Dias

2018

International Journal of E-Health and Medical Communications

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Radiotherapy is one of the main cancer treatments available today, together with chemotherapy and surgery. Radiotherapy treatments have to be planned for each patient in an individualized manner. The knowledge acquired from one single treatment can be used to improve the treatment planning and outcome of several other patients. In the last years, attention has been drawn to the added value of using data analysis for radiotherapy treatment planning, prediction of treatment outcomes, survival analysis and quality assurance. In this article, existing literature is reviewed.

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Data mining to aid beam angle selection for intensity-modulated radiation therapy

Cited by 3 publications

References 42 publications

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

Efficient and Reliable Data Extraction in Radiation Oncology using Python Programming Language

IMRT QA using machine learning: A multi‐institutional validation

Data Analysis in Radiotherapy Treatments

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