Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics
| Autor: | Ana Vaniqui, Cecile J A Wolfs, C. Hazelaar, Wouter van Elmpt, R. Canters, Femke Vaassen, Indra Lubken, Kirsten Kremer |
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| Přispěvatelé: | Radiotherapie, RS: GROW - R2 - Basic and Translational Cancer Biology, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
medicine.medical_specialty Computer science MODULATED RADIATION-THERAPY ARC THERAPY medicine.medical_treatment lcsh:R895-920 Dose metrics VMAT Overlap volume histogram (OVH) Dose–distance relation lcsh:RC254-282 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine CLINICAL VALIDATION Treatment plan Prediction model Histogram Knowledge based treatment planning medicine Radiology Nuclear Medicine and imaging Medical physics Original Research Article IMRT OPTIMIZATION Radiation Dose-distance relation business.industry lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens MODEL Radiation therapy 030220 oncology & carcinogenesis Organ at risk Cohort business Quality assurance Cohort study Treatment planning QA |
| Zdroj: | Physics and Imaging in Radiation Oncology, Vol 16, Iss, Pp 74-80 (2020) Physics and Imaging in Radiation Oncology Physics & Imaging in Radiation Oncology, 16, 74-80. Elsevier Ireland Ltd |
| ISSN: | 2405-6316 |
| Popis: | Background and purpose: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis.Material and methods: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort).Results: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and -4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: -0.1 ± 1.1 Gy and -0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning.Conclusion: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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