On the pre-clinical validation of a commercial model-based optimisation engine: application to volumetric modulated arc therapy for patients with lung or prostate cancer
| Autor: | Marta Scorsetti, Luca Cozzi, Piera Navarria, Antonella Fogliata, Giorgia Nicolini, Francesca Belosi, Eugenio Vanetti, Alessandro Clivio |
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| Rok vydání: | 2014 |
| Předmět: |
Male
Risk medicine.medical_specialty Lung Neoplasms Prostate cancer Prostate Medicine Humans Radiology Nuclear Medicine and imaging Lung cancer Radiometry Gynecology Lung business.industry Radiotherapy Planning Computer-Assisted Prostatic Neoplasms Radiotherapy Dosage Hematology Prostate carcinoma medicine.disease Volumetric modulated arc therapy medicine.anatomical_structure Oncology Radiology Radiotherapy Intensity-Modulated Benchmark data business Previously treated |
| Zdroj: | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 113(3) |
| ISSN: | 1879-0887 |
| Popis: | Purpose To evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy applied to advanced lung cancer and to low risk prostate carcinoma patients. Methods and materials Two sets each of 27 previously treated patients, were selected to train models for the prediction of dose–volume constraints. The models were validated on the same sets of plans (closed-loop) and on further two sets each of 25 patients not used for the training (open-loop). Results Quantitative improvements (statistically significant for the majority of the analysed dose–volume parameters) were observed between the benchmark and the test plans. In the pass–fail analysis, the rate of criteria not fulfilled was reduced in the lung patient group from 11% to 7% in the closed-loop and from 13% to 10% in the open-loop studies; in the prostate patient group it was reduced from 4% to 3% in the open-loop study. Conclusions Plans were optimised using a knowledge-based model to determine the dose–volume constraints. The results showed dosimetric improvements when compared to the benchmark data, particularly in the sparing of organs at risk. The data suggest that the new engine is reliable and could encourage its application to clinical practice. |
| Databáze: | OpenAIRE |
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