Engineering topics
Publications and source records attributed to Lindsay, Patricia.
To explore and quantify the relationship between esophageal dose and toxicity in the setting of lung stereotactic body radiation therapy (SBRT).
Purpose: To identify risk factors associated with a decline in liver function after stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma. Methods and Materials: Data were analyzed from patients with hepatocellular carcinoma treated on clinical trials of 6-fraction SBRT. Liver toxicity was defined as an increase in Child-Pugh (CP) score ≥2 three months after SBRT. Clinical factors, SBRT details, and liver dose-volume histogram (DVH) parameters were tested for association with toxicity using logistic regression. CP class B patients were analyzed separately. Results: Among CP class A patients, 101 were evaluable, with a baseline score of A5 (72%) or A6 (28%). Fifty-three percent had portal vein thrombus. The median liver volume was 1286 cc (range, 766-3967 cc), and the median prescribed dose was 36 Gy (range, 27-54 Gy). Toxicity was seen in 26 patients (26%). Thrombus, baseline CP of A6, and lower platelet count were associated with toxicity on univariate analysis, as were several liver DVH-based parameters. Absolute and spared liver volumes were not significant. On multivariate analysis for CP class A patients, significant associations were found for baseline CP score of A6 (odds ratio [OR], 4.85), lower platelet count (OR, 0.90; median, 108 × 10{sup 9}/L vs 150 × 10{sup 9}/L), higher mean liver dose (OR, 1.33; median, 16.9 Gy vs 14.7 Gy), and higher dose to 800 cc of liver (OR, 1.11; median, 14.3 Gy vs 6.0 Gy). With 13 CP-B7 patients included or when dose to 800 cc of liver was replaced with other DVH parameters (eg, dose to 700 or 900 cc of liver) in the multivariate analysis, effective volume and portal vein thrombus were associated with an increased risk. Conclusions: Baseline CP scores and higher liver doses (eg, mean dose, effective volume, doses to 700-900 cc) were strongly associated with liver function decline 3 months after SBRT. A lower baseline platelet count and portal vein thrombus were also associated with an increased risk.
Purpose: We have developed a semi-automated dose accumulation workflow for Head and Neck Cancer (HNC) patients to evaluate volumetric and dosimetric changes that take place during radiotherapy. This work will be used to assess how dosimetric changes affect both toxicity and disease control, hence inform the feasibility and design of a prospective HNC adaptive trial. Methods: RayStation 4.5.2 features deformable image registration (DIR), where structures already defined on the planning CT image set can be deformably mapped onto cone-beam computed tomography (CBCT) images, accounting for daily treatment set-up shifts and changes in patient anatomy. The daily delivered dose can be calculated on each CBCT and mapped back to the planning CT to allow dose accumulation. The process is partially automated using Python scripts developed in collaboration with RaySearch. Results: To date we have performed dose accumulation on 18 HNC patients treated at our institution during 2013–2015 under REB approval. Our semi-automated process establishes clinical feasibility. Generally, dose accumulation for the entire treatment course of one case takes 60–120 minutes: importing all CBCTs requires 20–30 minutes as each patient has 30 to 40 treated fractions; image registration and dose accumulation require 60–90 minutes. This is in contrast to the process without automated scripts where dose accumulation alone would take 3–5 hours. Conclusions: We have developed a reliable workflow for retrospective dose tracking in HNC using RayStation. The process has been validated for HNC patients treated on both Elekta and Varian linacs with CBCTs acquired on XVI and OBI platforms respectively.
Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume coverage and organ at risk sparing and were superior over plans produced with fixed sets of common beam angles. The great majority of the automatic plans (93%) were approved as clinically acceptable by three radiation therapy specialists. Conclusions: The results demonstrated the feasibility of utilizing a learning-based approach for automatic selection of beam angles in thoracic IMRT planning. The proposed method may assist in reducing the manual planning workload, while sustaining plan quality.