The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer.
Yoshiro IekoNoriyuki KadoyaTakayuki KanaiYujiro NakajimaKazuhiro AraiTakahiro KatoKengo ItoYuya MiyasakaKen TakedaTakeo IwaiKenji NemotoKeiichi JinguPublished in: Radiological physics and technology (2020)
Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients' 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5 Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6 Gy, 5.2 Gy, and 3.8 Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.
Keyphrases
- radiation therapy
- early stage
- locally advanced
- radiation induced
- computed tomography
- high resolution
- cardiac resynchronization therapy
- magnetic resonance imaging
- end stage renal disease
- squamous cell carcinoma
- machine learning
- heart failure
- chronic kidney disease
- palliative care
- oxidative stress
- deep learning
- mechanical ventilation
- respiratory failure
- mass spectrometry
- case report
- risk assessment
- artificial intelligence
- replacement therapy
- big data
- extracorporeal membrane oxygenation
- positron emission tomography
- prognostic factors
- acute respiratory distress syndrome
- fluorescent probe