1. Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac
- Author
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Matteo Maspero, Ramona K. Charaghvandi, Jeanine E. Vasmel, M.E.P. Philippens, Antonetta C. Houweling, Stefano Mandija, C Kontaxis, H.J.G.D. Van den Bongard, Sara S. Hackett, B. Van Asselen, Y J M de Hond, M. Groot Koerkamp, Radiotherapy, CCA - Imaging and biomarkers, and CCA - Cancer Treatment and Quality of Life
- Subjects
convolutional networks ,Supine position ,medicine.medical_treatment ,pseudo-CT ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14] ,0302 clinical medicine ,Breast cancer ,immune system diseases ,Hounsfield scale ,hemic and lymphatic diseases ,Humans ,Medicine ,Radiology, Nuclear Medicine and imaging ,MRI-only radiotherapy ,Mri linac ,Radiological and Ultrasound Technology ,business.industry ,partial breast irradiation ,Radiotherapy Planning, Computer-Assisted ,Partial Breast Irradiation ,Radiotherapy Dosage ,medicine.disease ,Magnetic Resonance Imaging ,Neoadjuvant Therapy ,Single fraction ,Radiation therapy ,surgical procedures, operative ,030220 oncology & carcinogenesis ,Ipsilateral breast ,Tomography, X-Ray Computed ,business ,Nuclear medicine ,MRI-linac ,therapeutics ,human activities - Abstract
A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1 plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2 plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCTBD1/sCTBD2/sCTBD3/sCTDL respectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVGTV was 0.15/0.00/0.00/โ0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCTDL. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTBD2 showed the optimal number of bulk-density levels for a bulk-density approach.
- Published
- 2021