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Item does not contain fulltext, 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 (sCT(BD1)); (2) volumes for sCT(BD1)plus chest wall and ipsilateral breast (sCT(BD2)); (3) volumes for sCT(BD2)plus ribs (sCT(BD3)); and a deep learning-generated sCT (sCT(DL)) 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 sCT(BD1)/sCT(BD2)/sCT(BD3)/sCT(DL)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 PTV(GTV)was 0.15/0.00/0.00/-0.07 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 sCT(DL). 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. Balan

Item does not contain fulltext, 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 (sCT(BD1)); (2) volumes for sCT(BD1)plus chest wall and ipsilateral breast (sCT(BD2)); (3) volumes for sCT(BD2)plus ribs (sCT(BD3)); and a deep learning-generated sCT (sCT(DL)) 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 sCT(BD1)/sCT(BD2)/sCT(BD3)/sCT(DL)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 PTV(GTV)was 0.15/0.00/0.00/-0.07 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 sCT(DL). 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. Balan

Item does not contain fulltext, 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 (sCT(BD1)); (2) volumes for sCT(BD1)plus chest wall and ipsilateral breast (sCT(BD2)); (3) volumes for sCT(BD2)plus ribs (sCT(BD3)); and a deep learning-generated sCT (sCT(DL)) 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 sCT(BD1)/sCT(BD2)/sCT(BD3)/sCT(DL)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 PTV(GTV)was 0.15/0.00/0.00/-0.07 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 sCT(DL). 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. Balan

Contains fulltext : 215382.pdf (publisher's version ) (Open Access), Background and purpose: The promise of the MR-linac is that one can visualize all anatomical changes during the course of radiotherapy and hence adapt the treatment plan in order to always have the optimal treatment. Yet, there is a trade-off to be made between the time spent for adapting the treatment plan against the dosimetric gain. In this work, the various daily plan adaptation methods will be presented and applied on a variety of tumour sites. The aim is to provide an insight in the behavior of the state-of-the-art 1.5T MRI guided on-line adaptive radiotherapy methods. Materials and methods: To explore the different available plan adaptation workflows and methods, we have simulated online plan adaptation for five cases with varying levels of inter-fraction motion, regions of interest and target sizes: prostate, rectum, esophagus and lymph node oligometastases (single and multiple target). The plans were evaluated based on the clinical dose constraints and the optimization time was measured. Results: The time needed for plan adaptation ranged between 17 and 485s. More advanced plan adaptation methods generally resulted in more plans that met the clinical dose criteria. Violations were often caused by insufficient PTV coverage or, for the multiple lymph node case, a too high dose to OAR in the vicinity of the PTV. With full online replanning it was possible to create plans that met all clinical dose constraints for all cases. Conclusion: Daily full online replanning is the most robust adaptive planning method for Unity. It is feasible for specific sites in clinically acceptable times. Faster methods are available, but before applying these, the specific use cases should be explored dosimetrically.

Contains fulltext : 215382.pdf (publisher's version ) (Open Access), Background and purpose: The promise of the MR-linac is that one can visualize all anatomical changes during the course of radiotherapy and hence adapt the treatment plan in order to always have the optimal treatment. Yet, there is a trade-off to be made between the time spent for adapting the treatment plan against the dosimetric gain. In this work, the various daily plan adaptation methods will be presented and applied on a variety of tumour sites. The aim is to provide an insight in the behavior of the state-of-the-art 1.5T MRI guided on-line adaptive radiotherapy methods. Materials and methods: To explore the different available plan adaptation workflows and methods, we have simulated online plan adaptation for five cases with varying levels of inter-fraction motion, regions of interest and target sizes: prostate, rectum, esophagus and lymph node oligometastases (single and multiple target). The plans were evaluated based on the clinical dose constraints and the optimization time was measured. Results: The time needed for plan adaptation ranged between 17 and 485s. More advanced plan adaptation methods generally resulted in more plans that met the clinical dose criteria. Violations were often caused by insufficient PTV coverage or, for the multiple lymph node case, a too high dose to OAR in the vicinity of the PTV. With full online replanning it was possible to create plans that met all clinical dose constraints for all cases. Conclusion: Daily full online replanning is the most robust adaptive planning method for Unity. It is feasible for specific sites in clinically acceptable times. Faster methods are available, but before applying these, the specific use cases should be explored dosimetrically.

Contains fulltext : 215382.pdf (publisher's version ) (Open Access), Background and purpose: The promise of the MR-linac is that one can visualize all anatomical changes during the course of radiotherapy and hence adapt the treatment plan in order to always have the optimal treatment. Yet, there is a trade-off to be made between the time spent for adapting the treatment plan against the dosimetric gain. In this work, the various daily plan adaptation methods will be presented and applied on a variety of tumour sites. The aim is to provide an insight in the behavior of the state-of-the-art 1.5T MRI guided on-line adaptive radiotherapy methods. Materials and methods: To explore the different available plan adaptation workflows and methods, we have simulated online plan adaptation for five cases with varying levels of inter-fraction motion, regions of interest and target sizes: prostate, rectum, esophagus and lymph node oligometastases (single and multiple target). The plans were evaluated based on the clinical dose constraints and the optimization time was measured. Results: The time needed for plan adaptation ranged between 17 and 485s. More advanced plan adaptation methods generally resulted in more plans that met the clinical dose criteria. Violations were often caused by insufficient PTV coverage or, for the multiple lymph node case, a too high dose to OAR in the vicinity of the PTV. With full online replanning it was possible to create plans that met all clinical dose constraints for all cases. Conclusion: Daily full online replanning is the most robust adaptive planning method for Unity. It is feasible for specific sites in clinically acceptable times. Faster methods are available, but before applying these, the specific use cases should be explored dosimetrically.