RESUMO
Introduction: Stereotactic radiotherapy (SBRT) potentially has a role in the management of oligometastatic melanoma. However, literature with data specific to this management is very limited. The objectives of this study were to evaluate the time to local control (LC) of extra-cranial melanoma metastases after SBRT treatment and to help establish if SBRT is a useful therapy for oligometastatic melanoma. Methods: A retrospective study was conducted with data collected from two referral centers in France between 2007 and 2020. The oligometastatic status of patients was reported based on the latest recommendations with a maximum of three lesions prior to treatment. Results: A total of 69 patients receiving SBRT for 88 oligometastatic melanoma metastases were included. The median follow-up time was 42.6 months. Most patients were treated for metachronous oligometastatic lesions. Occurrence of oligoprogression, oligorecurrence, and oligopersistence was reported in 42.0%, 39.1%, and 17.4% of cases, respectively. Treated lesions were mostly pulmonary (40.6%), followed by lymph node (34.8%) and hepatic sites (24.6%). Progression-free survival at 1, 2, and 3 years were 47.0% (35-59), 27.0% (16-39), and 25.0% (15.0-37.0), respectively. Time to LC rates at 1, 2, and 3 years were 94.2% (87.0-98.1), 90.3% (81.3-96.1), and 90.3% (81.3-96.1), respectively. Overall survival at 1, 2, and 3 years were 87% (76.0-93.0), 74.0% (76.0-93.0), and 61.0% (47.0-73.0), respectively. Only 17.4% of patients experienced acute, grade 1 or grade 2 toxicities with no reports of grade 3 or higher toxicities. Conclusion: SBRT demonstrated efficacy in managing melanoma patients with extracranial oligometastases and showed an overall low toxicity profile. Future randomized studies are needed to establish the role of SBRT in therapeutic approaches for patients with oligometastatic melanoma.
RESUMO
The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data. This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis. It takes on average of [Formula: see text] to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%. This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.