MR-guided stereotactic radiotherapy of infra-diaphragmatic oligometastases: Evaluation of toxicity and dosimetric parameters.

BACKGROUND AND PURPOSE: The SOFT trial is a prospective, multicenter, phase 2 trial investigating magnetic resonance (MR)-guided stereotactic ablative radiotherapy (SABR) for abdominal, soft tissue metastases in patients with oligometastatic disease (OMD) (clinicaltrials.gov ID NCT04407897). We present the primary endpoint analysis of 1-year treatment-related toxicity (TRAE). MATERIALS AND METHODS: Patients with up to five oligometastases from non-hematological cancers were eligible for inclusion. A risk-adapted strategy prioritized fixed organs at risk (OAR) constraints over target coverage. Fractionation schemes were 45-67.5 Gy in 3-8 fractions. The primary endpoint was grade ≥ 4 TRAE within 12 months post-SABR. The association between the risk of gastrointestinal (GI) toxicity and clinical and dosimetric parameters was tested using a normal tissue complication probability model. RESULTS: We included 121 patients with 147 oligometastatic targets, mainly located in the liver (41 %), lymph nodes (35 %), or adrenal glands (14 %). Nearly half of all targets (48 %, n = 71) were within 10 mm of a radiosensitive OAR. No grade 4 or 5 TRAEs, 3.5 % grade 3 TRAEs, and 43.7 % grade 2 TRAEs were reported within the first year of follow-up. We found a significant association between grade ≥ 2 GI toxicity and the parameters GI OAR D0.1cc, D1cc, and D20cc. CONCLUSION: In this phase II study of MR-guided SABR of oligometastases in the infra-diaphragmatic region, we found a low incidence of toxicity despite half of the lesions being within 10 mm of a radiosensitive OAR. GI OAR D0.1cc, D1cc, and D20cc were associated with grade ≥ 2 GI toxicity.

End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort.

Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.