Development and validation of a unifying pre-treatment decision tool for intracranial and extracranial metastasis-directed radiotherapy

BackgroundThough metastasis-directed therapy (MDT) has the potential to improve overall survival (OS), appropriate patient selection remains challenging. We aimed to develop a model predictive of OS to refine patient selection for clinical trials and MDT.Patients and methodsWe assembled a multi-institutional cohort of patients treated with MDT (stereotactic body radiation therapy, radiosurgery, and whole brain radiation therapy). Candidate variables for recursive partitioning analysis were selected per prior studies: ECOG performance status, time from primary diagnosis, number of additional non-target organ systems involved (NOS), and intracranial metastases.ResultsA database of 1,362 patients was assembled with 424 intracranial, 352 lung, and 607 spinal treatments (n=1,383). Treatments were split into training (TC) (70%, n=968) and internal validation (IVC) (30%, n=415) cohorts. The TC had median ECOG of 0 (interquartile range [IQR]: 0-1), NOS of 1 (IQR: 0-1), and OS of 18 months (IQR: 7-35). The resulting model components and weights were: ECOG = 0, 1, and > 1 (0, 1, and 2); 0, 1, and > 1 NOS (0, 1, and 2); and intracranial target (2), with lower scores indicating more favorable OS. The model demonstrated high concordance in the TC (0.72) and IVC (0.72). The score also demonstrated high concordance for each target site (spine, brain, and lung).ConclusionThis pre-treatment decision tool represents a unifying model for both intracranial and extracranial disease and identifies patients with the longest survival after MDT who may benefit most from aggressive local therapy. Carefully selected patients may benefit from MDT even in the presence of intracranial disease, and this model may help guide patient selection for MDT

Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

IntroductionOrgan-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data.MethodsTwo head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient.ResultsMean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p<0.05) for RO-revised DL contours compared to the RO-revised MDA contours, including volumetric Dice similarity coefficient (VDSC), surface DSC, added path length, and the 95%-Hausdorff distance. 32 OARs (76%) had mean VDSC greater than 0.8 for the RO-revised DL contours, compared to 20 (48%) for RO-revised MDA contours, and 34 (81%) for the unrevised DL OARs.ConclusionDL autosegmentation demonstrated significant time-savings for organ-at-risk contouring while improving agreement with the institutional GS, indicating comparable accuracy of DL model. Integration into the clinical practice with a prospective evaluation is currently underway

Adult Ewing Sarcoma: Survival and Local Control Outcomes in 102 Patients with Localized Disease

Objectives. To assess the clinical features and local control (LC) outcomes in adult patients with localized Ewing Sarcoma (ES). Methods. The records of 102 ES patients with localized disease ≥18 years of age seen from 1977 to 2007 were reviewed. Factors relevant to prognosis, survival, and LC were analyzed. Results. The 5-year overall survival (OS) and event-free survival (EFS) were 60% and 52%, respectively, for the entire cohort. Treatment era (1977–1992 versus 1993–2007) remained an independent prognostic factor for OS on multivariate analysis, with improved outcomes observed in the 1993–2007 era (P=0.02). The 5-year OS and EFS for the 1993–2007 era were 73% and 60%, respectively. Ifosfamide and etoposide based chemotherapy and surgery were more routinely used in the 1993–2007 era (P<0.01). The 5-year local failure rate (LFR) was 14%, with a 5-year LFR of 18% for surgery, 33% for radiation, and 0% for combined surgery and radiation in the 1993–2007 era (P=0.17). Conclusion. Modern survival outcomes for adults with localized ES are similar to multi-institutional results in children. This improvement over time is associated with treatment intensification with chemotherapy and increased use of surgery. Aggressive LC (combined surgery and radiation) may improve outcomes in poor prognosis patients

Ultra-low-dose (boom-boom) radiotherapy for management of recurrent ocular post-transplant lymphoproliferative disorder

Purpose: To report a case of recurrent iris post-transplant lymphoproliferative disorder (PTLD) treated with ultra-low-dose (boom-boom) radiotherapy (RT). Observations: A 12-year-old Caucasian male with a history of bilateral, recurrent iris PTLD of the extranodal marginal zone lymphoma (MALT) type presented with persistent bilateral anterior chamber cellular infiltration, which was incompletely controlled on topical corticosteroids, and with elevated intraocular pressure (IOP) in the right eye secondary to steroid response. The patient was diagnosed with PTLD recurrence and was successfully treated with ultra-low-dose RT to both eyes in 2 fractions of 2 Gy. At 15 month follow-up the patient maintained complete disease control with normal IOP off all topical ophthalmic medications. Conclusions and Importance: Ultra-low-dose RT for ocular PTLD of the MALT subtype represents a novel therapeutic approach that may provide a durable treatment response and could be considered as either primary or adjuvant therapy for this rare condition

Dosimetric impact of amino acid positron emission tomography imaging for target delineation in radiation treatment planning for high-grade gliomas

Background and purpose: The amino-acid positron emission tomography (PET) tracer 3,4-dihydroxy-6-[18F] fluoro-l-phenylalanine (18F-DOPA) has increased sensitivity for detecting regions of biologically aggressive tumors compared to T1 contrast-enhanced (T1-CE) magnetic resonance imaging (MRI). We performed dosimetric evaluation of treatment plans prepared with and without inclusion of 18F-DOPA-based biological target volume (BTV) evaluating its role in guiding radiotherapy of grade III/IV gliomas. Materials and methods: Eight patients (five T1-CE, three non-contrast-enhancing [NCE]) were included in our study. MRI only-guided anatomic plans and MRI+18FDOPA-PET-guided biologic plans were prepared for each patient, and dosimetric data for target volumes and organs at risk (OAR) were compared. High-dose BTV60Gy was defined as regions with tumor to normal brain (T/N) >2.0, while low-dose BTV51Gy was initially based on T/N >1.3, but refined per Nuclear Medicine expert. Results: For T1-CE tumors, planning target volumes (PTV) were larger than MRI-only anatomic target volumes. Despite increases in size of both gross target volumes and PTV, with volumetric-modulated arc therapy planning, no increase of dose to OAR was observed while maintaining similar target dose coverage. For NCE tumors, MRI+18F-DOPA PET biologic imaging identified a sub-region of the large, T2-FLAIR abnormal signal which may allow a smaller volume to receive the high dose (60 Gy) radiation. Conclusions: For T1-CE tumors, PTVs were larger than MRI-only anatomic target volumes with no increase of dose to OARs. Therefore, MRI+18F-DOPA PET-based biologic treatment planning appears feasible in patients with high-grade gliomas. Keywords: 18F-DOPA PET, PET-guided radiation therapy, Planning study, Amino acid PE