Marvaso, Giulia

Background Interventional radiotherapy (IRT), also known as brachytherapy, has become a cornerstone of radiation oncology due to its ability to maximize therapeutic efficacy while minimizing side effects to surrounding tissues. This bibliometric analysis aims to examine the evolution of IRT over the past decade. Material and Methods A string comprising IRT-related keywords was developed, and data were extracted from the Scopus database for English-language documents published between January 2014 and December 2024. The analysis was performed using Bibliometrix package Results A total of 15 755 documents from 2150 sources were analyzed, revealing an average annual growth rate of 1.03%. Publication trends were stable from 2014 to 2018, surged from 2019, and peaked in 2021, with subsequent levels remaining above pre-2019 figures. Original articles accounted for 72.7% of the literature, with key contributions from journals such as “Brachytherapy” and the “Journal of Contemporary Brachytherapy”. The analysis highlighted strong collaborative networks, with significant input from North American and European institutions, and identified focal research themes including uterine cervical cancer and prostate brachytherapy. Emerging topics, such as deep learning, artificial intelligence, and personalized dosimetry, underscore the field's shift towards precision medicine. Conclusions The findings reflect IRT's dynamic research landscape and its growing integration into modern oncologic practice, emphasizing the need for continued interdisciplinary efforts and clinical trials to advance this minimally invasive treatment modality further. Highlights • A decade-long bibliometric analysis of 15 755 publications on interventional radiotherapy (IRT) reveals stable growth, with a notable surge in research activity post-2019. • Uterine, cervical, and prostate cancers remain the most studied clinical indications for IRT globally. • North American and European institutions dominate collaborative networks, reflecting the strength of transatlantic research partnerships. • Emerging research themes include artificial intelligence, deep learning, and personalized dosimetry—marking a transition toward precision oncology. • The field's evolving landscape highlights the increasing clinical and scientific significance of IRT in contemporary cancer care.

160 Background: To our knowledge, no tools are available to guide care in oligometastatic prostate cancer (omPC). Here, we leveraged individual patient-level data pooled from 8 randomized trials in the X-Met collaboration to construct CEREBRO, a tool for predicting patient-specific responses to various treatment scenarios. Methods: Patients with omPC from the STOMP, ORIOLE, SABR COMET, EXTEND continuous ADT, EXTEND intermittent ADT, ARTO, RADIOSA, and RAVENS trials were included. 6 patients (1%) with missing data were excluded. Treatment scenarios were defined as active surveillance (AS), metastasis-directed therapy (MDT), systemic therapy (ST), or MDT+ST. To account for varying baseline hazards, Weibull models were fit for overall survival (OS); progression-free survival (PFS); and castration-resistance free survival (CRFS) for patients with hormone-sensitive disease. Endpoint definitions were harmonized per X-Met framework. Results: Of 600 patients included (treatment scenarios: AS=50; MDT=153; ST=172; MDT+ST=225), 405 (67%) had hormone-sensitive omPC. With median follow-up of 38 months, median PFS, CRFS, and OS of all patients were 18, 70, and 84 months. In order of relative contribution, inputs for predicting outcomes were: treatment scenario, stage, prostate-specific antigen annotated by ADT status, metachronous vs synchronous presentation, imaging type, number of metastases, hormone sensitivity, and age. The model for each endpoint showed robust fit, calibration, superiority against the null model (per the likelihood ratio test), and risk stratification (Table). Median PFS times for model-defined low-, intermediate-, and high-risk strata were 46 months, 18 months, and 7 months ( p <0.0001). In addition, the PFS and OS models also showed strong discrimination via c index and a large interval of risk probabilities with additive clinical utility per decision-curve analysis (Table). A public webpage will be made available to facilitate prospective trial stratification, patient counseling for patient-specific outcome predictions for each potential treatment scenario, and forecasts comparing outcomes between treatment scenarios. Conclusions: CEREBRO is a high-performance tool, trained on robust prospective data from 8 randomized trials with routinely collected clinical variables, for predicting risk and treatment outcomes in omPC. Given the uncertainties in optimal timing of MDT and combination with ST, this tool has been built to facilitate current decision making. External validation is planned. Model summary. Endpoint C index (95% CI) Calibration Likelihood ratio p Risk strata p Net clinical utility observed between thresholds PFS 0.72 (0.70-0.75) 1.04 <0.0001 <0.0001 0.2-1.0 CRFS 0.63 (0.60-0.70) 1.01 0.02 0.004 0.2-0.5 OS 0.81 (0.76-0.86) 1.01 <0.0001 <0.0001 0.05-1.0