Purpose: To evaluate local tumour control (LTC) by local ablation techniques (LAT) in liver malignancies. Materials and methods: In patients treated with LAT between January 2013 and October 2020 target lesions were characterised by histology, dimensions in three spatial axes, volume, vascularisation and challenging (CL) location. LAT used were: Radiofrequency Ablation (RFA), Microwave Ablation (MWA), Cryoablation (CRYO), Electrochemotherapy (ECT), and Interstitial Brachytherapy (IBT). Results: 211 LAT were performed in 155 patients. Mean follow-up including MRI for all patients was 11 months. Lesions treated with ECT and IBT were significantly larger and significantly more often located in CL in comparison to RFA, MWA and CRYO. Best LTC (all data for 12 months are given below) resulted after RFA (93%), followed by ECT (81%), CRYO (70%), IBT (68%) and MWA (61%), and further, entity-related for HCC (93%), followed by CRC (83%) and BrC (72%), without statistically significant differences. LTC in hypovascular lesions was worse (64%), followed by intermediate (82% p = 0.01) and hypervascular lesions (92% p = 0.07). Neither diameter (6 cm: 70%), nor volume (20 cm³: 67%), nor CL (75% in CL vs. 80% in non CL) had a significant impact on LTC. In CL, best LTC resulted after ECT (76%) and IBT (76%). Conclusion: With suitable LAT, similarly good local tumour control can be achieved regardless of lesion size and location of the target.

Minimal-invasive interventions considerably extend the therapeutic spectrum in oncology and open new dimensions in terms of survival, tolerability and patient-friendliness. Through the influence of image-guided interventions, many interdisciplinary therapy concepts have significantly evolved, and this process is by far not yet over. The rapid progression of minimal-invasive technologies offers hope for new therapeutic concepts in the short, medium and long term. Image-guided hybrid-technologies complement and even replace in selected cases classic surgery. In this newly begun era of immune-oncology, interdisciplinary collaboration and the focus on individualized and patient-friendly therapies are crucial.

Background: Large language models (LLMs) hold promise for supporting clinical tasks, particularly in data-driven and technical disciplines such as radiation oncology. While prior evaluation studies have focused on examination-style settings for evaluating LLMs, their performance in real-life clinical scenarios remains unclear. In the future, LLMs might be used as general AI assistants to answer questions arising in clinical practice. It is unclear how well a modern LLM, locally executed within the infrastructure of a hospital, would answer such questions compared with clinical experts. Objective: This study aimed to assess the performance of a locally deployed, state-of-the-art medical LLM in answering real-world clinical questions in radiation oncology compared with clinical experts. The aim was to evaluate the overall quality of answers, as well as the potential harmfulness of the answers if used for clinical decision-making. Methods: Physicians from 10 departments of European hospitals collected questions arising in the clinical practice of radiation oncology. Fifty of these questions were answered by 3 senior radiation oncology experts with at least 10 years of work experience, as well as the LLM Llama3-OpenBioLLM-70B (Ankit Pal and Malaikannan Sankarasubbu). In a blinded review, physicians rated the overall answer quality on a 5-point Likert scale (quality), assessed whether an answer might be potentially harmful if used for clinical decision-making (harmfulness), and determined if responses were from an expert or the LLM (recognizability). Comparisons between clinical experts and LLMs were then made for quality, harmfulness, and recognizability. Results: There were no significant differences between the quality of the answers between LLM and clinical experts (mean scores of 3.38 vs 3.63; median 4.00, IQR 3.00-4.00 vs median 3.67, IQR 3.33-4.00; P=.26; Wilcoxon signed rank test). The answers were deemed potentially harmful in 13% of cases for the clinical experts compared with 16% of cases for the LLM (P=.63; Fisher exact test). Physicians correctly identified whether an answer was given by a clinical expert or an LLM in 78% and 72% of cases, respectively. Conclusions: A state-of-the-art medical LLM can answer real-life questions from the clinical practice of radiation oncology similarly well as clinical experts regarding overall quality and potential harmfulness. Such LLMs can already be deployed within the local hospital environment at an affordable cost. While LLMs may not yet be ready for clinical implementation as general AI assistants, the technology continues to improve at a rapid pace. Evaluation studies based on real-life situations are important to better understand the weaknesses and limitations of LLMs in clinical practice. Such studies are also crucial to define when the technology is ready for clinical implementation. Furthermore, education for health care professionals on generative AI is needed to ensure responsible clinical implementation of this transforming technology.