Strahlentherapie und Onkologie最新文献

{"title":"Artificial intelligence for treatment delivery: image-guided radiotherapy.","authors":"Moritz Rabe, Christopher Kurz, Adrian Thummerer, Guillaume Landry","doi":"10.1007/s00066-024-02277-9","DOIUrl":"10.1007/s00066-024-02277-9","url":null,"abstract":"<p><p>Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"283-297"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Principles of artificial intelligence in radiooncology.","authors":"Yixing Huang, Ahmed Gomaa, Daniel Höfler, Philipp Schubert, Udo Gaipl, Benjamin Frey, Rainer Fietkau, Christoph Bert, Florian Putz","doi":"10.1007/s00066-024-02272-0","DOIUrl":"10.1007/s00066-024-02272-0","url":null,"abstract":"<p><strong>Purpose: </strong>In the rapidly expanding field of artificial intelligence (AI) there is a wealth of literature detailing the myriad applications of AI, particularly in the realm of deep learning. However, a review that elucidates the technical principles of deep learning as relevant to radiation oncology in an easily understandable manner is still notably lacking. This paper aims to fill this gap by providing a comprehensive guide to the principles of deep learning that is specifically tailored toward radiation oncology.</p><p><strong>Methods: </strong>In light of the extensive variety of AI methodologies, this review selectively concentrates on the specific domain of deep learning. It emphasizes the principal categories of deep learning models and delineates the methodologies for training these models effectively.</p><p><strong>Results: </strong>This review initially delineates the distinctions between AI and deep learning as well as between supervised and unsupervised learning. Subsequently, it elucidates the fundamental principles of major deep learning models, encompassing multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), diffusion-based generative models, and reinforcement learning. For each category, it presents representative networks alongside their specific applications in radiation oncology. Moreover, the review outlines critical factors essential for training deep learning models, such as data preprocessing, loss functions, optimizers, and other pivotal training parameters including learning rate and batch size.</p><p><strong>Conclusion: </strong>This review provides a comprehensive overview of deep learning principles tailored toward radiation oncology. It aims to enhance the understanding of AI-based research and software applications, thereby bridging the gap between complex technological concepts and clinical practice in radiation oncology.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"210-235"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The increasing role of artificial intelligence in radiation oncology: how should we navigate it?","authors":"Florian Putz, Rainer Fietkau","doi":"10.1007/s00066-025-02381-4","DOIUrl":"10.1007/s00066-025-02381-4","url":null,"abstract":"","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":"201 3","pages":"207-209"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143459650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Olaparib for high-risk biochemically recurrent prostate cancer following prostatectomy].","authors":"Katharina Hintelmann, Lukas Böckelmann","doi":"10.1007/s00066-024-02350-3","DOIUrl":"10.1007/s00066-024-02350-3","url":null,"abstract":"","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"343-345"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR-linac: role of artificial intelligence and automation.","authors":"Serena Psoroulas, Alina Paunoiu, Stefanie Corradini, Juliane Hörner-Rieber, Stephanie Tanadini-Lang","doi":"10.1007/s00066-024-02358-9","DOIUrl":"10.1007/s00066-024-02358-9","url":null,"abstract":"<p><p>The integration of artificial intelligence (AI) into radiotherapy has advanced significantly during the past 5 years, especially in terms of automating key processes like organ at risk delineation and treatment planning. These innovations have enhanced consistency, accuracy, and efficiency in clinical practice. Magnetic resonance (MR)-guided linear accelerators (MR-linacs) have greatly improved treatment accuracy and real-time plan adaptation, particularly for tumors near radiosensitive organs. Despite these improvements, MR-guided radiotherapy (MRgRT) remains labor intensive and time consuming, highlighting the need for AI to streamline workflows and support rapid decision-making. Synthetic CTs from MR images and automated contouring and treatment planning will reduce manual processes, thus optimizing treatment times and expanding access to MR-linac technology. AI-driven quality assurance will ensure patient safety by predicting machine errors and validating treatment delivery. Advances in intrafractional motion management will increase the accuracy of treatment, and the integration of imaging biomarkers for outcome prediction and early toxicity assessment will enable more precise and effective treatment strategies.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"298-305"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Can stereotactic radiotherapy for neovascular age-related macular degeneration (STAR trial) enhance quality of life?]","authors":"Stephanie Göller, Heinz Schmidberger","doi":"10.1007/s00066-024-02351-2","DOIUrl":"10.1007/s00066-024-02351-2","url":null,"abstract":"","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"346-348"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy.","authors":"Christian Trapp, Nina Schmidt-Hegemann, Michael Keilholz, Sarah Frederike Brose, Sebastian N Marschner, Stephan Schönecker, Sebastian H Maier, Diana-Coralia Dehelean, Maya Rottler, Dinah Konnerth, Claus Belka, Stefanie Corradini, Paul Rogowski","doi":"10.1007/s00066-024-02342-3","DOIUrl":"10.1007/s00066-024-02342-3","url":null,"abstract":"<p><strong>Background: </strong>This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.</p><p><strong>Methods: </strong>Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc., San Francisco, CA, USA), Gemini (Google LLC, Mountain View, CA, USA), Copilot (Microsoft Corp., Redmond, WA, USA), and Claude (Anthropic PBC, San Francisco, CA, USA)] via the respective web interfaces. Responses were evaluated for readability using the Flesch Reading Ease Index. Five radiation oncologists assessed the responses for relevance, correctness, and completeness using a five-point Likert scale. Additionally, 35 prostate cancer patients evaluated the responses from ChatGPT‑4 for comprehensibility, accuracy, relevance, trustworthiness, and overall informativeness.</p><p><strong>Results: </strong>The Flesch Reading Ease Index indicated that the responses from all LLMs were relatively difficult to understand. All LLMs provided answers that clinicians found to be generally relevant and correct. The answers from ChatGPT‑4, ChatGPT-4o, and Claude AI were also found to be complete. However, we found significant differences between the performance of different LLMs regarding relevance and completeness. Some answers lacked detail or contained inaccuracies. Patients perceived the information as easy to understand and relevant, with most expressing confidence in the information and a willingness to use ChatGPT‑4 for future medical questions. ChatGPT-4's responses helped patients feel better informed, despite the initially standardized information provided.</p><p><strong>Conclusion: </strong>Overall, LLMs show promise as a tool for patient education in prostate cancer radiotherapy. While improvements are needed in terms of accuracy and readability, positive feedback from clinicians and patients suggests that LLMs can enhance patient understanding and engagement. Further research is essential to fully realize the potential of artificial intelligence in patient education.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"333-342"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142955461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.","authors":"Ayhan Can Erdur, Daniel Rusche, Daniel Scholz, Johannes Kiechle, Stefan Fischer, Óscar Llorián-Salvador, Josef A Buchner, Mai Q Nguyen, Lucas Etzel, Jonas Weidner, Marie-Christin Metz, Benedikt Wiestler, Julia Schnabel, Daniel Rueckert, Stephanie E Combs, Jan C Peeken","doi":"10.1007/s00066-024-02262-2","DOIUrl":"10.1007/s00066-024-02262-2","url":null,"abstract":"<p><p>The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"236-254"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning.","authors":"Florian Putz, Sogand Beirami, Manuel Alexander Schmidt, Matthias Stefan May, Johanna Grigo, Thomas Weissmann, Philipp Schubert, Daniel Höfler, Ahmed Gomaa, Ben Tkhayat Hassen, Sebastian Lettmaier, Benjamin Frey, Udo S Gaipl, Luitpold V Distel, Sabine Semrau, Christoph Bert, Rainer Fietkau, Yixing Huang","doi":"10.1007/s00066-024-02313-8","DOIUrl":"10.1007/s00066-024-02313-8","url":null,"abstract":"<p><strong>Background: </strong>Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.</p><p><strong>Methods: </strong>Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice. Tumor boundaries were auto-segmented on contrast-enhanced T1w sequences. Out of the three auto-contours predicted by SA, accuracy was evaluated for the contour with the highest calculated IoU (Intersection over Union, \"oracle mask,\" simulating interactive model use with selection of the best tumor contour) and for the tumor contour with the highest model confidence (\"suggested mask\").</p><p><strong>Results: </strong>Mean best IoU (mbIoU) using the best predicted tumor contour (oracle mask) in full MRI slices was 0.762 (IQR 0.713-0.917). The best 2D mask was achieved after a mean of 6.6 interactive point prompts (IQR 5-9). Segmentation accuracy was significantly better for high- compared to low-grade glioma cases (mbIoU 0.789 vs. 0.668). Accuracy was worse using the suggested mask (0.572). Stacking best tumor segmentations from transverse MRI slices, mean 3D Dice score for tumor auto-contouring was 0.872, which was improved to 0.919 by combining axial, sagittal, and coronal contours.</p><p><strong>Conclusion: </strong>The Segment Anything foundation segmentation model can achieve high accuracy for glioma brain tumor segmentation in MRI datasets. The results suggest that foundation segmentation models could facilitate RT treatment planning when properly integrated in a clinical application.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":"255-265"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11839838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing treatment for Gleason 10 prostate cancer: radiation dose escalation and ⁶⁸Ga-PSMA-PET/CT staging.","authors":"Cem Onal, Ozan Cem Guler, Birhan Demirhan, Petek Erpolat, Aysenur Elmali, Melek Yavuz","doi":"10.1007/s00066-025-02376-1","DOIUrl":"https://doi.org/10.1007/s00066-025-02376-1","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the effects of dose escalation through focal boost (FB) to intraprostatic lesions (IPLs) as well as the role of gallium-68 prostate-specific membrane antigen positron-emission tomography (⁶⁸Ga-PSMA-PET/CT) for staging and treatment planning in patients with Gleason score (GS) 10 prostate cancer (PCa) receiving definitive radiotherapy (RT) and androgen deprivation therapy (ADT).</p><p><strong>Materials and methods: </strong>We retrospectively analyzed data of 92 patients with GS 10 PCa who underwent definitive RT and ADT from March 2010 to October 2022. Freedom from biochemical failure (FFBF), prostate cancer-specific survival (PCSS), distant metastasis-free survival (DMFS), and overall survival (OS) rates were calculated using the Kaplan-Meier method. Survival outcomes were compared between patients staged with ⁶⁸Ga-PSMA-PET/CT and those staged with conventional imaging modalities as well as between those who received a simultaneous integrated boost (SIB) and those who did not.</p><p><strong>Results: </strong>At a median follow-up time of 73 months, the 5‑year FFBF, PCSS, DMFS, and OS rates were 59.2%, 77.0%, 62.9%, and 67.6%, respectively. Disease progression was observed in 39 patients (42.4%), with most cases manifesting as distant metastasis (DM). A total of 56 patients (60.9%) were staged using ⁶⁸Ga-PSMA-PET/CT, while 43 patients (46.7%) received FB to IPLs. Patients staged with ⁶⁸Ga-PSMA-PET/CT had better FFBF and PCSS compared to those staged with conventional imaging. Patients undergoing an SIB had improved PCSS and DMFS. In the multivariable analysis, an ADT duration of 18 months or more was associated with improved FFBF, PCSS, DMFS, and OS. Application of an SIB was an additional independent predictor for improved FFBF, while staging with ⁶⁸Ga-PSMA-PET/CT was associated with better PCSS.</p><p><strong>Conclusion: </strong>We found that long-term ADT, increasing the radiation dose to primary tumor, and staging with ⁶⁸Ga-PSMA-PET/CT improved clinical outcomes. Additional research is needed for validation.</p>","PeriodicalId":21998,"journal":{"name":"Strahlentherapie und Onkologie","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}