Integrating computed tomography image features improves clinical prediction models for outcomes in nasopharyngeal carcinoma patients treated with (chemo)radiation [0.03%]
整合CT影像特征可改善放化疗鼻咽癌患者的临床预后模型预测效果
Guanzhi Zhou,Baoqiang Ma,Yan Li et al.
Guanzhi Zhou et al.
Background and purpose: Clinical prognostic models for nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT) with or without chemotherapy remain insufficient to capture tumour heterogeneity. ...
Systematic gravity-induced posterior tumour drift in free-breathing lung stereotactic ablative radiotherapy [0.03%]
重力引起的自由呼吸立体定向消融放射治疗肺肿瘤系统后漂移
Lars Merring-Mikkelsen,Hella M B Sand,Mads H Brincker
Lars Merring-Mikkelsen
Background and purpose: Intra-fractional respiratory tumour motion is a known challenge in lung stereotactic ablative radiotherapy (SABR), as it can compromise target dose coverage. Gravity-induced tumour baseline drift d...
Repeatability and reproducibility of MRI-derived radiomics on a 0.35 T MR-Linac using a tissue-mimicking phantom [0.03%]
基于组织 mimicking phantoms 的 MRI 导出的放射组学在 0.35T 磁共振放疗直线加速器中的再现性和可重复性研究
Florian Collard,Fanny Herault,Ludovic Vanquin et al.
Florian Collard et al.
Background and purpose: Magnetic resonance-guided radiotherapy provides real-time soft-tissue-based targeting during dose delivery. Beyond image guidance, magnetic resonance linear accelerator (MR-Linac) systems offer new...
Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy [0.03%]
基于深度学习的锥形束CT头颈部自适应放射治疗合成CT生成研究
Marcos M Sánchez,Carlos F Gracia,Concepción H Martínez et al.
Marcos M Sánchez et al.
Background and purpose: Synthetic computed tomography generation from cone-beam computed tomography enables adaptive radiotherapy on conventional linear accelerators, but its clinical implementation is limited. We develop...
A framework for planning organ at risk volume margin derivation for the small bowel during magnetic resonance-guided radiotherapy [0.03%]
基于磁共振引导放射治疗下小肠危及器官体积边界规划的框架模型研究
Saskia L C Damen,Astrid L H M W van Lier,Cornel Zachiu et al.
Saskia L C Damen et al.
Background and purpose: The small bowel is highly radiosensitive and prone to motion during abdominal and pelvic radiotherapy, risking overdosage. This study extended a previously presented small bowel tracking framework ...
Development of a cost-effective end-to-end commissioning test for simulation-omitted direct-to-unit adaptive radiotherapy [0.03%]
一种成本效益型端到端的验收测试开发，用于省略模拟的直接单元自适应放射治疗
Da Wang,Justin Visak,Sean Domal et al.
Da Wang et al.
Background and purpose: Direct-to-Unit (simulation-omitted) adaptive radiotherapy (ART) enables same-day treatment by generating treatment plans from diagnostic images, eliminating the need for computed tomography (CT) si...
Advancing stereotactic body radiotherapy through off-axis beam optimization to enable safer treatment of multi-isocenter coplanar targets [0.03%]
通过轴外射束优化推进立体定向体放射治疗，以实现多等中心共面靶区的安全治疗
Mohammad Ali Tajik-Mansoury,Ji N Lee,Ravneet Kaur et al.
Mohammad Ali Tajik-Mansoury et al.
Background and purpose: Oligometastatic lung cancer often requires multiple stereotactic body radiotherapy (SBRT) courses for spatially distinct lesions, increasing the risk of field overlap and side effect. While beam op...
Feasibility of in-vivo 4D prompt-gamma treatment verification in proton therapy for pancreatic cancer [0.03%]
胰腺癌质子治疗的在体四维原位伽马验证的可行性研究
Jonathan Berthold,Lena Nenoff,Stefanie Bertschi et al.
Jonathan Berthold et al.
Background and purpose: Prompt-gamma imaging (PGI) can detect deviations between planned and delivered proton spots. To date, PGI has been limited to body sites not influenced by regular motion or substantial intrafractio...
Federico Mastroleo,Mariana Borras-Osorio,Shiv P Patel et al.
Federico Mastroleo et al.
Digital twin research in radiation oncology has expanded rapidly across multiple domains, yet the field lacks definitional consensus and validated translational frameworks. A systematic search (PubMed, Scopus and Web of Science - September ...
Automated annotation error detection and correction for manual two-dimensional cinematic magnetic resonance imaging segmentations using segment anything 2 [0.03%]
基于Segment Anything 2的二维电影磁共振图像手动分割的自动化注释错误检测与纠正方法研究
Pia A W Görts,Mariska de Smet,Shyama U Tetar et al.
Pia A W Görts et al.
Performance of deep learning-based models for tumour tracking in magnetic resonance-guided radiotherapy hinges on high-quality labelled data. Medical images are prone to annotation errors, making data cleaning essential. We propose an autom...