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Advanced fiber optic systems for efficient medical image transmission: a telemedicine perspective. 用于高效医学图像传输的先进光纤系统:远程医疗视角。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1007/s13246-025-01622-1
Bengana Abdelfatih, Debbal Mohammed, Bouregaa Moueffeq, Bemmoussat Chemseddine

The increasing demand for secure, high-quality medical image transmission across healthcare institutions has posed a significant challenge to modern telemedicine systems. Traditional network infrastructures often fail to provide sufficient bandwidth and low latency required for transferring large volumes of high-resolution medical images, such as MRI and CT scans, over long distances. To address this limitation, a fiber-optic transmission framework was designed and evaluated with the objective of enhancing the speed, reliability, and accuracy of inter-hospital medical image sharing. In this study, a simulation-based approach was employed using OPTISYSTEM and MATLAB to model the optical transmission chain, including stages of image digitization, modulation, fiber propagation, and optical-to-electrical conversion at the receiving end. Various performance parameters such as Bit Error Rate (BER), Quality Factor (Q), transmission power, and noise levels were analyzed for different image resolutions and transmission distances. The results showed that Q-Factor values between 8.5 and 9.5 were obtained, with BER reaching values as low as 10⁻20, even for high-resolution images transmitted over distances up to 90 km. These results were compared to existing benchmarks in the literature and demonstrated superior performance. The proposed system exhibited strong robustness in handling large image datasets, with minimal signal distortion and negligible transmission errors. It was concluded that the adoption of this fiber-optic architecture could significantly improve the efficiency of telemedicine applications, offering a reliable and high-capacity solution for real-time diagnostic collaboration and patient monitoring between geographically distributed medical facilities.

医疗机构对安全、高质量医疗图像传输的需求日益增长,对现代远程医疗系统构成了重大挑战。传统的网络基础设施通常无法提供长距离传输大量高分辨率医学图像(如MRI和CT扫描)所需的足够带宽和低延迟。为了解决这一限制,设计并评估了光纤传输框架,目的是提高医院间医学图像共享的速度、可靠性和准确性。在本研究中,采用基于仿真的方法,利用OPTISYSTEM和MATLAB对光传输链进行建模,包括图像数字化、调制、光纤传播和接收端光电转换等阶段。分析了不同图像分辨率和传输距离下的各种性能参数,如误码率(BER)、质量因子(Q)、传输功率和噪声水平。结果显示,Q-Factor的值在8.5到9.5之间,BER的值低至10 - 20,即使对于传输距离高达90公里的高分辨率图像也是如此。这些结果与文献中现有的基准进行了比较,并证明了优越的性能。该系统在处理大型图像数据集时表现出较强的鲁棒性,信号失真最小,传输误差可忽略不计。结论是,采用这种光纤架构可以显著提高远程医疗应用的效率,为地理分布的医疗机构之间的实时诊断协作和患者监测提供可靠和高容量的解决方案。
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引用次数: 0
FrnOBSA: fractional order-based spectral analysis for arrhythmia detection. FrnOBSA:基于分数阶谱分析的心律失常检测。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-10-02 DOI: 10.1007/s13246-025-01634-x
Shikha Singhal, Manjeet Kumar
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引用次数: 0
Radiation exposure of young patients with abdominal neuroblastoma from therapeutic and imaging procedures: a phantom study. 年轻腹部神经母细胞瘤患者在治疗和成像过程中的辐射暴露:一项幻象研究。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-07-22 DOI: 10.1007/s13246-025-01601-6
Michalis Mazonakis, John Stratakis, Efrossyni Lyraraki, John Damilakis

This study calculated the radiation dose to young patients with high-risk abdominal neuroblastoma from therapeutic and imaging procedures. Computational XCAT phantoms representing typical patients aged 5-15 years were used. Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) plans were generated with 6 MV photons for a planning target volume (PTV) on the left and right abdominal side. Dose-volume-histograms from the plans were used to find the average dose (Dav) to critical normal abdominal and thoracic organs. The imaging dose to these organs and PTV was calculated by simulating kV cone-beam computed tomography (CBCT) scanning for patient setup before radiotherapy. Different CBCT protocols were simulated with Monte Carlo methods. The IMRT and VMAT plans provided similar PTV coverage and organ sparing. For a 21.6 Gy target dose, the Dav of the abdominal organs from the treatment plans was 3.6-19.6 Gy and that of thoracic organs was 0.1-2.3 Gy. Daily CBCT scans on 15-year-old patients with the standard adult protocol gave total PTV and organ doses of 95.3-485.3 mGy. The doses from the modified standard protocol for 5- and 10-year-old patients were 74.2-159.6 mGy. The dose calculations with a specially designed CBCT protocol for patients up to 10 years were 6.0-27.8 mGy. The total imaging dose to the PTV was up to 2.2% of the delivered therapeutic dose. The replacement of the modified adult CBCT protocol with a special protocol solely defined for children reduced the radiation dose to target and normal organs by more than five times.

本研究从治疗和影像学的角度计算了年轻高危腹部神经母细胞瘤患者的辐射剂量。使用代表5-15岁典型患者的计算XCAT模型。在左侧和右侧腹部的计划靶体积(PTV)上产生6 MV光子的强度调制放疗(IMRT)和体积调制电弧治疗(VMAT)计划。剂量-体积-直方图用于找出对关键正常腹部和胸部器官的平均剂量(Dav)。在放疗前,通过模拟千伏锥束计算机断层扫描(CBCT)来计算这些器官的成像剂量和PTV。用蒙特卡罗方法模拟了不同的CBCT方案。IMRT和VMAT计划提供类似的PTV覆盖和器官保留。靶剂量为21.6 Gy时,各治疗方案对腹部器官的Dav为3.6 ~ 19.6 Gy,对胸部器官的Dav为0.1 ~ 2.3 Gy。按照标准成人方案对15岁患者进行每日CBCT扫描,总PTV和器官剂量为95.3-485.3 mGy。5岁和10岁患者的修订标准方案剂量为74.2-159.6毫格瑞。使用特殊设计的CBCT方案计算的10岁以下患者的剂量为6.0-27.8 mGy。PTV的总成像剂量高达治疗剂量的2.2%。用专门为儿童制定的特殊方案取代修改后的成人CBCT方案,使靶器官和正常器官的辐射剂量减少了五倍以上。
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引用次数: 0
Hybrid deep-CNN and Bi-LSTM model with attention mechanism for enhanced ECG-based heart disease diagnosis. 基于注意机制的deep-CNN和Bi-LSTM混合模型增强心电诊断。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-07-22 DOI: 10.1007/s13246-025-01612-3
Gaurav Kumar, Neeraj Varshney

According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.

据世界卫生组织(WHO)称,每年有1790万人死于心血管疾病(cvd),包括心脏病发作。包括心脏病发作在内的心血管疾病导致全球32%的人死亡。目前的方法与心电图(ECG)信号的可变性作斗争,导致诊断错误。由于传统方法依赖于人工分析,这既耗时又容易出错,因此缺乏采用自动化和准确的心脏病检测模型。这项工作涵盖了心脏病诊断的关键主题,特别是心电图数据分析用于心血管疾病检测。基于注意机制的深度卷积神经网络(Deep-CNN)与双向长短期记忆(Bi-LSTM)模型的集成提高了心脏病分类的准确性和可靠性。Deep-CNN组件有效地从捕获的空间联系中提取特征,而Bi-LSTM层处理时间依赖性以识别患者随时间的健康模式。该模型对来自加州大学欧文分校(UCI)克利夫兰心脏病数据集的303例患者记录的14项临床特征进行了评估。该方法的准确率为97.23%,查全率为97.72%,查准率为96.90%。这些发现表明,所提出的体系结构比集成方法和混合模型更能提高诊断性能。
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引用次数: 0
Supervised versus unsupervised GAN for pseudo-CT synthesis in brain MR-guided radiotherapy. 有监督GAN与无监督GAN在脑磁共振引导放疗中伪ct合成的比较。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-07-22 DOI: 10.1007/s13246-025-01606-1
Milad Zeinali Kermani, Mohamad Bagher Tavakoli, Amir Khorasani, Iraj Abedi, Vahid Sadeghi, Alireza Amouheidari

Radiotherapy is a crucial treatment for brain tumor malignancies. To address the limitations of CT-based treatment planning, recent research has explored MR-only radiotherapy, requiring precise MR-to-CT synthesis. This study compares two deep learning approaches, supervised (Pix2Pix) and unsupervised (CycleGAN), for generating pseudo-CT (pCT) images from T1- and T2-weighted MR sequences. 3270 paired T1- and T2-weighted MRI images were collected and registered with corresponding CT images. After preprocessing, a supervised pCT generative model was trained using the Pix2Pix framework, and an unsupervised generative network (CycleGAN) was also trained to enable a comparative assessment of pCT quality relative to the Pix2Pix model. To assess differences between pCT and reference CT images, three key metrics (SSIM, PSNR, and MAE) were used. Additionally, a dosimetric evaluation was performed on selected cases to assess clinical relevance. The average SSIM, PSNR, and MAE for Pix2Pix on T1 images were 0.964 ± 0.03, 32.812 ± 5.21, and 79.681 ± 9.52 HU, respectively. Statistical analysis revealed that Pix2Pix significantly outperformed CycleGAN in generating high-fidelity pCT images (p < 0.05). There was no notable difference in the effectiveness of T1-weighted versus T2-weighted MR images for generating pCT (p > 0.05). Dosimetric evaluation confirmed comparable dose distributions between pCT and reference CT, supporting clinical feasibility. Both supervised and unsupervised methods demonstrated the capability to generate accurate pCT images from conventional T1- and T2-weighted MR sequences. While supervised methods like Pix2Pix achieve higher accuracy, unsupervised approaches such as CycleGAN offer greater flexibility by eliminating the need for paired training data, making them suitable for applications where paired data is unavailable.

放射治疗是脑肿瘤恶性肿瘤的重要治疗手段。为了解决基于ct的治疗计划的局限性,最近的研究已经探索了仅mr放射治疗,需要精确的mr - ct合成。本研究比较了两种深度学习方法,有监督(Pix2Pix)和无监督(CycleGAN),用于从T1和t2加权MR序列中生成伪ct (pCT)图像。收集3270张配对的T1和t2加权MRI图像,并与相应的CT图像进行配准。预处理后,使用Pix2Pix框架训练监督pCT生成模型,并训练无监督生成网络(CycleGAN),以便相对于Pix2Pix模型对pCT质量进行比较评估。为了评估pCT和参考CT图像之间的差异,使用了三个关键指标(SSIM, PSNR和MAE)。此外,对选定病例进行剂量学评估以评估临床相关性。Pix2Pix在T1图像上的平均SSIM、PSNR和MAE分别为0.964±0.03、32.812±5.21和79.681±9.52 HU。统计分析显示,Pix2Pix在生成高保真pCT图像方面明显优于CycleGAN (p 0.05)。剂量学评估证实了pCT和参考CT之间的剂量分布可比较,支持临床可行性。监督和非监督方法都证明了从常规T1和t2加权MR序列生成准确pCT图像的能力。虽然像Pix2Pix这样的监督方法实现了更高的准确性,但像CycleGAN这样的无监督方法通过消除对成对训练数据的需求提供了更大的灵活性,使其适用于无法获得成对数据的应用。
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引用次数: 0
PET-based radiomic analysis in multicentre lung cancer study and impact of feature domain harmonization. 基于pet的多中心肺癌研究放射学分析及特征域协调的影响。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-08-18 DOI: 10.1007/s13246-025-01625-y
Pooja Dwivedi, Sagar Barage, Rajshri Singh, Ashish Jha, Sayak Choudhury, Archi Agrawal, Venkatesh Rangarajan

Radiomic biomarkers have demonstrated significant potential in non-invasively assessing tumor biology and providing essential insights for precision medicine. However, the clinical translation is often hindered by challenges in multicenter studies, primarily due to a lack of standardization, such as variations in scanner models, acquisition protocols, reconstruction techniques, etc. This study aims to assess the impact of various harmonization methods in multicenter, 18 F-FDG PET-based radiomics for the classification of lung cancer histological subtypes using a machine learning model. Retrospective data included 178 lung cancer cohorts, comprising 117 adenocarcinomas and 61 squamous cell carcinomas from three different centers. PET DICOM image data was preprocessed with 3D ROI segmentation of the lung tumor and healthy liver, followed by the extraction of 111 radiomic features. Subsequently, Z-Score, Quantile, and ComBat were applied to generate three different harmonized datasets. Feature distribution was analyzed, and the top ten features were selected using recursive feature elimination. An eXtreme gradient boosting model was built on each dataset, and performance was assessed using accuracy, precision, sensitivity, specificity, and AUC with a 95% confidence interval. Variations in radiomic feature distribution and feature selection were observed after applying different harmonization methods. During validation of the trained model, AUC improved from 0.556 [95% CI 0.551-0.563] in the unharmonized data to 0.719 [95% CI 0.710-0.720], 0.952 [95% CI 0.951-0.954], and 0.996 [95% CI 0.995-0.996] in Z-Score, Quantile, and ComBat harmonized data, respectively, for classifying adenocarcinoma and squamous cell carcinoma subtypes. The study indicates that feature selection was affected by the different harmonization methods. The ComBat method was shown to significantly enhance the performance of AI-assisted PET radiomics.

放射组学生物标志物在非侵入性评估肿瘤生物学和为精准医学提供重要见解方面显示出巨大的潜力。然而,临床翻译常常受到多中心研究挑战的阻碍,主要是由于缺乏标准化,例如扫描仪模型、获取协议、重建技术等方面的差异。本研究旨在利用机器学习模型评估各种协调方法在多中心、18 F-FDG pet为基础的放射组学中对肺癌组织学亚型分类的影响。回顾性数据包括178个肺癌队列,包括来自三个不同中心的117个腺癌和61个鳞状细胞癌。对PET DICOM图像数据进行预处理,对肺肿瘤和健康肝脏进行三维ROI分割,提取111个放射学特征。随后,Z-Score, Quantile和ComBat被应用于生成三个不同的协调数据集。对特征分布进行分析,采用递归特征消去法筛选出最优的10个特征。在每个数据集上建立一个eXtreme梯度增强模型,并使用准确度、精度、灵敏度、特异性和AUC(95%置信区间)对性能进行评估。采用不同的调和方法,观察到放射学特征分布和特征选择的变化。在训练模型的验证过程中,用于分类腺癌和鳞状细胞癌亚型的Z-Score、Quantile和ComBat协调数据的AUC分别从未协调数据中的0.556 [95% CI 0.551-0.563]提高到0.719 [95% CI 0.710-0.720]、0.952 [95% CI 0.951-0.954]和0.996 [95% CI 0.995-0.996]。研究表明,不同的协调方法会影响特征选择。战斗方法被证明可以显著提高人工智能辅助PET放射组学的性能。
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引用次数: 0
Identifying suspicious naevi with dermoscopy via variational autoencoder auxiliary generative classifiers. 通过变分自动编码器辅助生成分类器用皮肤镜识别可疑痣。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-09-17 DOI: 10.1007/s13246-025-01636-9
Fatima Al Zegair, Brigid Betz-Stablein, Monika Janda, H Peter Soyer, Shekhar S Chandra

A naevus is a benign melanocytic skin tumour made up of naevus cells, characterised by variations in size, shape, and colour. Understanding naevi is essential due to their significant role as markers for the risk of developing melanoma. This study focused on creating a visual representation called a manifold that illustrates the distribution of two types of naevi: suspicious and non-suspicious. The research aimed to classify real naevi using generative adversarial networks (GANs), while also generating realistic synthetic samples and interpreting their distribution through a variational manifold. This inquiry holds promise for applying data-driven methods for early melanoma detection by identifying distinct features linked with suspicious naevi. Our variational autoencoder auxiliary classifier generative adversarial network (VAE-ACGAN) for suspicious naevi revealed a manifold with outstanding performance, including specificity, sensitivity, and area under the curve (AUC) scores, particularly representing suspicious naevi. These models surpassed various deep learning frameworks in key performance metrics while producing a manifold that indicated a significant distinction between the two categories in the resultant image, yielding high-quality and life-like representations of naevi. The results highlight the potential application of GANs in expanding data sets and enhancing the effectiveness of deep learning algorithms in dermatology. Accurate identification and categorisation of naevi could facilitate early melanoma detection and deepen our understanding of these skin lesions through an interpretable clustering method based on visual similarities.

痣是一种由痣细胞组成的良性黑色素细胞性皮肤肿瘤,其特征是大小、形状和颜色的变化。了解痣是至关重要的,因为它们是黑色素瘤发生风险的重要标志。这项研究的重点是创造一种被称为流形的视觉表现,来说明两种类型的痣的分布:可疑的和非可疑的。该研究旨在使用生成对抗网络(gan)对真实的naevi进行分类,同时生成真实的合成样本并通过变分流形解释其分布。这项研究有望通过识别与可疑痣相关的不同特征,应用数据驱动的方法进行早期黑色素瘤检测。我们针对可疑naevi的变分自编码器辅助分类器生成对抗网络(VAE-ACGAN)显示了具有出色性能的歧管,包括特异性,敏感性和曲线下面积(AUC)分数,特别是代表可疑naevi。这些模型在关键性能指标上超越了各种深度学习框架,同时产生了一个歧管,表明在生成的图像中两个类别之间存在显著区别,从而产生了高质量和逼真的naevi表示。这些结果突出了gan在扩展数据集和增强皮肤病学深度学习算法有效性方面的潜在应用。通过基于视觉相似性的可解释聚类方法,对痣的准确识别和分类可以促进黑色素瘤的早期发现,加深我们对这些皮肤病变的理解。
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引用次数: 0
FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection. FHESA:基于傅里叶分解和希尔伯特变换的脑电信号分析在阿尔茨海默病检测中的应用。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-09-29 DOI: 10.1007/s13246-025-01644-9
Kavita Bhatt, N Jayanthi, Manjeet Kumar

Alzheimer's Disease (AD) is a chronic neurological disorder that impairs the cognitive and behavioral abilities of older people. Early detection and treatment are crucial for minimizing the progression of the disease. Electroencephalogram (EEG) makes it possible to investigate the brain activities linked to various forms of disabilities experienced by individuals with AD. Nevertheless, the EEG signals are non-linear and non-stationary in nature making it difficult to retrieve the concealed information from the EEG signals. Therefore, a Fourier Decomposition Method (FDM) and Hilbert Transform (HT) based EEG signals analysis (FHESA) method is developed in this paper for the automated detection of AD. The FHESA method aims to efficiently analyze the EEG data to identify the important brain regions vulnerable to AD, and to assess the impact of various EEG channels for the timely and early detection of AD. The proposed FHESA method is divided into three primary stages. The first stage deals with the decomposition of the EEG signals into a finite number of Fourier Intrinsic Band Functions (FIBFs). In the second stage, HT is applied to all FIBFs to obtain instantaneous amplitude, frequency, and phase, that are then used to construct feature vectors. In the last stage, various Machine Learning (ML) algorithms are used to classify these feature vectors for efficient AD detection. Two distinct data sets are employed to assess the effectiveness of the proposed FHESA method. The outcome demonstrates that with dataset-I and dataset-II, the proposed methodology can detect AD with 98% and 99% accuracy, respectively. The performance of the proposed FHESA method is compared to other state-of-the-art methods used for AD detection. The promising results show that the proposed FHESA method can assist neurological experts in identifying and utilizing EEG signals for AD detection.

阿尔茨海默病(AD)是一种慢性神经系统疾病,会损害老年人的认知和行为能力。早期发现和治疗对于最大限度地减少疾病的进展至关重要。脑电图(EEG)使研究与AD患者所经历的各种形式的残疾相关的大脑活动成为可能。然而,由于脑电信号是非线性的、非平稳的,因此很难从脑电信号中提取出隐藏的信息。为此,本文提出了一种基于傅立叶分解(FDM)和希尔伯特变换(HT)的脑电信号分析(FHESA)方法,用于AD的自动检测。FHESA方法旨在有效分析脑电数据,识别AD易感的重要脑区,评估各种脑电通道对AD及时、早期发现的影响。本文提出的FHESA方法分为三个主要阶段。第一阶段处理脑电信号分解成有限数量的傅里叶内禀带函数(fibf)。在第二阶段,将HT应用于所有fibf以获得瞬时幅度,频率和相位,然后用于构建特征向量。在最后阶段,使用各种机器学习(ML)算法对这些特征向量进行分类,以实现有效的AD检测。采用两个不同的数据集来评估所提出的FHESA方法的有效性。结果表明,对于数据集i和数据集ii,所提出的方法可以分别以98%和99%的准确率检测AD。提出的FHESA方法的性能与用于AD检测的其他最先进的方法进行了比较。结果表明,本文提出的FHESA方法可以帮助神经学专家识别和利用脑电信号进行AD检测。
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引用次数: 0
Achieving greater accuracy in transcranial magnetic stimulation corticospinal evaluation and motor mapping by improving motor evoked potential recording: an emerging issue. 通过改进运动诱发电位记录,在经颅磁刺激皮质脊髓评估和运动制图中获得更高的准确性:一个新兴的问题。
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1007/s13246-025-01638-7
Marco Antonio Cavalcanti Garcia, Ana Carolina Borges Valente, Victor Hugo Moraes, Daniela Morales, Lucas Dos Santos Betioli
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引用次数: 0
Coplanar DCA-based hypofractionated stereotactic radiotherapy for very small brain metastasis from non-small cell lung cancer: treatment planning comparison with coplanar VMAT and preliminary clinical outcome. 基于共面dca的低分割立体定向放疗治疗非小细胞肺癌极小脑转移:治疗方案与共面VMAT的比较及初步临床结果
IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL Pub Date : 2025-12-01 Epub Date: 2025-10-01 DOI: 10.1007/s13246-025-01640-z
Shipai Zhu, An Li, Jia Liu, Qin Deng, Qingfang Li, Jialu Lai, Lin Zhou

To assess the clinical outcome of single-arc coplanar dynamic conformal arc (C-DCA) in three-fraction hypofractionated stereotactic radiotherapy (3F-HSRT) for single very small brain metastasis (BM; gross tumor volume [GTV] ≤ 1 cm3) from non-small cell lung cancer (NSCLC) and to compare treatment planning with single-arc coplanar volumetric modulated arc therapy (C-VMAT). From December 2019 to May 2023, twenty NSCLC patients with single BM treated with 3F-HSRT (24-39 Gy/3f) using C-DCA were enrolled in this study. Each plan was replanned using C-VMAT, and relevant planning indices were compared. Clinical outcome was evaluated following C-DCA treatment. C-VMAT yielded a higher homogeneity index (1.41 vs. 1.16, p < 0.001) and GTV D98% (38.10 Gy vs. 32.72 Gy, p = 0.008), with slightly smaller normal brain tissue (NBT) V23Gy, and V21Gy. However, C-DCA offered 31.37% lower monitor units (p = 0.008) and 36.55% shorter beam on time (p = 0.007) while achieving a significantly higher gamma passing rate for the 2%/1 mm criterion (p = 0.001). As of October 2023, the median follow-up time was 9.2 months. The intracranial disease control rate was 70%, with a median intracranial progression-free survival of 11.4 (95% CI 4.5-18.3) months and a 1-year intracranial control rate of 45.4%. The intracranial local disease control rate was 95%. Only two irradiated lesions progressed at the end of follow-up. The cerebral radiation necrosis rate of all patients was 5%. For small BM, C-DCA provided nearly equivalent target coverage and NBT sparing to the C-VMAT while maintaining higher delivery efficiency and accuracy. C-DCA HSRT also provided good local control and limited toxicity.

评价单弧共面动态适形电弧(C-DCA)在非小细胞肺癌(NSCLC)单发极小脑转移瘤(BM;总肿瘤体积[GTV]≤1 cm3)分段低分割立体定向放疗(3F-HSRT)中的临床效果,并与单弧共面体积调节电弧治疗(C-VMAT)的治疗方案进行比较。2019年12月至2023年5月,本研究纳入了20例使用C-DCA进行3f - hsrt (24-39 Gy/3f)治疗的单BM NSCLC患者。采用C-VMAT对各方案进行重新规划,并对相关规划指标进行比较。评估C-DCA治疗后的临床结果。C-VMAT具有较高的均匀性指数(1.41 vs. 1.16, p = 98%) (38.10 Gy vs. 32.72 Gy, p = 0.008),正常脑组织(NBT)的V23Gy和V21Gy略小。然而,C-DCA提供了31.37%的低监测单元(p = 0.008)和36.55%的短束时间(p = 0.007),同时实现了显著更高的2%/1 mm标准的伽马通良率(p = 0.001)。截至2023年10月,中位随访时间为9.2个月。颅内疾病控制率为70%,中位颅内无进展生存期为11.4个月(95% CI 4.5-18.3), 1年颅内控制率为45.4%。颅内局部疾病控制率为95%。在随访结束时,只有两个放射病灶进展。所有患者的脑放射性坏死率均为5%。对于小型BM, C-DCA在保持更高的投送效率和精度的同时,提供了几乎等同于C-VMAT的目标覆盖和NBT节约。C-DCA HSRT也具有良好的局部控制和有限的毒性。
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引用次数: 0
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Physical and Engineering Sciences in Medicine
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