Journal of X-Ray Science and Technology最新文献_第5页

Research on meshing method for industrial CT volume data based on iterative smooth signed distance surface reconstruction. 基于迭代光滑符号距离曲面重建的工业CT体数据网格划分方法研究。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI: 10.1177/08953996241306691

ShiBo Jiang, Shuo Xu, YueWen Sun, ZhiFang Wu

{"title":"Research on meshing method for industrial CT volume data based on iterative smooth signed distance surface reconstruction.","authors":"ShiBo Jiang, Shuo Xu, YueWen Sun, ZhiFang Wu","doi":"10.1177/08953996241306691","DOIUrl":"10.1177/08953996241306691","url":null,"abstract":"Industrial Computed Tomography (CT) technology is increasingly applied in fields such as additive manufacturing and non-destructive testing, providing rich three-dimensional information for various fields, which is crucial for internal structure detection, defect detection, and product development. In subsequent processes such as analysis, simulation, and editing, three-dimensional volume data models often need to be converted into mesh models, making effective meshing of volume data essential for expanding the application scenarios and scope of industrial CT. However, the existing Marching Cubes algorithm has issues with low efficiency and poor mesh quality during the volume data meshing process. To overcome these limitations, this study proposes an innovative method for industrial CT volume data meshing based on the Iterative Smooth Signed Surface Distance (iSSD) algorithm. This method first refines the segmented voxel model, accurately extracts boundary voxels, and constructs a high-quality point cloud model. By randomly initializing the normals of the point cloud and iteratively updating the point cloud normals, the mesh is reconstructed using the SSD algorithm after each iteration update, ultimately achieving high-quality, watertight, and smooth mesh model reconstruction, ensuring the accuracy and reliability of the reconstructed mesh. Qualitative and quantitative analyses with other methods have further highlighted the excellent performance of the method proposed in this paper. This study not only improves the efficiency and quality of volume data meshing but also provides a solid foundation for subsequent three-dimensional analysis, simulation, and editing, and has important industrial application prospects and academic value.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"340-349"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460452","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}

引用次数: 0

Preconditioned block Kaczmarz methods for linear equations with an application to computed tomography. 线性方程的预条件块卡兹马尔方法及其在计算机断层扫描中的应用。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-02-18 DOI: 10.1177/08953996251317421

Duo Liu, Wenli Wang, Gangrong Qu

{"title":"Preconditioned block Kaczmarz methods for linear equations with an application to computed tomography.","authors":"Duo Liu, Wenli Wang, Gangrong Qu","doi":"10.1177/08953996251317421","DOIUrl":"10.1177/08953996251317421","url":null,"abstract":"BackgroundPreconditioned Kaczmarz methods play a pivotal role in image reconstruction. A fundamental theoretical question lies in establishing the convergence conditions for these methods. Practically, devising an efficient block strategy to accelerate the reconstruction process is also critical.ObjectiveThis paper aims to introduce the convergence conditions for the preconditioned Kaczmarz methods and design the block strategy with corresponding preconditioners for these methods in computed tomography (CT).MethodsWe establish a kind of useful convergence conditions for the preconditioned block Kaczmarz methods and prove the dependence of the convergence limit on the initial guess. Tailored for the CT problem, we also propose a new method with a novel block strategy and specific preconditioners, which ensure accelerated convergence.ResultsNumerical experiments with the Shepp-Logan phantom and a real chest CT image demonstrate that our proposed block strategy and preconditioners effectively accelerate the reconstruction process by the preconditioned block Kaczmarz methods while maintaining satisfactory image quality.ConclusionsOur proposed method, which incorporates the designed block strategy and specific preconditioners, has superior performance compared to the traditional Landweber iteration and the block Kaczmarz iteration without preconditioners.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"472-487"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460450","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}

引用次数: 0

Study on the influence of square fiber diameter quality on the optical characteristics of lobster eye X-ray micro pore optics. 方形光纤直径质量对龙虾眼x射线微孔光学特性影响的研究。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI: 10.1177/08953996241306697

Yang Cao, Honggang Wang, Yanan Wang, Longhui Li, Yunsheng Qian, Yizheng Lang

{"title":"Study on the influence of square fiber diameter quality on the optical characteristics of lobster eye X-ray micro pore optics.","authors":"Yang Cao, Honggang Wang, Yanan Wang, Longhui Li, Yunsheng Qian, Yizheng Lang","doi":"10.1177/08953996241306697","DOIUrl":"10.1177/08953996241306697","url":null,"abstract":"BackgroundThe lobster eye micro pore optics (MPO) plays a pivotal role in X-ray focusing, composed of thousands of hollow square microfibers. The channel error in MPO can profoundly impact its focusing performance. Due to the complex manufacturing process of MPO, there are numerous factors that can contribute to channel errors.ObjectiveThis paper investigates the impact of two key quality indicators of fiber, i.e., diameter precision and ovality, on the focusing performance of flat MPO.MethodsDuring the actual production process of MPO, fibers with varying diameter precision and ovality are utilized, and point-to-point vacuum X-ray focusing equipment is used to assess MPO's focusing performance. Channel error models related to fiber diameter accuracy and ovality are established in the simulation.ResultsExperiments show that both the diameter precision and ovality of fiber influence MPO focusing abilities, with diameter precision primarily affecting the intensity and uniformity of the central point focus and the parallelism of the line foci, while ovality mainly affects the intensity and continuity of the line foci. Numerical simulation results reveal that tilt channel errors significantly affect the X-ray focusing effects.ConclusionsThese findings hold important guiding significance for the preparation process of square fibers and high quality X-ray focusing device.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"350-359"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460016","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}

引用次数: 0

Exploiting commercial micro X-ray fluorescence systems for stereoscopic soft X-ray imaging. 开发商用微x射线荧光系统用于立体软x射线成像。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-19 DOI: 10.1177/08953996241291356

Ricardo Baettig, Ben Ingram, Ricardo A Cabeza

{"title":"Exploiting commercial micro X-ray fluorescence systems for stereoscopic soft X-ray imaging.","authors":"Ricardo Baettig, Ben Ingram, Ricardo A Cabeza","doi":"10.1177/08953996241291356","DOIUrl":"10.1177/08953996241291356","url":null,"abstract":"BackgroundCommercial micro X-ray fluorescence (μXRF) systems often employ a tilted convergent beam, which can cause a misalignment between X-ray cartography and the corresponding visible images. This misalignment is typically considered a disadvantage, as it hinders the accurate spatial correlation of elemental information. However, this apparent drawback can be exploited to facilitate X-ray stereoscopy.ObjectiveTo demonstrate the use of unmodified commercial μXRF equipment to estimate the 3D configurations of metals and voids within a low-atomic-weight matrix, specifically polymethyl methacrylate, and to explore the implications for enhancing μXRF mapping techniques. This approach could have applications in materials science, archaeology, and other fields requiring non-destructive 3D chemical mapping.MethodsUsing unmodified commercial μXRF equipment, we leveraged both XRF and Compton scattering effects to quantitatively reconstruct the size, position, and depth of embedded tungsten, copper, and silver objects. The study specifically examines how beam divergence affects the acutance of objects located deeper within the sample.ResultsOur findings indicate a depth estimation bias ranging from 4% to 15% for depths between 24 mm, and a size estimation bias below 3.2%. These results validate the methodology and highlight the robustness of our approach under typical operational settings, suggesting that the technique could be applied to a wide range of samples with minimal modifications to existing μXRF systems.ConclusionsThe study confirms that the inclination-induced misalignment in μXRF systems can be harnessed to enhance three-dimensional imaging capabilities. Our work establishes a foundation for advancing current μXRF mapping techniques and interpretation strategies, potentially broadening the applications of μXRF in various scientific and industrial fields.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"285-296"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460443","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}

引用次数: 0

Orthogonal limited-angle CT reconstruction method based on anisotropic self-guided image filtering. 基于各向异性自引导图像滤波的正交限角 CT 重建方法。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI: 10.1177/08953996241300013

Gong Changcheng, Song Qiang

{"title":"Orthogonal limited-angle CT reconstruction method based on anisotropic self-guided image filtering.","authors":"Gong Changcheng, Song Qiang","doi":"10.1177/08953996241300013","DOIUrl":"10.1177/08953996241300013","url":null,"abstract":"Computed tomography (CT) reconstruction from incomplete projection data is significant for reducing radiation dose or scanning time. In this work, we investigate a special sampling strategy, which performs two limited-angle scans. We call it orthogonal limited-angle sampling. The X-ray source trajectory covers two limited-angle ranges, and the angle bisectors of the two angular ranges are orthogonal. This sampling method avoids rapid switching of tube voltage in few-view sampling, and reduces data correlation of projections in limited-angle sampling. It has the potential to become a practical imaging strategy. Then we propose a new reconstruction model based on anisotropic self-guided image filtering (ASGIF) and present an algorithm to solve this model. We construct adaptive weights to guide image reconstruction using the gradient information of reconstructed image itself. Additionally, since the shading artifacts are related to the scanning angular ranges and distributed in two orthogonal directions, anisotropic image filtering is used to preserve image edges. Experiments on a digital phantom and real CT data demonstrate that ASGIF method can effectively suppress shading artifacts and preserve image edges, outperforming other competing methods.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"325-339"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460448","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}

引用次数: 0

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture. 基于曼巴结构的胰腺囊性肿瘤深度学习检测方法。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-02-18 DOI: 10.1177/08953996251313719

Junlong Dai, Cong He, Liang Jin, Chengwei Chen, Jie Wu, Yun Bian

{"title":"A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.","authors":"Junlong Dai, Cong He, Liang Jin, Chengwei Chen, Jie Wu, Yun Bian","doi":"10.1177/08953996251313719","DOIUrl":"10.1177/08953996251313719","url":null,"abstract":"Objective: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses the technical challenge posed by the tumors' complex morphological features in medical images.Methods: This study develops an innovative deep learning network architecture, M-YOLO (Mamba YOLOv10), which combines the advantages of Mamba and YOLOv10 and aims to improve the accuracy and efficiency of pancreatic cystic neoplasm(PCN) detection. The Mamba architecture, with its superior sequence modeling capabilities, is ideally suited for processing the rich contextual information contained in medical images. At the same time, YOLOv10's fast object detection feature ensures the system's viability for application in clinical practice.Results: M-YOLO has a high sensitivity of 0.98, a specificity of 0.92, a precision of 0.96, an F1 value of 0.97, an accuracy of 0.93, as well as a mean average precision (mAP) of 0.96 at 50% intersection-to-union (IoU) threshold on the dataset provided by Changhai Hospital.Conclusions: M-YOLO(Mamba YOLOv10) enhances the identification performance of PCN by integrating the deep feature extraction capability of Mamba and the fast localization technique of YOLOv10.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"461-471"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460302","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}

引用次数: 0

An effective COVID-19 classification in X-ray images using a new deep learning framework. 使用新的深度学习框架在x射线图像中有效分类COVID-19。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-19 DOI: 10.1177/08953996241290893

P Thilagavathi, R Geetha, S Jothi Shri, K Somasundaram

{"title":"An effective COVID-19 classification in X-ray images using a new deep learning framework.","authors":"P Thilagavathi, R Geetha, S Jothi Shri, K Somasundaram","doi":"10.1177/08953996241290893","DOIUrl":"10.1177/08953996241290893","url":null,"abstract":"BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 2019 (COVID-19). Artificial Intelligence (AI) based methods are emerging technologies that help to identify COVID-19 in chest X-ray images quickly.MethodIn this study, the publically accessible database COVID-19 Chest X-ray is used to diagnose lung-related disorders using a hybrid deep-learning approach. This dataset is pre-processed using an Improved Anisotropic Diffusion Filtering (IADF) method. After that, the features extraction methods named Grey-level Co-occurrence Matrix (GLCM), uniform Local Binary Pattern (uLBP), Histogram of Gradients (HoG), and Horizontal-vertical neighbourhood local binary pattern (hvnLBP) are utilized to extract the useful features from the pre-processed dataset. The dimensionality of a feature set is subsequently reduced through the utilization of an Adaptive Reptile Search Optimization (ARSO) algorithm, which optimally selects the features for flawless classification. Finally, the hybrid deep learning algorithm, Multi-head Attention-based Bi-directional Gated Recurrent unit with Deep Sparse Auto-encoder Network (MhA-Bi-GRU with DSAN), is developed to perform the multiclass classification problem. Moreover, a Dynamic Levy-Flight Chimp Optimization (DLF-CO) algorithm is applied to minimize the loss function in the hybrid algorithm.ResultsThe whole simulation is performed using the Python language in which the 0.001 learning rate accomplishes the proposed method's higher classification accuracy of 0.95%, and 0.98% is obtained for a 0.0001 learning rate. Overall, the performance of the proposed methodology outperforms all existing methods employing different performance parameters.ConclusionThe proposed hybrid deep-learning approach with various feature extraction, and optimal feature selection effectively diagnoses disease using Chest X-ray images demonstrated through classification accuracy.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"297-316"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460408","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}

引用次数: 0

Ultrasound and advanced imaging techniques in prostate cancer diagnosis: A comparative study of mpMRI, TRUS, and PET/CT. 超声和先进成像技术在前列腺癌诊断中的应用：mpMRI、TRUS和PET/CT的比较研究。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-02-12 DOI: 10.1177/08953996241304988

Ying Dong, Peng Wang, Hua Geng, Yankun Liu, Enguo Wang

{"title":"Ultrasound and advanced imaging techniques in prostate cancer diagnosis: A comparative study of mpMRI, TRUS, and PET/CT.","authors":"Ying Dong, Peng Wang, Hua Geng, Yankun Liu, Enguo Wang","doi":"10.1177/08953996241304988","DOIUrl":"10.1177/08953996241304988","url":null,"abstract":"ObjectiveThis study aims to assess and compare the diagnostic performance of three advanced imaging modalities-multiparametric magnetic resonance imaging (mpMRI), transrectal ultrasound (TRUS), and positron emission tomography/computed tomography (PET/CT)-in detecting prostate cancer in patients with elevated PSA levels and abnormal DRE findings.MethodsA retrospective analysis was conducted on 150 male patients aged 50-75 years with elevated PSA and abnormal DRE. The diagnostic accuracy of each modality was assessed through sensitivity, specificity, and the area under the curve (AUC) to compare performance in detecting clinically significant prostate cancer (Gleason score ≥ 7).ResultsMpMRI demonstrated the highest diagnostic performance, with a sensitivity of 90%, specificity of 85%, and AUC of 0.92, outperforming both TRUS (sensitivity 76%, specificity 78%, AUC 0.77) and PET/CT (sensitivity 82%, specificity 80%, AUC 0.81). MpMRI detected clinically significant tumors in 80% of cases. Although TRUS and PET/CT had similar detection rates for significant tumors, their overall accuracy was lower. Minor adverse events occurred in 5% of patients undergoing TRUS, while no significant complications were associated with mpMRI or PET/CT.ConclusionThese findings suggest that mpMRI is the most reliable imaging modality for early detection of clinically significant prostate cancer. It reduces the need for unnecessary biopsies and optimizes patient management.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"436-447"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460246","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}

引用次数: 0

A cross-type multi-dimensional network based on feature enhancement and triple interactive attention for LDCT denoising. 基于特征增强和三重交互关注的交叉型多维网络LDCT去噪。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-29 DOI: 10.1177/08953996241306696

Lina Jia, Beibei Jia, Zongyang Li, Yizhuo Zhang, Zhiguo Gui

{"title":"A cross-type multi-dimensional network based on feature enhancement and triple interactive attention for LDCT denoising.","authors":"Lina Jia, Beibei Jia, Zongyang Li, Yizhuo Zhang, Zhiguo Gui","doi":"10.1177/08953996241306696","DOIUrl":"10.1177/08953996241306696","url":null,"abstract":"BackgroundNumerous deep leaning methods for low-dose computed technology (CT) image denoising have been proposed, achieving impressive results. However, issues such as loss of structure and edge information and low denoising efficiency still exist.ObjectiveTo improve image denoising quality, an enhanced multi-dimensional hybrid attention LDCT image denoising network based on edge detection is proposed in this paper.MethodsIn our network, we employ a trainable Sobel convolution to design an edge enhancement module and fuse an enhanced triplet attention network (ETAN) after each <math><mn>3</mn><mo>×</mo><mn>3</mn></math> convolutional layer to extract richer features more comprehensively and suppress useless information. During the training process, we adopt a strategy that combines total variation loss (TVLoss) with mean squared error (MSE) loss to reduce high-frequency artifacts in image reconstruction and balance image denoising and detail preservation.ResultsCompared with other advanced algorithms (CT-former, REDCNN and EDCNN), our proposed model achieves the best PSNR and SSIM values in CT image of the abdomen, which are 34.8211and 0.9131, respectively.ConclusionThrough comparative experiments with other related algorithms, it can be seen that the algorithm proposed in this article has achieved significant improvements in both subjective vision and objective indicators.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"393-404"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460298","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}

引用次数: 0

A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data. 使用临床和影像学数据进行乳腺癌风险分层的多模型机器学习框架。

IF 1.7 3区医学

Journal of X-Ray Science and Technology Pub Date : 2025-03-01 Epub Date: 2025-01-27 DOI: 10.1177/08953996241308175

Lu Miao, Zidong Li, Jinnan Gao

{"title":"A multi-model machine learning framework for breast cancer risk stratification using clinical and imaging data.","authors":"Lu Miao, Zidong Li, Jinnan Gao","doi":"10.1177/08953996241308175","DOIUrl":"10.1177/08953996241308175","url":null,"abstract":"PurposeThis study presents a comprehensive machine learning framework for assessing breast cancer malignancy by integrating clinical features with imaging features derived from deep learning.MethodsThe dataset included 1668 patients with documented breast lesions, incorporating clinical data (e.g., age, BI-RADS category, lesion size, margins, and calcifications) alongside mammographic images processed using four CNN architectures: EfficientNet, ResNet, DenseNet, and InceptionNet. Three predictive configurations were developed: an imaging-only model, a hybrid model combining imaging and clinical data, and a stacking-based ensemble model that aggregates both data types to enhance predictive accuracy. Twelve feature selection techniques, including ReliefF and Fisher Score, were applied to identify key predictive features. Model performance was evaluated using accuracy and AUC, with 5-fold cross-valida tion and hyperparameter tuning to ensure robustness.ResultsThe imaging-only models demonstrated strong predictive performance, with EfficientNet achieving an AUC of 0.76. The hybrid model combining imaging and clinical data reached the highest accuracy of 83% and an AUC of 0.87, underscoring the benefits of data integration. The stacking-based ensemble model further optimized accuracy, reaching a peak AUC of 0.94, demonstrating its potential as a reliable tool for malignancy risk assessment.ConclusionThis study highlights the importance of integrating clinical and deep imaging features for breast cancer risk stratification, with the stacking-based model.","PeriodicalId":49948,"journal":{"name":"Journal of X-Ray Science and Technology","volume":" ","pages":"360-375"},"PeriodicalIF":1.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460382","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}

引用次数: 0