To model and predict the dynamics of conductive nonmagnetic objects moved within the MRI room under the influence of Lenz effect. High frequency motions, like vibrations induced by gradient eddy currents are not taken into account.
Methods:
The dynamics are described by an ordinary differential equation and the Lenz effect approximated under the assumption of negligible skin effect. This allows to separate the Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape.
Results:
The proposed model and numerical procedure were validated with experimental data recording the rotation of an aluminium plate falling inside a 1.5 T MRI scanner. The model was also applied for studying the translation of an aluminium plate pushed with constant force towards the MRI bore through the fringe field.
Conclusion:
The collected results showed that it is possible to obtain accurate predictions of motion in the presence of Lenz effect by neglecting the skin effect while determining the motional eddy currents induced in the metallic object.
目的：模拟和预测在伦兹效应的影响下，在核磁共振室内移动的导电非磁性物体的动力学。高频运动，如由梯度涡流引起的振动没有被考虑在内。方法：用常微分方程描述动力学，并在可忽略集肤效应的假设下近似地描述Lenz效应。这允许分离依赖于物体位置和速度的伦茨效应，导致任何形状的物体的一个简单的数值过程。结果：所提出的模型和数值过程通过记录铝板落在1.5 T MRI扫描仪内的旋转的实验数据得到验证。该模型还应用于研究铝板在恒力作用下通过边缘场向核磁共振成像孔的平移。结论：收集到的结果表明，在确定金属物体中产生的运动涡流时，忽略集肤效应可以获得存在Lenz效应时的准确运动预测。
{"title":"Lenz effect in conductive nonmagnetic objects moved in MRI environments","authors":"Alessandro Arduino , Oriano Bottauscio , Michael Steckner , Umberto Zanovello , Luca Zilberti","doi":"10.1016/j.mri.2025.110605","DOIUrl":"10.1016/j.mri.2025.110605","url":null,"abstract":"<div><h3>Purpose:</h3><div>To model and predict the dynamics of conductive nonmagnetic objects moved within the MRI room under the influence of Lenz effect. High frequency motions, like vibrations induced by gradient eddy currents are not taken into account.</div></div><div><h3>Methods:</h3><div>The dynamics are described by an ordinary differential equation and the Lenz effect approximated under the assumption of negligible skin effect. This allows to separate the Lenz effect dependency on the object position and velocity, leading to a simple numerical procedure for objects of any shape.</div></div><div><h3>Results:</h3><div>The proposed model and numerical procedure were validated with experimental data recording the rotation of an aluminium plate falling inside a 1.5 T MRI scanner. The model was also applied for studying the translation of an aluminium plate pushed with constant force towards the MRI bore through the fringe field.</div></div><div><h3>Conclusion:</h3><div>The collected results showed that it is possible to obtain accurate predictions of motion in the presence of Lenz effect by neglecting the skin effect while determining the motional eddy currents induced in the metallic object.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110605"},"PeriodicalIF":2.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.mri.2025.110607
Tommaso Zajac , Gloria Menegaz , Marco Pizzolato
Microscopic Propagator Imaging (MPI) is a novel diffusion MRI technique that estimates properties, referred to as indices, of the microscopic propagator. This is the probability distribution of water displacements within tissue microstructures. Unlike conventional mean apparent propagator methods, MPI is designed to minimize the sensitivity of indices to mesoscopic confounds such as axonal orientation dispersion, yielding diagnostic maps that more directly reflect presence, integrity, and shape of microstructures rather than their directional arrangement within the voxel. The method is implemented as a machine learning framework that exploits zonal relationships among spherical harmonic coefficients of multi-shell diffusion data to map such data to the microscopic propagator indices. Applied to human brain data, MPI yields reliable voxelwise estimates, with resulting maps exhibiting expected spatial patterns and systematic differences relative to the corresponding mean apparent propagator indices. These findings suggest that MPI provides microscopic-specific and complementary information beyond classical propagator methods, with potential to improve the characterization of brain tissue microstructure.
{"title":"Microscopic Propagator Imaging with diffusion MRI","authors":"Tommaso Zajac , Gloria Menegaz , Marco Pizzolato","doi":"10.1016/j.mri.2025.110607","DOIUrl":"10.1016/j.mri.2025.110607","url":null,"abstract":"<div><div>Microscopic Propagator Imaging (MPI) is a novel diffusion MRI technique that estimates properties, referred to as indices, of the microscopic propagator. This is the probability distribution of water displacements within tissue microstructures. Unlike conventional mean apparent propagator methods, MPI is designed to minimize the sensitivity of indices to mesoscopic confounds such as axonal orientation dispersion, yielding diagnostic maps that more directly reflect presence, integrity, and shape of microstructures rather than their directional arrangement within the voxel. The method is implemented as a machine learning framework that exploits zonal relationships among spherical harmonic coefficients of multi-shell diffusion data to map such data to the microscopic propagator indices. Applied to human brain data, MPI yields reliable voxelwise estimates, with resulting maps exhibiting expected spatial patterns and systematic differences relative to the corresponding mean apparent propagator indices. These findings suggest that MPI provides microscopic-specific and complementary information beyond classical propagator methods, with potential to improve the characterization of brain tissue microstructure.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110607"},"PeriodicalIF":2.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.mri.2025.110606
Shuluan Chen , Shunan Che , Mengying Yang , Yufei Chen , Yuan Tian , Kun Ma , Sicong Wang , Jing Li
Objectives
This study aimed to develop and validate a prognostic nomogram integrating baseline MRI and clinicopathological features to predict disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).
Materials and methods
A retrospective cohort of 402 invasive breast cancer patients who underwent pre-treatment MRI, NAC, and surgery between January 2014 and December 2021 was analyzed. Patients were randomly assigned to a training group (n = 280) and a validation group (n = 122). Variables were selected via univariate Cox regression and Lasso-Cox analyses, with significant factors used to construct nomogram models. The clinicopathological, baseline MRI and combined models were constructed. Model performance was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curves. A risk score derived from the combined model facilitated stratification into high- and low-risk groups, with log-rank test used for survival comparison.
Results
Key predictors in the clinicopathological model included clinical T stage, pathological complete response (pCR) in primary tumor, pCR in axillary lymph nodes, and lymphovascular invasion. MRI-based predictors included multifocal or multicentric lesions, subcutaneous edema, and ipsilateral suspicious internal mammary lymph nodes. The combined model outperformed the clinicopathological (training C-index = 0.67, validation C-index = 0.754) and baseline MRI models (training C-index = 0.665, validation C-index = 0.605), achieving C-indices of 0.706 and 0.719 in the training and validation groups, respectively. A risk score cut-off of −0.35 effectively stratified patients into high- and low-risk groups.
Conclusion
This combined nomogram integrating clinicopathological and MRI features offers improved predictive accuracy for DFS in breast cancer patients after NAC, enabling enhanced risk stratification and individualized follow-up strategies.
{"title":"Development and validation of a prognostic prediction nomogram incorporating MRI and clinicopathological features in breast cancer patients after neoadjuvant chemotherapy","authors":"Shuluan Chen , Shunan Che , Mengying Yang , Yufei Chen , Yuan Tian , Kun Ma , Sicong Wang , Jing Li","doi":"10.1016/j.mri.2025.110606","DOIUrl":"10.1016/j.mri.2025.110606","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop and validate a prognostic nomogram integrating baseline MRI and clinicopathological features to predict disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).</div></div><div><h3>Materials and methods</h3><div>A retrospective cohort of 402 invasive breast cancer patients who underwent pre-treatment MRI, NAC, and surgery between January 2014 and December 2021 was analyzed. Patients were randomly assigned to a training group (<em>n</em> = 280) and a validation group (<em>n</em> = 122). Variables were selected via univariate Cox regression and Lasso-Cox analyses, with significant factors used to construct nomogram models. The clinicopathological, baseline MRI and combined models were constructed. Model performance was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curves. A risk score derived from the combined model facilitated stratification into high- and low-risk groups, with log-rank test used for survival comparison.</div></div><div><h3>Results</h3><div>Key predictors in the clinicopathological model included clinical T stage, pathological complete response (pCR) in primary tumor, pCR in axillary lymph nodes, and lymphovascular invasion. MRI-based predictors included multifocal or multicentric lesions, subcutaneous edema, and ipsilateral suspicious internal mammary lymph nodes. The combined model outperformed the clinicopathological (training C-index = 0.67, validation C-index = 0.754) and baseline MRI models (training C-index = 0.665, validation C-index = 0.605), achieving C-indices of 0.706 and 0.719 in the training and validation groups, respectively. A risk score cut-off of −0.35 effectively stratified patients into high- and low-risk groups.</div></div><div><h3>Conclusion</h3><div>This combined nomogram integrating clinicopathological and MRI features offers improved predictive accuracy for DFS in breast cancer patients after NAC, enabling enhanced risk stratification and individualized follow-up strategies.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110606"},"PeriodicalIF":2.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.mri.2025.110608
Yu Weng , Jufeng Zhao , Christakis Damianou , Kun Luo , Guangmang Cui
Magnetic resonance imaging (MRI) is an advanced imaging technique that is used to aid in medical diagnosis. However, common noises such as Gaussian and Rician noise can blur details and structures, affect contrast and reduce signal-to-noise ratio (SNR), so MRI denoising technique becomes an critical step to get noise-free MRI images. Traditional methods still have limitations in effectively balancing noise removal and the preservation of image details and structural information. To address the challenge, this paper proposes an MRI image denoising model that combines Nonlinear Mapping Network (NLMap) and Attention Mechanism-guided Adaptive Total Variation Regularization (ATVR). The model includes a NLMap-ATVR network, a crafted joint loss function and a Bayesian optimization framework. Firstly, the network uses an encoder-decoder architecture, combined with ATVR to ensure noise removal. Secondly, the joint loss function includes mean square error (MSE) loss, perceptual loss and ATVR loss, which are used to consider pixel-level and feature-level spatial structural errors to preserve details and structures. Thirdly, a Bayesian optimization framework is applied to automatically tune the hyperparameters to obtain optimal parameters. Compared with State-of-the-art methods, both subjective and objective evaluations based on experimental results demonstrate that the proposed method not only effectively removes noise but also significantly preserves details and structural information, which greatly improves SNR.
{"title":"NLMap-ATVR: A novel combination of nonlinear mapping network and adaptive total variation regularization for MRI denoising","authors":"Yu Weng , Jufeng Zhao , Christakis Damianou , Kun Luo , Guangmang Cui","doi":"10.1016/j.mri.2025.110608","DOIUrl":"10.1016/j.mri.2025.110608","url":null,"abstract":"<div><div>Magnetic resonance imaging (MRI) is an advanced imaging technique that is used to aid in medical diagnosis. However, common noises such as Gaussian and Rician noise can blur details and structures, affect contrast and reduce signal-to-noise ratio (SNR), so MRI denoising technique becomes an critical step to get noise-free MRI images. Traditional methods still have limitations in effectively balancing noise removal and the preservation of image details and structural information. To address the challenge, this paper proposes an MRI image denoising model that combines Nonlinear Mapping Network (NLMap) and Attention Mechanism-guided Adaptive Total Variation Regularization (ATVR). The model includes a NLMap-ATVR network, a crafted joint loss function and a Bayesian optimization framework. Firstly, the network uses an encoder-decoder architecture, combined with ATVR to ensure noise removal. Secondly, the joint loss function includes mean square error (MSE) loss, perceptual loss and ATVR loss, which are used to consider pixel-level and feature-level spatial structural errors to preserve details and structures. Thirdly, a Bayesian optimization framework is applied to automatically tune the hyperparameters to obtain optimal parameters. Compared with State-of-the-art methods, both subjective and objective evaluations based on experimental results demonstrate that the proposed method not only effectively removes noise but also significantly preserves details and structural information, which greatly improves SNR.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110608"},"PeriodicalIF":2.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).
Methods
We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (n = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (d), extracellular diffusion coefficient (Dex), intracellular volume fraction (Vin), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples t-test or Mann-Whitney U test (significance threshold: P < 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to β-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.
Results
The ICC group exhibited significantly higher Dex values compared to the HCC group (P < 0.05), whereas d, Vin, and cellularity were significantly lower in the ICC group (P < 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for Dex, 0.779 for d, 0.833 for Vin, and 0.733 for cellularity. The d value measured by Td-dMRI showed a significant positive correlation with pathological results (r = 0.634, P < 0.05). Notably, combining Vin and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.
The ICC group exhibited a significantly higher extracellular diffusivity (Dex) compared to the HCC group, whereas the cell diameter (d), intracellular volume fraction (Vin), and cellularity were significantly lower (all P < 0.05). The area under the.
Conclusion
Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.
{"title":"Time-dependent diffusion-weighted MRI discriminates hepatocellular carcinoma from intrahepatic cholangiocarcinoma: A prospective animal model study","authors":"Yong-Mei Huang , Yu-Chen Wei , Xing-Qing Qin , Meng-Na Lan , Jin-Yuan Liao","doi":"10.1016/j.mri.2025.110601","DOIUrl":"10.1016/j.mri.2025.110601","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate the diagnostic potential of microstructural parameters derived from time-dependent diffusion magnetic resonance imaging (Td-dMRI) for distinguishing hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC).</div></div><div><h3>Methods</h3><div>We established nude mouse models bearing subcutaneous xenografts of HCC (MHCC97H, HepG2 cell lines) and ICC (QBC939 cell line) (<em>n</em> = 30). All models underwent Td-dMRI scanning. Microstructural parameters, including cell diameter (<em>d</em>), extracellular diffusion coefficient (<em>D</em><sub>ex</sub>), intracellular volume fraction (<em>V</em><sub>in</sub>), and cellularity, were calculated based on the IMPULSED model. Intergroup differences were assessed using independent samples <em>t</em>-test or Mann-Whitney <em>U</em> test (significance threshold: <em>P</em> < 0.05). The diagnostic performance of each parameter was evaluated by receiver operating characteristic (ROC) curve analysis. Post-operative liver tissue specimens were subjected to <em>β</em>-catenin immunohistochemical staining to validate the correlation between imaging parameters and pathological findings.</div></div><div><h3>Results</h3><div>The ICC group exhibited significantly higher <em>D</em><sub>ex</sub> values compared to the HCC group (<em>P</em> < 0.05), whereas <em>d</em>, <em>V</em><sub>in</sub>, and cellularity were significantly lower in the ICC group (<em>P</em> < 0.05). The areas under the ROC curve (AUCs) for differentiating HCC from ICC were 0.838 for <em>D</em><sub>ex</sub>, 0.779 for <em>d</em>, 0.833 for <em>V</em><sub>in</sub>, and 0.733 for cellularity. The <em>d</em> value measured by Td-dMRI showed a significant positive correlation with pathological results (<em>r</em> = 0.634, <em>P</em> < 0.05). Notably, combining <em>V</em><sub>in</sub> and cellularity parameters enhanced the AUC to 0.95, outperforming any single parameter.</div><div>The ICC group exhibited a significantly higher extracellular diffusivity (<em>D</em><sub>ex</sub>) compared to the HCC group, whereas the cell diameter (<em>d</em>), intracellular volume fraction (<em>V</em><sub>in</sub>), and cellularity were significantly lower (all <em>P</em> < 0.05). The area under the.</div></div><div><h3>Conclusion</h3><div>Td-dMRI enables non-invasive differentiation between HCC and ICC by quantifying distinct tumor microstructural environments. The parameters derived from this technique show promise as potential imaging biomarkers for subtyping liver cancers.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110601"},"PeriodicalIF":2.0,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145857030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Magnetic Resonance Imaging (MRI) as a representative of noninvasive medical imaging, it has excellent soft tissue resolution, multi-parameter imaging capability and no radiation, thus it has been widely used in the fields of disease diagnosis, treatment and drug development. However, MRI technology still has problems such as long time consuming, sensitive to motion artefacts, and difficult to balance the imaging effect and speed. In this paper, a Nesterov accelerated spectral conjugate gradient algorithm (TV_NASCG) for MRI with TV regularisation is established from optimising reconstruction algorithms based on Total Variation (TV) regularised MRI model. The algorithm is based on a set of conjugate and spectral parameters proposed in this paper, and inspired by Nesterov acceleration technique, a set of Nesterov acceleration extrapolation step and acceleration parameter are proposed. In this paper, we also use step size acceleration technique and Powell restart strategy to further improve performance of the algorithm. In addition, we give a convergence analysis of TV_NASCG algorithm, which proves that it is sufficiently descent and globally convergent. Finally, in order to verify MRI performance of TV_NASCG algorithm, it was tested in MRI experiment and compared with other algorithms, and experimental results show that TV_NASCG algorithm can improve MRI quality and shorten imaging time.
{"title":"Nesterov accelerated spectral conjugate gradient algorithm for magnetic resonance imaging with TV regularisation","authors":"YueHong Ding , ZhiBin Zhu , Shuo Wang , BenXin Zhang","doi":"10.1016/j.mri.2025.110604","DOIUrl":"10.1016/j.mri.2025.110604","url":null,"abstract":"<div><div>Magnetic Resonance Imaging (MRI) as a representative of noninvasive medical imaging, it has excellent soft tissue resolution, multi-parameter imaging capability and no radiation, thus it has been widely used in the fields of disease diagnosis, treatment and drug development. However, MRI technology still has problems such as long time consuming, sensitive to motion artefacts, and difficult to balance the imaging effect and speed. In this paper, a Nesterov accelerated spectral conjugate gradient algorithm (TV_NASCG) for MRI with TV regularisation is established from optimising reconstruction algorithms based on Total Variation (TV) regularised MRI model. The algorithm is based on a set of conjugate and spectral parameters proposed in this paper, and inspired by Nesterov acceleration technique, a set of Nesterov acceleration extrapolation step and acceleration parameter are proposed. In this paper, we also use step size acceleration technique and Powell restart strategy to further improve performance of the algorithm. In addition, we give a convergence analysis of TV_NASCG algorithm, which proves that it is sufficiently descent and globally convergent. Finally, in order to verify MRI performance of TV_NASCG algorithm, it was tested in MRI experiment and compared with other algorithms, and experimental results show that TV_NASCG algorithm can improve MRI quality and shorten imaging time.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110604"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To explore the value of time-dependent diffusion MRI(Td-dMRI) in predicting the expression status of cytokeratin 19(CK19) in hepatocellular carcinoma(HCC) before surgery.
Materials and methods
Prospective inclusion of 72 HCC patients confirmed by surgical pathology (43 CK19-negative and 29 CK19-positive). All patients underwent time-dependent diffusion MRI (Td-dMRI) using a 3.0 T MR scanner before surgery, and quantitative parameters were calculated. Clinical data and MRI features of the patients were collected. Using univariate and multivariate logistic regression analysis to identify the risk factors for CK19 positive expression and establish a predictive model. The diagnostic performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration analysis.
Results
CK19-positive HCC exhibited significantly lower d and higher cellularity compared to CK19-negative HCC. CK19-positive HCC demonstrated significantly higher proportions of AFP (>200 ng/ml), CEA (>5 ng/ml), arterial phase rim enhancement, peritumoral hypointensity on hepatobiliary phase, peritumoral arterial hyperenhancement, and intratumoral necrosis/hemorrhage than CK19-negative HCC. Univariate and multivariate logistic regression analyses identified cellularity, AFP (>200 ng/ml), arterial phase rim enhancement, and peritumoral hypointensity on hepatobiliary phase as independent predictors of CK19 positivity. The combined model incorporating these four factors achieved an AUC of 0.889 (95 % CI: 0.809–0.968), with a sensitivity of 82.8 % and specificity of 86.0 %.
Conclusions
The cellularity value based on Td-dMRI was a potential quantitative biomarker for predicting CK19-positive HCC.
{"title":"Time-dependent diffusion MRI combined with enhanced MRI and clinical indicators for preoperative prediction of CK19 expression status in hepatocellular carcinoma: a prospective study","authors":"Yu-chen Wei , Xing-Qing Qin , Jian-sun Li , Yuan-fang Tao , Chongze Yang , Qing ling Huang , Yan-yan Yu , Huiting Zhang , Haodong Qin , Thorsten Feiweier , Jin-yuan Liao","doi":"10.1016/j.mri.2025.110602","DOIUrl":"10.1016/j.mri.2025.110602","url":null,"abstract":"<div><h3>Objective</h3><div>To explore the value of time-dependent diffusion MRI(Td-dMRI) in predicting the expression status of cytokeratin 19(CK19) in hepatocellular carcinoma(HCC) before surgery.</div></div><div><h3>Materials and methods</h3><div>Prospective inclusion of 72 HCC patients confirmed by surgical pathology (43 CK19-negative and 29 CK19-positive). All patients underwent time-dependent diffusion MRI (Td-dMRI) using a 3.0 T MR scanner before surgery, and quantitative parameters were calculated. Clinical data and MRI features of the patients were collected. Using univariate and multivariate logistic regression analysis to identify the risk factors for CK19 positive expression and establish a predictive model. The diagnostic performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration analysis.</div></div><div><h3>Results</h3><div>CK19-positive HCC exhibited significantly lower d and higher cellularity compared to CK19-negative HCC. CK19-positive HCC demonstrated significantly higher proportions of AFP (>200 ng/ml), CEA (>5 ng/ml), arterial phase rim enhancement, peritumoral hypointensity on hepatobiliary phase, peritumoral arterial hyperenhancement, and intratumoral necrosis/hemorrhage than CK19-negative HCC. Univariate and multivariate logistic regression analyses identified cellularity, AFP (>200 ng/ml), arterial phase rim enhancement, and peritumoral hypointensity on hepatobiliary phase as independent predictors of CK19 positivity. The combined model incorporating these four factors achieved an AUC of 0.889 (95 % CI: 0.809–0.968), with a sensitivity of 82.8 % and specificity of 86.0 %.</div></div><div><h3>Conclusions</h3><div>The cellularity value based on Td-dMRI was a potential quantitative biomarker for predicting CK19-positive HCC.</div></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"127 ","pages":"Article 110602"},"PeriodicalIF":2.0,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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