Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 pat...Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 patients with breast diseases were selected as samples and grouped according to disease type.Twenty-eight patients with breast cancer were included in Group A,and 28 patients with NPM were included in Group B.All patients underwent multimodal MRI enhancement scanning and diffusion-weighted imaging.The MRI results,time-signal intensity curves,ADC values,lesion intensity,and imaging signs were compared between the two groups.Results:There were no significant differences in enhancement characteristics,lymph node enlargement,and margins between Group A and Group B(P>0.05).The proportion of outflow curves in Group A was higher than that in Group B(P<0.05).The ADC value in Group A was lower than that in Group B,and the lesion intensity was higher than that in Group B(P<0.05).There were significant differences in imaging signs,such as abscess or sinus,ascending time-signal curve,and mammary duct dilation between Group A and Group B(P<0.05).Conclusion:Multimodal MRI enhancement scanning and diffusion-weighted imaging techniques can be used to diagnose breast diseases.Comprehensive analysis of time-signal intensity curves,lesion intensity,imaging signs,and ADC values can differentiate between NPM and breast cancer.展开更多
critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study pr...critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.展开更多
Magnetic resonance imaging(MRI)is a powerful tool for diagnosing and monitoring brain diseases,but its low sensitivity can hinder early detection.To address this challenge,we utilized chemical exchange saturation tran...Magnetic resonance imaging(MRI)is a powerful tool for diagnosing and monitoring brain diseases,but its low sensitivity can hinder early detection.To address this challenge,we utilized chemical exchange saturation transfer(CEST)MRI,which greatly enhances sensitivity for detecting low-concentration compounds.In this study,we developed a CEST contrast agent based on a recombinant adeno-associated viruses(rAAVs)encoding the protamine-1(PRM1)MRI reporter gene.CEST MRI revealed that PRM1 contrast agent effectively highlighted caudate putamen region after injection of the rAAVs into the mouse brain,clearly distinguishing it from the surrounding tissue,with no observable damage.This method provides a sensitive,metal-free CEST contrast agent for in vivo brain cell detection,demonstrating potential for both diagnostic and therapeutic applications in brain diseases.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
文摘Objective:To explore the value of multimodal MRI enhancement scanning and diffusion-weighted imaging in differentiating non-puerperal mastitis(NPM)and breast cancer.Methods:From September 2022 to September 2024,56 patients with breast diseases were selected as samples and grouped according to disease type.Twenty-eight patients with breast cancer were included in Group A,and 28 patients with NPM were included in Group B.All patients underwent multimodal MRI enhancement scanning and diffusion-weighted imaging.The MRI results,time-signal intensity curves,ADC values,lesion intensity,and imaging signs were compared between the two groups.Results:There were no significant differences in enhancement characteristics,lymph node enlargement,and margins between Group A and Group B(P>0.05).The proportion of outflow curves in Group A was higher than that in Group B(P<0.05).The ADC value in Group A was lower than that in Group B,and the lesion intensity was higher than that in Group B(P<0.05).There were significant differences in imaging signs,such as abscess or sinus,ascending time-signal curve,and mammary duct dilation between Group A and Group B(P<0.05).Conclusion:Multimodal MRI enhancement scanning and diffusion-weighted imaging techniques can be used to diagnose breast diseases.Comprehensive analysis of time-signal intensity curves,lesion intensity,imaging signs,and ADC values can differentiate between NPM and breast cancer.
基金funded by the Ministry of Higher Education(MoHE)Malaysia through the Fundamental Research Grant Scheme—Early Career Researcher(FRGS-EC),grant number FRGSEC/1/2024/ICT02/UNIMAP/02/8.
文摘critical for guiding treatment and improving patient outcomes.Traditional molecular subtyping via immuno-histochemistry(IHC)test is invasive,time-consuming,and may not fully represent tumor heterogeneity.This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes.Four pretrained models,including two Convolutional Neural Networks(MobileNet_V3_Large and VGG-16)and two Vision Transformers(ViT_B_16 and ViT_Base_Patch16_Clip_224)were fine-tuned to classify images into HER2-enriched,Luminal,Normal-like,and Triple Negative subtypes.Hyperparameter tuning,including learning rate adjustment and layer freezing strategies,was applied to optimize performance.Among the evaluated models,ViT_Base_Patch16_Clip_224 achieved the highest test accuracy(94.44%),with equally high precision,recall,and F1-score of 0.94,demonstrating excellent generalization.MobileNet_V3_Large achieved the same accuracy but showed less training stability.In contrast,VGG-16 recorded the lowest performance,indicating a limitation in its generalizability for this classification task.The study also highlighted the superior performance of the Vision Transformer models over CNNs,particularly due to their ability to capture global contextual features and the benefit of CLIP-based pretraining in ViT_Base_Patch16_Clip_224.To enhance clinical applicability,a graphical user interface(GUI)named“BCMS Dx”was developed for streamlined subtype prediction.Deep learning applied to mammography has proven effective for accurate and non-invasive molecular subtyping.The proposed Vision Transformer-based model and supporting GUI offer a promising direction for augmenting diagnostic workflows,minimizing the need for invasive procedures,and advancing personalized breast cancer management.
基金financially supported by the National Natural Science Foundation of China(82127802,22374157)Strategic Priority Research Program,CAS(XDB0540000,XDC0170000)CAS Youth Interdisciplinary Team(JCTD-2022-13).In addition,Xin Zhou acknowledges the support from the Tencent Foundation through the XPLORER PRIZE.
文摘Magnetic resonance imaging(MRI)is a powerful tool for diagnosing and monitoring brain diseases,but its low sensitivity can hinder early detection.To address this challenge,we utilized chemical exchange saturation transfer(CEST)MRI,which greatly enhances sensitivity for detecting low-concentration compounds.In this study,we developed a CEST contrast agent based on a recombinant adeno-associated viruses(rAAVs)encoding the protamine-1(PRM1)MRI reporter gene.CEST MRI revealed that PRM1 contrast agent effectively highlighted caudate putamen region after injection of the rAAVs into the mouse brain,clearly distinguishing it from the surrounding tissue,with no observable damage.This method provides a sensitive,metal-free CEST contrast agent for in vivo brain cell detection,demonstrating potential for both diagnostic and therapeutic applications in brain diseases.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
