Computerized Medical Imaging and Graphics最新文献

{"title":"Classification of glioma grade and Ki-67 level prediction in MRI data: A SHAP-driven interpretation","authors":"E.H. Bhuiyan ,&nbsp;M.M. Khan ,&nbsp;S.A. Hossain ,&nbsp;R. Rahman ,&nbsp;Q. Luo ,&nbsp;M.F. Hossain ,&nbsp;K. Wang ,&nbsp;M.S.I. Sumon ,&nbsp;S. Khalid ,&nbsp;M. Karaman ,&nbsp;J. Zhang ,&nbsp;M.E.H. Chowdhury ,&nbsp;W. Zhu ,&nbsp;X.J. Zhou","doi":"10.1016/j.compmedimag.2025.102578","DOIUrl":"10.1016/j.compmedimag.2025.102578","url":null,"abstract":"<div><div>This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and SHapley Additive exPlanations (SHAP). This IRB-approved study included 101 patients with glioma brain tumor acquired MR images with the T2W-FLAIR sequence. We extracted DL bottleneck features using ResNet50 from glioma MR images. Principal component analysis (PCA) was deployed for dimensionality reduction. XAI was used to identify potential features. The XGBosst classified the histologic grades of the glioma and the level of Ki-67. We integrated potential DL features with patient demographics (age and sex) and Ki-67 biomarkers, utilizing SHAP to determine the model’s essential features and interactions. Glioma grade classification and Ki-67 level predictions achieved overall accuracies of 0.94 and 0.91, respectively. It achieved precision scores of 0.92, 0.94, and 0.96 for glioma grades 2, 3, and 4, and 0.88, 0.94, and 0.97 for Ki-67 levels (low: <span><math><mrow><mn>5</mn><mtext>%</mtext><mo>≤</mo><mi>K</mi><mi>i</mi><mo>−</mo><mn>67</mn><mo>&lt;</mo><mn>10</mn><mtext>%</mtext></mrow></math></span>, moderate: <span><math><mrow><mn>10</mn><mtext>%</mtext><mo>≤</mo><mi>K</mi><mi>i</mi><mo>−</mo><mn>67</mn><mo>≤</mo><mn>20</mn></mrow></math></span>, and high: <span><math><mrow><mi>K</mi><mi>i</mi><mo>−</mo><mn>67</mn><mo>&gt;</mo><mn>20</mn><mtext>%</mtext></mrow></math></span>). Corresponding F1-scores were 0.95, 0.88, and 0.96 for glioma grades and 0.92, 0.93, and 0.87 for Ki-67 levels. SHAP analysis further highlighted a strong association between bottleneck DL features and Ki-67 biomarkers, demonstrating their potential to differentiate glioma grades and Ki-67 levels while offering valuable insights into glioma aggressiveness. This study demonstrates the precise classification of glioma grades and the prediction of Ki-67 levels to underscore the potential of AI-driven MRI analysis to enhance clinical decision-making in glioma management.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102578"},"PeriodicalIF":5.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-step-guided visual prediction of glioblastoma recurrence from multimodality images","authors":"Chen Zhao ,&nbsp;Meidi Chen ,&nbsp;Xiaobo Wen ,&nbsp;Jianping Song ,&nbsp;Yifan Yuan ,&nbsp;Qiu Huang","doi":"10.1016/j.compmedimag.2025.102585","DOIUrl":"10.1016/j.compmedimag.2025.102585","url":null,"abstract":"<div><div>Accurately predicting glioblastoma (GBM) recurrence is crucial for guiding the planning of target areas in subsequent radiotherapy and radiosurgery for glioma patients. Current prediction methods can determine the likelihood and type of recurrence but cannot identify the specific region or visually display location of the recurrence. To efficiently and accurately predict the recurrence of GBM, we proposed a three-step-guided prediction method consisting of feature extraction and segmentation (FES), radiomics analysis, and tag constraints to narrow the predicted region of GBM recurrence and standardize the shape of GBM recurrence prediction. Particularly in FES we developed an adaptive fusion module and a modality fusion module to fuse feature maps from different modalities. In the modality fusion module proposed, we designed different convolution modules (Conv-D and Conv-P) specifically for diffusion tensor imaging (DTI) and Positron Emission Computed Tomography (PET) images to extract recurrence-related features. Additionally, model fusion is proposed in the stable diffusion training process to learn and integrate the individual and typical properties of the recurrent tumors from different patients. Contrasted with existing segmentation and generation methods, our three-step-guided prediction method improves the ability to predict distant recurrence of GBM, achieving a 28.93 Fréchet Inception Distance (FID), and a 0.9113 Dice Similarity Coefficient (DSC). Quantitative results demonstrate the effectiveness of the proposed method in predicting the recurrence of GBM with the type and location. To the best of our knowledge, this is the first study combines the stable diffusion and multimodal images fusion with PET and DTI from different institutions to predict both distant and local recurrence of GBM in the form of images.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102585"},"PeriodicalIF":5.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Bayesian brain extraction by integrating structural subspace-based spatial prior into deep neural networks","authors":"Yunpeng Zhang ,&nbsp;Huixiang Zhuang ,&nbsp;Yue Guan ,&nbsp;Yao Li","doi":"10.1016/j.compmedimag.2025.102572","DOIUrl":"10.1016/j.compmedimag.2025.102572","url":null,"abstract":"<div><div>Accurate and robust brain extraction, or skull stripping, is essential for studying brain development, aging, and neurological disorders. However, brain images exhibit substantial data heterogeneity due to differences in contrast and geometric characteristics across various diseases, medical institutions and age groups. A fundamental challenge lies in effectively capturing the high-dimensional spatial-intensity distributions of the brain. This paper introduces a novel Bayesian brain extraction method that integrates a structural subspace-based prior, represented as a mixture-of-eigenmodes, with deep learning-based classification to achieve accurate and robust brain extraction. Specifically, we used structural subspace model to effectively capture global spatial-structural distributions of the normal brain. Leveraging this global spatial prior, a multi-resolution, position-dependent neural network is employed to effectively model the local spatial-intensity distributions. A patch-based fusion network is then used to combine these global and local spatial-intensity distributions for final brain extraction. The proposed method has been rigorously evaluated using multi-institutional datasets, including healthy scans across lifespan, images with lesions, and images affected by noise and artifacts, demonstrating superior segmentation accuracy and robustness over the state-of-the-art methods. Our proposed method holds promise for enhancing brain extraction in practical clinical applications.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102572"},"PeriodicalIF":5.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-informed neural networks for denoising high b-value diffusion-weighted images","authors":"Qiaoling Lin ,&nbsp;Fan Yang ,&nbsp;Yang Yan ,&nbsp;Haoyu Zhang ,&nbsp;Qing Xie ,&nbsp;Jiaju Zheng ,&nbsp;Wenze Yang ,&nbsp;Ling Qian ,&nbsp;Shaoxing Liu ,&nbsp;Weigen Yao ,&nbsp;Xiaobo Qu","doi":"10.1016/j.compmedimag.2025.102579","DOIUrl":"10.1016/j.compmedimag.2025.102579","url":null,"abstract":"<div><div>Diffusion-weighted imaging (DWI) is widely applied in tumor diagnosis by measuring the diffusion of water molecules. To increase the sensitivity to tumor identification, faithful high b-value DWI images are expected by setting a stronger strength of gradient field in magnetic resonance imaging (MRI). However, high b-value DWI images are heavily affected by reduced signal-to-noise ratio due to the exponential decay of signal intensity. Thus, removing noise becomes important for high b-value DWI images. Here, we propose a Physics-Informed neural Network for high b-value DWI images Denoising (PIND) by leveraging information from physics-informed loss and prior information from low b-value DWI images with high signal-to-noise ratio. Experiments are conducted on a prostate DWI dataset that has 125 subjects. Compared with the original noisy images, PIND improves the peak signal-to-noise ratio from 31.25 dB to 36.28 dB, and structural similarity index measure from 0.77 to 0.92. Our schemes can save 83% data acquisition time since fewer averages of high b-value DWI images need to be acquired, while maintaining 98% accuracy of the apparent diffusion coefficient value, suggesting its potential effectiveness in preserving essential diffusion characteristics. Reader study by 4 radiologists (3, 6, 13, and 18 years of experience) indicates PIND’s promising performance on overall quality, signal-to-noise ratio, artifact suppression, and lesion conspicuity, showing potential for improving clinical DWI applications.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102579"},"PeriodicalIF":5.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedBLIP: A multimodal method of medical question-answering based on fine-tuning large language model","authors":"Lejun Gong ,&nbsp;Jiaming Yang ,&nbsp;Shengyuan Han ,&nbsp;Yimu Ji","doi":"10.1016/j.compmedimag.2025.102581","DOIUrl":"10.1016/j.compmedimag.2025.102581","url":null,"abstract":"<div><div>Medical visual question answering is crucial for effectively interpreting medical images containing clinically relevant information. This study proposes a method called MedBLIP (Medical Treatment Bootstrapping Language-Image Pretraining) to tackle visual language generation tasks related to chest X-rays in the medical field. The method combine an image encoder with a large-scale language model, and effectively generates medical question-answering text through a strategy of freezing the image encoder based on the BLIP-2 model. Firstly, chest X-ray images are preprocessed, and an image sample generation algorithm is used to enhance the text data of doctor-patient question-answering, thereby increasing data diversity. Then, a multi-layer convolutional image feature extractor is introduced to better capture the feature representation of medical images. During the fine-tuning process of the large language generation model, a new unfreezing strategy is proposed, which is to unfreeze different proportions of the weights of the fully connected layer to adapt to the data in the medical field. The image feature extractor is responsible for extracting key features from images, providing the model with rich visual information, while the text feature extractor accurately captures the essential requirements of the user's question. Through their synergistic interaction, the model can more effectively integrate medical images and user inquiries, thereby generating more accurate and relevant output content. The experimental results show that unfreezing 31.25 % of the weights of the fully connected layer can significantly improve the performance of the model, with ROUGE-L reaching 66.12 %, and providing a more accurate and efficient answer generation solution for the medical field. The method of this study has potential applications in the field of medical language generation tasks. Although the proposed model cannot yet fully replace human radiologists, it plays an indispensable role in improving diagnostic efficiency, assisting decision-making, and supporting medical research. With continuous technological advancements, the model's performance will be further enhanced, and its application value in the medical field will become even more significant. The algorithm implementation can be obtained from <span><span>https://github.com/JiminFohill/MedicalChat.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102581"},"PeriodicalIF":5.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of uncertainty estimation methods in medical image segmentation: Exploring the usage of uncertainty in clinical deployment","authors":"Shiman Li ,&nbsp;Mingzhi Yuan ,&nbsp;Xiaokun Dai ,&nbsp;Chenxi Zhang","doi":"10.1016/j.compmedimag.2025.102574","DOIUrl":"10.1016/j.compmedimag.2025.102574","url":null,"abstract":"<div><div>Uncertainty estimation methods are essential for the application of artificial intelligence (AI) models in medical image segmentation, particularly in addressing reliability and feasibility challenges in clinical deployment. Despite their significance, the adoption of uncertainty estimation methods in clinical practice remains limited due to the lack of a comprehensive evaluation framework tailored to their clinical usage. To address this gap, a simulation of uncertainty-assisted clinical workflows is conducted, highlighting the roles of uncertainty in model selection, sample screening, and risk visualization. Furthermore, uncertainty evaluation is extended to pixel, sample, and model levels to enable a more thorough assessment. At the pixel level, the Uncertainty Confusion Metric (UCM) is proposed, utilizing density curves to improve robustness against variability in uncertainty distributions and to assess the ability of pixel uncertainty to identify potential errors. At the sample level, the Expected Segmentation Calibration Error (ESCE) is introduced to provide more accurate calibration aligned with Dice, enabling more effective identification of low-quality samples. At the model level, the Harmonic Dice (HDice) metric is developed to integrate uncertainty and accuracy, mitigating the influence of dataset biases and offering a more robust evaluation of model performance on unseen data. Using this systematic evaluation framework, five mainstream uncertainty estimation methods are compared on organ and tumor datasets, providing new insights into their clinical applicability. Extensive experimental analyses validated the practicality and effectiveness of the proposed metrics. This study offers clear guidance for selecting appropriate uncertainty estimation methods in clinical settings, facilitating their integration into clinical workflows and ultimately improving diagnostic efficiency and patient outcomes.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102574"},"PeriodicalIF":5.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A segmentation network based on CNNs for identifying laryngeal structures in video laryngoscope images","authors":"Jinjing Wu ,&nbsp;Wenhui Guo ,&nbsp;Zhanheng Chen ,&nbsp;Huixiu Hu ,&nbsp;Houfeng Li ,&nbsp;Ying Zhang ,&nbsp;Jing Huang ,&nbsp;Long Liu ,&nbsp;Zhenghao Xu ,&nbsp;Tianying Xu ,&nbsp;Miao Zhou ,&nbsp;Chenglong Zhu ,&nbsp;Haipo Cui ,&nbsp;Wenyun Xu ,&nbsp;Zui Zou","doi":"10.1016/j.compmedimag.2025.102573","DOIUrl":"10.1016/j.compmedimag.2025.102573","url":null,"abstract":"<div><div>Video laryngoscopes have become increasingly vital in tracheal intubation, providing clear imaging that significantly improves success rates, especially for less experienced clinicians. However, accurate recognition of laryngeal structures remains challenging, which is critical for successful first-attempt intubation in emergency situations. This paper presents MPE-UNet, a deep learning model designed for precise segmentation of laryngeal structures from video laryngoscope images, aiming to assist clinicians in performing tracheal intubation more accurately and efficiently. MPE-UNet follows the classic U-Net architecture, which features an encoder–decoder structure and enhances it with advanced modules and innovative techniques at every stage. In the encoder, we designed an improved multi-scale feature extraction module, which better processes complex throat images. Additionally, a pyramid fusion attention module was incorporated into the skip connections, enhancing the model’s ability to capture details by dynamically weighting and merging features from different levels. Moreover, a plug-and-play attention mechanism module was integrated into the decoder, further refining the segmentation process by focusing on important features. The experimental results show that the performance of the proposed method outperforms state-of-the-art methods.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102573"},"PeriodicalIF":5.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis","authors":"Yuan Zhang ,&nbsp;Hu Wang ,&nbsp;David Butler ,&nbsp;Brandon Smart ,&nbsp;Yutong Xie ,&nbsp;Minh-Son To ,&nbsp;Steven Knox ,&nbsp;George Condous ,&nbsp;Mathew Leonardi ,&nbsp;Jodie C. Avery ,&nbsp;M. Louise Hull ,&nbsp;Gustavo Carneiro","doi":"10.1016/j.compmedimag.2025.102575","DOIUrl":"10.1016/j.compmedimag.2025.102575","url":null,"abstract":"<div><div>Endometriosis is a serious multifocal condition that can involve various pelvic structures, with Pouch of Douglas (POD) obliteration being a significant clinical indicator for diagnosis. To circumvent the need for invasive diagnostic procedures like laparoscopy, research has increasingly focused on imaging-based methods such as transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI). The limited diagnostic accuracy achieved through manual interpretation of these imaging techniques has driven the development of automated classifiers that can effectively utilize both modalities. However, patients often undergo only one of these two examinations, resulting in unpaired data for training and testing POD obliteration classifiers, where TVUS models tend to be more accurate than MRI models, but TVUS scanning are more operator dependent. This prompts a crucial question: Can a model be trained with unpaired TVUS and MRI data to enhance the performance of a model exclusively trained with MRI, while maintaining the high accuracy of the model individually trained with TVUS? In this paper we aim to answer this question by proposing a novel multi-modal POD obliteration classifier that is trained with unpaired TVUS and MRI data and tested using either MRI or TVUS data. Our method is the first POD obliteration classifier that can flexibly take either the TVUS or MRI data, where the model automatically focuses on the uterus region within MRI data, eliminating the need for any manual intervention. Experiments conducted on our endometriosis dataset show that our method significantly improves POD obliteration classification accuracy using MRI from AUC=0.4755 (single-modal training and testing, without automatically focusing on the uterus region) to 0.8023 (unpaired multi-modal training and single modality MRI testing, with automatic uterus region detection), while maintaining the accuracy using TVUS with AUC=0.8921 (single modality TVUS testing using either an unpaired multi-modal training or a single-modality training).</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102575"},"PeriodicalIF":5.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OSAM-NET: A multi-feature fusion model for measuring fetal head flexion during labor with transformer multi-head self-attention","authors":"Shijie Zhang ,&nbsp;Shaozheng He ,&nbsp;Jingjing Wu ,&nbsp;Dandan Wang ,&nbsp;Pan Zeng ,&nbsp;Guorong Lyu","doi":"10.1016/j.compmedimag.2025.102582","DOIUrl":"10.1016/j.compmedimag.2025.102582","url":null,"abstract":"<div><div>Fetal head flexion is essential during labor. The current assessment presents technical challenges for unskilled ultrasound operators. Therefore, the study aimed to propose an occiput-spine angle measurement network (OSAM-NET) to improve the accuracy and intrapartum applicability of fetal head flexion assessment. We used YOLOv8 to extract key anatomical structures (occiput and spine) and Vision Transformer to extract global features on ultrasound images of the posterior mid-sagittal section of the fetus (PMSF). Then, by fusing the geometric location information and global features through the multi-head self-attention mechanism, we constructed the multi-feature fusion model OSAM-NET. The model was able to extract intricate features from multi-dimensional information, effectively boosting the accuracy of occiput-spine angle (OSA) prediction. We curated the first OSA dataset comprising 1688 high-quality, clear PMSF ultrasound images and annotations to train and test our model. We evaluated the performance of OSAM-NET and compared it with other models. The results showed that OSAM-NET outperformed the comparison models on all evaluation metrics, with R² increasing by nearly 13 %, and root mean square error (RMSE) and mean absolute error (MAE) decreasing by approximately 15 % and 20 %, respectively. The intraclass correlation coefficient (ICC) improved by about 8 %, with the average value reaching 0.89, indicating good agreement with the measurements of ultrasound experts. The multi-feature fusion model OSAM-NET demonstrates strong applicability and predictive accuracy for complex ultrasound images. This study provides a reliable and efficient tool for automatically evaluating fetal head flexion. The real-world application potential has been validated on prospective test dataset.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102582"},"PeriodicalIF":5.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144223277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study","authors":"Xuan Chu ,&nbsp;Tengfei Wang ,&nbsp;Meiwen Chen ,&nbsp;Jingyu Li ,&nbsp;Luyao Wang ,&nbsp;Chengjie Wang ,&nbsp;Hongzhi Wang ,&nbsp;Stephen TC Wong ,&nbsp;Yongchao Chen ,&nbsp;Hai Li","doi":"10.1016/j.compmedimag.2025.102576","DOIUrl":"10.1016/j.compmedimag.2025.102576","url":null,"abstract":"<div><div>The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical practice. However, most current research focuses on single-modal images and does not fully leverage the comprehensive information from multimodal medical images, limiting model performance. To enhance screening accuracy, this study uses a deep learning framework that integrates high-dimensional convolutions of B-mode ultrasound (BMUS) and strain elastography (SE) images to predict the malignancy of TI-RADS 4 thyroid nodules with high-risk features. First, we extract nodule regions from the images and expand the boundary areas. Then, adaptive particle swarm optimization (APSO) and contrast limited adaptive histogram equalization (CLAHE) algorithms are applied to enhance ultrasound image contrast. Finally, deep learning techniques are used to extract and fuse high-dimensional features from both ultrasound modalities to classify benign and malignant thyroid nodules. The proposed model achieved an AUC of 0.937 (95 % CI 0.917–0.949) and 0.927 (95 % CI 0.907–0.948) in the test and external validation sets, respectively, demonstrating strong generalization ability. When compared with the diagnostic performance of three groups of radiologists, the model outperformed them significantly. Meanwhile, with the model's assistance, all three radiologist groups showed improved diagnostic performance. Furthermore, heatmaps generated by the model show a high alignment with radiologists' expertise, further confirming its credibility. The results indicate that our model can assist in clinical thyroid nodule diagnosis, reducing the risk of missed and misdiagnosed diagnoses, particularly for high-risk populations, and holds significant clinical value.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"124 ","pages":"Article 102576"},"PeriodicalIF":5.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}