BMC Medical Imaging最新文献

{"title":"Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.","authors":"Yu-Feng Qian, Wan-Liang Guo","doi":"10.1186/s12880-025-01582-8","DOIUrl":"10.1186/s12880-025-01582-8","url":null,"abstract":"<p><strong>Purposes: </strong>To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.</p><p><strong>Methods: </strong>A total of 3665 cases of intussusception were retrospectively collected from January 2017 to December 2022. The cohort was randomly assigned to training and validation sets at a 6:4 ratio. Two types of images were processed: abdominal grayscale US images and abdominal radiographs. These images served as inputs for the deep learning algorithm and were individually processed by five detection models for training, with each model predicting its respective categories and probabilities. The optimal models were selected individually for decision fusion to obtain the final predicted categories and their probabilities.</p><p><strong>Results: </strong>With US, the VGG11 model showed the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.669 (95% CI: 0.635-0.702). In contrast, with radiographs, the ResNet18 model excelled with an AUC of 0.809 (95% CI: 0.776-0.841). We then employed two fusion methods. In the averaging fusion method, the two models were combined to reach a diagnostic decision. Specifically, a soft voting scheme was used to average the probabilities predicted by each model, resulting in an AUC of 0.877 (95% CI: 0.846-0.908). In the stacking fusion method, a meta-model was built based on the predictions of the two optimal models. This approach notably enhanced the overall predictive performance, with LightGBM emerging as the top performer, achieving an AUC of 0.897 (95% CI: 0.869-0.925). Both fusion methods demonstrated excellent performance.</p><p><strong>Conclusions: </strong>Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"67"},"PeriodicalIF":2.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11874663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of brain microstructure changes in surviving fetus of monochorionic twin pregnancies with single intrauterine fetal death using diffusion weighted imaging: a MRI-based cohort study.","authors":"Aonan Wang, Ran Huo, Yuan Wei, Xiaoyue Guo, Zheng Wang, Qiang Zhao, Ying Liu, Huishu Yuan","doi":"10.1186/s12880-025-01609-0","DOIUrl":"10.1186/s12880-025-01609-0","url":null,"abstract":"<p><strong>Background: </strong>Single intrauterine fetal death (sIUFD) will lead to an increased risk of adverse events such as fetal brain abnormalities in the survivor. However, how to detect these anomalies in the early stages remains to be explored.</p><p><strong>Objective: </strong>To compare apparent diffusion coefficient (ADC) values of fetal brain in cases of single intrauterine fetal death (sIUFD) with twins control and singleton control using diffusion weighted imaging (DWI), and to perform follow-up study to reveal the underlying cerebral microstructure changes.</p><p><strong>Materials and methods: </strong>In this prospective MRI-based cohort study, we compared 43 surviving fetuses of sIUFD (18 following selective fetal reduction, 2 following laser ablation treatment for twin-to-twin transfusion syndrome, and 23 spontaneous) with 2 control cohorts ( 43 healthy twin fetuses, 43 singletons). All fetuses underwent fetal brain MRI. DWI was performed and ADC map was reconstructed. ADC values of certain regions were compared among the three groups.</p><p><strong>Results: </strong>ADC values were lower in bilateral white matter of frontal, parietal, temporal lobes and cerebellum in surviving fetuses compared with twins control and singleton control, respectively. ADC values of bilateral basal ganglia, thalamus and cerebellum in surviving fetuses, that of bilateral frontal lobes, cerebellum in twins control and that of right temporal lobe, left basal ganglia, and bilateral cerebellum in singleton control, were negatively correlated with gestational age. ADC values of left cerebellum in surviving fetuses were positively correlated with interval time.</p><p><strong>Conclusions: </strong>DWI is a very useful sequence for detecting underlying changes. ADC value might be a effective indicator of subtle anomalies in surviving fetuses.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"70"},"PeriodicalIF":2.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11877868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.","authors":"Ying Yu, Gang-Feng Li, Wei-Xiong Tan, Xiao-Yan Qu, Tao Zhang, Xing-Yi Hou, Yuan-Bo Zhu, Zhi-Ying Ma, Lu Yang, Ya Gao, Mei Yu, Cui Yue, Zhen Zhou, Yang Yang, Lin-Feng Yan, Guang-Bin Cui","doi":"10.1186/s12880-025-01596-2","DOIUrl":"10.1186/s12880-025-01596-2","url":null,"abstract":"<p><strong>Objective: </strong>By discussing the difference, stability and classification ability of tumor contour extracted by artificial intelligence and doctors, can a more stable method of tumor contour extraction be obtained?</p><p><strong>Methods: </strong>We propose a novel framework for the automatic segmentation of lung tumor contours and the differential diagnosis of downstream tasks. This framework integrates four key modules: tumor segmentation, extraction of radiomic features, feature selection, and the development of diagnostic models for clinical applications. Using this framework, we conducted a study involving a cohort of 1,429 patients suspected of lung cancer. Four automatic segmentation methods (RNN, UNET, WFCM, and SNAKE) were evaluated against manual segmentation performed by three radiologists with varying levels of expertise. We further studied the consistency of radiomic features extracted from these methods and evaluates their diagnostic performance across three downstream tasks: benign vs. malignant classification, lung adenocarcinoma infiltration, and lung nodule density classification.</p><p><strong>Results: </strong>The Dice coefficient of RNN is the highest among the four automatic segmentation methods (0.803 > 0.751, 0.576, 0.560), and all P < 0.05. In the consistency comparison of the seven contour-extracted radiomic features, that the features extracted by RNN and S1 (the senior radiologist) showed the highest similarity which was higher than the other automatic segmentation methods and doctors with low seniority. In all three downstream tasks, the radiomic features extracted from RNN segmentation contours showed the highest diagnostic discrimination. In the classification of benign and malignant nodules, the RNN method performed slightly better than the S1 method, with an AUC of 0.840 ± 0.01 and 0.824 ± 0.015, respectively, and significantly better than the other five methods. Similarly, the RNN method had an AUC value of 0.946 in lung adenocarcinoma infiltration, and a kappa value of 0.729 in lung nodule density classification, both of which were better than the other six methods.</p><p><strong>Conclusions: </strong>Our findings suggest that AI-driven tumor segmentation methods can enhance clinical decision-making by providing reliable and reproducible results, ultimately emphasizing the auxiliary role of automated tumor contouring in clinical practice. The findings will have important implications for the application of radiomics in clinical practice.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"63"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using deep learning to differentiate among histology renal tumor types in computed tomography scans.","authors":"Hung-Cheng Kan, Po-Hung Lin, I-Hung Shao, Shih-Chun Cheng, Tzuo-Yau Fan, Ying-Hsu Chang, Liang-Kang Huang, Yuan-Cheng Chu, Kai-Jie Yu, Cheng-Keng Chuang, Chun-Te Wu, See-Tong Pang, Syu-Jyun Peng","doi":"10.1186/s12880-025-01606-3","DOIUrl":"10.1186/s12880-025-01606-3","url":null,"abstract":"<p><strong>Background: </strong>This study employed a convolutional neural network (CNN) to analyze computed tomography (CT) scans with the aim of differentiating among renal tumors according to histologic sub-type.</p><p><strong>Methods: </strong>Contrast-enhanced CT images were collected from patients with renal tumors. The patient cohort was randomly split to create a training dataset (90%) and a testing dataset (10%). Following image dataset augmentation, Inception V3 and Resnet50 models were used to differentiate between renal tumors subtypes, including angiomyolipoma (AML), oncocytoma, clear cell renal cell carcinoma (ccRCC), chromophobe renal cell carcinoma (chRCC), and papillary renal cell carcinoma (pRCC). 5-fold cross validation was then used to evaluate the models in terms of classification performance.</p><p><strong>Results: </strong>The study cohort comprised 554 patients, including those with angiomyolipoma (n = 67), oncocytoma (n = 34), clear cell renal cell carcinoma (n = 246), chromophobe renal cell carcinoma (n = 124), and papillary renal cell carcinoma (n = 83). Dataset augmentation of the training dataset included this to 4238 CT images for analysis. The accuracy of the models was as follows: Inception V3 (0.830) and Resnet 50 (0.849).</p><p><strong>Conclusion: </strong>This study demonstrated the efficacy of using deep learning models for the classification of renal tumor subtypes from contrast-enhanced CT images. While the models showed promising accuracy, further development is necessary to improve their clinical applicability.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"66"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric measurement of cranial cavity and cerebral ventricular system with 3D Slicer software based on CT data.","authors":"Yanming Huang, Junxiang Huang, Celin Guan, Tianqing Liu, Shuanglin Que","doi":"10.1186/s12880-025-01591-7","DOIUrl":"10.1186/s12880-025-01591-7","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to evaluate the clinical utility of using 3D Slicer software for volumetric measurement of the cranial cavity and cerebral ventricular system, particularly in hydrocephalus patients. We also provide detailed steps for performing the measurements.</p><p><strong>Methods: </strong>Volumetric measurements were performed on 186 healthy volunteers, 117 hydrocephalus patients with intact skulls, and 72 hydrocephalus patients with incomplete skulls using 3D Slicer based on computed tomography (CT) data. CT scans were performed using a GE Discovery750 scanner and analyzed with 3D Slicer software (version 5.0.2). Cranial cavity volumes were measured using two methods: the Swiss Skull Stripper module and the Segment Editor tool. Ventricular volumes were assessed by segmenting the ventricles and periventricular structures with anatomical markers. Data were analyzed for consistency and accuracy using SPSS version 25.0, with statistical significance set at p ≤ 0.05.</p><p><strong>Results: </strong>Intracranial volume measurements showed no significant differences between healthy controls and HANPH patients, nor between different measurement methods. In healthy controls, males had larger ventricular volumes than females, and older individuals had larger volumes, except for the fourth ventricle. The left lateral ventricle was larger than the right. No discrepancies were found between measurements taken by two neurosurgeons.</p><p><strong>Conclusion: </strong>The volumetric measurement of cranial cavity and cerebral ventricular system with 3D Slicer software based on CT data are accurate, repeatable and consistent, providing methodological and technical support for hydrocephalus research, especially for incomplete skull patients, the third ventricle and the fourth ventricle.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"64"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features.","authors":"Gang Liang, Suxin Zhang, Yiquan Zheng, Wenqing Chen, Yuan Liang, Yumeng Dong, Lizhen Li, Jianding Li, Caixian Yang, Zengyu Jiang, Sheng He","doi":"10.1186/s12880-025-01607-2","DOIUrl":"10.1186/s12880-025-01607-2","url":null,"abstract":"<p><strong>Background: </strong>To develop a predictive nomogram for breast cancer lympho-vascular invasion (LVI), based on digital breast tomography (DBT) data obtained from intra- and peri-tumoral regions.</p><p><strong>Methods: </strong>One hundred ninety-two breast cancer patients were enrolled in this retrospective study from 2 institutions, in which Institution 1 served as the basis for training (n = 113) and testing (n = 49) sets, while Institution 2 served as the external validation set (n = 30). Tumor regions of interest (ROI) were manually-delineated on DBT images, in which peri-tumoral ROI was defined as 1 mm around intra-tumoral ROI. Radiomics features were extracted, and logistic regression was used to construct intra-, peri-, and intra- + peri-tumoral radiomics models. Patient clinical data was analyzed by both uni- and multi-variable logistic regression analyses to identify independent risk factors for the non-radiomics clinical imaging model, and the combination of both the most optimal radiomics and clinical imaging models comprised the comprehensive model. The best-performing model out of the 3 types (radiomics, clinical imaging, comprehensive) was identified using receiver operating characteristic (ROC) curve analysis, and used to construct the predictive nomogram.</p><p><strong>Results: </strong>The most optimal radiomics model was the intra- + peri-tumoral model, and 3 independent risk factors for LVI, maximum tumor diameter (odds ratio [OR] = 1.486, 95% confidence interval [CI] = 1.082-2.041, P = 0.014), suspicious malignant calcification (OR = 2.898, 95% CI = 1.232 ~ 6.815, P = 0.015), and axillary lymph node (ALN) metastasis (OR = 3.615, 95% CI = 1.642-7.962, P < 0.001) were identified by the clinical imaging model. Furthermore, the comprehensive model was the most accurate in predicting LVI occurrence, with areas under the curve (AUCs) of 0.889, 0.916, and 0.862, for, respectively, the training, testing and external validation sets, compared to radiomics (0.858, 0.849, 0.844) and clinical imaging (0.743, 0.759, 0.732). The resulting nomogram, incorporating radiomics from the intra- + peri-tumoral model, as well as maximum tumor diameter, suspicious malignant calcification, and ALN metastasis, had great correspondence with actual LVI diagnoses under the calibration curve, and was of high clinical utility under decision curve analysis.</p><p><strong>Conclusions: </strong>The predictive nomogram, derived from both radiomics and clinical imaging features, was highly accurate in identifying future LVI occurrence in breast cancer, demonstrating its potential as an assistive tool for clinicians to devise individualized treatment regimes.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"65"},"PeriodicalIF":2.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical application of the quantitative fetal heart quantification in the evaluation of right heart function in fetuses with redundancy foramen ovale flap.","authors":"Xiao-Rong Su, Ai-Lin Wang, Hong-Xia Tie, Qiong-Yu Yang, Shu-Na Cao, Tian-Gang Li","doi":"10.1186/s12880-025-01601-8","DOIUrl":"10.1186/s12880-025-01601-8","url":null,"abstract":"<p><strong>Background: </strong>To investigate the clinical value of fetal heart quantification (fetal HQ) in the evaluation of right ventricular size, morphology and cardiac function in fetuses with redundancy foramen ovale flap (RFOF).</p><p><strong>Methods: </strong>31 fetuses diagnosed with RFOF through echocardiography from September 2021 to December 2023 were selected as the control group, and 62 healthy fetuses that matched the age and gestational period of the pregnant women in the RFOF group were chosen as the control group. Fetal HQ software provided by GE Voluson E10 was employed to automatically track endocardial parameters of the right ventricle in 24 segments.</p><p><strong>Results: </strong>The internal diameter of foramen ovale in RFOF group was significantly smaller than that of normal fetal diameter in control group, with statistical significance (P < 0.05). Comparing the morphological parameters of the fetuses in the RFOF group and the control group, there was no statistically significant difference in the GSI scores (P > 0.05), but the RV-LED of the fetuses in the RFOF group in the segments of 1-24 were higher than the fetuses in the normal control group (both P < 0.05), and the RV-SI was lower than that in the normal control group (all P < 0.05).</p><p><strong>Conclusions: </strong>The Fetal HQ technique enables accurate localisation of the site of the RFOF foetal lesion by rapid quantitative analysis of morphological and functional parameters of the right ventricle of the foetal heart.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"62"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the impact of rectal susceptibility artifacts in prostate magnetic resonance imaging on subjective evaluation and deep learning: a two-center retrospective study.","authors":"Zheng Wang, Peng Lu, Song Liu, Chengzhi Fu, Yong Ye, Chengxin Yu, Lei Hu","doi":"10.1186/s12880-025-01602-7","DOIUrl":"10.1186/s12880-025-01602-7","url":null,"abstract":"<p><strong>Background: </strong>To compare the influence of rectal susceptibility artifacts on the subjective evaluation and deep learning (DL) in prostate cancer (PCa) diagnosis.</p><p><strong>Methods: </strong>This retrospective two-center study included 1052 patients who underwent MRI and biopsy due to clinically suspected PCa between November 2019 and November 2023. The extent of rectal artifacts in these patients' images was evaluated using the Likert four-level method. The PCa diagnosis was performed by six radiologists and an automated PCa diagnosis DL method. The performance of DL and radiologists was evaluated using the area under the receiver operating characteristic curve (AUC) and the area under the multi-reader multi-case receiver operating characteristic curve, respectively.</p><p><strong>Results: </strong>Junior radiologists and DL demonstrated statistically significantly higher AUCs in patients without artifacts compared to those with artifacts (R1: 0.73 vs. 0.64; P = 0.01; R2: 0.74 vs. 0.67; P = 0.03; DL: 0.77 vs. 0.61; P < 0.001). In subgroup analysis, no statistically significant differences in the AUC were observed among different grades of rectal artifacts for both all radiologists (0.08 ≤ P ≤ 0.90) and DL models (0.12 ≤ P ≤ 0.96). The AUC for DL without artifacts significantly exceeded those with artifacts in both the peripheral zone (PZ) and transitional zone (TZ) (DL_PZ: 0.78 vs. 0.61; P = 0.003; DL_TZ: 0.73 vs. 0.59; P = 0.011). Conversely, there were no statistically significant differences in AUC with and without artifacts for all radiologists in PZ and TZ (0.08 ≤ P ≤ 0.98).</p><p><strong>Conclusions: </strong>Rectal susceptibility artifacts have significant negative effects on subjective evaluation of junior radiologists and DL.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"61"},"PeriodicalIF":2.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative diagnostic performance of VI-RADS based on biparametric and multiparametric MRI in predicting muscle invasion in bladder cancer.","authors":"Peikun Liu, Lingkai Cai, Linjing Jiang, Haonan Chen, Qiang Cao, Kexin Bai, Rongjie Bai, Qikai Wu, Xiao Yang, Qiang Lu","doi":"10.1186/s12880-025-01595-3","DOIUrl":"10.1186/s12880-025-01595-3","url":null,"abstract":"<p><strong>Background: </strong>Vesical Imaging-Reporting and Data System (VI-RADS) based on multiparametric magnetic resonance imaging (mp-MRI) performed well in diagnosing muscle-invasive bladder cancer (MIBC). However, certain cases may present challenges in determining the final VI-RADS score using only T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, especially in the absence of dynamic contrast-enhanced (DCE) imaging. This study aims to evaluates whether biparametric MRI (bp-MRI) achieve comparable diagnostic performance to mp-MRI for predicting MIBC and seeks to identify the most suitable bp-MRI criterion by establishing four specific conditions based on T2WI and DWI.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 447 patients who underwent preoperative mp-MRI. Images were evaluated according to the VI-RADS protocol by three independent readers. In the bp-DWI and bp-DWI Plus criteria, DWI was the primary sequence used for lesion assessment, while T2WI was the primary sequence for bp-T2WI and bp-T2WI Plus criteria. The Plus criteria (bp-DWI Plus and bp-T2WI Plus) assigned a final VI-RADS score of 4 when both T2WI and DWI scores were 3. The gold standard for diagnosis was histopathological evaluation after surgery. Diagnostic performance was evaluated by comparing the area under the curve (AUC), sensitivity, specificity, and inter-reader agreement using Cohen's kappa analysis.</p><p><strong>Results: </strong>Among 447 patients, 304 confirmed as NMIBC and 143 as MIBC. The kappa values were 0.876, 0.873, 0.873, 0.642, and 0.642 for mp-MRI, bp-DWI, bp-DWI Plus, bp-T2WI, and bp-T2WI Plus, respectively, when VI-RADS cutoff > 2. Similarly, when cutoff > 3, the kappa values were 0.848, 0.811, 0.873, 0.811, and 0.873. No significant differences were observed between mp-MRI and bp-DWI (AUC: 0.916 vs. 0.912, p = 0.498), but mp-MRI and bp-DWI had higher AUCs compared to bp-DWI Plus, bp-T2WI, and bp-T2WI Plus.</p><p><strong>Conclusions: </strong>Both mp-MRI and bp-DWI demonstrate excellent performance in predicting MIBC, with bp-DWI being an alternative to mp-MRI.</p><p><strong>Trial registration: </strong>retrospectively.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"60"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11853285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A prior information-based multi-population multi-objective optimization for estimating ¹⁸F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma.","authors":"Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang","doi":"10.1186/s12880-024-01534-8","DOIUrl":"10.1186/s12880-024-01534-8","url":null,"abstract":"<p><strong>Background: </strong>¹⁸F fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. A prior-based multi-population multi-objective optimization (p-MPMOO) approach using two sub-populations based on two categories of prior information was preliminarily proposed for estimating the ¹⁸F-FDG PET/CT pharmacokinetics of patients with hepatocellular carcinoma.</p><p><strong>Methods: </strong>PET data from 24 hepatocellular carcinoma (HCC) tumors of 5-min dynamic PET/CT supplemented with 1-min static PET at 60 min were prospectively collected. A reversible double-input three-compartment model and kinetic parameters (K₁, k₂, k₃, k₄, f_a, and [Formula: see text]) were used to quantify the metabolic information. The single-individual Levenberg-Marquardt (LM) algorithm, single-population algorithms (Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA)) and p-MPMO optimization algorithms (p-MPMOPSO, p-MPMODE, and p-MPMOGA) were used to estimate the parameters.</p><p><strong>Results: </strong>The areas under the curve (AUCs) of the three p-MPMO methods were significantly higher than other methods in K₁ and k₄ (P < 0.05 in the DeLong test) and the single population optimization in k₂ and k₃ (P < 0.05), and did not differ from other methods in f_a and v_b (P > 0.05). Compared with single-population optimization, the three p-MPMO methods improved the significant differences between K₁, k₂, k₃, and k₄. The p-MPMOPSO showed significant differences (P < 0.05) in the parameter estimation of k₂, k₃, k₄, and f_a. The p-MPMODE is implemented on K₁, k₂, k₃, k₄, and f_a; The p-MPMOGA does it on all six parameters.</p><p><strong>Conclusions: </strong>The p-MPMOO approach proposed in this paper performs well for distinguishing HCC tumors from normal liver tissue.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"59"},"PeriodicalIF":2.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11854238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}