BMC Medical Imaging最新文献

{"title":"Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.","authors":"Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki","doi":"10.1186/s12880-025-01710-4","DOIUrl":"https://doi.org/10.1186/s12880-025-01710-4","url":null,"abstract":"<p><strong>Background: </strong>Basal cell carcinoma (BCC) is the most common skin cancer, placing a significant burden on healthcare systems globally. Developing high-precision automated diagnostics requires large annotated datasets, which are costly and difficult to obtain. This study aimed to fine-tune a weakly supervised machine learning model to classify BCC in preoperative punch biopsies using transfer learning. By addressing challenges of scalability and variability, this approach seeks to enhance generalizability and diagnostic accuracy.</p><p><strong>Methods: </strong>The Basal Cell Classification (BCCC) dataset included 514 WSIs of punch biopsies (261 with BCC and 253 tumor-free slides), divided into training (70%), validation (15%), and test sets (15%). WSIs were split into patches, and features were extracted using a pretrained simCLR model trained on 1,435 WSIs from BCC excisions. Features were formed into graphs for spatial information and the processed by a Vision Transformer. Testing included finetuned and non-finetuned pre-trained models as well as a model trained from the scratch, evaluated on 78 WSIs from the BCCC dataset. The COBRA dataset of 3,588 WSIs (1,794 with BCC and 1,794 without) was used for external validation. Models classified no-tumor vs. tumor (two classes), no-tumor vs. low-risk vs. high-risk tumors (three classes), and no-tumor vs. four BCC subtypes (five classes).</p><p><strong>Results: </strong>The fine-tuned model significantly outperformed the non-fine-tuned pretrained model and the model trained from the scratch with accuracies of 91.7%, 82.1%, and 75.3% and with AUCs of 0.98, 0.95-0.98, and 0.91-0.97 for two, three, and five-class classification. On the external validation, accuracies were 84.9% and 70.5%, with AUCs of 0.92 and 0.89-0.91 for two and three-class classification, respectively. The ablation study revealed that the fine-tuned model outperformed the model trained from scratch, improving mean accuracy by 10.6%, 11.7%, and 13.1% on the BCCC dataset, as well as by 29.6% and 19.2% on the COBRA dataset.</p><p><strong>Conclusions: </strong>The results suggest that transfer learning not only enhances model performance on small datasets but also supports robust feature extraction in complex histopathology tasks. These findings reinforce the utility of pre-trained models in computational pathology, where access to large, labeled datasets is often limited, and task-specific challenges require nuanced understanding of the visual data.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"166"},"PeriodicalIF":2.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma.","authors":"Xiaoxia Li, Yi Guo, Shunfa Huang, Funan Wang, Chenchen Dai, Jianjun Zhou, Dengqiang Lin","doi":"10.1186/s12880-025-01715-z","DOIUrl":"https://doi.org/10.1186/s12880-025-01715-z","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate a computed tomography (CT)-based intratumoral and peritumoral radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized clear cell renal cell carcinoma (ccRCC).</p><p><strong>Methods: </strong>This two-center study included 447 patients with localized ccRCC. Patients from Center A were randomly split into a training set (n = 281) and an internal validation set (IVS) (n = 114) in a 7:3 ratio, while 52 patients from Center B formed the external validation set (EVS). Radiomics features from preoperative CT were obtained from the internal area of tumor (IAT), the internal and peritumoral areas of the tumor at 3 mm (IPAT 3 mm), and 5 mm (IPAT 5 mm). The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a radiomics score to develop radiomics model (RM). A clinical model (CM) was also established using significant clinical factors. Furthermore, a fusion model (FM) was developed by integrating independent predictors from both clinical factors and the radiomics score (Radscore) through multivariate Cox proportional hazards regression. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell's concordance index (C-index), and decision curve analysis (DCA).</p><p><strong>Results: </strong>Compared to both the IAT model and the IPAT 3 mm model, the IPAT 5 mm radiomics model demonstrated superior predictive performance for tumor recurrence (C-index: 0.924 vs. 0.915-0.923 in the IVS; 0.952 vs. 0.920-0.944 in the EVS). Therefore, the IPAT 5 mm radiomics score was incorporated into the development of the fusion model. The FM exhibited outstanding predictive accuracy, achieving a C-index of 0.938 in the IVS, significantly outperforming the CM (0.889, P = 0.03). Notably, in the EVS, the RM surpassed both the CM and FM (C-index: 0.952 vs. 0.904-0.940, P > 0.05). Furthermore, decision curve analysis indicated that the FM provided the highest net clinical benefit in the IVS, while both the FM and RM demonstrated substantially greater net benefit than the CM in the EVS.</p><p><strong>Conclusions: </strong>The radiomics model and the fusion model, which integrate both intratumoral and peritumoral features, offer accurate prediction of recurrence risk in patients with localized ccRCC. These models have the potential to aid in personalized treatment planning, optimized surveillance strategies, and treatment strategies for patients with clear cell renal cell carcinoma.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"167"},"PeriodicalIF":2.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outlier data in volume calculations of uterine fibroids comparing ellipsoid formula and voxel-based segmentation.","authors":"Viktor Bérczi, Kolos György Turtóczki, Szuzina Fazekas, Anna Dolla-Takács, Róbert Stollmayer, Pál Novák Kaposi, Ildikó Kalina, Bettina Katalin Budai","doi":"10.1186/s12880-025-01672-7","DOIUrl":"https://doi.org/10.1186/s12880-025-01672-7","url":null,"abstract":"<p><strong>Background: </strong>The ellipsoidal formula is the most common method used to determine the volume of fibroids on MR images. Labor-intensive manual segmentation provides the opportunity to measure the volume of a given lesion on a voxel basis. The aim of this study is to compare the volume of the uterine fibroid calculated using voxel-based segmentation and the ellipsoid formula.</p><p><strong>Methods: </strong>In this study, pretreatment MRI scans of patients who underwent uterine artery embolization due to symptomatic fibroids were retrospectively collected between 2016 and 2022. The volume data of the largest fibroids was determined by segmentation (group S) as the reference standard. In addition, the largest diameters of the fibroids in three planes (D1/D2/D3) were also measured and the volumes were also estimated by using the ellipsoidal formula (D1*D2*D3*0.5233) (group E). The interobserver reproducibility of the diameter measurements was tested. The volume values (median, IQR) were compared; in addition, the differences between the segmented and ellipsoidal volumes were recorded. Statistical analysis was performed using the Kruskal-Wallis test, Wilcoxon's two-sided signed rank test, intraclass correlation (ICC) analysis, and Bland-Altman plots.</p><p><strong>Results: </strong>Pretreatment MRI scans of 113 patients were identified. Fibroids where the interobserver difference of diameter-based ellipsoidal volumes reached 30% were excluded resulting in 99 patients in the final dataset. The volumes of group S and group E showed no significant differences with 134.1 (257.3) cm³ and 133.5 (269.1) cm³, respectively, with an average difference of 3.47 cm³ (0.25%; p = 0.377). The agreement between the two methods was excellent (ICC = 0.979), without difference across fibroid locations. In 46 cases (46.5%), group S values were larger, and in 53 fibroids (53.5%), group E volume values were larger. However, volume difference was outside the ± 20% range in 21 cases (21.2%) and outside the ± 30% range in 10 cases (10.1%); the largest difference was approximately 56.5% (156.5 cm³).</p><p><strong>Conclusions: </strong>The ellipsoid formula-based and the voxel-based volume calculation showed no significant difference for the group as a whole. However, there was a difference of > 20% in 21.2% of cases and > 30% in 10.1% of cases. In the era of personalized medicine, it is not only the average difference between the two methods that need to be considered but also cases where there is a 20% or 30% difference in results should be highlighted, as these may change the treatment plan in individual cases. This methodology should also be tested for other tumor-type volume calculations.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"165"},"PeriodicalIF":2.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A proposed imaging scoring system to differentiate dural-based metastasis from meningioma using MR and CT images.","authors":"Akeel A Alali, Maryam Aljaafary, Khalid AlBaker","doi":"10.1186/s12880-025-01723-z","DOIUrl":"10.1186/s12880-025-01723-z","url":null,"abstract":"<p><strong>Background and purpose: </strong>The differentiation between dural-based metastasis and meningioma, which is the most common benign extra axial tumor, is crucial, particularly when staging patients with known primary neoplasms. The purpose of this study was to assess CT and MR imaging features and to validate a proposed imaging scoring system to differentiate between the two pathologies.</p><p><strong>Materials and methods: </strong>A total of 84 patients with pathologically proven meningioma and 31 dural-based metastases were included in this retrospective study. The CT and MR imaging features, including the mean apparent diffusion coefficient (ADC), presence of edema, cystic changes, dural tail, leptomeningeal enhancement, calcifications, bone destruction and hyperostosis, were evaluated. The efficacy of the proposed imaging method for meningioma and its benign findings was evaluated.</p><p><strong>Results: </strong>There was a significant difference in most of the imaging features between patients with meningiomas and those with dural-based metastasis. The presence of vasogenic edema, leptomeningeal enhancement and bone destruction was significantly greater in patients with dural-based metastasis. Bone destruction and leptomeningeal enhancement showed the highest specificity for dural-based metastasis. There was also a significant difference between the two pathologies according to the proposed scoring system, with a P value < 0.001. Receiver Operator Characteristic (ROC) curve analysis was done to optimize the cutoff point which was identified as score 2 and above which has high 89.6% diagnostic accuracy for meningioma.</p><p><strong>Conclusion: </strong>The proposed imaging scoring system could be an effective tool for predicting the diagnosis of meningioma. This can be utilized to discriminate between meningioma and dural-based metastasis, particularly when staging patients with known primary neoplasms.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"163"},"PeriodicalIF":2.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075876","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":"Segmentation of the thoracolumbar fascia in ultrasound imaging: a deep learning approach.","authors":"Lorenza Bonaldi, Carmelo Pirri, Federico Giordani, Chiara Giulia Fontanella, Carla Stecco, Francesca Uccheddu","doi":"10.1186/s12880-025-01720-2","DOIUrl":"10.1186/s12880-025-01720-2","url":null,"abstract":"<p><strong>Background: </strong>Only in recent years it has been demonstrated that the thoracolumbar fascia is involved in low back pain (LBP), thus highlighting its implications for treatments. Furthermore, an easily accessible and non-invasive way to investigate the fascia in real time is the ultrasound examination, which to be reliable as is, it must overcome the challenges related to the configuration of the machine and the experience of the operator. Therefore, the lack of a clear understanding of the fascial system combined with the penalty related to the setting of the ultrasound acquisition has generated a gap that makes its effective evaluation difficult during clinical routine. The aim of the present work is to fill this gap by investigating the effectiveness of using a deep learning approach to segment the thoracolumbar fascia from ultrasound imaging.</p><p><strong>Methods: </strong>A total of 538 ultrasound images of the thoracolumbar fascia of LBP subjects were finally used to train and test a deep learning network. An additional test set (so-called Test set 2) was collected from another center, operator, machine manufacturer, patient cohort, and protocol to improve the generalizability of the study.</p><p><strong>Results: </strong>A U-Net-based architecture was demonstrated to be able to segment these structures with a final training accuracy of 0.99 and a validation accuracy of 0.91. The accuracy of the prediction computed on a test set (87 images not included in the training set) reached the 0.94, with a mean intersection over union index of 0.82 and a Dice-score of 0.76. These latter metrics were outperformed by those in Test set 2. The validity of the predictions was also verified and confirmed by two expert clinicians.</p><p><strong>Conclusions: </strong>Automatic identification of the thoracolumbar fascia has shown promising results to thoroughly investigate its alteration and target a personalized rehabilitation intervention based on each patient-specific scenario.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"164"},"PeriodicalIF":2.9,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075933","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":"Alterations of gray matter volume and structural covariance network in unilateral frontal lobe low-grade gliomas.","authors":"Yue Hu, Jianxun Xu, Ji Xiong, Kun Lv, Daoying Geng","doi":"10.1186/s12880-025-01716-y","DOIUrl":"https://doi.org/10.1186/s12880-025-01716-y","url":null,"abstract":"<p><strong>Purpose: </strong>To explore the alterations of gray matter volume (GMV) and structural covariant network (SCN) in unilateral frontal lobe low-grade gliomas (FLGGs).</p><p><strong>Materials and methods: </strong>The three dimensional (3D) T1 structural images of 117 patients with unilateral FLGGs and 68 age- and sex-matched healthy controls (HCs) were enrolled. The voxel-based morphometry (VBM) analysis and graph theoretical analysis of SCN were conducted to investigate the impact of unilateral FLGGs on the brain structure. This represents the first structural MRI study integrating both voxel-level morphometric changes and network-level reorganization patterns in unilateral FLGGs.</p><p><strong>Results: </strong>Through VBM analysis, we found that unilateral FLGGs can cause increased GMV in contralesional amygdala, calcarine, and angular gyrus, ipsilesional amygdala as well as vermis_6. The SCN of contralesional cerebrum, ipsilesional unaffected regions and cerebellum in both patients and HCs have typical small-world properties (Sigma > 1, Lambda ≈ 1 and Gamma > 1). Compared to HCs, global and nodal network metrics changed significantly in patients.</p><p><strong>Conclusion: </strong>The combination of VBM and SCN analysis revealed both focal GMV enlargement and topological alterations in patients with unilateral FLGGs, and provide a novel perspective of cross regional morphological collaborative changes for understanding the glioma-related neuroadaptation. These findings may suggest potential neuroimaging correlates of adaptive changes, which could inform future investigations into personalized treatment approaches.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"162"},"PeriodicalIF":2.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12079902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075898","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":"Temporal evolution of MRI findings and survival outcomes in patients with brain metastases after stereotactic radiosurgery.","authors":"Ali Salbas, Ali Murat Koc, Mehmet Coskun, Emine Merve Horoz, Adem Sengul, Mustafa Fazil Gelal","doi":"10.1186/s12880-025-01713-1","DOIUrl":"https://doi.org/10.1186/s12880-025-01713-1","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to investigate the temporal evolution of magnetic resonance imaging (MRI) findings in brain metastases following stereotactic radiosurgery (SRS) and their correlation with treatment response and survival outcomes. By analyzing volumetric changes in tumor size, perilesional edema, and necrotic components, we seek to identify imaging biomarkers that predict prognosis and treatment efficacy.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 97 patients (200 metastatic lesions) who underwent SRS for brain metastases between 2010 and 2022. Multiparametric MRI (MPMRI) scans were analyzed at four distinct follow-up periods: 1 to 3 months, 3 to 8 months, 8 to 16 months, and 16 to 24 months post-SRS. Volumetric measurements of tumor size, perilesional edema, and necrosis were obtained using semi-automated segmentation. Apparent diffusion coefficient (ADC) values and relative cerebral blood volume (rCBV) ratios were also assessed. Statistical analyses, including Kaplan-Meier survival curves and ROC analysis, were performed to determine prognostic imaging biomarkers.</p><p><strong>Results: </strong>The most significant reduction in tumor and perilesional edema volume occurred within the first 1 to 3 months post-SRS and continued until the 8th month. A transient increase in lesion size (pseudoprogression) was observed in 31.5% of cases, predominantly between 3 and 8 months post-SRS. Pretreatment tumor volume was found to be significantly associated with treatment response. ROC analysis identified 1.22 cm³ as the optimal cutoff value for differentiating between Group A (good response) and Group B (poor response) lesions (AUC = 0.754, sensitivity = 87.0%, specificity = 57.1%). Survival analysis revealed that higher pretreatment tumor volume, larger necrotic volume, and extensive perilesional edema were associated with shorter survival times (p < 0.05). No significant association was found between survival and ADC or rCBV.</p><p><strong>Conclusion: </strong>Following SRS, early reductions in tumor and edema volume were observed, while 31.5% of cases showed transient enlargement. Smaller tumors responded better to SRS, whereas larger volume, extensive edema, and necrosis were linked to shorter survival. Given the high rate of pseudoprogression, not every post-treatment size increase indicates true progression. A wait-and-see approach may help avoid unnecessary interventions in selected cases.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"161"},"PeriodicalIF":2.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075935","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 right atrial volume measurements using single-plane area-length and stack-of-short-axis methods: A 3.0 T cardiac magnetic resonance study.","authors":"Nabhat Noparatkailas, Ankavipar Saprungruang, Piyanun Sanguanwong, Angkana Sunthornram, Yongkasem Vorasettakarnkij, Monravee Tumkosit, Pairoj Chattranukulchai, Nonthikorn Theerasuwipakorn","doi":"10.1186/s12880-025-01708-y","DOIUrl":"https://doi.org/10.1186/s12880-025-01708-y","url":null,"abstract":"<p><strong>Purpose: </strong>The stack-of-short-axis volumes (SAX) summation and single-plane area-length (AL) methods are established approaches for right atrial (RA) volume quantification in cardiovascular magnetic resonance (CMR) imaging. However, data regarding the reliability and agreement between these methods are limited. Furthermore, there is no validation on whether to include the right atrial appendage (RAA) in the analysis. This study aims to evaluate the reliability of the single-plane AL and SAX methods for measuring RA volumes and to assess the agreement between these two approaches.</p><p><strong>Methods: </strong>CMR (3.0T, Siemens) data from 40 healthy volunteers were analyzed to quantify RA volumes, both including and excluding RAA volume, using the SAX and single-plane (4-chamber view) AL methods.</p><p><strong>Results: </strong>The mean age of 40 participants was 33.6 ± 6.1 years (50% male). RA volumes measured by the SAX method were significantly larger than those obtained by the single-plane AL method (maximum RA volume including RAA: 84.9 ± 22.9 vs. 63.7 ± 16.0 ml, p-value < 0.001; minimum RA volume including RAA: 45.3 ± 15.9 vs. 34.7 ± 12.2 ml, p-value < 0.001). RA ejection fraction (RAEF) was the only parameter that showed no statistical difference between the two methods. Bland-Altman plots demonstrated poor agreement between the techniques, with substantial biases and wide limits of agreement. Both methods exhibited excellent reproducibility when the RAA volume was included (ICC = 0.89-0.96). However, reproducibility was reduced when the RAA volume was excluded, particularly in terms of inter-observer agreement (ICC = 0.73-0.96).</p><p><strong>Conclusions: </strong>The single-plane AL method underestimates RA volumes compared to the SAX method, and the poor agreement between the two techniques suggests they should not be used interchangeably. RA volume measurements should be interpreted using method-specific reference values. Additionally, including the RAA in RA volume quantification-regardless of the method-may improve measurement reproducibility.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"160"},"PeriodicalIF":2.9,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12080051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075848","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":"Study on heterogeneity of vascularity and cellularity via multiparametric MRI habitat imaging in breast cancer.","authors":"Xiaolei Zhang, Xiaoyan Chen, Yao Fu, Han Zhou, Yan Lin","doi":"10.1186/s12880-025-01698-x","DOIUrl":"10.1186/s12880-025-01698-x","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to visually analyze the heterogeneity of vascularity and cellularity across different sub-regions of breast cancer using habitat imaging (HI) to predict human epidermal growth factor receptor 2 (HER2) expression and evaluate the effectiveness of neoadjuvant therapy (NAT) in breast cancer patients.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 76 patients diagnosed with breast cancer. Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) sequences were utilized to acquire MR images. Apparent diffusion coefficient (ADC), K^trans, K_ep, and V_e values were measured for each sub-region, and the percentage of each sub-region relative to the total lesion was calculated. Statistical analyses, including t-tests, rank-sum tests, chi-square tests, and Spearman correlation, were performed.</p><p><strong>Results: </strong>Three distinct sub-regions within breast cancer lesions were identified through HI, characterized physiologically as: low vascularity-high cellularity (LV-HC), low vascularity-low cellularity (LV-LC), and high vascularity-low cellularity (HV-LC). Significant differences were observed in the proportions of these tumor sub-regions between HER2-positive and HER2-negative breast cancers. Additionally, HER2-low and HER2-zero breast cancers demonstrated statistical differences in the second sub-region (LV-LC). Furthermore, the proportion of the first sub-region (LV-HC) was negatively correlated with the efficacy of NAT in breast cancer patients.</p><p><strong>Conclusions: </strong>Habitat imaging can identify distinct sub-regions within breast cancer lesions, providing a noninvasive imaging biomarker for predicting HER2 expression levels and assessing the efficacy of NAT in breast cancer patients.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"159"},"PeriodicalIF":2.9,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062020","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":"Efficacy of CEUS-guided biopsy for thoracic and pulmonary lesions: a systematic review and meta-analysis.","authors":"Zhe Sun, Xuejiao Yu, Jiaojiao Ma, Tongtong Zhou, Bo Zhang","doi":"10.1186/s12880-025-01700-6","DOIUrl":"10.1186/s12880-025-01700-6","url":null,"abstract":"<p><strong>Background: </strong>This study compares the success rate, diagnostic accuracy, and safety of contrast-enhanced ultrasound (CEUS)-guided biopsy versus conventional ultrasound (US)-guided biopsy for thoracic and pulmonary lesions.</p><p><strong>Methods: </strong>A systematic search of PubMed, EMBASE, Web of Science, and Cochrane Library was conducted. The primary outcomes included success rate and diagnostic accuracy, and the secondary outcome was the odds ratio of adverse effects. A random-effects meta-analysis pooled the data, with heterogeneity assessed by I² and publication bias evaluated using Egger's test and funnel plot analysis. Sensitivity analysis was performed to confirm result robustness. Subgroup analysis and meta-regression were conducted to explore the sources of heterogeneity.</p><p><strong>Results: </strong>Sixteen studies with 3,459 patients were included. CEUS-guided biopsy demonstrated higher success rate (99.18%, 95% CI: 98.00-99.90%) and diagnostic accuracy (95.96%, 95% CI: 94.84-96.96%) than US-guided biopsy (success rate: 97.26%, 95% CI: 95.45-98.68%; diagnostic accuracy: 85.87%, 95% CI: 82.05-89.31%). Complications were more frequent in the US-guided group, with an odds ratio of 1.65 (95% CI: 1.15-2.37). Heterogeneity was low, and publication bias was minimal, except for diagnostic accuracy in the US group. Sensitivity analysis confirmed result robustness.</p><p><strong>Conclusion: </strong>Compared with conventional ultrasound, CEUS-guided biopsy demonstrates a comparable success rate, superior diagnostic accuracy, and a lower incidence of complications, underscoring its clinical value as a preferred approach for thoracic and pulmonary lesion assessment.</p><p><strong>Systematic review registration: </strong>This study was registered with PROSPERO under the registration number CRD42024608627.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"158"},"PeriodicalIF":2.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143960395","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}