BMC Medical Imaging最新文献

{"title":"CT-based radiomics models using intralesional and different perilesional signatures in predicting the microvascular density of hepatic alveolar echinococcosis.","authors":"Juan Hou, Simiao Zhang, Shouxian Li, Zicheng Zhao, Longfei Zhao, Tieliang Zhang, Wenya Liu","doi":"10.1186/s12880-025-01612-5","DOIUrl":"10.1186/s12880-025-01612-5","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the performance of CT-based intralesional combined with different perilesional radiomics models in predicting the microvascular density (MVD) of hepatic alveolar echinococcosis (HAE).</p><p><strong>Methods: </strong>This study retrospectively analyzed preoperative CT data from 303 patients with HAE confirmed by surgical pathology (MVD positive, n = 182; MVD negative, n = 121). The patients were randomly divided into the training cohort (n = 242) and test cohort (n = 61) at a ratio of 8:2. The radiomics features were extracted from CT images on the portal vein phase. Four radiomics models were constructed based on gross lesion volume (GLV), gross combined 10 mm perilesional volume (GPLV_10mm), gross combined 15 mm perilesional volume (GPLV_15mm) and gross combined 20 mm perilesional volume (GPLV_20mm). The best radiomics signature model and clinical features were combined to establish a nomogram. Receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the predictive performance of models.</p><p><strong>Results: </strong>Among the four radiomics models, the GPLV_20mm model performed the highest prediction performance with the area under the curves (AUCs) in training cohort and test cohort was 0.876 and 0.802, respectively. The AUC of the clinical model was 0.753 in the training cohort and 0.699 in the test cohort. The AUC of the nomogram model based clinical and GPLV_20mm radiomic signatures was 0.922 in the training cohort and 0.849 in the test cohort. The DCA showed that the nomogram had greater benefits among the three models.</p><p><strong>Conclusion: </strong>CT-based GPLV_20mm radiomics model can better predict MVD of HAE. The nomogram model showed the best predictive performance.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"84"},"PeriodicalIF":2.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11895365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596146","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":"Stapes footplate's posterior border protrudes the vestibule in healthy ears: anatomical insights from ultra-high-resolution CT.","authors":"Ruowei Tang, Ning Xu, Zhengyu Zhang, Zhongrui Chen, Heyu Ding, Zhenghan Yang, Zhenchang Wang, Pengfei Zhao","doi":"10.1186/s12880-025-01622-3","DOIUrl":"10.1186/s12880-025-01622-3","url":null,"abstract":"<p><strong>Background: </strong>The stapes footplate (SF) and annular ligament (AL) in the oval window region are of paramount significances. This study aims to assess the visibility of AL and the relative positioning of the SF and vestibule using ultra-high-resolution computed tomography (U-HRCT).</p><p><strong>Methods: </strong>U-HRCT images between September 2020 and April 2023 were retrospectively reviewed, and 479 ears deemed healthy from both clinical and radiological perspectives were included. AL was considered visible when manifesting as linear low attenuation between the SF and oval window, and the visibility was assessed for its four borders (anterior, posterior, superior and inferior). Two neuroradiologists conducted measurements independently for the SF length, SF-oval window angle, and SF protrusion depth into the vestibule. The results were described on the entire cohort, and compared by age and by sex.</p><p><strong>Results: </strong>A cohort comprising 479 participants [median age 58 years (interquartile range 35-65); 269 females] with 479 healthy ears were included. The inferior border of the AL region demonstrated the highest visibility (476/479, 99.4%), whereas the posterior border exhibited the lowest visibility rate (394/479, 82.3%). The median protrusion depth of the SF posterior border into the vestibule was 0.4 mm (interquartile range 0.3-0.5 mm). Statistically significant differences were observed within age and sex groups for the SF length and the SF protrusion depth (all P < 0.05).</p><p><strong>Conclusions: </strong>This study established radiological features for the SF and AL in healthy ears through U-HRCT. The findings are essential for providing normative references, expediting disease diagnosis, and aiding in selection of surgical strategy.</p><p><strong>Trial registration: </strong>Retrospectively registered.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"82"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584522","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":"¹⁸F-FDG PET/CT metabolic parameter changes to assess vascular inflammatory response in patients with diffuse large B-cell lymphoma.","authors":"Wenli Xie, Lixiu Cao, Jing Yu, Aijuan Tian, Jin Wang, Runlong Lin","doi":"10.1186/s12880-025-01617-0","DOIUrl":"10.1186/s12880-025-01617-0","url":null,"abstract":"<p><strong>Objective: </strong>To study the changes in positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (¹⁸F-FDG PET/CT) aortic target-to-background ratio (TBR) and aortic calcification scores before and after 6 cycles of chemotherapy with the rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) regimen in patients with diffuse large B-cell lymphoma (DLBCL).</p><p><strong>Patients and methods: </strong>We selected 161 patients with DLBCL who received 6 cycles of R-CHOP standard chemotherapy and underwent baseline and 6-cycle efficacy evaluations using ¹⁸F-FDG PET/CT examinations at the Second Hospital of Dalian Medical University from July 2017 to June 2023. Additionally, 125 patients who underwent ¹⁸F-FDG PET/CT for physical examination during the same period, without active malignancy or systemic inflammatory disease, were chosen as the control group. We measured metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of systemic lymphoma lesions in tumor patients. Aortic wall FDG uptake was semi quantitatively analyzed as TBR (target-to-blood pool ratio) in five different vascular regions using oncological ¹⁸F-FDG PET/CT. The aortic TBR difference (ΔTBR) was the difference between the post- and pre-chemotherapy TBR values. The degree of arterial segmental wall calcification was assessed using the CT semiquantitative method.</p><p><strong>Results: </strong>Comparison of the pre-treatment group of DLBCL with the control group showed that aortic TBR (1.28 ± 0.17 vs. 1.22 ± 0.18, P < 0.05) were higher in the former group. Additionally, comparing different stage groups of patients with DLBCL revealed that aortic TBR (1.30 ± 0.18 vs. 1.22 ± 0.15, P < 0.05) were higher in the Stage III/IV group compared to the Stage I/II group. Aortic TBR was positively correlated with TLG (P = 0.016, R = 0.19) and MTV (P = 0.032, R = 0.17). Analysis of changes in aortic ¹⁸F-FDG uptake in patients with DLBCL after 6 cycles of treatment revealed that aortic TBR levels were significantly higher post-treatment compared to pre-treatment(P < 0.05). The aortic ΔTBR value was significantly higher in the progression group than in the complete remission group(P < 0.05).</p><p><strong>Conclusion: </strong>Aortic wall ¹⁸F-FDG uptake is related to disease severity and prognosis, indicating a possible vascular effect of lymphoma and its therapeutic interventions. This work highlights an additional potential role of PET/CT in imaging oncology for evaluating disease severity and its consequences on the vasculature.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"81"},"PeriodicalIF":2.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584521","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":"Magnetic resonance diffusion-derived vessel density (DDVD) as a valuable tissue perfusion biomarker for isocitrate dehydrogenase genotyping in diffuse gliomas.","authors":"Chen-Xi Ni, Ruo-Lan Lin, Dian-Qi Yao, Fu-Zhao Ma, Yu-Ting Shi, Ying-Ying He, Yang Song, Guang Yang, Ri-Feng Jiang, Yì Xiáng J Wáng","doi":"10.1186/s12880-025-01605-4","DOIUrl":"10.1186/s12880-025-01605-4","url":null,"abstract":"<p><strong>Background: </strong>Determining isocitrate dehydrogenase (IDH) mutation is crucial for glioma clinical management. MR diffusion-derived 'vessel density' (DDVD) offers non-invasive tissue perfusion evaluation within the tumor microenvironment. The study attempts to distinguish IDH genotypes of diffuse gliomas using DDVD in whole tumor parenchyma and its habitats.</p><p><strong>Methods: </strong>This study enrolled 63 patients with diffuse gliomas (30 IDH-mutant and 33 IDH-wildtype) who underwent diffusion-weighted (DW) imaging at 3T. DDVD_b0b10 was the signal difference between the b = 0 and b = 10 s/mm² DW images. DDVD_{b0b10_b10b20} is DDVD_b0b10 minus DDVD_b10b20. nDDVD was DDVD divided by signal intensity at b = 0 s/mm² DW image. Correlations between DDVD metrics/intravoxel incoherent motion (IVIM) imaging metrics (D and f) and IDH genotypes/Ki-67 status were studied.</p><p><strong>Results: </strong>In tumor parenchyma, DDVD_{b0b10_b10b20} and nDDVD_{b0b10_b10b20} were lower, whereas D was higher in IDH-mutant gliomas [median (interquartile range): 12.76 (9.79-14.60); 15.14 (11.61-19.29); 1.31 (1.19-1.39)] compared to IDH-wildtype gliomas [14.48 (2.93-18.60), p = 0.008; 20.55 (15.89-24.02), p < 0.001; 1.16 (0.98-1.27), p = 0.003]. Habitat analysis improved the diagnostic performance for IDH genotyping, with the highest AUC of 0.823 found for the nDDVD_{b0b10_b10b20} derived from the high DDVD_b0b10 value habitat. Diagnostic efficacy of the combined model of nDDVD_{b0b10_b10b20} with D was superior to that of combined model of f with D. The habitat model incorporating age, sex, and Karnofsky Performance Status further significantly enhanced the diagnostic efficacy, with an AUC reaching 0.979. Additionally, DDVD and f showed a positive correlation with Ki-67, while D exhibited a negative correlation with Ki-67 (all p < 0.05).</p><p><strong>Conclusion: </strong>DDVD, as a novel biomarker of microvascular perfusion, effectively differentiates IDH genotypes in gliomas. The habitat analysis improves the diagnostic accuracy for IDH genotyping.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"79"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887403/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571935","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":"MTMU: Multi-domain Transformation based Mamba-UNet designed for unruptured intracranial aneurysm segmentation.","authors":"Bing Li, Nian Liu, Jianbin Bai, Jianfeng Xu, Yi Tang, Yan Liu","doi":"10.1186/s12880-025-01611-6","DOIUrl":"10.1186/s12880-025-01611-6","url":null,"abstract":"<p><p>The management of Unruptured Intracranial aneurysm (UIA) depends on the shape parameters assessment of lesions, which requires target segmentation. However, the segmentation of UIA is a challenging task due to the small volume of the lesions and the indistinct boundary between the lesion and the parent arteries. To relieve these issues, this article proposes a multi-domain transformation-based Mamba-UNet (MTMU) for UIA segmentation. The model employs a U-shaped segmentation architecture, equipped with the feature encoder consisting of a set of Mamba and Flip (MF) blocks. It endows the model with the capability of long-range dependency perceiving while balancing computational cost. Fourier Transform (FT) based connection allows for the enhancement of edge information in feature maps, thereby mitigating the difficulties in feature extraction caused by the small size of the target and the limited number of foreground pixels. Additionally, a sub task providing target geometry constrain (GC) is utilized to constrain the model training, aiming at splitting aneurysm dome from its parent artery accurately. Extensive experiments have been conducted to demonstrate the superior performance of the proposed method compared to other competitive medical segmentation methods. The results prove that the proposed method have great clinical application prospects.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"80"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571937","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":"LoG-staging: a rectal cancer staging method with LoG operator based on maximization of mutual information.","authors":"Ge Zhang, Hao Dang, Qian Zuo, Zhen Tian","doi":"10.1186/s12880-025-01610-7","DOIUrl":"10.1186/s12880-025-01610-7","url":null,"abstract":"<p><p>Deep learning methods have been migrated to rectal cancer staging as a classification process based on magnetic resonance images (MRIs). Typical approaches suffer from the imperceptible variation of images from different stage. The data augmentation also introduces scale invariance and rotation consistency problems after converting MRIs to 2D visible images. Moreover, the correctly labeled images are inadequate since T-staging requires pathological examination for confirmation. It is difficult for classification model to characterize the distinguishable features with limited labeled data. In this article, Laplace of Gaussian (LoG) filter is used to enhance the texture details of converted MRIs and we propose a new method named LoG-staging to predict the T stages of rectal cancer patients. We first use the LoG operator to clarify the fuzzy boundaries of rectal cancer cell proliferation. Then, we propose a new feature clustering method by leveraging the maximization of mutual information (MMI) mechanism which jointly learns the parameters of a neural network and the cluster assignments of features. The assignments are used as labels for the next round of training, which compensate the inadequacy of labeled training data. Finally, we experimentally verify that the LoG-staging is more accurate than the nonlinear dimensionality reduction in predicting the T stages of rectal cancer. We innovatively implement information bottleneck (IB) method in T-staging of rectal cancer based on image classification and impressive results are obtained.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"78"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571854","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":"Relationship between MRI features and HIF-1α, GLUT1 and Ki-67 expression in pituitary adenoma with cystic degeneration.","authors":"Fangfang Zhang, Zhenhong Pan, Jianwu Wu, Yinxing Huang","doi":"10.1186/s12880-025-01574-8","DOIUrl":"10.1186/s12880-025-01574-8","url":null,"abstract":"<p><strong>Background: </strong>Pituitary adenomas (PAs) are prevalent tumors that often exhibit ischemia, hypoxia, and cystic transformations, impacting their prognosis. The relationship between cystic degeneration in PAs and the expressions of hypoxia-inducible factor-1α (HIF-1α), glucose transporter 1 (GLUT1), and Ki-67 remains unclear. This study aims to analyze the correlation between MRI characteristics of cystic PA and the expression of these proteins.</p><p><strong>Methods: </strong>This is a retrospective analysis. A total of 74 patients with cystic PA and 30 PA patients without cystic degeneration were enrolled. Their MRI signs were analyzed. According to the T2WI signs of PA, they were divided into the fluid level group (n = 26), non-fluid level group (n = 48), and non-cyst group (n = 30). Immunohistochemistry was performed to evaluate the expression levels of HIF-lα, GLUT1, and Ki-67 protein. Univariate and multinomial logistic regression analyses were used to evaluate the factors affecting MRI signs of PA. Spearman correlation was also performed.</p><p><strong>Results: </strong>There was no significant difference in gender, age, and HIF-1α protein expression among the three groups. Significant differences were found in invasiveness (P = 0.008), GLUT1 (P < 0.001), and Ki-67 protein expression (P = 0.009) among the three groups. Pairwise comparisons revealed statistically significant differences in invasiveness, GLUT1, and Ki-67 protein expressions between the non-fluid level group and the non-cyst group. Furthermore, GLUT1 protein expression was significantly different between the non-fluid level group and the fluid level group. Notably, GLUT1 was identified as an independent factor for the non-fluid level cystic characteristics of PA. Additionally, GLUT1 was positively correlated with invasiveness and Ki-67.</p><p><strong>Conclusion: </strong>The non-fluid level cystic PA has higher invasiveness and higher proliferation than fluid level cystic PA and non-cyst PA, which may be related to high glucose metabolism as indicated by GLUT1 expression.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"76"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571939","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":"UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation.","authors":"Ibtissam Bakkouri, Siham Bakkouri","doi":"10.1186/s12880-025-01616-1","DOIUrl":"10.1186/s12880-025-01616-1","url":null,"abstract":"<p><p>Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential for preserving vision, making vessel segmentation crucial for identifying vascular diseases that pose a threat to vision. However, accurately segmenting blood vessels in fundus images is challenging due to factors such as significant variability in vessel scale and appearance, occlusions, complex backgrounds, variations in image quality, and the intricate branching patterns of retinal vessels. To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. UGS-M3F leverages its Unified Multi-Context Feature Fusion (UM2F) and Gated Boundary-Aware Swin Transformer (GBS-T) modules to capture contextual information across different levels. The UM2F module enhances the extraction of detailed vessel features, while the GBS-T module emphasizes small vessel detection and ensures extensive coverage of large vessels. Extensive experimental results on publicly available datasets, including FIVES, DRIVE, STARE, and CHAS_DB1, show that UGS-M3F significantly outperforms existing state-of-the-art methods. Specifically, UGS-M3F achieves a Dice Coefficient (DC) improvement of 2.12% on FIVES, 1.94% on DRIVE, 2.52% on STARE, and 2.14% on CHAS_DB1 compared to the best-performing baseline. This improvement in segmentation accuracy has the potential to revolutionize diagnostic techniques, allowing for more precise disease identification and management across a range of ocular conditions.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"77"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571952","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":"Analysis of features of papillary thyroid carcinoma on color Doppler ultrasound images: implications for lymph node metastasis.","authors":"Lu Cao, Ying Cao, Xiangru Wang, Xinxin Lu, Fangxi Zhao, Lei Sun, Hua Wang, Xiaopeng Li","doi":"10.1186/s12880-025-01615-2","DOIUrl":"10.1186/s12880-025-01615-2","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to describe the color Doppler flow features of papillary thyroid carcinoma (PTC) and to further investigate the associations between these features and lymph node metastasis (LNM).</p><p><strong>Methods: </strong>A retrospective analysis of the clinical data of 287 PTC patients confirmed by postoperative pathology at the Second Affiliated Hospital of Xi'an Jiaotong University from January 2022 to April 2023 was conducted. The Adler grading system and novel blood flow patterns were used to analyze the vascularity of the PTC lesions on color Doppler images. Univariate and multivariate logistic regression analyses were conducted to evaluate the independent effects of blood flow characteristics on LNM, and a logistic regression model was established to assess their predictive value for PTC-related LNM.</p><p><strong>Results: </strong>In all, 287 PTC lesions were analyzed using color Doppler ultrasonography, which identified five main reference patterns: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%). The Adler blood flow grading was as follows: 0 (32.75%), I (18.82%), II (19.16%), and III (29.27%). A univariate analysis revealed that the Adler grade was not significantly associated with LNM (P > 0.05), whereas the garland pattern was significantly associated with LNM (P < 0.05). A multivariate analysis revealed that the garland pattern was an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156-0.893]). The incorporation of the garland pattern into the model improved the predictive accuracy for LNM in PTC patients, and the AUC increased from 0.727 [95% CI: 0.669-0.786] to 0.767 [95% CI: 0.731-0.821].</p><p><strong>Conclusions: </strong>This study classifies PTC into five types on the basis of color Doppler flow features and highlights the garland pattern as a potential predictor of LNM risk.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"75"},"PeriodicalIF":2.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883915/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571853","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 subregional prediction model for radiation-induced hypothyroidism.","authors":"Wenting Ren, Ziqi Pan, Kuo Men, Bin Liang, Qingfeng Xu, Junlin Yi, Jianrong Dai","doi":"10.1186/s12880-025-01619-y","DOIUrl":"10.1186/s12880-025-01619-y","url":null,"abstract":"<p><strong>Background: </strong>Considering the potential association between radiation-induced hypothyroidism (RHT) and the thyroid subregions as well as the received radiation dose in each subregion, this study aims to develop a subregional prediction model for RHT.</p><p><strong>Methods: </strong>CT images and dose images of 128 patients with nasopharyngeal carcinoma were collected retrospectively. The thyroid subregion was obtained by clustering thyroid voxels and voxel entropy. After extracting 1781 radiomics features and 1767 dosiomics features, a subregional RHT prediction model was established, and its performance was compared with that of the whole thyroid model. The phenotype and dosimetry parameters of each subregion were analyzed by AUC, T test and Delong test.</p><p><strong>Results: </strong>Three subregions (S1, S2, S3) were identified. The subregional prediction model was constructed based on 34 radiomics and dosiomics features. According to the Delong test, the prediction performance of the subregional model was significantly superior than that of the whole thyroid model (0.813 VS 0.624, p = 0.038). Subregional analysis suggests that S1 and S3 regions may have higher radiosensitivity than S2 regions.</p><p><strong>Conclusions: </strong>In this study, a subregional model for predicting RHT was established and the radiosensitivity of the relevant subregions was evaluated. The subregion-based RHT prediction model may help to improve radiotherapy plan design for better thyroid function protection.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"74"},"PeriodicalIF":2.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11881320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555729","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}