Quantitative Imaging in Medicine and Surgery最新文献

{"title":"Relapse of ascending aortic pseudoaneurysm and aortopulmonary fistula after first resection: a case description.","authors":"Huihui Wu, Ting Gao, Qingdong Zhang, He Liu, Lihong Wang","doi":"10.21037/qims-24-2277","DOIUrl":"10.21037/qims-24-2277","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6590-6593"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bone scintigraphy and SPECT/CT imaging characteristics in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis syndrome.","authors":"Xiaohui Wang, Qiwen Li, Peng Yang, Zhenzhen Peng, Chunyan Wang, Ming Chen","doi":"10.21037/qims-2024-2920","DOIUrl":"10.21037/qims-2024-2920","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6584-6589"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-level channel-spatial attention and light-weight scale-fusion network (MCSLF-Net): multi-level channel-spatial attention and light-weight scale-fusion transformer for 3D brain tumor segmentation.","authors":"Mingzhe Zhou, Jinbao Li, Yahong Guo","doi":"10.21037/qims-2025-354","DOIUrl":"10.21037/qims-2025-354","url":null,"abstract":"<p><strong>Background: </strong>Gliomas, the most aggressive primary tumors in the central nervous system, are characterized by high morphological heterogeneity and diffusely infiltrating boundaries. Such complexity poses significant challenges for accurate segmentation in clinical practice. Although deep learning methods have shown promising results, they often struggle to achieve a satisfactory trade-off among precise boundary delineation, robust multi-scale feature representation, and computational efficiency, particularly when processing high-resolution three-dimensional (3D) magnetic resonance imaging (MRI) data. Therefore, the aim of this study is to develop a novel 3D segmentation framework that specifically addresses these challenges, thereby improving clinical utility in brain tumor analysis. To accomplish this, we propose a multi-level channel-spatial attention and light-weight scale-fusion network (MCSLF-Net), which integrates a multi-level channel-spatial attention mechanism (MCSAM) and a light-weight scale-fusion module. By strategically enhancing subtle boundary features while maintaining a compact network design, our approach seeks to achieve high accuracy in delineating complex glioma morphologies, reduce computational burden, and provide a more clinically feasible segmentation solution.</p><p><strong>Methods: </strong>We propose MCSLF-Net, a network integrating two key components: (I) MCSAM: by strategically inserting a 3D channel-spatial attention module at critical semantic layers, the network progressively emphasizes subtle, infiltrative edges and small, easily overlooked contours. This avoids reliance on an additional edge detection branch while enabling fine-grained localization in ambiguous transitional regions. (II) Light-weight scale fusion unit (LSFU): leveraging depth-wise separable convolutions combined with multi-scale atrous (dilated) convolutions, LSFU enhances computational efficiency and adapts to varying feature requirements at different network depths. In doing so, it effectively captures small infiltrative lesions as well as extensive tumor areas. By coupling these two modules, MCSLF-Net balances global contextual information with local fine-grained features, simultaneously reducing the computational burden typically associated with 3D medical image segmentation.</p><p><strong>Results: </strong>Extensive experiments on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets validated the effectiveness of our approach. On BraTS 2021, MCSLF-Net achieved a mean Dice similarity coefficient (DSC) of 0.8974 and a mean 95<sup>th</sup> percentile Hausdorff distance (HD95) of 2.52 mm. Notably, it excels in segmenting intricate transitional areas, including the enhancing tumor (ET) region and the tumor core (TC), thereby demonstrating superior boundary delineation and multi-scale feature fusion capabilities relative to existing methods.</p><p><strong>Conclusions: </strong>These findings underscore the clinical potential of d","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6301-6325"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preliminary study on the ability of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics to predict vessels that encapsulate tumor clusters and prognosis in hepatocellular carcinoma.","authors":"Siqi Hu, Qiong Zou, Zijie Shen, Yujie Xie, Shi Li, Ju Jiao, Hong Zhang, Muhua Cheng, Yong Zhang","doi":"10.21037/qims-2024-2734","DOIUrl":"10.21037/qims-2024-2734","url":null,"abstract":"<p><strong>Background: </strong>Recently, a novel vascular pattern characterized by vessels encapsulating tumor clusters (VETC) was reported to be related to poor clinical outcomes in patients with hepatocellular carcinoma (HCC). The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics features derived from ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) to preoperatively predict VETC and prognosis in HCC patients.</p><p><strong>Methods: </strong>A total of 149 patients diagnosed with HCC from two institutions (The Third Affiliated Hospital of Sun Yat-sen University and Sun Yat-sen Memorial Hospital) were retrospectively enrolled and subsequently divided into a training cohort (n=103) and a test cohort (n=46) as external validation. The correlation between traditional image features on computed tomography (CT)/magnetic resonance imaging (MRI) and ¹⁸F-FDG PET/CT and VETC status were evaluated and compared. Radiomics features were extracted from ¹⁸F-FDG PET/CT images, followed by calculation of a radiomics score (Radscore). Univariate and multivariate logistic regression analyses were used to screen out the independent indicators. A nomogram model was developed based on Radscore and clinical indicators, and a clinical model was developed based on clinical indicators. The performance of the nomogram, clinical model, Radscore, as well as traditional PET parameter tumor-to-liver ratio (TLR) were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Disease-free survival (DFS) and overall survival (OS) rates were assessed using Kaplan-Meier survival analysis.</p><p><strong>Results: </strong>The difference in FDG parameter TLR between VETC-positive and VETC-negative HCC was found to be statistically significant (P<0.05), which was consistent with traditional CT/MRI imaging features. The Radscore was derived by calculating 13 selected radiomics features, comprising of six PET radiomics features and seven CT radiomics features. The nomogram model exhibited an area under the curve (AUC) of 0.908 [95% confidence interval (CI): 0.852-0.963; sensitivity: 0.855; specificity: 0.833] and 0.762 (95% CI: 0.624-0.900; sensitivity: 0.739; specificity: 0.739) in the training and test cohort, respectively. The disparity in the prediction of VETC status based on the nomogram model between DFS and OS was statistically comparable to that observed between VETC-positive and VETC-negative cases through pathological analysis (P<0.05).</p><p><strong>Conclusions: </strong>FDG metabolism is significantly associated with VETC status in HCC patients. A comprehensive nomogram model based on PET/CT radiomics and clinical indicators has potential for preoperative prediction of VETC as well as patient prognosis.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6217-6233"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surgical anesthesia using a combination of lumbar plexus and sacral plexus block for knee joint compound injury in severe scoliosis: a case description.","authors":"Cai Cheng, Jie Bai, Jun Jin, Sheng-Hui Huang","doi":"10.21037/qims-2024-2762","DOIUrl":"10.21037/qims-2024-2762","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6567-6570"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"^99mTc-MAA SPECT/CT imaging before ⁹⁰Y-SIRT for reflecting the distribution of ⁹⁰Y resin microspheres in tumors and the liver: a comparison with ⁹⁰Y PET/CT verification imaging.","authors":"Yong Cheng, Feng Wei, Hongkun Yu, Xueying Ling, Bin Guo, JingJie Shang, Qijun Cai, Yingxin Li, Xiaoxin Yang, Bingyu Ran, Mingrong Cao, Chengzhi Li, Hao Xu, Jian Gong","doi":"10.21037/qims-24-2232","DOIUrl":"10.21037/qims-24-2232","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;&lt;sup&gt;90&lt;/sup&gt;Y microsphere selective internal radiation therapy (&lt;sup&gt;90&lt;/sup&gt;Y-SIRT) has been widely used for the treatment of unresectable primary or metastatic liver tumors. &lt;sup&gt;99m&lt;/sup&gt;Tc-macroaggregated albumin single-photon emission computed tomography-computed tomography (&lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT) and &lt;sup&gt;90&lt;/sup&gt;Y positron emission tomography-computed tomography (&lt;sup&gt;90&lt;/sup&gt;Y PET/CT) are performed before and after &lt;sup&gt;90&lt;/sup&gt;Y-SIRT, respectively, for pretreatment dose distribution assessment and posttreatment verification of microsphere distribution. The purpose of this study was to evaluate the predictive value of &lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT for dose distribution assessment and analyze its potential impact on the calculation of absorbed dose to normal liver tissue. To achieve this, the differences in tumor imaging, liver tumor-to-normal ratio (TNR), and normal liver tissue absorbed dose (Dliver) between &lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT and &lt;sup&gt;90&lt;/sup&gt;Y PET/CT were analyzed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Thirty-eight patients with primary or metastatic liver cancer who underwent &lt;sup&gt;90&lt;/sup&gt;Y-SIRT were retrospectively enrolled. Their &lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT and &lt;sup&gt;90&lt;/sup&gt;Y PET/CT images were analyzed, and the TNR measured by &lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT (TNR&lt;sub&gt;SPECT&lt;/sub&gt;) and &lt;sup&gt;90&lt;/sup&gt;Y PET/CT (TNR&lt;sub&gt;PET&lt;/sub&gt;) was compared. The absolute differences in TRN (ΔTNR) and percent differences in TNR (%TNR) between the two values were also calculated. Correlation analysis was conducted with patient's age, body mass index, total liver volume, and target tumor volume. The absolute differences in D&lt;sub&gt;liver&lt;/sub&gt; (ΔD&lt;sub&gt;liver&lt;/sub&gt;) and percent differences in D&lt;sub&gt;liver&lt;/sub&gt; (%D&lt;sub&gt;liver&lt;/sub&gt;) as calculated by TNR&lt;sub&gt;SPECT&lt;/sub&gt; and TNR&lt;sub&gt;PET&lt;/sub&gt; were respectively compared.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;TNR&lt;sub&gt;SPECT&lt;/sub&gt; and TNR&lt;sub&gt;PET&lt;/sub&gt; were not significantly different across imaging modes (P=0.118) and were highly correlated (r=0.854; P&lt;0.01). When stratification by tumor type (primary: n=27; metastatic: n=37) and location (left lobe: n=20; right lobe: n=44) was applied, significant TNR differences emerged only in the left-lobe group (TNR&lt;sub&gt;SPECT&lt;/sub&gt; 5.9 &lt;i&gt;vs.&lt;/i&gt; TNR&lt;sub&gt;PET&lt;/sub&gt; 4.7; P=0.015). Bland-Altman analysis demonstrated good agreement in both total and subgroup TNR measurements. The mean ΔTNR was 0.4±1.7, with %TNR ≤92.1%. Dliver calculations showed no significant intermodal difference (P=0.198). Analyses of ΔD&lt;sub&gt;liver&lt;/sub&gt; and %D&lt;sub&gt;liver&lt;/sub&gt; revealed maximum overestimations of 37.3 Gy (ΔD&lt;sub&gt;liver&lt;/sub&gt; &gt;0) and underestimations of 44.5 Gy (ΔD&lt;sub&gt;liver&lt;/sub&gt; &lt;0), with peak %D&lt;sub&gt;liver&lt;/sub&gt; reaching 92.0%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Comparison with &lt;sup&gt;90&lt;/sup&gt;Y PET/CT imaging indicated that &lt;sup&gt;99m&lt;/sup&gt;Tc-MAA SPECT/CT could accurately reflect TNR and D&lt;sub&gt;liver&lt;/sub&gt; in &lt;sup&gt;90&lt;/sup&gt;Y-SIRT. However, TNR&lt;sub&gt;SPECT&lt;/sub&gt; ma","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6272-6286"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impaired white-matter structural network mediates the relationship between the glymphatic system and emotional abnormalities in patients with chronic kidney disease.","authors":"Wei Du, Yuan Li, Yuhan Jiang, Chun Yang, Yimiao Bao, Jiajun Cao, Yilin Wang, Yanwei Miao","doi":"10.21037/qims-24-1692","DOIUrl":"10.21037/qims-24-1692","url":null,"abstract":"<p><strong>Background: </strong>Patients with chronic kidney disease (CKD) tend to have white-matter network and glymphatic system dysfunction, and may experience cognitive decline and psychiatric abnormalities. This study explored the relationships between graph theory-based white-matter networks and diffusion tensor image analysis along the perivascular space (DTI-ALPS) and the choroid plexus volume (CPV).</p><p><strong>Methods: </strong>Sixty-one CKD patients, including 22 non-dialysis dependent (NDD) and 39 dialysis-dependent (DD) patients, and 43 age-, gender-, and education-matched healthy control (HC) participants were recruited for the study. All participants underwent blood biochemical tests, neuropsychological assessments, and magnetic resonance imaging scans. Preprocessed images were used to construct fractional anisotropy networks, and calculate the DTI-ALPS index and CPV. Intergroup comparisons were performed for all measurements, and correlations were analyzed.</p><p><strong>Results: </strong>Compared to the HCs, the DD-CKD patients had elevated small-worldness (σ) and normalized clustering coefficient (P<0.01) values, while no significant statistical differences were observed between NDD-CKD patients and the HCs. The clustering coefficient (P<0.05), and global and local efficiency (P<0.01) values were decreased, while the characteristic path length (P<0.01) was increased in the CKD patients, especially those who were DD. The CKD patients also had decreased DTI-ALPS index (P=0.001) and increased CPV (P<0.001) values. These indicators were correlated with verbal memory, anxiety, and depression levels (all P<0.05). The mediation analysis revealed that σ partially mediated the effect of the DTI-ALPS index on depression levels (mediation effect: -5.0404), σ fully mediated the effect of the CPV on depression levels (mediation effect: 0.0013), and σ fully mediated the effect of the DTI-ALPS index on anxiety levels (mediation effect: -5.9291).</p><p><strong>Conclusions: </strong>CKD patients exhibit impaired glymphatic system function and abnormal white-matter network topology, both of which are correlated with cognition, verbal memory, and emotional states. The structural integrity and organizational efficiency of white-matter networks may be a critical factor linking glymphatic dysfunction to emotional health in CKD patients.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6016-6031"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between intraneural blood flow of the sciatic nerve, peripheral neuropathies, and foot ulcers in patients with diabetes.","authors":"Shun-Ping Chen, Ruo-Yu Li, Jing Hong, Ting-Ting Ye, Hong Zhu","doi":"10.21037/qims-2024-2773","DOIUrl":"10.21037/qims-2024-2773","url":null,"abstract":"<p><strong>Background: </strong>Intraneural blood flow (INBF) serves as a useful marker for diagnosing peripheral neuropathies. However, the specific relationship between sciatic nerve INBF (SN_inbf) and the occurrence of diabetic peripheral neuropathies (DPNs) and diabetic foot ulcers (DFUs) remains ambiguous. Thus, the purpose of this study was to investigate the association between SN_inbf, as detected by power Doppler ultrasound, and the presence of DPNs and DFUs in patients with diabetes.</p><p><strong>Methods: </strong>A retrospective cross-sectional study was performed on patients with diabetes between June 2018 and July 2024. The study population was categorized into two groups based on electroneurophysiological findings: a DPN and a non-DPN group. Within the DPN group, the patients were subdivided as DFU and non-DFU subgroups. Clinical characteristics and ultrasound parameters, including SN_inbf, were compared between those groups and subgroups. Logistic regression analysis was employed to investigate the relationships between SN_inbf and the presence of DPN and DFU. Additionally, the diagnostic performance of SN_inbf was assessed through the areas under the receiver operating characteristic curves (AUROCs) for identifying DPN and DFU. Meanwhile, sciatic nerves (SNs) of healthy controls (HCs) were taken as normal controls to determine whether SN_inbf can be detected in HC by power Doppler ultrasound.</p><p><strong>Results: </strong>The study included 113 patients with diabetes [mean age ± standard deviation (SD), 63.81±12.31 years; 71 males] (82 had DPN and 31 did not). The subgroup analysis of DPN patients included 61 individuals with DFU and 21 without. Adjusted logistic regression analysis revealed a significant independent association between SN_inbf and the presence of DPN in patients with diabetes [odds ratio (OR): 7.399; 95% confidence interval (CI): 1.959-27.944; P=0.004]. Further analysis within the DPN subgroup confirmed a significant association between SN_inbf and the presence of DFUs (OR: 12.255; 95% CI: 2.881-52.135; P=0.001). The AUROC for SN_inbf in diagnosing DPN and DFU was 0.755 and 0.727, respectively (all P<0.05), with corresponding sensitivities and specificities of 67.1% and 83.9% for DPN and 78.7% and 66.7% for DFU, respectively. Additionally, there were 19 HCs (38 SNs, matched gender and age with patients with diabetes). Among those HC, no SN_inbf was detected by power Doppler ultrasound.</p><p><strong>Conclusions: </strong>SN_inbf is positively correlated with DPN in patients with diabetes and with DFU in those with DPN, suggesting that SN_inbf is a valuable indicator for diagnosing both DPN and DFU.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6234-6251"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic measurement and reference values setting of intracranial cerebrospinal fluid volume: a large-scale analysis of computed tomography images.","authors":"Ruhang Ma, Jiacheng Hao, Jing Ding, Haomiao Yu, Shuang Xia","doi":"10.21037/qims-2024-2571","DOIUrl":"10.21037/qims-2024-2571","url":null,"abstract":"<p><strong>Background: </strong>In textbooks and literature, intracranial cerebrospinal fluid (iCSF) volume is typically presented as a generalized range without population-specific references. The volume of iCSF in normal populations varies with age and brain parenchyma volume, and exhibits sex-related differences. This study aimed to develop an automated extraction model for quantitatively measuring iCSF volume using brain non-contrast computed tomography (CT) images, and to establish reference values for iCSF in populations of varying age and brain atrophy groups based on sex.</p><p><strong>Methods: </strong>Brain non-contrast CT images from 400 individuals were retrospectively collected. The iCSF was delineated manually slice by slice. The UNet Transformers (UNETR) architecture was employed to develop an iCSF extraction and automatic quantitative measurement model. Subsequently, the model was used to extract iCSF automatically from 1,600 cases, and reference values were set for different ages and degrees of brain atrophy for males and females.</p><p><strong>Results: </strong>The automatic extraction and quantitative volume measurement model for iCSF achieved a Dice coefficient 0.78. In age groups of ≤44, 45-59, 60-74, and ≥75 years, the reference values of total iCSF volume for males were 62.0, 91.2, 116.9, and 127.0 mL, respectively, and for females were 59.1, 91.2, 113.6, and 122.4 mL, respectively. In the no, mild, moderate, and severe brain atrophy groups, the reference values of total iCSF volume for males were 58.4, 79.6, 99.1, and 127.4 mL, respectively, and for females were 59.1, 78.4, 98.0, and 125.1 mL, respectively.</p><p><strong>Conclusions: </strong>Given the good performance of the model, the established reference values for iCSF volume could significantly aid research related to iCSF.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"6185-6199"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain iron deficiency in children with attention deficit hyperactivity disorder using iterAtive magnetic suscePtibility sources sepARaTion (APART-QSM).","authors":"Yufen Li, Huan Wang, Huasong Cai, Shasha Lan, Yan Dai, Boyan Xu, Shu Su, Hongyu Zhang, Zhiyun Yang, Yingqian Chen","doi":"10.21037/qims-2024-2800","DOIUrl":"10.21037/qims-2024-2800","url":null,"abstract":"<p><strong>Background: </strong>IterAtive magnetic suscePtibility sources sepARaTion (APART-QSM), a recently proposed susceptibility source separation method, can differentiate paramagnetic and diamagnetic susceptibility distributions related to iron and myelin, respectively. This study aimed to investigate whether paramagnetic susceptibility values of deep gray matter structures combined with machine learning algorithms could be used to identify individuals with attention-deficit/hyperactivity disorder (ADHD) and to further explore ADHD-related pathogenesis.</p><p><strong>Methods: </strong>Thirty-six ADHD and 35 age, sex-matched healthy controls (HCs) were recruited. The paramagnetic susceptibility mapping obtained by using APART-QSM method was normalized and the positive susceptibility values of deep gray matter structures, including the bilateral caudate nucleus, putamen, pallidum, and thalamus, were extracted. Random forest (RF) and support vector machine (SVM) were adopted to build machine learning models based on regional positive susceptibility values. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performance.</p><p><strong>Results: </strong>Lower positive susceptibility values of the left caudate nucleus and bilateral pallidum were found (Caudate_L: 0.0231±0.0045 <i>vs.</i> 0.0261±0.0051, Pallidum_L: 0.0431±0.0114 <i>vs.</i> 0.0503±0.0141, Pallidum_R: 0.0426±0.0119 <i>vs.</i> 0.0488±0.0120, P<0.05, uncorrected). However, no significant correlations were found between decreased iron levels and attention performance. Both classifiers achieved good performance, particularly the RF model with an AUC of 0.756, sensitivity of 77.8%, specificity of 68.6% and accuracy of 73.2%.</p><p><strong>Conclusions: </strong>Our findings revealed iron deficiency of deep gray matter nuclei in children with ADHD, and machine learning models combined with APART-QSM could be used to distinguish ADHD from HCs, providing a potential biomarker for further understanding of ADHD pathophysiology and facilitating early diagnosis.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 7","pages":"5980-5990"},"PeriodicalIF":2.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12290788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}