European Radiology Experimental最新文献

{"title":"Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study.","authors":"Luca Salhöfer, Mathias Holtkamp, Francesco Bonella, Lale Umutlu, Johannes Wienker, Dirk Westhölter, Matthias Welsner, Christian Taube, Kaid Darwiche, Judith Kohnke, Jannis Straus, Nikolas Beck, Marko Frings, Sebastian Zensen, Rene Hosch, Giulia Baldini, Felix Nensa, Marcel Opitz, Johannes Haubold","doi":"10.1186/s41747-024-00536-z","DOIUrl":"10.1186/s41747-024-00536-z","url":null,"abstract":"<p><strong>Background: </strong>Non-malignant chronic diseases remain a major public health concern. Given the alterations in lipid metabolism and deposition in the lung and its association with fibrotic interstitial lung disease (fILD) and chronic obstructive pulmonary disease (COPD), this study aimed to detect those alterations using computed tomography (CT)-based analysis of pulmonary fat attenuation volume (CTpfav).</p><p><strong>Methods: </strong>This observational retrospective single-center study involved 716 chest CT scans from three subcohorts: control (n = 279), COPD (n = 283), and fILD (n = 154). Fully automated quantification of CTpfav based on lung segmentation and HU-thresholding. The pulmonary fat index (PFI) was derived by normalizing CTpfav to the CT lung volume. Statistical analyses were conducted using Kruskal-Wallis with Dunn's post hoc tests.</p><p><strong>Results: </strong>Patients with fILDs demonstrated a significant increase in CTpfav (median 71.0 mL, interquartile range [IQR] 59.7 mL, p < 0.001) and PFI (median 1.9%, IQR 2.4%, p < 0.001) when compared to the control group (CTpfav median 43.6 mL, IQR 16.94 mL; PFI median 0.9%, IQR 0.5%). In contrast, individuals with COPD exhibited significantly reduced CTpfav (median 36.2 mL, IQR 11.4 mL, p < 0.001) and PFI (median 0.5%, IQR 0.2%, p < 0.001).</p><p><strong>Conclusion: </strong>The study underscores the potential of CTpfav and PFI as imaging biomarkers for detecting changes in lung lipid metabolism and deposition and demonstrates a possibility of tracking these alterations in patients with COPD and ILDs. Further research is needed to validate these findings and explore the clinical relevance of CTpfav and PFI in lung disease management.</p><p><strong>Relevance statement: </strong>This study introduces a fully automated method for quantifying CTpfav, potentially establishing it as a new imaging biomarker for chronic lung diseases.</p><p><strong>Key points: </strong>This retrospective observational study employed an open-source, automated algorithm for the quantification of CT pulmonary fat attenuation volume (CTpfav). Patients with fibrotic interstitial lung disease (fILD) showed a significantly higher CTpfav and pulmonary fat index (PFI), i.e., CTpfav/CT lung volume, compared to a control group. Patients with chronic obstructive pulmonary disease (COPD) showed significantly lower CTpfav and PFI compared to the control group. CTpfav and PFI may each serve as imaging biomarkers for various lung diseases and warrant further investigation.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"139"},"PeriodicalIF":3.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the physiological effects and safety of transpulmonary chemoembolization with doxorubicin on pulmonary tissue using a human-isolated lung perfusion model.","authors":"Alexis Slama, Hannah Steinberg, Stéphane Collaud, Özlem Okumus, Ralph-Axel Hilger, Sebastian Bauer, Hans-Ulrich Schildhaus, Clemens Aigner, Benedikt M Schaarschmidt","doi":"10.1186/s41747-024-00532-3","DOIUrl":"10.1186/s41747-024-00532-3","url":null,"abstract":"<p><strong>Background: </strong>Whole lung transpulmonary chemoembolization using a combination of doxorubicin (DXO) and degradable starch microspheres (DSM-TPCE) might be a promising treatment option in soft tissue sarcoma. To pave the way for clinical studies, this study aimed to evaluate the short-term effects of DSM-TPCE with DXO using an ex vivo isolated lung perfusion (ILP) model.</p><p><strong>Methods: </strong>Nine lung specimens retrieved from patients undergoing lobectomy underwent ex vivo ILP. In groups of three, lung specimens were either treated with sole DXO, sole DSM, or combined substances (DSM + DXO). During ex vivo ILP, histological samples were obtained from each lung every 15 min. Quantitative DXO analysis and histopathological grading of possible tissue damage using a five-point Likert scale was performed. Two-way repeated measures ANOVA tested for differences between treatment groups and changes over time.</p><p><strong>Results: </strong>We created a preclinical ex vivo ILP model to simulate the effects of DSM-TPCE. In histopathological analysis, only two specimens, treated with only DXO, showed an increase in parenchymal damage over time. No significant effect of time (3.3%, p = 0.305) or group (23.3; p = 0.331) was identified. Within the lung tissue, the DXO concentration ranged from 205 to 1,244 ng/g. No significant effects could be detected regarding different treatment groups (4.9% of total variation, p = 0.103).</p><p><strong>Conclusion: </strong>In an ex vivo ILP model using human lung lobes, the physiological effects of DSM-TPCE with DXO could be tested. Neither increased DXO concentrations in lung tissue nor histopathological changes indicating early lung toxicity were observed.</p><p><strong>Relevance statement: </strong>An ex vivo ILP model using human lung specimens did not show any signs of early lung toxicity after transpulmonary chemoembolization with DXO. These results support further evaluation of DSM-TPCE in phase I/II trials.</p><p><strong>Key points: </strong>Transpulmonary chemoembolization can be investigated in an ex vivo ILP model. DSM did not increase DXO in normal lung tissue. DSM did not increase parenchymal toxicity compared to the control groups.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"137"},"PeriodicalIF":3.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Focal liver lesions: multiparametric microvasculature characterization via super-resolution ultrasound imaging.","authors":"Qian-Qian Zeng, Shi-Zhe An, Chao-Nan Chen, Zhen Wang, Jia-Cheng Liu, Ming-Xi Wan, Yu-Jin Zong, Xiao-Hua Jian, Jie Yu, Ping Liang","doi":"10.1186/s41747-024-00540-3","DOIUrl":"10.1186/s41747-024-00540-3","url":null,"abstract":"<p><strong>Background: </strong>Noninvasive and functional imaging of the focal liver lesion (FLL) vasculature at microscopic scales is clinically challenging. We investigated the feasibility of using super-resolution ultrasound (SR-US) imaging for visualizing and quantifying the microvasculature of intraparenchymal FLLs.</p><p><strong>Methods: </strong>Patients with FLLs between June 2022 and February 2023 were prospectively screened. Following bolus injection of microbubbles at clinical concentration, SR-US was performed using a high frame rate (350-500 Hz) modified ultrasound scanner and a convex array transducer with a central frequency of 3.1 MHz.</p><p><strong>Results: </strong>In total, 47 pathologically proven FLLs at a depth of 5.7 ± 1.7 cm (mean ± standard deviation) were included: 30 hepatocellular carcinomas (HCC), 11 liver metastases (LM), and 6 focal nodular hyperplasias (FNH). The smallest detectable vessel size of the hepatic microvasculature was 128.4 ± 18.6 μm (mean ± standard deviation) at a depth of 8 cm. Significant differences were observed among the three types of lesions in terms of pattern categories, vessel density, minimum flow velocity, and perfusion index. We observed higher vessel density for FNH versus liver parenchyma (p < 0.001) as well as fractal dimension and local flow direction entropy value for FNH versus HCC (p = 0.002 and p < 0.001, respectively) and for FNH versus LM (p = 0.006 and p = 0.002, respectively).</p><p><strong>Conclusion: </strong>Multiparametric SR-US showed that these three pathological types of FLLs have specific microvascular phenotypes. Vessel density, fractal dimension and local flow direction entropy served as valuable parameters in distinguishing between benign and malignant FLLs.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov (NCT06018142).</p><p><strong>Relevance statement: </strong>Multiparametric SR-US imaging offers precise morphological and functional assessment of the microvasculature of intraparenchymal focal liver lesions, providing insights into tumor heterogeneity and angiogenesis.</p><p><strong>Key points: </strong>Super-resolution (SR)-US imaging allowed morphological and functional evaluation of intraparenchymal hepatic lesion microvasculature. Hepatocellular carcinoma, liver metastasis, and focal nodular hyperplasia exhibit distinct microvascular architectures and hemodynamic profiles. Multiparametric microvasculature characterization via SR-US imaging facilitates the differentiation between benign and malignant microvascular phenotypes.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"138"},"PeriodicalIF":3.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the value of arterial spin labeling and six diffusion MRI models in differentiating solid benign and malignant renal tumors.","authors":"Mengmeng Gao, Shichao Li, Guanjie Yuan, Weinuo Qu, Kangwen He, Zhouyan Liao, Ting Yin, Wei Chen, Qian Chu, Zhen Li","doi":"10.1186/s41747-024-00537-y","DOIUrl":"10.1186/s41747-024-00537-y","url":null,"abstract":"<p><strong>Objective: </strong>To explore the value of three-dimensional arterial spin labeling (ASL) and six diffusion magnetic resonance imaging (MRI) models in differentiating solid benign and malignant renal tumors.</p><p><strong>Methods: </strong>This retrospective study included 89 patients with renal tumors. All patients underwent ASL and ZOOMit diffusion-weighted imaging (DWI) examinations and were divided into three groups: clear cell renal cell carcinoma (ccRCC), non-ccRCC, and benign renal tumors (BRT). The mean and peak renal blood flow (RBFmean and RBFpeak) from ASL and fourteen diffusion parameters from mono-exponential DWI (Mono_DWI), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), fractional order calculus (FROC), and continuous-time random-walk (CTRW) model were analyzed. Binary logistic regression was used to determine the optimal parameter combinations. The diagnostic performance of various MRI-derived parameters and their combinations was compared.</p><p><strong>Results: </strong>Among the six diffusion models, the SEM model achieved the highest performance in differentiating ccRCC from non-ccRCC (area under the receiver operating characteristic curve [AUC] 0.880) and from BRT (AUC 0.891). IVIM model achieved the highest AUC (0.818) in differentiating non-ccRCC from BRT. Among all the MRI-derived parameters, RBFpeak combined with DKI_MK yielded the highest AUC (0.970) in differentiating ccRCC from non-ccRCC, and the combination of RBFpeak, SEM_DDC, and FROC_μ yielded the highest AUC (0.992) for differentiating ccRCC from BRT.</p><p><strong>Conclusion: </strong>ASL and all diffusion models showed similar diagnostic performance in differentiating ccRCC from non-ccRCC or BRT, while the IVIM model performed better in distinguishing non-ccRCC from BRT. Combining ASL with diffusion models can provide additional value in predicting ccRCC.</p><p><strong>Relevance statement: </strong>Considering the increasing detection rate of incidental renal masses, accurate discrimination of benign and malignant renal tumors is crucial for decision-making. Combining ASL with diffusion MRI models offers a promising solution to this clinical issue.</p><p><strong>Key points: </strong>All assessed models were effective for differentiating ccRCC from non-ccRCC or BRT. ASL and all diffusion models showed similar performance in differentiating ccRCC from non-ccRCC or BRT. Combining ASL with diffusion models significantly improved diagnostic efficacy in predicting ccRCC. IVIM model could better differentiate non-ccRCC from BRT.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"135"},"PeriodicalIF":3.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiautomatic volume measure of kidney vascular territories on CT angiography to plan aortic aneurysm repair in patients with horseshoe kidney.","authors":"Axel Bartoli, Alberto Colombo, Francesco Pisu, Tommaso Galliena, Chiara Gnasso, Enrico Rinaldi, Germano Melissano, Anna Palmisano, Antonio Esposito","doi":"10.1186/s41747-024-00531-4","DOIUrl":"10.1186/s41747-024-00531-4","url":null,"abstract":"<p><p>Surgical repair of abdominal aortic aneurism (AAA) with horseshoe kidney (HK) is challenging because of several accessory renal arteries (RAs), variable in number, branches, and vascular territories, with subsequent variable renal damage. The identification of RAs and vascular territories could contribute to surgical planning. We developed a semiautomatic presurgical computed tomography angiography (CTA)-based model to measure the renal volume of each RA, validated on postsurgical CTA in patients with HK treated for AAA. Renal parenchyma volume was extracted on both CTAs (Vol_Tot_pre and Vol_Tot_post) after labeling RAs ostia and vascular endpoints by two observers using a semiautomatic model by assigning each renal voxel to the closest vascular ending, obtaining volumes for each vascular territory. Number of RAs number was 4.0 ± 1.4 (mean ± standard deviation (SD)), Vol_Tot_pre 360 ± 76.5 cm³; kidney volume loss at surgery (KVLS) (Vol_Tot_pre minus Vol_Tot_post) 51.9 ± 35.4 cm³; percentage of kidney loss 15.2 ± 11.6%. KVLS and predicted kidney volume loss on preoperative CTA (PKVL) were strongly correlated (r = 0.93; p = 0.023). Interobserver agreement was good (mean bias = 0.000001 ± 1.96 SD of 19.1 cm³). Presurgical semiautomatic segmentation of vascular territories in patients with HK and AAA is feasible. RELEVANCE STATEMENT: This software allowed the preoperative calculation of renal volume perfused by each renal artery in the challenging association of the horseshoe kidney and abdominal aortic aneurism. It helps to determine the feasibility of surgical resection of arteries, thereby improving surgical planning and reducing the risk of postoperative renal function deterioration. KEY POINTS: The association between horseshoe kidney and abdominal aortic aneurism is a challenging condition that may require renal vascular resection. A semiautomatic model measures renal volume perfused by each artery on preoperative computed tomography angiography with high accuracy. Customized use of this tool could improve surgical management by determining which arteries can be safely resected during surgery.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"133"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of hypoxia and its dynamic evolution in glioblastoma via qBOLD MRI: a comparative study with metformin treatment.","authors":"Dongdong Wang, Jie Chen, Yinwei Ying, Xinxin Zhao, Nan Mei, Xuanxuan Li, Yuqi Zhu, Jin Cui, Pu-Yeh Wu, Yiping Lu, Bo Yin","doi":"10.1186/s41747-024-00533-2","DOIUrl":"10.1186/s41747-024-00533-2","url":null,"abstract":"<p><strong>Background: </strong>To investigate the accuracy of quantitative blood oxygen level-dependent (qBOLD) magnetic resonance imaging (MRI) in identifying hypoxia within glioblastoma and explore dynamic changes in oxygenation status of glioblastoma with and without metformin administration.</p><p><strong>Methods: </strong>Three healthy and seven C6-bearing rats underwent 7-T qBOLD MRI. Oxygen extraction fraction (OEF) and cerebral metabolism rate of O₂ (CMRO₂) were calculated from qBOLD data. Tumor tissues were stained using hypoxia-inducible factor-1 <math><mi>α</mi></math> (HIF-1 <math><mi>α</mi></math> ) and pimonidazole. The correlation between the hypoxia markers and corresponding qBOLD-based parameters was analyzed. Six C6-bearing rats were divided into metformin-treated and control groups for a longitudinal study of qBOLD imaging changes, with scans conducted on the 12th, 15th, and 18th day post-tumor implantation.</p><p><strong>Results: </strong>In healthy rats, gray matter showed higher values than white matter in T2, T2*, cerebral blood volume (CBV), and cerebral blood flow (CBF), whereas OEF was lower. Glioblastoma tissues exhibited elevated T2, T2*, CBV, and CBF but decreased OEF and CMRO₂ relative to normal-appearing white matter. No significant correlation was found between staining scores from HIF-1 <math><mi>α</mi></math> and pimonidazole. T2* and T2 values were negatively correlated with pimonidazole scores in tumor regions. As the tumor progressed, OEF values increased with intra-tissue variations, whereas CMRO₂ decreased. Metformin delayed the reduction of T2 and T2* values, with significant differences in OEF and CMRO₂ values compared to controls on day 18.</p><p><strong>Conclusion: </strong>T2* and T2 values were significantly associated with the hypoxia status in glioma. Metformin could potentially mitigate the progression of hypoxia in glioblastoma, which can be tracked by qBOLD parameters.</p><p><strong>Relevance statement: </strong>This study demonstrates the potential of qBOLD parameters in assessing glioma dynamic oxygen metabolism and the efficacy of metformin as an anti-hypoxic agent, providing insights into improving glioblastoma treatment strategies.</p><p><strong>Key points: </strong>The study investigated qBOLD imaging's accuracy in identifying hypoxia status within glioblastoma. qBOLD effectively assesses hypoxia and its dynamic evolution in glioblastoma. qBOLD parameters assist in identifying a suitable patient demographic for metformin treatment.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"134"},"PeriodicalIF":3.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142773188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.","authors":"Inge A H van den Berk, Colin Jacobs, Maadrika M N P Kanglie, Onno M Mets, Miranda Snoeren, Alexander D Montauban van Swijndregt, Elisabeth M Taal, Tjitske S R van Engelen, Jan M Prins, Shandra Bipat, Patrick M M Bossuyt, Jaap Stoker","doi":"10.1186/s41747-024-00518-1","DOIUrl":"10.1186/s41747-024-00518-1","url":null,"abstract":"<p><strong>Background: </strong>To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).</p><p><strong>Methods: </strong>In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared.</p><p><strong>Results: </strong>During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0-2) AI mark per ULDCT.</p><p><strong>Conclusion: </strong>The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×).</p><p><strong>Relevance statement: </strong>AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities.</p><p><strong>Trial registration: </strong>The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl).</p><p><strong>Key points: </strong>An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED. AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives). AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities. AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"132"},"PeriodicalIF":3.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of pulmonary artery pressure, blood indices, and myocardial microcirculation in rats returning from high altitude to moderate altitude.","authors":"Chunlong Yan, Jinfeng Ma, Dengfeng Tian, Tingjun Yan, Chenhong Zhang, Fengjuan Zhang, Yuchun Zhao, Shihan Fu, Qiang Zhang, Mengxue Xia, Yue Li, Yanqiu Sun","doi":"10.1186/s41747-024-00514-5","DOIUrl":"10.1186/s41747-024-00514-5","url":null,"abstract":"<p><strong>Background: </strong>To investigate changes in pulmonary artery pressure (PAP), blood indices, and myocardial microcirculation in rats returning from high altitude (HA) to moderate altitude (MA).</p><p><strong>Methods: </strong>Forty 4-week-old male Sprague-Dawley rats were randomly divided into four groups with ten rats in each group. One group was transported to the MA area (MA-group), and the other three groups were transported to HA (HA-group-A, HA-group-B, and HA-group-C). After 28 weeks of age, the rats from the HA area were transported to the MA area for 0 days, 10 days, and 20 days, respectively. PAP, routine blood tests, and computed tomography myocardial perfusion indices were measured.</p><p><strong>Results: </strong>Compared with the MA-group, the body weight of HA-groups decreased (p < 0.05), and PAP in HA-group-A and HA-group-B increased (p < 0.05). In the HA groups, PAP initially increased and then decreased. Compared with the MA-group, red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) of rats in HA-group-A increased (p < 0.05). Compared with the HA-group-A, RBC, HGB, and HCT of HA-group-B gradually decreased (p < 0.05) while MCV decreased (p < 0.05), and PLT of HA-group-C increased (p < 0.05). Compared with the MA group, blood flow (BF) and blood volume (BV) of the HA-group-A decreased (p < 0.05). Compared with the HA-group-A, TTP increased first and then decreased (p < 0.05), and BF and BV increased gradually (p < 0.05). Pathological results showed that myocardial fiber arrangement was disordered, and cell space widened in the HA group.</p><p><strong>Conclusion: </strong>PAP, blood parameters, and myocardial microcirculation in rats returning from high to MA exhibited significant changes.</p><p><strong>Relevance statement: </strong>This study provides an experimental basis for understanding the physiological and pathological mechanisms during the process of deacclimatization to HA and offers new insights for the prevention and treatment of deacclimatization to HA syndrome.</p><p><strong>Key points: </strong>Forty rats were raised in a real plateau environment. Myocardial microcirculation was detected by CT myocardial perfusion imaging. The PAP of the unacclimated rats increased first and then decreased. The myocardial microcirculation of the deacclimated rats showed hyperperfusion changes.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"131"},"PeriodicalIF":3.7,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image biomarkers and explainable AI: handcrafted features versus deep learned features.","authors":"Leonardo Rundo, Carmelo Militello","doi":"10.1186/s41747-024-00529-y","DOIUrl":"10.1186/s41747-024-00529-y","url":null,"abstract":"<p><p>Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN for both deep feature extraction and end-to-end approaches. We discuss the curse of dimensionality, along with dimensionality reduction techniques. Despite the outstanding performance of deep learning (DL) approaches, the use of handcrafted features instead of deep learned features needs to be considered for each specific study. Dataset size is a key factor: large-scale datasets with low sample diversity could lead to overfitting; limited sample sizes can provide unstable models. The dataset must be representative of all the \"facets\" of the clinical phenomenon/disease investigated. The access to high-performance computational resources from graphics processing units is another key factor, especially for the training phase of deep architectures. The advantages of multi-institutional federated/collaborative learning are described. When large language models are used, high stability is needed to avoid catastrophic forgetting in complex domain-specific tasks. We highlight that non-DL approaches provide model explainability superior to that provided by DL approaches. To implement explainability, the need for explainable AI arises, also through post hoc mechanisms. RELEVANCE STATEMENT: This work aims to provide the key concepts for processing the imaging features to extract reliable and robust image biomarkers. KEY POINTS: The key concepts for processing the imaging features to extract reliable and robust image biomarkers are provided. The main differences between radiomics and representation learning approaches are highlighted. The advantages and disadvantages of handcrafted versus learned features are given without losing sight of the clinical purpose of artificial intelligence models.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"130"},"PeriodicalIF":3.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11576747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical feasibility of automated blur detection in digital mammography using convolutional neural network.","authors":"S Nowakowska, V Vescoli, T Schnitzler, C Ruppert, K Borkowski, A Boss, C Rossi, B Wein, A Ciritsis","doi":"10.1186/s41747-024-00527-0","DOIUrl":"10.1186/s41747-024-00527-0","url":null,"abstract":"<p><strong>Background: </strong>The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mammography.</p><p><strong>Methods: </strong>A retrospective dataset consisting of 152 examinations acquired with mammography machines from three different vendors was utilized. The blurred areas were contoured by expert breast radiologists. Normalized Wiener spectra (nWS) were extracted in a sliding window manner from each mammogram. These spectra served as input for a convolutional neural network (CNN) generating the probability of the spectra originating from a blurred region. The resulting blur probability mask, upon thresholding, facilitated the classification of a mammogram as either blurred or sharp. Ground truth for the test set was defined by the consensus of two radiologists.</p><p><strong>Results: </strong>A significant correlation between the view (p < 0.001), as well as between the laterality and the presence of blur (p = 0.004) was identified. The developed model AUROC of 0.808 (95% confidence interval 0.794-0.821) aligned with the consensus in 78% (67-83%) of mammograms classified as blurred. For mammograms classified by consensus as sharp, the model achieved agreement in 75% (67-83%) of them.</p><p><strong>Conclusion: </strong>A model for blur detection was developed and assessed. The results indicate that a robust approach to blur detection, based on feature extraction in frequency space, tailored to radiologist expertise regarding clinical relevance, could eliminate the subjectivity associated with the visual assessment.</p><p><strong>Relevance statement: </strong>This blur detection model, if implemented in clinical practice, could provide instantaneous feedback to technicians, allowing for prompt mammogram retakes and ensuring that only high-quality mammograms are sent for screening and diagnostic tasks.</p><p><strong>Key points: </strong>Blurring in mammography limits radiologist interpretation and diagnostic accuracy. This objective blur detection tool ensures image quality, and reduces retakes and unnecessary exposures. Wiener spectrum analysis and CNN enabled automated blur detection in mammography.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"129"},"PeriodicalIF":3.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11574226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}