European Journal of Radiology Open最新文献

{"title":"Construction of a nomogram combining CEUS and MRI imaging for preoperative diagnosis of microvascular invasion in hepatocellular carcinoma","authors":"Feiqian Wang ,&nbsp;Kazushi Numata ,&nbsp;Akihiro Funaoka ,&nbsp;Takafumi Kumamoto ,&nbsp;Kazuhisa Takeda ,&nbsp;Makoto Chuma ,&nbsp;Akito Nozaki ,&nbsp;Litao Ruan ,&nbsp;Shin Maeda","doi":"10.1016/j.ejro.2024.100587","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100587","url":null,"abstract":"<div><h3>Purpose</h3><p>To use Sonazoid contrast-enhanced ultrasound (S-CEUS) and Gadolinium-Ethoxybenzyl-Diethylenetriamine Penta-Acetic Acid magnetic-resonance imaging (EOB-MRI), exploring a non-invasive preoperative diagnostic strategy for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).</p></div><div><h3>Methods</h3><p>111 newly developed HCC cases were retrospectively collected. Both S-CEUS and EOB-MRI examinations were performed within one month of hepatectomy. The following indicators were investigated: size; vascularity in three phases of S-CEUS; margin, signal intensity, and peritumoral wedge shape in EOB-MRI; tumoral homogeneity, presence and integrity of the tumoral capsule in S-CEUS or EOB-MRI; presence of branching enhancement in S-CEUS; baseline clinical and serological data. The least absolute shrinkage and selection operator regression and multivariate logistic regression analysis were applied to optimize feature selection for the model. A nomogram for MVI was developed and verified by bootstrap resampling.</p></div><div><h3>Results</h3><p>Of the 16 variables we included, wedge and margin in HBP of EOB-MRI, capsule integrity in AP or HBP/PVP images of EOB-MRI/S-CEUS, and branching enhancement in AP of S-CEUS were identified as independent risk factors for MVI and incorporated into construction of the nomogram. The nomogram achieved an excellent diagnostic efficiency with an area under the curve of 0.8434 for full data training set and 0.7925 for bootstrapping validation set for 500 repetitions. In evaluating the nomogram, Hosmer–Lemeshow test for training set exhibited a good model fit with <em>P</em> &gt; 0.05. Decision curve analysis of nomogram model yielded excellent clinical net benefit with a wide range (5–80 % and 85–94 %) of risk threshold.</p></div><div><h3>Conclusions</h3><p>The MVI Nomogram established in this study may provide a strategy for optimizing the preoperative diagnosis of MVI, which in turn may improve the treatment and prognosis of MVI-related HCC.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100587"},"PeriodicalIF":1.8,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235204772400042X/pdfft?md5=ce4e261f345e48950b915bc3a9621dcf&pid=1-s2.0-S235204772400042X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582328","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":"Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction","authors":"Noriko Nishioka ,&nbsp;Noriyuki Fujima ,&nbsp;Satonori Tsuneta ,&nbsp;Masato Yoshikawa ,&nbsp;Rina Kimura ,&nbsp;Keita Sakamoto ,&nbsp;Fumi Kato ,&nbsp;Haruka Miyata ,&nbsp;Hiroshi Kikuchi ,&nbsp;Ryuji Matsumoto ,&nbsp;Takashige Abe ,&nbsp;Jihun Kwon ,&nbsp;Masami Yoneyama ,&nbsp;Kohsuke Kudo","doi":"10.1016/j.ejro.2024.100588","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100588","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI).</p></div><div><h3>Methods</h3><p>This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue.</p></div><div><h3>Results</h3><p>In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p&lt;0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p&lt;0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p&lt;0.0001, p=0.0014, respectively).</p></div><div><h3>Conclusion</h3><p>Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100588"},"PeriodicalIF":1.8,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000431/pdfft?md5=b9b782fb0ca622347ec25b1eacf0e51d&pid=1-s2.0-S2352047724000431-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141582329","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":"The association of magnetic resonance imaging features with five molecular subtypes of breast cancer","authors":"Van Thi Nguyen ,&nbsp;Duc Huu Duong ,&nbsp;Quang Thai Nguyen ,&nbsp;Duy Thai Nguyen ,&nbsp;Thi Linh Tran ,&nbsp;Tra Giang Duong","doi":"10.1016/j.ejro.2024.100585","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100585","url":null,"abstract":"<div><h3>Objective</h3><p>To identify the association of magnetic resonance imaging (MRI) features with molecular subtypes of breast cancer (BC).</p></div><div><h3>Materials and methods</h3><p>A retrospective study was conducted on 112 invasive BC patients with preoperative breast MRI. The confirmed diagnosis and molecular subtypes of BC were based on the postoperative specimens. MRI features were collected by experienced radiologists. The association of MRI features of each subtype was compared to other molecular subtypes in univariate and multivariate logistic regression analyses.</p></div><div><h3>Results</h3><p>The proportions of luminal A, luminal B HER2-negative, luminal B HER2-positive, HER2-enriched, and triple-negative BC were 14.3 %, 52.7 %, 12.5 %, 10.7 %, and 9.8 %, respectively. Luminal A was associated with hypo-isointensityon T2-weighted images (OR=6.214, 95 % CI: 1.163–33.215) and non-restricted diffusion on DWI-ADC (OR=6.694, 95 % CI: 1.172–38.235). Luminal B HER2-negative was related to the presence of mass (OR=7.245, 95 % CI: 1.760–29.889) and slow/medium initial enhancement pattern (OR=3.654, 95 % CI: 1.588–8.407). There were no associations between MRI features and luminal B HER2-positive. HER2-enriched tended to present as non-mass enhancement lesions (OR=20.498, 95 % CI: 3.145–133.584) with fast uptake in the initial postcontrast phase (OR=9.788, 95 % CI: 1.689–56.740), and distortion (OR=11.471, 95 % CI: 2.250–58.493). Triple-negative were associated with unifocal (OR=7.877, 95 % CI: 1.180–52.589), hyperintensityon T2-weighted images (OR=14.496, 95 % CI: 1.303–161.328), rim-enhanced lesions (OR=18.706, 95 % CI: 1.915–182.764), and surrounding tissue edema (OR=5.768, 95 % CI: 1.040–31.987).</p></div><div><h3>Conclusion</h3><p>Each molecular subtype of BC has distinct features on breast MRI. These characteristics can serve as an adjunct to immunohistochemistry in diagnosing molecular subtypes, particularly in cases, where traditional methods yield equivocal results.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100585"},"PeriodicalIF":1.8,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000406/pdfft?md5=653b1d0e0dd64eddc9d162db001bb6f2&pid=1-s2.0-S2352047724000406-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479760","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":"CT-based radiomics combined with clinical features for invasiveness prediction and pathological subtypes classification of subsolid pulmonary nodules","authors":"Miaozhi Liu ,&nbsp;Rui Duan ,&nbsp;Zhifeng Xu ,&nbsp;Zijie Fu ,&nbsp;Zhiheng Li ,&nbsp;Aizhen Pan ,&nbsp;Yan Lin","doi":"10.1016/j.ejro.2024.100584","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100584","url":null,"abstract":"<div><h3>Purpose</h3><p>To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.</p></div><div><h3>Materials and Methods</h3><p>This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023. Models based on CT radiomics and clinical features were constructed for classification of AAH/AIS and MIA, MIA and IAC, as well as lepidic-predominant adenocarcinoma (LPA) and acinar-predominant adenocarcinoma (APA). Receiver operating characteristic (ROC) curve was used to evaluate the model performance. Finally, the nomograms based on the optimal models were established.</p></div><div><h3>Results</h3><p>The nomogram based on the combined model (AAH/AIS versus MIA) consisting of lobulation, the GGN-vessel relationship, diameter, CT value, consolidation tumor ratio (CTR) and rad-score performed the best (AUC=0.841), while age, CT value, CTR and rad-score were the significant features for distinguishing MIA from IAC, the nomogram based on these features performed the best (AUC=0.878). There were no significant differences in clinical features between LPA and APA, while the radiomics model based on rad-score showed good performance for distinguishing LPA from APA (AUC=0.926).</p></div><div><h3>Conclusions</h3><p>The nomograms based on radiomics and clinical features could predict the invasiveness of SSNs accurately. Moreover, radiomics models showed good performance in distinguishing LPA from APA.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100584"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235204772400039X/pdfft?md5=e2c65049a8c3da3633ab27931e354524&pid=1-s2.0-S235204772400039X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479762","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":"Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation","authors":"Varatharajan Nainamalai ,&nbsp;Hemin Ali Qair ,&nbsp;Egidijus Pelanis ,&nbsp;Håvard Bjørke Jenssen ,&nbsp;Åsmund Avdem Fretland ,&nbsp;Bjørn Edwin ,&nbsp;Ole Jakob Elle ,&nbsp;Ilangko Balasingham","doi":"10.1016/j.ejro.2024.100582","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100582","url":null,"abstract":"<div><h3>Objective</h3><p>Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.</p></div><div><h3>Methods</h3><p>We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.</p></div><div><h3>Results</h3><p>Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.</p></div><div><h3>Conclusion</h3><p>This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100582"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000376/pdfft?md5=425200236243651a581a0c649773fdeb&pid=1-s2.0-S2352047724000376-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479759","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 pulmonary function in COPD patients using dynamic digital radiography: A novel approach utilizing lung signal intensity changes during forced breathing","authors":"Noriaki Wada ,&nbsp;Akinori Tsunomori ,&nbsp;Takeshi Kubo ,&nbsp;Takuya Hino ,&nbsp;Akinori Hata ,&nbsp;Yoshitake Yamada ,&nbsp;Masako Ueyama ,&nbsp;Mizuki Nishino ,&nbsp;Atsuko Kurosaki ,&nbsp;Kousei Ishigami ,&nbsp;Shoji Kudoh ,&nbsp;Hiroto Hatabu","doi":"10.1016/j.ejro.2024.100579","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100579","url":null,"abstract":"<div><h3>Objectives</h3><p>To investigate the association of lung signal intensity changes during forced breathing using dynamic digital radiography (DDR) with pulmonary function and disease severity in patients with chronic obstructive pulmonary disease (COPD).</p></div><div><h3>Methods</h3><p>This retrospective study included 46 healthy subjects and 33 COPD patients who underwent posteroanterior chest DDR examination. We collected raw signal intensity and gray-scale image data. The lung contour was extracted on the gray-scale images using our previously developed automated lung field tracking system and calculated the average of signal intensity values within the extracted lung contour on gray-scale images. Lung signal intensity changes were quantified as SImax/SImin, representing the maximum ratio of the average signal intensity in the inspiratory phase to that in the expiratory phase. We investigated the correlation between SImax/SImin and pulmonary function parameters, and differences in SImax/SImin by disease severity.</p></div><div><h3>Results</h3><p>SImax/SImin showed the highest correlation with VC (r_s = 0.54, P &lt; 0.0001), followed by FEV₁ (r_s = 0.44, P &lt; 0.0001), both of which are key indicators of COPD pathophysiology. In a multivariate linear regression analysis adjusted for confounding factors, SImax/SImin was significantly lower in the severe COPD group compared to the normal group (P = 0.0004) and mild COPD group (P=0.0022), suggesting its potential usefulness in assessing COPD severity.</p></div><div><h3>Conclusion</h3><p>This study suggests that the signal intensity changes of lung fields during forced breathing using DDR reflect the pathophysiology of COPD and can be a useful index in assessing pulmonary function in COPD patients, potentially improving COPD diagnosis and management.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100579"},"PeriodicalIF":1.8,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000340/pdfft?md5=eedbb14257c44e6f9294f86130763009&pid=1-s2.0-S2352047724000340-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479761","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":"Minimal dose CT for left ventricular ejection fraction and combination with chest-abdomen-pelvis CT","authors":"Martin Weber Kusk ,&nbsp;Søren Hess ,&nbsp;Oke Gerke ,&nbsp;Lone Deibjerg Kristensen ,&nbsp;Christina Stolzenburg Oxlund ,&nbsp;Tina Elisabeth Ormstrup ,&nbsp;Janus Mølgaard Christiansen ,&nbsp;Shane J. Foley","doi":"10.1016/j.ejro.2024.100583","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100583","url":null,"abstract":"<div><h3>Objectives</h3><p>This prospective study tested the diagnostic accuracy, and absolute agreement with MRI of a low-dose CT protocol for left ventricular ejection fraction (LVEF) measurement. Furthermore we assessed its potential for combining it with Chest-Abdomen-Pelvis CT (CAP-CT) for a one-stop examination.</p></div><div><h3>Materials &amp; methods</h3><p>Eighty-two patients underwent helical low-dose CT. Cardiac magnetic resonance imaging (MRI) was the reference standard. In fifty patients, CAP-CT was performed concurrently, using a modified injection protocol. In these, LVEF was measured with radioisotope cardiography (MUGA). Patients &gt;18 years, without contrast media or MRI contraindications, were included. Bias was measured with Bland-Altman analysis, classification accuracy with Receiver Operating Characteristics, and inter-reader agreement with Intra-Class Correlation Coefficient (ICC). Correlation was examined using Pearson's correlation coefficients. CAP image quality was compared to previous scans with visual grading characteristics.</p></div><div><h3>Results</h3><p>The mean CT dose-length-product (DLP) was 51.8 mGycm, for an estimated effective dose of 1.4 mSv, compared to 5.7 mSv for MUGA. CT LVEF bias was between 2 % and 10 %, overestimating end-diastolic volume. When corrected for bias, sensitivity and specificity of 100 and 98.5 % for classifying reduced LVEF (50 % MRI value) was achieved. ICC for MUGA was significantly lower than MRI and CT. Distinction of renal medulla and cortex was reduced in the CAP scan, but proportion of diagnostic scans was not significantly different from standard protocol.</p></div><div><h3>Conclusion</h3><p>When corrected for inter-modality bias, CT classifies patients with reduced LVEF with high accuracy at a quarter of MUGA dose and can be combined with CAP-CT without loss of diagnostic quality.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100583"},"PeriodicalIF":1.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000388/pdfft?md5=d3624d446dfd93a7467c41937a54a97b&pid=1-s2.0-S2352047724000388-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479705","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":"Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure","authors":"Alexa E. Golbus ,&nbsp;John L. Schuzer ,&nbsp;Chloe Steveson ,&nbsp;Shirley F. Rollison ,&nbsp;James Matthews ,&nbsp;Joseph Henry-Ellis ,&nbsp;Marco Razeto ,&nbsp;Marcus Y. Chen","doi":"10.1016/j.ejro.2024.100578","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100578","url":null,"abstract":"<div><h3>Purpose</h3><p>Traditional CT acquisition planning is based on scout projection images from planar anterior-posterior and lateral projections where the radiographer estimates organ locations. Alternatively, a new scout method utilizing ultra-low dose helical CT (3D Landmark Scan) offers cross-sectional imaging to identify anatomic structures in conjunction with artificial intelligence based Anatomic Landmark Detection (ALD) for automatic CT acquisition planning. The purpose of this study is to quantify changes in scan length and radiation dose of CT examinations planned using 3D Landmark Scan and ALD and performed on next generation wide volume CT versus examinations planned using traditional scout methods. We additionally aim to quantify changes in radiation dose reduction of scans planned with 3D Landmark Scan and performed on next generation wide volume CT.</p></div><div><h3>Methods</h3><p>Single-center retrospective analysis of consecutive patients with prior CT scan of the same organ who underwent clinical CT using 3D Landmark Scan and automatic scan planning. Acquisition length and dose-length-product (DLP) were collected. Data was analyzed by paired t-tests.</p></div><div><h3>Results</h3><p>104 total CT examinations (48.1 % chest, 15.4 % abdomen, 36.5 % chest/abdomen/pelvis) on 61 individual consecutive patients at a single center were retrospectively analyzed. 79.8 % of scans using 3D Landmark Scan had reduction in acquisition length compared to the respective prior acquisition. Median acquisition length using 3D Landmark Scan was 26.7 mm shorter than that using traditional scout methods (p &lt; 0.001) with a 23.3 % median total radiation dose reduction (245.6 (IQR 150.0–400.8) mGy cm vs 320.3 (IQR 184.1–547.9) mGy cm). CT dose index similarly was overall decreased for scans planned with 3D Landmark and ALD and performed on next generation CT versus traditional methods (4.85 (IQR 3.8–7) mGy vs. 6.70 (IQR 4.43–9.18) mGy, respectively, p &lt; 0.001).</p></div><div><h3>Conclusion</h3><p>Scout imaging using reduced dose 3D Landmark Scan images and Anatomic Landmark Detection reduces acquisition range in chest, abdomen, and chest/abdomen/pelvis CT scans. This technology, in combination with next generation wide volume CT reduces total radiation dose.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100578"},"PeriodicalIF":2.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000339/pdfft?md5=e5ed7f7f3246ee284cee1840d28083d1&pid=1-s2.0-S2352047724000339-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424359","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":"A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients","authors":"TaoHu Zhou ,&nbsp;Yu Guan ,&nbsp;XiaoQing Lin ,&nbsp;XiuXiu Zhou ,&nbsp;Liang Mao ,&nbsp;YanQing Ma ,&nbsp;Bing Fan ,&nbsp;Jie Li ,&nbsp;WenTing Tu ,&nbsp;ShiYuan Liu ,&nbsp;Li Fan","doi":"10.1016/j.ejro.2024.100580","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100580","url":null,"abstract":"<div><h3>Purpose</h3><p>It is vital to develop noninvasive approaches with high accuracy to discriminate the preserved ratio impaired spirometry (PRISm) group from the chronic obstructive pulmonary disease (COPD) groups. Radiomics has emerged as an image analysis technique. This study aims to develop and confirm the new radiomics-based noninvasive approach to discriminate these two groups.</p></div><div><h3>Methods</h3><p>Totally 1066 subjects from 4 centers were included in this retrospective research, and classified into training, internal validation or external validation sets. The chest computed tomography (CT) images were segmented by the fully automated deep learning segmentation algorithm (Unet231) for radiomics feature extraction. We established the radiomics signature (Rad-score) using the least absolute shrinkage and selection operator algorithm, then conducted ten-fold cross-validation using the training set. Last, we constructed a radiomics signature by incorporating independent risk factors using the multivariate logistic regression model. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).</p></div><div><h3>Results</h3><p>The Rad-score, including 15 radiomic features in whole-lung region, which was suitable for diffuse lung diseases, was demonstrated to be effective for discriminating between PRISm and COPD. Its diagnostic accuracy was improved through integrating Rad-score with a clinical model, and the area under the ROC (AUC) were 0.82(95 %CI 0.79–0.86), 0.77(95 %CI 0.72–0.83) and 0.841(95 %CI 0.78–0.91) for training, internal validation and external validation sets, respectively. As revealed by analysis, radiomics nomogram showed good fit and superior clinical utility.</p></div><div><h3>Conclusions</h3><p>The present work constructed the new radiomics-based nomogram and verified its reliability for discriminating between PRISm and COPD.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100580"},"PeriodicalIF":2.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000352/pdfft?md5=4350c77666fa3e691e8c4b13e144787a&pid=1-s2.0-S2352047724000352-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141323985","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":"Three-dimensional sectional measurement approach for serial volume changes in shoulder muscles after arthroscopic rotator cuff repair","authors":"Keita Nagawa ,&nbsp;Yuki Hara ,&nbsp;Hirokazu Shimizu ,&nbsp;Koichiro Matsuura ,&nbsp;Kaiji Inoue ,&nbsp;Eito Kozawa ,&nbsp;Katsunobu Sakaguchi ,&nbsp;Mamoru Niitsu","doi":"10.1016/j.ejro.2024.100577","DOIUrl":"https://doi.org/10.1016/j.ejro.2024.100577","url":null,"abstract":"<div><h3>Purpose</h3><p>This study assessed the serial volume changes in multiple shoulder muscles simultaneously following arthroscopic rotator cuff repair (ARCR) by a three-dimensional (3D) modeling-based sectional measurement. These volume changes were correlated with background preoperative factors.</p></div><div><h3>Methods</h3><p>Four consecutive magnetic resonance imaging scans (preoperatively and postoperatively at 3, 6, and 12 months) of 33 shoulders from 31 patients who underwent arthroscopic rotator cuff repair were examined. We focused on the sectional volume differences of the supraspinatus, infraspinatus, teres minor, and subscapularis between preoperatively and 3 months postoperatively (Dif.pre.3mo) and between 3 and 12 months postoperatively (Dif.3.12mo). The correlation between volume differences and clinical/demographic parameters was determined by a multivariate analysis.</p></div><div><h3>Results</h3><p>No statistically significant differences were observed for most serial changes in the shoulder muscle volumes. The tear-site muscles (supraspinatus and infraspinatus) showed similar tendencies for volume changes, whereas the non-tear-site muscles (teres minor and subscapularis) differed. A negative correlation was observed between Dif.pre.3mo and Dif.3.12mo for the supraspinatus, infraspinatus, and teres minor. These perioperative volume differences might correlate with tear size and symptom duration in the supraspinatus, as well as with a history of steroid injections and work and sports activity levels in the infraspinatus and teres minor.</p></div><div><h3>Conclusion</h3><p>The serial volume changes in multiple shoulder muscles after ARCR measured using our 3D sectional approach exhibited different tendencies and clinical implications depending on the primary and non-primary site of tears. Our method may serve as a potential indicator to facilitate muscle recovery and prevent the progression of postoperative muscle atrophy.</p></div>","PeriodicalId":38076,"journal":{"name":"European Journal of Radiology Open","volume":"13 ","pages":"Article 100577"},"PeriodicalIF":2.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352047724000327/pdfft?md5=9255e03712724df64d87828167e8caa1&pid=1-s2.0-S2352047724000327-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141314334","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}