Research in diagnostic and interventional imaging最新文献

{"title":"Grading of soft tissues sarcomas using radiomics models: Choice of imaging methods and comparison with conventional visual analysis","authors":"Bailiang Chen ,&nbsp;Olivier Steinberger ,&nbsp;Roman Fenioux ,&nbsp;Quentin Duverger ,&nbsp;Tryphon Lambrou ,&nbsp;Gauthier Dodin ,&nbsp;Alain Blum ,&nbsp;Pedro Augusto Gondim Teixeira","doi":"10.1016/j.redii.2022.100009","DOIUrl":"10.1016/j.redii.2022.100009","url":null,"abstract":"<div><h3>Purpose</h3><p>To determine which combination of imaging modalities/contrast, radiomics models, and how many features provides the best diagnostic performance for the differentiation between low- and high-grade soft tissue sarcomas (STS) using a radiomics approach.</p></div><div><h3>Methods</h3><p>MRI and CT from 39 patients with a histologically confirmed STS were prospectively analyzed. Images were evaluated both quantitatively by radiomics models and qualitatively by visual evaluation (used as reference) for grading (low-grade vs high-grade). In radiomics analysis, 120 radiomic features were extracted and contributed into three models: least absolute shrinkage and selection operator with logistic regression(LASSO-LR), recursive feature elimination and cross-validation (RFECV-SVC) and analysis of variance with SVC (ANOVA-SVC). Those were applied to different combinations of imaging modalities acquisition, with and without contrast medium administration, as well as selected number of features.</p></div><div><h3>Results</h3><p>Fat-saturated T2w (FS-T2w) MR images using RFECV-SVC radiomic models involving five features yielded the best results with mean sensitivity, specificity, and accuracy of 92% ± 10%, 78% ± 30%, and 89% ± 12%, respectively. The performance of radiomics was better than that of conventional analysis (67% accuracy) for STS grading. Combination of multiple contrast or imaging modalities did not increase the diagnostic performance.</p></div><div><h3>Conclusion</h3><p>FS-T2w MR images alone with a five-feature radiomics analysis usingh REFCV-SVC model may be able to provide sufficient diagnositic performance compared to conventional visual evaluation with multiple MRI contrast and CT imaging.</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"2 ","pages":"Article 100009"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772652522000096/pdfft?md5=7b415543fdbd132cdb3a7dc72a83f160&pid=1-s2.0-S2772652522000096-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47915158","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":"Investigating of the role of CT scan for cancer patients during the first wave of COVID-19 pandemic","authors":"Sylvain Bourdoncle ,&nbsp;Thomas Eche ,&nbsp;Jeremy McGale ,&nbsp;Kevin Yiu ,&nbsp;Ephraïm Partouche ,&nbsp;Randy Yeh ,&nbsp;Samy Ammari ,&nbsp;Hervé Rousseau ,&nbsp;Laurent Dercle ,&nbsp;Fatima-Zohra Mokrane","doi":"10.1016/j.redii.2022.100004","DOIUrl":"10.1016/j.redii.2022.100004","url":null,"abstract":"<div><h3>Introduction</h3><p>Amidst this current COVID-19 pandemic, we undertook this systematic review to determine the role of medical imaging, with a special emphasis on computed tomography (CT), on guiding the care and management of oncologic patients.</p></div><div><h3>Material and Methods</h3><p>Study selection focused on articles from 01/02/2020 to 04/23/2020. After removal of irrelevant articles, all systematic or non-systematic reviews, comments, correspondence, editorials, guidelines and meta-analysis and case reports with less than 5 patients were also excluded. Full-text articles of eligible publications were reviewed to select all imaging-based publications, and the existence or not of an oncologic population was reported for each publication. Two independent reviewers collected the following information: ( 1) General publication data; (2) Study design characteristics; (3) Demographic, clinical and pathological variables with percentage of cancer patients if available; (4) Imaging performances. The sensitivity and specificity of chest CT (C-CT) were pooled separately using a random-effects model. The positive predictive value (PPV) and negative predictive value (NPV) of C-CT as a test was estimated for a wide range of disease prevalence rates.</p></div><div><h3>Results</h3><p>A total of 106 publications were fully reviewed. Among them, 96 were identified to have extractable data for a two-by-two contingency table for CT performance. At the end, 53 studies (including 6 that used two different populations) were included in diagnosis accuracy analysis (<em>N</em> = 59). We identified 53 studies totaling 11,352 patients for whom the sensitivity (95CI) was 0.886 (0.880; 0.894), while specificity remained low: in 93% of cases (55/59), specificity was ≤ 0.5. Among all the 106 reviewed studies, only 7 studies included oncologic patients and were included in the final analysis for C-CT performances. The percentage of patients with cancer in these studies was 0.3% (34/11352 patients), lower than the global prevalence of cancer. Among all these studies, only 1 (0.9%, 1/106) reported performance specifically in a cohort of cancer patients, but it however only reported true positives.</p></div><div><h3>Discussion</h3><p>There is a concerning lack of COVID-19 studies involving oncologic patients, showing there is a real need for further investigation and evaluation of the performance of the different medical imaging modalities in this specific patient population.</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"1 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8970534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963336","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":"Quantitative and qualitative evaluation of liver metastases with intraprocedural cone beam CT prior to transarterial radioembolization as a predictor of treatment response","authors":"Florian Messmer MD ,&nbsp;Juliana Zgraggen ,&nbsp;Adrian Kobe MD ,&nbsp;Lyubov Chaykovska MD ,&nbsp;Gilbert Puippe MD ,&nbsp;Caecilia S. Reiner MD ,&nbsp;Thomas Pfammatter MD","doi":"10.1016/j.redii.2022.100005","DOIUrl":"10.1016/j.redii.2022.100005","url":null,"abstract":"<div><h3>Purpose</h3><p>To investigate, by quantitative and qualitative enhancement measurements, the correlation between tumor enhancement on cone beam computed tomography (CBCT) images and treatment response at 6 months in patients undergoing transarterial radioembolization (TARE) for liver metastases.</p></div><div><h3>Materials and Methods</h3><p>36 patients (56% male; median age 62.5 years) with 104 metastases were retrospectively included. Quantitative and qualitative enhancement of liver metastases were evaluated on CBCT images before TARE. Quantitative analysis consisted of lesion enhancement measurements (ROI HU lesion – ROI HU relative to inferior vena cava). Qualitative analysis consisted of subjective enhancement pattern analysis (diffuse, sparse, rim-like or non-enhancing). Morphologic tumor response was evaluated according to RECIST 1.1 criteria on follow-up CT or MR imaging.</p></div><div><h3>Results</h3><p>At a mean follow up of 6.5 ± 3.7 months, progressive disease (PD) was found in 4 patients, partial response (PR) in 11 and stable disease (SD) in 21. Relative lesion enhancement was significantly different between these groups (-37.5±154.2 HU vs. 103.8±93.4 vs. 181±144 HU in PD vs. SD vs. PR group, respectively; p&lt;0.01). ROC analysis of relative lesion enhancement to predict progressive disease showed an area under the curve of 0.86 (p&lt;0.01). For qualitative lesion enhancement analysis, no difference between groups was found.</p></div><div><h3>Conclusion</h3><p>Quantitative enhancement measurements derived from intraprocedural contrast enhanced CBCT may identify responders to TARE in patients with liver metastases.</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"1 ","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772652522000059/pdfft?md5=22d0d2b8eddf9bfdc73351bc0e2cf2ee&pid=1-s2.0-S2772652522000059-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47726773","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":"Value and prognostic impact of a deep learning segmentation model of COVID-19 lung lesions on low-dose chest CT","authors":"Axel Bartoli MD ,&nbsp;Joris Fournel ,&nbsp;Arnaud Maurin MD ,&nbsp;Baptiste Marchi MD ,&nbsp;Paul Habert MD ,&nbsp;Maxime Castelli MD ,&nbsp;Jean-Yves Gaubert MD ,&nbsp;Sebastien Cortaredona MD ,&nbsp;Jean-Christophe Lagier MD, PhD ,&nbsp;Matthieu Million MD, PhD ,&nbsp;Didier Raoult MD, PhD ,&nbsp;Badih Ghattas MCU ,&nbsp;Alexis Jacquier MD, PhD","doi":"10.1016/j.redii.2022.100003","DOIUrl":"10.1016/j.redii.2022.100003","url":null,"abstract":"<div><h3>Objectives</h3><p>1) To develop a deep learning (DL) pipeline allowing quantification of COVID-19 pulmonary lesions on low-dose computed tomography (LDCT). 2) To assess the prognostic value of DL-driven lesion quantification.</p></div><div><h3>Methods</h3><p>This monocentric retrospective study included training and test datasets taken from 144 and 30 patients, respectively. The reference was the manual segmentation of 3 labels: normal lung, ground-glass opacity(GGO) and consolidation(Cons). Model performance was evaluated with technical metrics, disease volume and extent. Intra- and interobserver agreement were recorded. The prognostic value of DL-driven disease extent was assessed in 1621 distinct patients using C-statistics. The end point was a combined outcome defined as death, hospitalization&gt;10 days, intensive care unit hospitalization or oxygen therapy.</p></div><div><h3>Results</h3><p>The Dice coefficients for lesion (GGO+Cons) segmentations were 0.75±0.08, exceeding the values for human interobserver (0.70±0.08; 0.70±0.10) and intraobserver measures (0.72±0.09). DL-driven lesion quantification had a stronger correlation with the reference than inter- or intraobserver measures. After stepwise selection and adjustment for clinical characteristics, quantification significantly increased the prognostic accuracy of the model (0.82 vs. 0.90; <em>p</em>&lt;0.0001).</p></div><div><h3>Conclusions</h3><p>A DL-driven model can provide reproducible and accurate segmentation of COVID-19 lesions on LDCT. Automatic lesion quantification has independent prognostic value for the identification of high-risk patients.</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"1 ","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9909529","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":"Abdominal imaging in ICU patients with viral pneumonia: Are findings in COVID-19 patients really different from those observed with non-SARS-CoV-2 viral pneumonia?","authors":"Edouard Reizine ,&nbsp;Sebastien Mule ,&nbsp;Nicolas De Prost ,&nbsp;Nicolas Mongardon ,&nbsp;Jean-François Deux ,&nbsp;Hicham Kobeiter ,&nbsp;Alain Luciani","doi":"10.1016/j.redii.2022.01.001","DOIUrl":"10.1016/j.redii.2022.01.001","url":null,"abstract":"<div><h3>Purpose</h3><p>To evaluate and compare the prevalence and type of abdominal involvements identified on CT scans in COVID-19 critically ill patients to those observed in critically ill patients with non-SARS-CoV-2 viral pneumonia.</p></div><div><h3>Methods</h3><p>Monocentric IRB approved retrospective study comparing all abdominal CT scans performed for patients admitted in the ICU with COVID-19 and those performed in a historical cohort of ICU patients with non-SARS-CoV-2 viral pneumonia. For each patient, gallbladder abnormality, acute pancreatitis signs, acute adrenal infarction, renal infarcts, bowel wall thickening and CT scan signs of bowel ischemia were assessed. Results were then compared between critically ill COVID-19 and non-COVID-19 patients (Chi-2 or Fisher exact tests for categorical data and Student t-test or Mann-Whitney U test for continuous data as appropriate).</p></div><div><h3>Results</h3><p>Ninety-nine COVID-19 patients and 45 non-COVID-19 patients were included.</p><p>No difference was found between the rate of abnormal findings comparing COVID-19 patients and patients with other viral pneumonia (63/99 [64%] vs 27/45 [61%], p=0.94). Acute pancreatitis signs were more commonly seen in COVID-19 patients but without statistically difference between groups (14/99 [14%] vs 3/45 [6.7%], p=0.31). Bowel wall thickening was slightly more commonly seen in patients with other viral pneumonia (18/99 [18%] vs 11/45 [24%], p=0.52), however ischemic features were observed in higher rate in the COVID-19 group, although without reaching statistically significant differences (7/99 [7.1%] vs 2/45 [4.4%], p=0.75).</p></div><div><h3>Conclusion</h3><p>The rate and severity of abdominal involvement demonstrated by CT in ICU patients hospitalized for COVID-19 although high were not different to that observed in patients with other severe viral pneumoniae</p></div>","PeriodicalId":74676,"journal":{"name":"Research in diagnostic and interventional imaging","volume":"1 ","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8867080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10294629","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}