Followed Journals
Journal you Follow: 0
 
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Journals are published.
Already have an account? Sign In to see the journals you follow.
Similar Journals
Journal Cover
European Radiology
Journal Prestige (SJR): 1.943
Citation Impact (citeScore): 4
Number of Followers: 19  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1432-1084 - ISSN (Online) 0938-7994
Published by Springer-Verlag Homepage  [2658 journals]
  • Infratentorial lesions in multiple sclerosis patients: intra- and
           inter-rater variability in comparison to a fully automated segmentation
           using 3D convolutional neural networks

    • Free pre-print version: Loading...

      Abstract: Objective Automated quantification of infratentorial multiple sclerosis lesions on magnetic resonance imaging is clinically relevant but challenging. To overcome some of these problems, we propose a fully automated lesion segmentation algorithm using 3D convolutional neural networks (CNNs). Methods The CNN was trained on a FLAIR image alone or on FLAIR and T1-weighted images from 1809 patients acquired on 156 different scanners. An additional training using an extra class for infratentorial lesions was implemented. Three experienced raters manually annotated three datasets from 123 MS patients from different scanners. Results The inter-rater sensitivity (SEN) was 80% for supratentorial lesions but only 62% for infratentorial lesions. There was no statistically significant difference between the inter-rater SEN and the SEN of the CNN with respect to the raters. For supratentorial lesions, the CNN featured an intra-rater intra-scanner SEN of 0.97 (R1 = 0.90, R2 = 0.84) and for infratentorial lesion a SEN of 0.93 (R1 = 0.61, R2 = 0.73). Conclusion The performance of the CNN improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input and matches the detection performance of experienced raters. Furthermore, for infratentorial lesions the CNN was more robust against repeated scans than experienced raters. Key Points • A 3D convolutional neural network was trained on MRI data from 1809 patients (156 different scanners) for the quantification of supratentorial and infratentorial multiple sclerosis lesions. • Inter-rater variability was higher for infratentorial lesions than for supratentorial lesions. The performance of the 3D convolutional neural network (CNN) improved significantly for infratentorial lesions when specifically trained on infratentorial lesions using a T1 image as an additional input. • The detection performance of the CNN matches the detection performance of experienced raters.
      PubDate: 2021-10-13
       
  • A deep learning–based automatic analysis of cardiovascular borders on
           chest radiographs of valvular heart disease: development/external
           validation

    • Free pre-print version: Loading...

      Abstract: Objectives Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning–based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). Methods We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. Results CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. Conclusions The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. Key Points • A deep learning–based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.
      PubDate: 2021-10-13
       
  • Comparison of 7 T and 3 T vessel wall MRI for the evaluation of
           intracranial aneurysm wall

    • Free pre-print version: Loading...

      Abstract: Objectives To compare the visibility of intracranial aneurysm wall and thickness quantification between 7 and 3 T vessel wall imaging and evaluate the association between aneurysm size and wall thickness. Methods Twenty-nine patients with 29 unruptured intracranial aneurysms were prospectively recruited for 3D T1-weighted vessel wall MRI at both 3 T and 7 T with 0.53 mm (3 T) and 0.4 mm (7 T) isotropic resolution, respectively. Two neuroradiologists independently evaluated wall visibility (0–5 Likert scale), quantified the apparent wall thickness (AWT) using a semi-automated full-width-half-maximum method, calculated wall sharpness, and measured the wall-to-lumen contrast ratio (CRwall/lumen). Results Twenty-four patients with 24 aneurysms were included in this study. 7 T achieved significantly better aneurysm wall visibility than 3 T (3.6 ± 1.1 vs 2.7 ± 0.8, p = 0.003). AWT measured on 3 T and 7 T had a good correlation (averaged r = 0.63 ± 0.19). However, AWT on 3 T was 15% thicker than that on 7 T (0.52 ± 0.07 mm vs 0.45 ± 0.05 mm, p < 0.001). Wall sharpness on 7 T was 57% higher than that on 3 T (1.95 ± 0.32 mm−1 vs 1.24 ± 0.15 mm−1, p < 0.001). CRwall/lumen on 3 T and 7 T was comparable (p = 0.424). AWT on 7 T was positively correlated with aneurysm size (saccular: r = 0.58, q = 0.046; fusiform: r = 0.67, q = 0.049). Conclusions 7 T provides better visualization of intracranial aneurysm wall with higher sharpness than 3 T. 3 T overestimates the wall thickness relative to 7 T. Aneurysm wall thickness is positively correlated with aneurysm size. 7 T MRI is a promising tool to evaluate aneurysm wall in vivo. Key Points • 7 T provides better visualization of intracranial aneurysm wall with higher sharpness than 3 T. • 3 T overestimates the wall thickness comparing with 7 T. • Aneurysm wall thickness is positively correlated with aneurysm size.
      PubDate: 2021-10-13
       
  • Response prediction of neoadjuvant chemoradiation therapy in locally
           advanced rectal cancer using CT-based fractal dimension analysis

    • Free pre-print version: Loading...

      Abstract: Objectives There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC. Methods In this retrospective study, 215 patients (average age: 57 years (18–87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set. Results Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively. Conclusion FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions. Key Points • Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.
      PubDate: 2021-10-13
       
  • Diagnostic reference levels and median doses for common clinical
           indications of CT: findings from an international registry

    • Free pre-print version: Loading...

      Abstract: Ob jectives The European Society of Radiology identified 10 common indications for computed tomography (CT) as part of the European Study on Clinical Diagnostic Reference Levels (DRLs, EUCLID), to help standardize radiation doses. The objective of this study is to generate DRLs and median doses for these indications using data from the UCSF CT International Dose Registry. Methods Standardized data on 3.7 million CTs in adults were collected between 2016 and 2019 from 161 institutions across seven countries (United States of America (US), Switzerland, Netherlands, Germany, UK, Israel, Japan). DRLs (75th percentile) and median doses for volumetric CT-dose index (CTDIvol) and dose-length product (DLP) were assessed for each EUCLID category (chronic sinusitis, stroke, cervical spine trauma, coronary calcium scoring, lung cancer, pulmonary embolism, coronary CT angiography, hepatocellular carcinoma (HCC), colic/abdominal pain, appendicitis), and US radiation doses were compared with European. Results The number of CT scans within EUCLID categories ranged from 8,933 (HCC) to over 1.2 million (stroke). There was greater variation in dose between categories than within categories (p < .001), and doses were significantly different between categories within anatomic areas. DRLs and median doses were assessed for all categories. DRLs were higher in the US for 9 of the 10 indications (except chronic sinusitis) than in Europe but with a significantly higher sample size in the US. Conclusions DRLs for CTDIvol and DLP for EUCLID clinical indications from diverse organizations were established and can contribute to dose optimization. These values were usually significantly higher in the US than in Europe. Key Points • Registry data were used to create benchmarks for 10 common indications for CT identified by the European Society of Radiology. • Observed US radiation doses were higher than European for 9 of 10 indications (except chronic sinusitis). • The presented diagnostic reference levels and median doses highlight potentially unnecessary variation in radiation dose.
      PubDate: 2021-10-13
       
  • In-patient care trends in peripheral artery disease in the German
           healthcare system over the past decade

    • Free pre-print version: Loading...

      Abstract: Objectives To analyze trends of in-hospital treatment of patients admitted due to peripheral artery disease (PAD) from 2009 to 2018 with special focus on comorbidities, revascularization procedures, resulting costs, and outcome. Methods Using data from the research data center of the German Federal Statistical Office, we included all hospitalizations due to PAD Fontaine stage IIb or higher from 2009 to 2018. To analyze comorbidities, Elixhauser diagnostic groups and linear van Walraven score (vWS) were assessed. Results A total of 1.8 million hospitalizations resulting in €10.3 billion in reimbursement costs were included. From 2009 to 2018, the absolute number of hospitalizations due to PAD increased by 13.3% (163,547 to 185,352). The average cost per hospitalization increased by 20.8% from €5,261 to €6,356. The overall in-hospital mortality decreased from 3.1 to 2.6%. Median vWS of all PAD cases increased by 3 points (2 to 5). The number of percutaneous transluminal angioplasties (PTA) increased by 43.9% while some surgical procedures such as bypasses and embolectomies decreased by 30.8% and 6.8%, respectively. Many revascularization procedures showed a disproportionate increase of those performed in vessels below the knee for example in PTA (+ 68.5%) or in endarterectomies (+ 38.8%). Conclusions This decade-long nationwide analysis shows a rising number of hospitalizations due to PAD with more comorbid patients resulting in increasing reimbursement costs. Interventions are shifting from surgical to endovascular approaches with a notable trend towards interventions in smaller vessels below the knee. Key Points • The number of hospitalizations due to peripheral artery disease is rising and it is associated with increasing reimbursement costs. • Admitted patients are older and show an increasing number of comorbidities while overall in-hospital mortality is decreasing. • Revascularization procedures are shifting from surgical to endovascular approaches and show a trend towards intervention in smaller vessels below the knee. • Major amputations are decreasing while the number of minor amputations is increasing.
      PubDate: 2021-10-13
       
  • Magnetic resonance imaging before breast cancer surgery: results of an
           observational multicenter international prospective analysis (MIPA)

    • Free pre-print version: Loading...

      Abstract: Objectives Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue. Methods This observational study enrolled women aged 18–80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases. Results Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p < 0.001 for all comparisons). Mastectomy was planned based on conventional imaging in 22.4% (MRI group) versus 14.4% (noMRI group) (p < 0.001). The additional planned mastectomy rate in the MRI group was 11.3%. The overall performed first- plus second-line mastectomy rate was 36.3% (MRI group) versus 18.0% (noMRI group) (p < 0.001). In women receiving conserving surgery, MRI group had a significantly lower reoperation rate (8.5% versus 11.7%, p < 0.001). Conclusions Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup. Key Points • In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making.
      PubDate: 2021-10-13
       
  • Impact of the COVID-19 lockdown in France on the diagnosis and staging of
           breast cancers in a tertiary cancer centre

    • Free pre-print version: Loading...

      Abstract: Objectives Due to COVID-19, a lockdown took place between March 17 and May 1, 2020, in France. This study evaluates the impact of the lockdown on the diagnosis and staging of breast cancers in a tertiary cancer centre. Methods Our database was searched for all consecutive invasive breast cancers diagnosed in our institution during the lockdown (36 working days), during equivalent periods of 36 working days before and after lockdown and a reference period in 2019. The number and staging of breast cancers diagnosed during and after lockdown were compared to the pre-lockdown and reference periods. Tumour maximum diameters were compared using the Mann–Whitney test. Proportions of tumour size categories (T), ipsilateral axillary lymph node invasion (N) and presence of distant metastasis (M) were compared using Fisher’s exact test. Results Compared to the reference period (n = 40 in average), the number of breast cancers diagnosed during lockdown (n = 32) decreased by 20% but increased by 48% after the lockdown (n = 59). After the lockdown, comparatively to the reference period, breast cancers were more often symptomatic (86% vs 57%; p = 0.001) and demonstrated bigger tumour sizes (p = 0.0008), the rates of small tumours (T1) were reduced by 38%, locally advanced cancers (T3, T4) increased by 80% and lymph node invasion increased by 64%. Conclusion The COVID-19 lockdown was associated with a 20% decrease in the number of diagnosed breast cancers. Because of delayed diagnosis, breast cancers detected after the lockdown had poorer prognosis with bigger tumour sizes and higher rates of node invasion. Key Points • The number of breast cancer diagnosed in a large tertiary cancer centre in France decreased by 20% during the first COVID-19 lockdown. • Because of delayed diagnosis, breast cancers demonstrated bigger tumour size and more frequent axillary lymph node invasion after the lockdown. • In case of a new lockdown, breast screening programme and follow-up examinations should not be suspended and patients with clinical symptoms should be encouraged to seek attention promptly.
      PubDate: 2021-10-13
       
  • Predicting the molecular subtype of breast cancer and identifying
           interpretable imaging features using machine learning algorithms

    • Free pre-print version: Loading...

      Abstract: Objectives To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. Methods We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. Results The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. Conclusions This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. Key Points • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent.
      PubDate: 2021-10-13
       
  • Prediction of recurrence after surgery based on preoperative MRI features
           in patients with pancreatic neuroendocrine tumors

    • Free pre-print version: Loading...

      Abstract: Objectives To investigate useful MRI features in pancreatic neuroendocrine tumor (PNET) patients for predicting recurrence and its timing after surgery. Methods A total of 99 patients with PNET who underwent MRI and surgery from 2000 to 2018 were enrolled. Two radiologists independently assessed MRI findings, including size, location, margin, T1 and T2 signal intensity, enhancement patterns, common bile duct (CBD) or main pancreatic duct (MPD) dilatation, vascular invasion, lymph node enlargement, DWI, and ADC value. Imaging findings associated with recurrence and disease-free survival (DFS) were assessed using logistic regression analysis and Cox proportional hazard regression analysis. Results The median follow-up period was 40.4 months, and recurrence after surgery occurred in 12.1% (12/99). Among them, 6 patients experienced recurrence within 1 year, and 9 patients experienced recurrence within 2 years after surgery. In multivariate analysis, major venous invasion (OR 10.76 [1.14–101.85], p = 0.04) was associated with recurrence within 1 year, and portal phase iso- to hypoenhancement (OR 51.89 [1.73–1557.89], p = 0.02), CBD or MPD dilatation (OR 10.49 [1.35–81.64], p = 0.03) and larger size (OR 1.05 [1.00–1.10], p = 0.046) were associated with recurrence within 2 years. The mean DFS was 116.4 ± 18.5 months, and the 5-year DFS rate was 85.7%. In multivariate analysis, portal phase iso- to hypoenhancement (HR 21.36 [2.01–197.77], p = 0.01), ductal dilatation (HR 5.22 [1.46–18.68], p = 0.01), major arterial invasion (HR 42.90 [3.66–502.48], p = 0.003), and larger size (HR 1.04 [1.01–1.06], p = 0.01) showed a significant effect on poor DFS. Conclusion MRI features, including size, enhancement pattern, vascular invasion, and ductal dilatation, are useful in predicting recurrence and poor DFS after surgery in PNET. Key Points • MRI features are useful for predicting prognosis in patients with PNET after surgery. • PV or SMV invasion (OR 10.49 [1.35–81.64], p = 0.04) was significantly associated with 1-year recurrence. • Portal phase iso- to hypoenhancement (HR 21.36), CBD or MPD dilatation (HR 5.22), arterial invasion (HR 42.90), and larger size (HR 1.04) had significant effects on poor DFS (p < 0.05).
      PubDate: 2021-10-13
       
  • Added-value of dynamic contrast-enhanced MRI on prediction of tumor
           recurrence in locally advanced cervical cancer treated with
           chemoradiotherapy

    • Free pre-print version: Loading...

      Abstract: Objectives To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT). Methods Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system. Results Lymph node metastasis (LNM) and the mean value of ve (ve_mean) were independently associated with tumor recurrence (all p < 0.05). The prediction model based on T stage, LNM, and ve_mean demonstrated a moderate predictive capability in identifying LACC patients with a high risk of tumor recurrence; the model was more accurate than the FIGO staging system alone (c-index: 0.735 vs. 0.661) and the combination of ve_mean and the FIGO staging system (c-index: 0.735 vs. 0.688). Moreover, patients were grouped into low-, medial-, and high-risk levels based on the advanced T stage, positive LNM, and ve_mean < 0.361, with which the 2-year DFS was significantly stratified (p < 0.001). Conclusions The ve_mean from DCE-MRI could be used as a useful biomarker to predict DFS in LACC patients treated with CCRT as an assistant of LNM and T stage. Key Points Lower ve_mean is an independent predictor of poor prognosis for disease-free survival in locally advanced cervical cancer patients treated with concurrent chemoradiotherapy (hazard ratio [HR]: 0.016, p<0.023). A combined prediction model based on advanced T stage, LNM, and ve_mean performed better than the FIGO staging system alone.
      PubDate: 2021-10-13
       
  • Comparison of HU histogram analysis and BMD for proximal femoral fragility
           fracture assessment: a retrospective single-center case–control study

    • Free pre-print version: Loading...

      Abstract: Objectives To evaluate the feasibility of HU histogram analysis (HUHA) to assess proximal femoral fragility fractures with respect to BMD. Methods This retrospective study included 137 patients with femoral fragility fractures who underwent hip CT and 137 control patients without fractures who underwent abdominal CT between January 2018 and February 2019. HUHA was calculated with the 3D volume of interest from the femoral head to the lesser trochanter. HUHAfat (percentage of negative HU values) and HUHAbone (percentage of HU values ≥ 125 HU) were assumed to be fat and bone components, respectively. Statistical significance was assessed using the area under the receiver operating characteristic curve (AUC), Spearman correlation (ρ), and odds ratio. Results HUHAfat was strongly positively correlated (ρ = 0.56) and BMD was moderately negatively correlated with fragility fractures (ρ =  − 0.37). AUC of HUHAfat (0.82, 95% CI [0.77, 0.87]) significantly differed from that of BMD (0.69, 95% CI [0.63, 0.75]) (p < .001). The cutoff value was 15.8% for HUHAfat (sensitivity: 90.4%; specificity: 67.7%) and 0.709 g/cm2 for BMD (sensitivity: 87.5%; specificity: 51.5%), with higher HUHAfat and lower BMD values indicating fragility fractures. The odds ratio of HUHAfat was 19.5 (95% CI [9.9, 38.2], p < .001), which was higher than that of BMD, 7.4 (95% CI [4.0, 13.6], p < .001). Conclusion HUHAfat revealed better performance than BMD and demonstrated feasibility in assessing proximal femoral fragility fractures. Key Points • HUHA fat showed a strong positive correlation (Spearman ρ = 0.56, p < .001), and BMD showed a moderate negative correlation (Spearman ρ =  − 0.37, p < .001) with proximal femoral fragility fractures. • HUHA fat (AUC = 0.82) performed significantly better than BMD in assessing proximal femoral fragility fractures (AUC = 0.69) (p < .001). • The odds ratio of HUHA fat for proximal femoral fragility fractures was higher than that of BMD (19.5 and 7.4, respectively; p < .001).
      PubDate: 2021-10-13
       
  • Reduced magnetic resonance angiography signal intensity in the middle
           cerebral artery ipsilateral to severe carotid stenosis may be a practical
           index of high oxygen extraction fraction

    • Free pre-print version: Loading...

      Abstract: Objectives Angiographic “slow flow” in the middle cerebral artery (MCA), caused by carotid stenosis, may be associated with high oxygen extraction fraction (OEF). If the MCA slow flow is associated with a reduced relative signal intensity (rSI) of the MCA on MR angiography, the reduced rSI may be associated with a high OEF. We investigated whether the MCA slow flow ipsilateral to carotid stenosis was associated with a high OEF and aimed to create a practical index to estimate the high OEF. Methods We included patients who underwent digital subtraction angiography (DSA) and MRA between 2015 and 2019 to evaluate carotid stenosis. MCA slow flow by image count using DSA, MCA rSI, minimal luminal diameter (MLD) of the carotid artery, carotid artery stenosis rate (CASr), and whole-brain OEF (wb-OEF) was evaluated. When MCA slow flow was associated with a high wb-OEF, the determinants of MCA slow flow were identified, and their association with high wb-OEF was evaluated. Results One hundred and twenty-seven patients met our inclusion criteria. Angiographic MCA slow flow was associated with high wb-OEF. We identified MCA rSI and MLD as determinants of angiographic MCA slow flow. The upper limits of MCA rSI and MLD for angiographic MCA slow flow were 0.89 and 1.06 mm, respectively. The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination. Conclusions The combination of reduced MCA rSI and ipsilateral narrow MLD is a straightforward index of high wb-OEF. Key Points • The whole-brain OEF in patients with angiographic slow flow in the MCA ipsilateral to high-grade carotid stenosis was higher than in patients without it. • Independent determinants of MCA slow flow were MCA relative signal intensity (rSI) on MRA or minimal luminal diameter (MLD) of the carotid stenosis. • The wb-OEF was higher in patients with an MCA rSI ≤ 0.89 and ipsilateral MLD ≤ 1.06 mm than patients without this combination.
      PubDate: 2021-10-12
       
  • Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the
           differentiation of parotid gland tumors

    • Free pre-print version: Loading...

      Abstract: Objective To assess the usefulness of combined diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of parotid gland tumors. Methods Seventy patients with 80 parotid gland tumors who underwent DKI and DCE-MRI were retrospectively enrolled and divided into four groups: pleomorphic adenomas (PAs), Warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). DCE-MRI and DKI quantitative parameters were measured. The Kruskal–Wallis H test and post hoc test with Bonferroni correction and ROC curve were used for statistical analysis. Results WTs demonstrated the highest Kep value (median 1.89, interquartile range [1.46–2.31] min−1) but lowest Ve value (0.20, [0.15–0.25]) compared with PAs (Kep, 0.34 [0.21–0.55] min−1; Ve, 0.36 [0.24–0.43]), OBTs (Kep, 1.22 [0.27–1.67] min−1; Ve, 0.28 [0.25–0.41]), and MTs (Kep, 0.71 [0.50–1.23] min−1; Ve, 0.35 [0.26–0.45]) (all p < .05). MTs had the lower D value (1.10, [0.88–1.29] × 10−3 mm2/s) compared with PAs (1.81, [1.60–2.20] × 10−3 mm2/s) and OBTs (1.57, [1.32–1.89] × 10−3 mm2/s) (both p < .05). PAs had the lower Ktrans value (0.12, [0.07–0.18] min−1) compared with OBTs (0.28, [0.11–0.50] min−1) (p < .05). The cutoff values of combined Kep and Ve, D, and Ktrans to distinguish WTs, MTs, and PAs sequentially were 1.06 min−1, 0.28, 1.46 × 10−3 mm2/s, and 0.21 min−1, respectively (accuracy, 89% [71/80], 91% [73/80], 78% [62/80], respectively). Conclusion The combined use of DKI and DCE-MRI may help differentiate parotid gland tumors. Key Points • The combined use of DKI and DCE-MRI could facilitate the understanding of the pathophysiological characteristics of parotid gland tumors. • A stepwise diagnostic diagram based on the combined use of DCE-MRI parameters and the diffusion coefficient is helpful for accurate preoperative diagnosis in parotid gland tumors and may further facilitate the clinical management of patients.
      PubDate: 2021-10-12
       
  • Histogram analysis of diffusion-weighted imaging and dynamic
           contrast-enhanced MRI for predicting occult lymph node metastasis in
           early-stage oral tongue squamous cell carcinoma

    • Free pre-print version: Loading...

      Abstract: Objectives To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC). Materials and methods This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM. Results MRI-determined depth of invasion and ADCentropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC10th. ADCuniformity and wash-inskewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC10th, ADCuniformity, and wash-inskewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82. Conclusions Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC. Key Points • Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC 10th , ADC uniformity , and wash-in skewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.
      PubDate: 2021-10-12
       
  • Fuhrman nuclear grade prediction of clear cell renal cell carcinoma:
           influence of volume of interest delineation strategies on machine
           learning-based dynamic enhanced CT radiomics analysis

    • Free pre-print version: Loading...

      Abstract: Objective To investigate the influence of different volume of interest (VOI) delineation strategies on machine learning–based predictive models for discrimination between low and high nuclear grade clear cell renal cell carcinoma (ccRCC) on dynamic contrast-enhanced CT. Methods This study retrospectively collected 177 patients with pathologically proven ccRCC (124 low-grade; 53 high-grade). Tumor VOI was manually segmented, followed by artificially introducing uncertainties as: (i) contour-focused VOI, (ii) margin erosion of 2 or 4 mm, and (iii) margin dilation (2, 4, or 6 mm) inclusive of perirenal fat, peritumoral renal parenchyma, or both. Radiomics features were extracted from four-phase CT images (unenhanced phase (UP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP)). Different combinations of four-phasic features for each VOI delineation strategy were used to build 176 classification models. The best VOI delineation strategy and superior CT phase were identified and the top-ranked features were analyzed. Results Features extracted from UP and EP outperformed features from other single/combined phase(s). Shape features and first-order statistics features exhibited superior discrimination. The best performance (ACC 81%, SEN 67%, SPE 87%, AUC 0.87) was achieved with radiomics features extracted from UP and EP based on contour-focused VOI. Conclusion Shape and first-order features extracted from UP + EP images are better feature representations. Contour-focused VOI erosion by 2 mm or dilation by 4 mm within peritumor renal parenchyma exerts limited impact on discriminative performance. It provides a reference for segmentation tolerance in radiomics-based modeling for ccRCC nuclear grading. Key Points • Lesion delineation uncertainties are tolerated within a VOI erosion range of 2 mm or dilation range of 4 mm within peritumor renal parenchyma for radiomics-based ccRCC nuclear grading. • Radiomics features extracted from unenhanced phase and excretory phase are superior to other single/combined phase(s) at differentiating high vs low nuclear grade ccRCC. • Shape features and first-order statistics features showed superior discriminative capability compared to texture features.
      PubDate: 2021-10-12
       
  • Osteomyelitis on MR imaging as a key predictor of recurrent septic
           arthritis of the shoulder

    • Free pre-print version: Loading...

      Abstract: Objectives To investigate the clinical and radiologic predictors of postoperative recurrent septic arthritis of the shoulder (SAS) using multivariable analysis. Methods Forty-three patients (mean age, 65 years; 24 women) who underwent surgery for SAS between January 2011 and October 2019 were retrospectively enrolled. An orthopedic surgeon assessed clinical (age, sex, comorbidity, duration from symptom onset to MR imaging and surgery, surgical method, antibiotic usage), laboratory (serum white blood cell [WBC] count, C-reactive protein [CRP] level, synovial cell count), and surgical findings (culture/biopsy results). Two musculoskeletal radiologists evaluated MR imaging findings (bone marrow [reactive bone marrow edema, osteomyelitis, osteochondral erosion] and soft tissue [synovitis, bursitis, muscle edema, abscess] abnormalities). Recurrent SAS was evaluated at ≥ 12 months postoperatively. Univariable and multivariable analyses were performed to determine the best predictor of recurrent SAS. Results The overall recurrent SAS rate was 33% (14/43). On univariable analysis, mean age (without recurrence vs. recurrence: 68 vs. 59 years, p = .04), mean duration from symptom onset to surgery (18 vs. 25 days, p = .02), serum WBC count (12,000 vs. 9,000 cells/mL3, p = .04), CRP level (13 vs. 6 mg/L, p = .01), and osteomyelitis on MR imaging (p < .01 for both readers) significantly differed between patients with and without recurrence; on multivariable analysis, only osteomyelitis on MR imaging was significantly associated with recurrent SAS for both readers (p = .02 and .01 for each reader respectively). The inter-reader agreement was good (κ = .62–1.0) for all MR imaging findings, except for muscle edema (fair, κ = .37). Conclusion Osteomyelitis on MR imaging was the best predictor of recurrent SAS. Key Points • Osteomyelitis on preoperative MR imaging was the best predictor associated with recurrent septic arthritis of the shoulder on multivariable analysis including clinical, laboratory, and MR findings. • In multivariable analyses focused on each bone marrow abnormality, with adjustment for clinical and laboratory parameters, the presence of reactive bone marrow edema and osteochondral erosion on MR imaging showed no significant association with recurrent septic arthritis of the shoulder.
      PubDate: 2021-10-12
       
  • Estimation of pancreatic fibrosis and prediction of postoperative
           pancreatic fistula using extracellular volume fraction in multiphasic
           contrast-enhanced CT

    • Free pre-print version: Loading...

      Abstract: Objective To investigate the diagnostic performance of the extracellular volume (ECV) fraction in multiphasic contrast-enhanced computed tomography (CE-CT) for estimating histologic pancreatic fibrosis and predicting postoperative pancreatic fistula (POPF). Methods Eighty-five patients (49 men; mean age, 69 years) who underwent multiphasic CE-CT followed by pancreaticoduodenectomy with pancreaticojejunal anastomosis between January 2012 and December 2018 were retrospectively included. The ECV fraction was calculated from absolute enhancements of the pancreas and aorta between the precontrast and equilibrium-phase images, followed by comparisons among histologic pancreatic fibrosis grades (F0‒F3). The diagnostic performance of the ECV fraction in advanced fibrosis (F2‒F3) was evaluated using receiver operating characteristic curve analysis. Multivariate logistic regression analysis was used to evaluate the associations of the risk of POPF development with patient characteristics, histologic findings, and CT imaging parameters. Results The mean ECV fraction of the pancreas was 34.4% ± 9.5, with an excellent intrareader agreement of 0.811 and a moderate positive correlation with pancreatic fibrosis (r = 0.476; p < 0.001). The mean ECV fraction in advanced fibrosis was significantly higher than that in no/mild fibrosis (44.4% ± 10.8 vs. 31.7% ± 6.7; p < 0.001), and the area under the receiver operating characteristic curve for the diagnosis of advanced fibrosis was 0.837. Twenty-two patients (25.9%) developed clinically relevant POPF. Multivariate logistic regression analysis demonstrated that the ECV fraction was a significant predictor of POPF. Conclusions The ECV fraction can offer quantitative information for assessing pancreatic fibrosis and POPF after pancreaticojejunal anastomosis. Key Points • There was a moderate positive correlation of the extracellular volume (ECV) fraction of the pancreas in contrast-enhanced CT with the histologic grade of pancreatic fibrosis (r = 0.476; p < 0.001). • The ECV fraction was higher in advanced fibrosis (F2‒F3) than in no/mild fibrosis (F0‒F1) (p < 0.001), with an AUC of 0.837 for detecting advanced fibrosis. • The ECV fraction was an independent risk factor for predicting subclinical (odds ratio, 0.81) and clinical (odds ratio, 0.80) postoperative pancreatic fistula.
      PubDate: 2021-10-12
       
  • Multiparametric magnetic resonance imaging-derived radiomics for the
           prediction of disease-free survival in early-stage squamous cervical
           cancer

    • Free pre-print version: Loading...

      Abstract: Objective To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC). Methods A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOIentire, VOI+5 mm, and VOI−5 mm) to build 3 rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to build combined model incorporating rad-scores with clinical risk factors and compared with clinical model alone. Kaplan–Meier analysis was used to further validate prognostic value of selected clinical and radiomics characteristics. Results Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with a C-index of 0.750 in the training set and 0.839 in the validation set. Combined model was constructed by incorporating age categorized by 55, Federation of Gynecology and Obstetrics (Figo) stage, and lymphovascular space invasion with rad-score (VOI+5 mm). Combined model performed better than clinical model in DFS prediction in both the training set (C-index 0.815 vs 0.709; p = 0.024) and the validation set (C-index 0.866 vs 0.719; p = 0.001). Conclusion Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making. Key Points • Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI+5 mm) performed best with favorable C-index values. • Combined model incorporating multiparametric MRI-based radiomics with clinical risk factors performed significantly better in DFS prediction than the clinical model. • Combined model presented as a nomogram can be easily used to predict survival, thereby facilitating clinical decision-making.
      PubDate: 2021-10-12
       
  • Multiparametric quantitative MRI for the evaluation of dysthyroid optic
           neuropathy

    • Free pre-print version: Loading...

      Abstract: Objective To evaluate the ability of quantitative MRI parameters for predicting dysthyroid optic neuropathy (DON). Methods We retrospectively collected and analyzed the clinical features and 3.0 T MRI data of 59 patients with Graves orbitopathy (GO), with (n = 26) and without DON (n = 33). We compared MRI quantitative parameters, including the modified muscle index (mMI), proptosis, volume of intra-orbital fat, mean apparent diffusion coefficient value, and T2 value of the optic nerve among patients with and without DON. A logistic regression analysis was performed to identify the risk factors associated with DON. Moreover, we performed a receiver operating characteristic curve analysis and decision curve analysis to evaluate the diagnostic performance of the identified parameters for DON. Results We studied 118 orbits (43 and 75 with and without DON, respectively). The mMI and mean T2 value of the optic nerve were significantly greater in orbits with DON (p < 0.001). A greater mMI at 21 mm (odds ratio (OR), 1.039; 95% confidence interval (CI): 1.019, 1.058) and higher mean T2 value of the optic nerve (OR, 1.035; 95% CI: 1.017, 1.054) were associated with a higher risk of DON. A model combining the mMI at 21 mm and mean T2 values for the optic nerve effectively predicted DON in patients with GO, with a sensitivity and specificity of 95.3% and 76%, respectively. Conclusion A quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve can be an effective imaging marker for identifying DON. Key Points • Patients with GO and DON had greater mMI than those without DON. • Optic nerves in patients with DON demonstrated an increased T2 value. • The quantitative MRI parameter combining the mMI at 21 mm and mean T2 value of the optic nerve is the most effective method for diagnosing DON.
      PubDate: 2021-10-12
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.237.16.210
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-