Sinclairhermansen8492

ution in situations requiring bone healing. Trial registration number Local ethics committee registration number 5176-07/16.

The use of cementless prosthesis has increased in the last 30years with the aim of improving the long-term results of total hip arthroplasties in young and active patients. Encouraging results have recently been reported for cementless titanium and cobalt chromium stems. However, there are few studies with long-term follow-up, and the majority have analysed several models of uncemented stems due to their modifications over the years. Therefore, the aim was to assess the long-term survival rate of the Mittelmeier Mark III or Autophor 900-S stem.

A retrospective cohort study of both gender patients under 70years old with at least one implanted Mittelmeier Mark III uncemented stem was performed. Survival rate was defined as the proportion of stems that did not need a surgical revision from any cause. Clinical status was evaluated using the Merle d'Aubigne scale modified by Matta (excellent/good/fair/poor).

Between 1990 and 1999, 73 stems were implanted. The mean (SD) age at surgical time was 49.3 (9.9) years, and the median (range) of follow-up was 22 (1-28) years. The overall survival rate was 93% (68/73, 95%CI 85-97%). The stem revisions were due to stem breakage (n = 2), to aseptic loosening (n = 2) and to septic loosening (n = 1). Clinical results were excellent 84%, good 15% and fair 1.5%.

The Mittelmeier Mark III stem had an excellent survival rate with a stable long-term fixation and excellent clinical outcomes.

The Mittelmeier Mark III stem had an excellent survival rate with a stable long-term fixation and excellent clinical outcomes.

To investigate the predictive value of four-phase contrast-enhanced CT (CECT) for early complete response (CR) to drug-eluting-bead transarterial chemoembolization (DEB-TACE), with a particular focus on the quantitatively assessed wash-in and wash-out.

A retrospective analysis of preprocedural CECTs was performed for 129 HCC nodules consecutively subjected to DEB-TACE as first-line therapy. Lesion size, location, and margins were recorded. For the quantitative analysis, the following parameters were computed contrast enhancement ratio (CER) and lesion-to-liver contrast ratio (LLC) as estimates of wash-in; absolute and relative wash-out (WO

and WO

) and delayed percentage attenuation ratio (DPAR) as estimates of wash-out. The early radiological response of each lesion was assessed by the mRECIST criteria and dichotomized in CR versus others (partial response, stable disease, and progressive disease).

All quantitatively assessed wash-out variables had significantly higher rates for CR lesions (WO

p =wash-out, and delayed percentage attenuation ratio), the latter (DPAR), based only on the delayed phase, is the most predictive (AUC = 0.80), showing a significant association with complete response for values above 120.

To develop and evaluate a deep learning-based model capable of detecting primary hepatic malignancies in multiphase CT images of patients at high risk for hepatocellular carcinoma (HCC).

A total of 1350 multiphase CT scans of 1280 hepatic malignancies (1202 HCCs and 78 non-HCCs) in 1320 patients at high risk for HCC were retrospectively analyzed. Following the delineation of the focal hepatic lesions according to reference standards, the CT scans were categorized randomly into the training (568 scans), tuning (193 scans), and test (589 scans) sets. Multiphase CT information was subjected to multichannel integration, and livers were automatically segmented before model development. A deep learning-based model capable of detecting malignancies was developed using a mask region-based convolutional neural network. The thresholds of the prediction score and the intersection over union were determined on the tuning set corresponding to the highest sensitivity with < 5 false-positive cases per CT scan. The seration of multiphase CT and automatic liver segmentation, enabled the application of a deep learning-based model to detect primary hepatic malignancy. • Our model exhibited a sensitivity of 84.8% with a false-positive rate of 4.80 per CT scan.

This study aimed to compare the cardiac function among different sub-types of pulmonary hypertension (PH) and to explore the independent predictors of major adverse cardiovascular events (MACE).

Eighty-seven PH patients diagnosed by right heart catheterization (RHC) were recruited. Patients underwent cardiac magnetic resonance (CMR) and RHC examination within 2 weeks. The CMR images were analyzed to calculate the cardiac functional parameters including right ventricle (RV) and left ventricle (LV) end-diastolic volume index (EDVI), end-systolic volume index (ESVI), stroke volume index (SVI), ejection fraction (EF), tricuspid annular plane systolic excursion (TAPSE), and myocardial mass (MM). The median follow-up time was 46.5 months (interquartile range 26-65.5 months), and the endpoints were the occurrence of MACE.

RVEDVI, LVEDVI, and LVESVI were higher in congenital heart disease-related PH (CHD-PH) than in other sub-types (p < 0.05). RVMM, RVSVI, and RVCI were highest in CHD-PH. There was no significant difference in the prognosis among different sub-types (p > 0.05). Comparing with the non-MACE group, RVEF, TAPSE, and LVSVI significantly decreased in the MACE group, while the RVESVI significantly increased (p < 0.05). TAPSE ≤ 15.65 mm and LVSVI ≤ 30.27 mL/m

were significant independent predictors of prognosis in PH patients.

CHD-PH had a higher RV function reserve but lowest LVEF comparing to other subgroups. TAPSE and LVSVI could contribute to the prediction of MACE in PH patients.

• CMR imaging is a noninvasive and accurate tool to assess ventricular function. • CHD-PH had higher RV function reserve but lowest LVEF. • TAPSE and LVSVI could contribute to the prediction of MACE in PH patients.

• CMR imaging is a noninvasive and accurate tool to assess ventricular function. • CHD-PH had higher RV function reserve but lowest LVEF. see more • TAPSE and LVSVI could contribute to the prediction of MACE in PH patients.

To evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging.

A total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed. The models were validated in the test set consisting of 45 patients with brain metastases (203 lesions) and 49 patients without brain metastases.

The combined 3D BB and 3D GRE model yielded better performance than the 3D GRE model (sensitivities of 93.1% vs 76.8%, p < 0.001), and this effect was significantly stronger in subgroups with small metastases (p interaction < 0.001). For metastases < 3 mm, ≥ 3 mm and < 10 mm, and ≥ 10 mm, the sensitivities were 82.4%, 93.2%, and 100%, respectively. The combined 3D BBB and 3D GRE model showed a false-positive rate per case of 0.59 in the test set. • The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while the 3D GRE model showed a lower Dice coefficient of 0.756.

To explore the optimum diameter threshold for solid nodules to define positive results at baseline screening low-dose CT (LDCT) and to compare two-dimensional and volumetric measurement of lung nodules for the diagnosis of lung cancers.

We included consecutive participants from the Korean Lung Cancer Screening project between 2017 and 2018. The average transverse diameter and effective diameter (diameter of a sphere with the same volume) of lung nodules were measured by semi-automated segmentation. Diagnostic performances for lung cancers diagnosed within 1 year after LDCT were evaluated using area under receiver-operating characteristic curves (AUCs), sensitivities, and specificities, with diameter thresholds for solid nodules ranging from 6 to 10 mm. The reduction of unnecessary follow-up LDCTs and the diagnostic delay of lung cancers were estimated for each threshold.

Fifty-two lung cancers were diagnosed among 10,424 (10,141 men; median age 62 years) participants within 1 year after LDCT. Average tration of the diameter threshold for solid nodules from 6 to 9 mm can substantially reduce unnecessary follow-up LDCTs with a small proportion of diagnostic delay of lung cancers. • The average transverse and effective diameters of lung nodules showed similar performances for the prediction of a lung cancer diagnosis.

To compare the value of reduced field-of-view (FOV) intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and arterial spin labeling (ASL) for assessing renal allograft fibrosis and predicting long-term dysfunction.

This prospective study included 175 renal transplant recipients undergoing reduced FOV IVIM DWI, ASL, and biopsies. Renal allograft fibrosis was categorized into ci0, ci1, ci2, and ci3 fibrosis according to biopsy results. A total of 83 participants followed for a median of 39 (IQR, 21-42) months were dichotomized into stable and impaired allograft function groups based on follow-up estimated glomerular filtration rate. Total apparent diffusion coefficient (ADC

), pure diffusion ADC, pseudo-perfusion ADC, perfusion fraction f from IVIM DWI, and renal blood flow (RBF) from ASL were calculated and compared. The area under the receiver operating characteristic curve (AUC) was calculated to assess the diagnostic and predictive performances.

RBF was different in ci0 vs ci1 (147.9 .

An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.

In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.

The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001).

A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.

• The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

• The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.