European radiology
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Predicting post-hepatectomy liver failure (PHLF) after extended right hepatectomy following portal vein embolization (PVE) from serial gadoxetic acid-enhanced magnetic resonance imaging (MRI). ⋯ • To predict the likelihood of post-hepatectomy liver failure, it is important to estimate not only future liver remnant volume prior to extended liver resection but also future liver remnant function. • Future liver remnant function can be predicted by performing gadoxetic acid-enhanced MRI as an imaging-based liver function test before and after portal vein embolization. • A reduction of relative enhancement of the liver in gadoxetic acid-enhanced MRI after portal vein embolization of 0.044 predicts post-hepatectomy liver failure with 75.0% sensitivity and 92.6% specificity.
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To evaluate the efficiency of radiomics model on CT images of intratumoral and peritumoral lung parenchyma for preoperative prediction of lymph node (LN) metastasis in clinical stage T1 peripheral lung adenocarcinoma patients. ⋯ • Radiomics from peritumoral lung parenchyma increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT. • A radiomic nomogram was developed and validated to predict LN metastasis. • Different scan parameters on CT showed that radiomics signature had good predictive performance.
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Comparative Study
Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.
This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction. ⋯ • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.
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To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. ⋯ • Multiparametric MRI may help in the preoperative grading of BCa tumors. • The Joint_Model established from T2WI, DWI, and ADC feature subsets demonstrated a high diagnostic accuracy for preoperative prediction of pathological grade in BCa tumors. • The radiomics approach has the potential to preoperatively assess tumor grades in BCa and avoid subjectivity.
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To assess the diagnostic accuracy of automated 3D volumetry of central pulmonary arteries using computed tomography pulmonary angiography (CTPA) for suspected pulmonary hypertension alone and in combination with echocardiography. ⋯ • This diagnostic accuracy study derived a regression model for noninvasive prediction of invasively measured mean pulmonary arterial pressure as assessed by gold standard right heart catheterisation. • This regression model using automated 3D volumetry of the central pulmonary arteries based on CT pulmonary angiography in conjunction with the echocardiographic pressure estimate predicted pulmonary arterial pressure and the presence of pulmonary hypertension with good diagnostic accuracy. • The combination of automated 3D volumetry and echocardiographic pressure estimate in the regression model provided superior diagnostic accuracy compared to each parameter alone.