Coupling texture analysis and physiological modeling for liver dynamic MRI interpretation.

International audienceWe coupled our physiological model of the liver, to a MRI simulator (SIMRI) in order to find image markers of the tumor growth. Some pathological modifications related to the development of Hepatocellular carcinoma are simulated (flows, permeability, vascular density). Corresponding images simulated at typical acquisition phases (arterial, portal) are compared to real images. The evolution of some textural features with arterial flow is also presented

Can Dynamic Contrast-Enhanced Magnetic Resonance Imaging Combined with Texture Analysis Differentiate Malignant Glioneuronal Tumors from Other Glioblastoma?

An interesting approach has been proposed to differentiate malignant glioneuronal tumors (MGNTs) as a subclass of the WHO grade III and IV malignant gliomas. MGNT histologically resemble any WHO grade III or IV glioma but have a different biological behavior, presenting a survival twice longer as WHO glioblastomas and a lower occurrence of metastases. However, neurofilament protein immunostaining was required for identification of MGNT. Using two complementary methods, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and texture analysis (MRI-TA) from the same acquisition process, the challenge is to in vivo identify MGNT and demonstrate that MRI postprocessing could contribute to a better typing and grading of glioblastoma. Results are obtained on a preliminary group of 19 patients a posteriori selected for a blind investigation of DCE T1-weighted and TA at 1.5 T. The optimal classification (0/11 misclassified MGNT) is obtained by combining the two methods, DCE-MRI and MRI-TA

Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma?

International audienceAn interesting approach has been proposed to differentiate malignant glioneuronal tumors (MGNTs) as a subclass of the WHO grade III and IV malignant gliomas. MGNT histologically resemble any WHO grade III or IV glioma but have a different biological behavior, presenting a survival twice longer as WHO glioblastomas and a lower occurrence of metastases. However, neurofilament protein immunostaining was required for identification of MGNT. Using two complementary methods, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and texture analysis (MRI-TA) from the same acquisition process, the challenge is to in vivo identify MGNT and demonstrate that MRI postprocessing could contribute to a better typing and grading of glioblastoma. Results are obtained on a preliminary group of 19 patients a posteriori selected for a blind investigation of DCE T1-weighted and TA at 1.5 T. The optimal classification (0/11 misclassified MGNT) is obtained by combining the two methods, DCE-MRI and MRI-TA

Role of preoperative optimization of the liver for resection in patients with hilar cholangiocarcinoma type III.

International audienceBACKGROUND: Long-term survival after complete resection of hilar cholangiocarcinoma remains disappointing. The aim of this retrospective study was to assess the impact of liver optimization on postoperative outcome of hilar cholangiocarcinoma type III. MATERIALS AND METHODS: In a retrospective, single-center analysis, outcomes in patients with hilar cholangiocarcinoma type III who underwent resection after preoperative liver optimization (preoperative transhepatic biliary drainage [PTBD], bile replacement, and/or portal vein embolization [PVE]) were compared with nonoptimized controls. RESULTS: Of 41 patients undergoing surgery, 38 patients undergoing curative intent procedures were identified, of whom 15 underwent preoperative optimization. After PTBD, direct bilirubin decreased from 218.0 ± 184.2 to 75.9 ± 42.7 μmol/L (P = 0.03), and there was a trend toward decreased AST and ALT levels. Overall, 3- and 5-year survival rates were 47.9 ± 9.1 and 41.9 ± 9.8%. The primary endpoint, 5-year survival after surgery, was not significantly different between groups. Preoperative jaundice was identified as an independent prognostic factor for poor outcome (hazard ratio [HR] 2.12, P = 0.02). Four patients (10.5%) without preoperative optimization died of liver failure within the first 30 days postsurgery, preceded in three cases by intra-abdominal abscesses. PTBD was associated with a lower rate of postoperative intra-abdominal abscesses; however this factor was not independently predictive of higher survival. CONCLUSION: Preoperative optimization of the liver in hilar cholangiocarcinoma Type III reduced the incidence of intra-abdominal abscesses, but its impact on postoperative survival remains unclear

Differentiation of focal nodular hyperplasia and hepatocellular adenoma using qualitative and quantitative imaging features and classification and regression tree analysis

Purpose To assess qualitative and quantitative analysis of gadoxetate disodium-enhanced hepatobiliary phase MR imaging (MRI) and assess the performance of classification and regression tree analysis for the differentiation of focal nodular hyperplasia (FNH) and hepatocellular adenoma (HCA). Materials and methods This retrospective study was approved by our local ethics committee. One hundred seventy patients suspected of having FNH or HCA underwent gadoxetate disodium-enhanced MRI. The reference standard was either pathology or follow-up imaging. Two readers reviewed images to identify qualitative imaging features and measure signal intensity on unenhanced, dynamic, and hepatobiliary phase images. For quantitative analysis, contrast enhancement ratio (CER), lesion-to-liver contrast (LLC), signal intensity ratio (SIR), and relative signal enhancement ratio (RSER) were calculated. A classification and regression tree (CART) analysis was developed. Results Eighty-five patients met the inclusion criteria, with a total of 97 FNHs and 43 HCAs. For qualitative analysis, the T1 signal intensity on the hepatobiliary phase provided the highest overall classification performance (91.9% sensitivity, 90.1% specificity, and 90.9% accuracy). For quantitative analysis, RSER in the hepatobiliary phase with a threshold of 0.723 provided the highest classification performance (92.6% sensitivity and 89.4% specificity) to differentiate FNHs from HCAs. A CART model based on five qualitative imaging features provided an accuracy of 94.4% (95% confidence interval 90.0–98.9%). Conclusion Gadoxetate disodium-enhanced hepatobiliary phase provides high diagnostic performance as demonstrated in quantitative and qualitative analysis in differentiation of FNH and HCA, supported by a CART decision model

Machine learning based on quantitative ultrasound for assessment of chronic liver disease

Chronic liver disease (CLD) is a highly prevalent condition characterized by the coexistence of histopathological changes, including liver steatosis, inflammation and fibrosis. Based on a multi-parametric approach, the goal was to assess the ancillary value of quantitative US (QUS) parameters to point shear-wave elastography (pSWE), based on random forests, on a cohort of subjects with CLD. Ninety-one individuals were recruited in this prospective institutional review board approved study, and 82 patients were included after applying exclusion criteria. Measurements of pSWE and radiofrequency ultrasound images were acquired with a clinical scanner using a convex probe. QUS features were extracted from homodyned-K parametric maps. Total and local attenuation coefficient slopes were also included as spectral QUS features, based on reference phantom methods. Dichotomous classification of grades and stages were performed. Receiver operating characteristics (ROC) curves were estimated with bootstrapping, which yielded area under each ROC curve (AUC). The reference standard was histopathological analysis of liver biopsy specimens for grading steatosis and inflammation, and staging fibrosis. QUS parameters improved the classification of liver steatosis, inflammation, and fibrosis compared to pSWE alone. For instance, to classify liver steatosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, AUCs increased from 0.60, 0.63, and 0.62 to 0.90, 0.81, and 0.78, respectively. Examples of parametric maps are reported