Is There a Place for Elastography in the Diagnosis of Hepatocellular Carcinoma?

Mapping the strain-stiffening behavior of the lung and lung cancer at microscale resolution using the crystal ribcage

Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung's material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung's microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor's stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor's mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.

The Clinical Value of Multimodal Ultrasound for the Differential Diagnosis of Hepatocellular Carcinoma from Other Liver Tumors in Relation to Histopathology

Recent advances in the field of ultrasonography offer promising tools for the evaluation of liver tumors. We aim to assess the value of multimodal ultrasound in differentiating hepatocellular carcinomas (HCCs) from other liver lesions. We prospectively included 66 patients with 72 liver tumors. The histological analysis was the reference standard for the diagnosis of malignant liver lesions, and partially for benign tumors. All liver lesions were assessed by multiparametric ultrasound: standard ultrasound, contrast-enhanced ultrasound (CEUS), the point shear wave elastography (pSWE) using shear wave measurement (SWM) method and real-time tissue elastography (RTE). To diagnose HCCs, CEUS achieved a sensitivity, specificity, accuracy and positive predictive value (PPV) of 69.05%, 92.86%, 78.57% and 93.55%, respectively. The mean shear-wave velocity (Vs) value in HCCs was 1.59 ± 0.29 m/s, which was lower than non-HCC malignancies (p < 0.05). Using a cut-off value of 1.58 m/s, SWM achieved a sensitivity of 54.76%, and 82.35% specificity, for differentiating HCCs from other malignant lesions. The combination of SWM and CEUS showed higher sensitivity (79.55%) compared with each technique alone, while maintaining a high specificity (89.29%). In RTE, most HCCs (61.53%) had a mosaic pattern with dominant blue areas corresponding to type "c" elasticity. Elasticity type "c" was 70.59% predictive for HCCs. In conclusion, combining B-mode ultrasound, CEUS, pSWE and RTE can provide complementary diagnostic information and potentially decrease the requirements for other imaging modalities.

Multiparametric Ultrasound Approach Using a Tree-Based Decision Classifier for Inconclusive Focal Liver Lesions Evaluated by Contrast Enhanced Ultrasound

BACKGROUND

Multiparametric ultrasound (MPUS) is a concept whereby the examiner is encouraged to use the latest features of an ultrasound machine. The aim of this study was to reanalyze inconclusive focal liver lesions (FLLs) that had been analyzed via contrast enhanced ultrasound (CEUS) using the MPUS approach with the help of a tree-based decision classifier.

MATERIALS AND METHODS

We retrospectively analyzed FLLs that were inconclusive upon CEUS examination in our department, focusing our attention on samples taken over a period of two years (2017-2018). MPUS reanalysis followed a three-step algorithm, taking into account the liver stiffness measurement (LSM), time-intensity curve analysis (TIC), and parametric imaging (PI). After processing all steps of the algorithm, a binary decision tree classifier (BDTC) was used to achieve a software-assisted decision.

RESULTS

Area was the only TIC-CEUS parameter that showed a significant difference between malign and benign lesions with a cutoff of >-19.3 dB for washout phenomena (AUROC = 0.58, Se = 74.0%, Sp = 45.7%). Using the binary decision tree classifier (BDTC) algorithm, we correctly classified 71 out of 91 lesions according to their malignant or benignant status, with an accuracy of 78.0% (sensitivity = 62%, specificity = 45%, and precision = 80%).

CONCLUSIONS

By reevaluating inconclusive FLLs that had been analyzed via CEUS using MPUS, we managed to determine that 78% of the lesions were malignant and, in 28% of them, we established the lesion type.