Cancers Metastatic to the Liver

Altered glycosylation modulates the structure and function of disease-related proteins. The associations between serotransferrin (STF) N-glycosylation and liver diseases (LDs) have been revealed. However, how intact N-glycopeptides vary among different types of liver diseases remains unclear.

Intact STF N-glycopeptides from patients with chronic liver disease (CLD, n = 92), primary liver cancer (PLC, n = 123), metastatic liver cancer (MLC, n = 57), and healthy controls (HCs, n = 59) were determined using high-resolution mass spectrometry.

Significant changes were displayed in STF glycosylation among 4 groups. The LD screening model, including Asn432 G1S/G2S, Asn432 G2S/G2S2, and Asn630 G2NS2/G2FNS2, was constructed to differentiate LDs from HCs, with a AUC of 0.92. The liver cancer (LC) diagnostic model, a combination of Asn432 G1-N/G1S-N, Asn432 G1/G2, Asn432 G2FS/G2FS2, and Asn630 G1S-N /G1S, showed good performance in discriminating LC from CLD (AUC = 0.93). Moreover, AFP-negative LC patients (93 %) were successfully predicted by the LC diagnostic model. Furthermore, the MLC triage model, composed of Asn432 G1/G2, Asn432 G3F/G3FS, Asn630 G2/G2S, Asn630 G2S2/G2NS2, and Asn630 G3FS/G3FS2, yielded an AUC of 0.98 between PLC and MLC.

STF N-glycosylation is a potential biomarker for the accurate classification of different LDs.

The computer-based approach is required for the non-invasive detection of chronic liver diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we review the computer-aided diagnosis of hepatic lesions in view of diffuse- and focal liver disorders. This survey mainly focuses on three image acquisition modalities: ultrasonography, computed tomography, and magnetic resonance imaging. We present the insightful analysis with pros and cons for each preliminary step, particularly preprocessing, attribute analysis, and classification techniques to accomplish clinical diagnostic tasks. In preprocessing, we explore and compare commonly used denoising, deblurring and segmentation methods. Denoising is mainly performed with nonlinear models. In contrast, deep neural networks are frequently applied for deblurring and automatic segmentation of region-of-interest. In attribute analysis, the most common approach comprises texture properties. For classification, the support vector machine is mainly utilized across three image acquisition modalities. However, comparative analysis shows the best performance is obtained by deep learning-based convolutional neural networks. Considering biopsy samples or pathological factors such as overall stage, margin, and differentiation can be helpful for improving the prediction performance. In addition, technique breakthrough is expected soon with advances in machine learning models to address data limitation problems and improve the prediction performance.

Bulk ultrasound ablation is a thermal therapy approach in which tissue is heated by unfocused or weakly focused sonication (average intensities on the order of 100 W/cm²) to achieve coagulative necrosis within a few minutes exposure time. Assessing the role of bubble activity, including acoustic cavitation and tissue vaporization, in bulk ultrasound ablation may help in making bulk ultrasound ablation safer and more effective for clinical applications. Here, two series of ex vivo ablation trials were conducted to investigate the role of bubble activity and tissue vaporization in bulk ultrasound ablation. Fresh bovine liver tissue was ablated with unfocused, continuous-wave ultrasound using ultrasound image-ablate arrays sonicating at 31 W/cm² (0.9 MPa amplitude) for either 20 min at a frequency of 3.1 MHz or 10 min at 4.8 MHz. Tissue specimens were maintained at a static overpressure of either 0.52 or 1.2 MPa to suppress bubble activity and tissue vaporization or at atmospheric pressure for control groups. A passive cavitation detector was used to record subharmonic (1.55 or 2.4 MHz), broadband (1.2–1.5 MHz) and low-frequency (5–20 kHz) acoustic emissions. Treated tissue was stained with 2% triphenyl tetrazolium chloride to evaluate thermal lesion dimensions. Subharmonic emissions were significantly reduced in overpressure groups compared with control groups. Correlations observed between acoustic emissions and lesion dimensions were significant and positive for the 3.1-MHz series, but significant and negative for the 4.8-MHz series. The results indicate that for bulk ultrasound ablation, where both acoustic cavitation and tissue vaporization are possible, bubble activity can enhance ablation in the absence of tissue vaporization, but can reduce thermal lesion dimensions in the presence of vaporization.