Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma

Application of surface-enhanced Raman spectroscopy to human serum for diagnosing liver cancer

This study investigates the application of surface-enhanced Raman spectroscopy (SERS) in the diagnosis of liver cancer using Ag@SiO2 nanoparticles as SERS substrates. A SERS test was conducted on serum samples obtained from patients with liver cancer and healthy individuals. After repeated several times experiments, it was found that the best SERS spectrum was obtained when the volume ratio of serum to deionized water was 1:2. Moreover, data preprocessing was performed on the tested SERS spectrum, and the preprocessed spectral data were combined with principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) for further analysis to classify the serum samples of patients with liver cancer and healthy individuals. The results showed that the classification effect of standard normal variate spectral data combined with the OPLS-DA was the best for the serum samples, with a classification accuracy of 97.98%, sensitivity of 97.14%, and specificity of 98.44%. Therefore, the SERS technology can be developed as a favorable method for the accurate diagnosis of liver cancer in the future.

Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features

Aim

Cancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.

Methods

72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.

Results

It was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.

Conclusion

Binary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

Synergistic Nanomedicine: Photodynamic, Photothermal and Photoimmune Therapy in Hepatocellular Carcinoma: Fulfilling the Myth of Prometheus?

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with high morbidity and mortality, which seriously threatens the health and life expectancy of patients. The traditional methods of treatment by surgical ablation, radiotherapy, chemotherapy, and more recently immunotherapy have not given the expected results in HCC. New integrative combined therapies, such as photothermal, photodynamic, photoimmune therapy (PTT, PDT, PIT), and smart multifunctional platforms loaded with nanodrugs were studied in this review as viable solutions in the synergistic nanomedicine of the future. The main aim was to reveal the latest findings and open additional avenues for accelerating the adoption of innovative approaches for the multi-target management of HCC. High-tech experimental medical applications in the molecular and cellular research of photosensitizers, novel light and laser energy delivery systems and the features of photomedicine integration via PDT, PTT and PIT in immuno-oncology, from bench to bedside, were introspected. Near-infrared PIT as a treatment of HCC has been developed over the past decade based on novel targeted molecules to selectively suppress cancer cells, overcome immune blocking barriers, initiate a cascade of helpful immune responses, and generate distant autoimmune responses that inhibit metastasis and recurrences, through high-tech and intelligent real-time monitoring. The process of putting into effect new targeted molecules and the intelligent, multifunctional solutions for therapy will bring patients new hope for a longer life or even a cure, and the fulfillment of the myth of Prometheus.

Technical note: Noninvasive monitoring of normal tissue radiation damage using spectral quantitative ultrasound spectroscopy

BACKGROUND

While radiation therapy (RT) is a critical component of breast cancer therapy and is known to decrease overall local recurrence rates, recent studies have shown that normal tissue radiation damage may increase recurrence risk. Fibrosis is a well-known consequence of RT, but the specific sequence of molecular and mechanical changes induced by RT remains poorly understood.

PURPOSE

To improve cancer therapy outcomes, there is a need to understand the role of the irradiated tissue microenvironment in tumor recurrence. This study seeks to evaluate the use of spectral quantitative ultrasound (spectral QUS) for real time determination of the normal tissue characteristic radiation response and to correlate these results to molecular features in irradiated tissues.

METHODS

Murine mammary fat pads (MFPs) were irradiated to 20 Gy, and spectral QUS was used to analyze tissue physical properties pre-irradiation as well as at 1, 5, and 10 days post-irradiation. Tissues were processed for scanning electron microscopy imaging as well as histological and immunohistochemical staining to evaluate morphology and structure.

RESULTS

Tissue morphological and structural changes were observed non-invasively following radiation using mid-band fit (MBF), spectral slope (SS), and spectral intercept (SI) measurements obtained from spectral QUS. Statistically significant shifts in MBF and SI indicate structural tissue changes in real time, which matched histological observations. Radiation damage was indicated by increased adipose tissue density and extracellular matrix (ECM) deposition.

CONCLUSIONS

Our findings demonstrate the potential of using spectral QUS to noninvasively evaluate normal tissue changes resulting from radiation damage. This supports further pre-clinical studies to determine how the tissue microenvironment and physical properties change in response to therapy, which may be important for improving treatment strategies.

Targeted Multifunctional Nanoplatform for Imaging-Guided Precision Diagnosis and Photothermal/Photodynamic Therapy of Orthotopic Hepatocellular Carcinoma

Background

Effective theranostic of hepatocellular carcinoma (HCC) in an early-stage is imminently demanded to improve its poor prognosis. Combination of the near-infrared (NIR) photoacoustic imaging (PAI) and fluorescence imaging (FLI) can provide high temporospatial resolution, outstanding optical contrast, and deep penetration and thus is promising for accurate and sensitive HCC diagnosis.

Methods

A versatile CXCR4-targeted Indocyanine green (ICG)/Platinum (Pt)-doped polydopamine melanin-mimic nanoparticle (designated ICG/Pt@PDA-CXCR4, referred to as IPP-c) is synthesized as an HCC-specific contrast agent for high-resolution precise diagnostic PAI/FLI and optical imaging-guided targeted photothermal therapy (PTT)/photodynamic therapy (PDT) of orthotopic small hepatocellular carcinoma (SHCC).

Results

The multifunctional targeted nanoparticle yields superior HCC specificity, high imaging contrast in both PAI and FLI, good stability, reliable biocompatibility, effective singlet oxygen generation and superior photothermal conversion efficiency (PCE, 58.7%) upon 808-nm laser irradiation. The targeting ability of IPP-c was validated in in vitro experiments on selectively killing the CXCR4-overexpressing HCC cells. Moreover, we test the efficient dual-modal optical precision diagnosis properties of IPP-c via in vivo experiments on targeted particle accumulation in an early-stage SHCC mouse model (tumor diameter about 1.2 mm). Then, under the guidance of real-time optical imaging, effective and mini-invasive PTT/PDT of orthotopic SHCCs were demonstrated without damaging adjacent liver tissues or other major organs.

Conclusion

This study presented a multifunctional CXCR4-targeted nanoparticle to conduct effective and mini-invasive phototherapeutics of orthotopic SHCCs via the real-time quantitative guidance by optical imaging, which provided a new perception for building a versatile targeted nanoplatform for phototheranostics of early-stage HCC.

Diagnostic Performance of 9 Quantitative Ultrasound Parameters for Detection and Classification of Hepatic Steatosis in Nonalcoholic Fatty Liver Disease

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease worldwide. Quantitative ultrasound (QUS) parameters based on radiofrequency raw data show promise in quantifying liver fat.

PURPOSE

The aim of this study was to evaluate the diagnostic performance of 9 QUS parameters compared with magnetic resonance imaging (MRI)-estimated proton density fat fraction (PDFF) in detecting and staging hepatic steatosis in patients with or suspected of NAFLD.

MATERIALS AND METHODS

In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved prospective study, 31 participants with or suspected of NAFLD, without other underlying chronic liver diseases (13 men, 18 women; average age, 52 years [range, 26-90 years]), were examined. The following parameters were obtained: acoustic attenuation coefficient (AC); hepatorenal index (HRI); Nakagami parameter; shear wave elastography measures such as shear wave elasticity, viscosity, and dispersion; and spectroscopy-derived parameters including spectral intercept (SI), spectral slope (SS), and midband fit (MBF). The diagnostic ability (area under the receiver operating characteristic curves and accuracy) of QUS parameters was assessed against different MRI-PDFF cutoffs (the reference standard): 6.4%, 17.4%, and 22.1%. Linearity with MRI-PDFF was evaluated with Spearman correlation coefficients (p).

RESULTS

The AC, SI, Nakagami, SS, HRI, and MBF strongly correlated with MRI-PDFF (P = 0.89, 0.89, 0.88, -0.87, 0.81, and 0.71, respectively [P < 0.01]), with highest area under the receiver operating characteristic curves (ranging from 0.85 to 1) for identifying hepatic steatosis using 6.4%, 17.4%, and 22.1% MRI-PDFF cutoffs. In contrast, shear wave elasticity, shear wave viscosity, and shear wave dispersion did not strongly correlate to MRI-PDFF (P = 0.45, 0.38, and 0.07, respectively) and had poor diagnostic performance.

CONCLUSION

The AC, Nakagami, SI, SS, MBF, and HRI best correlate with MRI-PDFF and show high diagnostic performance for detecting and classifying hepatic steatosis in our study population.

SUMMARY STATEMENT

Quantitative ultrasound is an accurate alternative to MRI-based techniques for evaluating hepatic steatosis in patients with or at risk of NAFLD.

KEY FINDINGS

Our preliminary results show that specific quantitative ultrasound parameters accurately detect different degrees of hepatic steatosis in NAFLD.

Diagnosis of liver cancer by FTIR spectra of serum

Liver cancer is the most common fatal malignant tumor in the world. Early diagnosis of liver cancer can improve the survival rate of the patients with liver disease. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with curve fitting and chemometrics was used to distinguish the serum from patients from that of healthy people. The curve fitting results in protein range of 1700-1600 cm^-1 showed that there were differences in the secondary structure of protein in serum between the patients with liver cancer and healthy people. Principal component analysis (PCA) in lipid range of 2900-2800 cm^-1 could distinguish the serum of patients with liver cancer from that of healthy people. The first two principal components PC1 and PC2 explained 95% of the total data variance. The sensitivity and specificity of partial least squares discriminant analysis (PLS-DA) in lipid range of 2900-2800 cm^-1 reached 92.85% and 95.23% respectively. It is shown that FTIR spectroscopy might be developed as an effective method for the diagnosis of liver cancer.