No abstract

Percutaneous microwave ablation and radiofrequency ablation are both effective methods in treating hepatocellular carcinomas. The local tumor control, complications related to treatment, and long-term survivals were equivalent for the two modalities.

The value of contrast-enhanced ultrasound (CEUS) in differential diagnosis between benign and malignant gallbladder diseases was investigated. Thirty-three patients with gallbladder carcinomas and 47 with benign gallbladder diseases underwent CEUS. The lesion enhancement time, enhancement extent, pattern, dynamic change of enhancement and the intactness of gallbladder wall were evaluated. In the early phase at CEUS, hyper-, iso-, hypo-, and non-enhancement were found in 84.8% (28/33), 9.1% (3/33), 6.1% (2/33), and 0% (0/33) of gallbladder carcinomas, and 70.3% (33/47), 17.0% (8/47), 2.1% (1/47), and 10.6% (5/47) of benign diseases (p > 0.05). Hyper-enhancement or iso-enhancement in the early phase and then fading out to hypo-enhancement within 35 s after contrast agent administration was found in 90.9% (30/33) of carcinomas and 17.0% (8/47) of benign lesions (p < 0.001). Destruction of the gallbladder wall intactness was absent in benign diseases, whereas it was present in 28 (84.8%) of the 33 carcinomas (p < 0.001). Destruction of gallbladder wall intactness on CEUS yielded the highest capability in differential diagnosis, with sensitivity, specificity, and Youden's index of 84.8% (28/33), 100% (47/47), and 0.85, respectively. Conventional US made correct original diagnoses in 55 (68.8%) patients, whereas CEUS in 77 (96.3%). Thus, CEUS is useful in differential diagnosis between malignant and benign gallbladder diseases.

Delivery of greater microwave energy with cooled-shaft antennas yielded large ablation zones in ex vivo and in vivo livers and in liver cancers. Effective local tumor control was achieved during one microwave ablation session.

MW ablation using a modified cooled-shaft antenna produces a larger ablation zone than RF ablation, with an efficacy similar to RF ablation in local tumour control. MW ablation is a safe and promising treatment of sHCC.

Supramolecular nanoparticles for photothermal therapy (PTT) have shown promising therapeutic efficacy in the primary tumor and great potential for turning the whole-body immune microenvironment from "cold" to "hot," which allows for the simultaneous treatment of the primary tumor and the metastatic site. In this work, we develop a liposome-based PTT nanoparticle through the self-assembly of FDA-approved intravenous injectable lipids and a photothermal agent, indocyanine green (ICG). The obtained ICG-liposome shows long-term storage stability, high ICG encapsulation efficiency (>95%), and enhanced near-infrared (NIR) light-triggered photothermal reaction both in vitro and in vivo. The ICG-liposome efficiently eradicated the primary tumor upon laser irradiation in two colon cancer animal models (CT26 and MC38) and promoted the infiltration of CD8 T cells to distant tumors. However, PTT from ICG-liposome shows only a minimal effect on the inhibition of distant tumor growth in long-term monitoring, predicting other immunosuppressive mechanisms that exist in the distant tumor. By immune-profiling of the tumor microenvironment, we find that the distant tumor growth after PTT highly correlates to compensatory upregulation of immune checkpoint biomarkers, including program death-1 (PD-1), T-cell immunoglobulin, and mucin domain-containing protein 3 (TIM-3), in tumor-infiltrating CD8 T cells. Based on this mechanism, we combine dual PD-1 and TIM-3 blockade with PTT in an MC38 tumor model. This combo successfully clears the primary tumor, generates a systemic immune response, and inhibits the growth of the distant tumor. The ICG-liposome-combined PD-1/TIM-3 blockade strategy sheds light on the future clinical use of supramolecular PTT for cancer immunotherapy.

Elevated platelets based inflammatory indices, especially APRI, was associated with adverse characteristic features and poor prognosis in HCC, especially for patients with HBV infection or cirrhosis. Antiplatelet treatment may represent a potential therapy for HBV-induced HCC recurrence.

Objective To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. Materials and methods In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). Results By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E ( P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 ( P = 0.01) compared to PT-E. Conclusion The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E. Electronic supplementary material The online version of this article (10.1186/s40644-019-0197-5) contains supplementary material, which is available to authorized users.