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.

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. 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). 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. 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.

Background: We aimed to evaluate the performance of a deep learning (DL)-based Radiomics strategy designed for analyzing contrast-enhanced ultrasound (CEUS) to not only predict the progression-free survival (PFS) of radiofrequency ablation (RFA) and surgical resection (SR) but also optimize the treatment selection between them for patients with very-early or early-stage hepatocellular carcinoma (HCC). Methods: We retrospectively enrolled 419 patients examined by CEUS within 1 week before receiving RFA or SR (RFA: 214, SR: 205) from January 2008 to 2016. Two Radiomics signatures were constructed by the Radiomics model R-RFA and R-SR to stratify PFS of different treatment groups. Then, RFA and SR nomograms were built by incorporating Radiomics signatures and significant clinical variables to achieve individualized 2-year PFS prediction. Finally, we applied both Radiomics models and both nomograms to each enrolled patient to investigate whether there were space for treatment optimization and how much prognostic improvement could be expected. Results: R-RFA and R-SR showed remarkable discrimination (C-index: 0.726 for RFA, 0.741 for SR). RFA and SR nomograms provided good 2-year PFS prediction accuracy and good calibrations. We identified 17.3% RFA patients and 27.3% SR patients should swap their treatment, so their average probability of 2-year PFS would increase 12 and 15%, respectively. Conclusions: The proposed Radiomics models and nomograms achieved accurate preoperative prediction of PFS for RFA and SR, and they could facilitate the optimized treatment selection between them for patients with very-early or early-stage HCC.

It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.

OBJECTIVE. The purpose of this meta-analysis was to summarize the evidence on the accuracy of various ultrasound findings for excluding a diagnosis of biliary atresia. MATERIALS AND METHODS. We searched MEDLINE and the Web of Science databases for the period from January 1990 to May 2015. To be included, studies had to satisfy two criteria. First, the data needed to include 2 × 2 contingency data on the diagnostic accuracy of ultrasound in identifying biliary atresia in at least 10 patients with and 10 patients without disease. Second, the study needed to use surgery or biopsy for biliary atresia and surgery, biopsy, clinical follow-up, or some combination of the three as the reference standard for the exclusion of biliary atresia. The methodologic quality of each study was assessed with version 2 of the Quality Assessment of Diagnostic Accuracy Studies tool. Estimated sensitivity and specificity of each ultrasound characteristic were calculated using a random-effects model. RESULTS. Twenty-three studies ...