Movable and Focus-Tunable Lens Based on Electrically Controllable Liquid: A Lattice Boltzmann Study.

Adjusting the focal length by changing the liquid interface of the liquid lens has become a potential method. In this paper, the lattice-Boltzmann-electrodynamic (LB-ED) method is used to numerically investigate the zooming process of a movable and focus-tunable electrowetting-on-dielectrics (EWOD) liquid lens by combining the LBM chemical potential model and the electrodynamic model. The LB method is used to solve the Navier-Stokes equation, and the Poisson-Boltzmann (PB) equation is introduced to solve the electric field distribution. The experimental results are consistent with the theoretical results of the Lippmann-Young equation. Through the simulation of a liquid lens zoom driven by EWOD, it is found that the lens changes from a convex lens to a concave lens with the voltage increases. The focal length change rate in the convex lens stage gradually increases with voltage. In the concave lens stage, the focal length change rate is opposite to that in the convex lens stage. During the zooming process, the low-viscosity liquid exhibits oscillation, and the high-viscosity liquid appears as overdamping. Additionally, methods were proposed to accelerate lens stabilization at low and high viscosities, achieving speed improvements of about 30% and 50%, respectively. Simulations of lens motion at different viscosities demonstrate that higher-viscosity liquids require higher voltages to achieve the same movement speed.

NIR responsive tumor vaccine in situ for photothermal ablation and chemotherapy to trigger robust antitumor immune responses.

BACKGROUND: Therapeutic tumor vaccine (TTV) that induces tumor-specific immunity has enormous potentials in tumor treatment, but high heterogeneity and poor immunogenicity of tumor seriously impair its clinical efficacy. Herein, a novel NIR responsive tumor vaccine in situ (HA-PDA@IQ/DOX HG) was prepared by integrating hyaluronic acid functionalized polydopamine nanoparticles (HA-PDA NPs) with immune adjuvants (Imiquimod, IQ) and doxorubicin (DOX) into thermal-sensitive hydrogel. RESULTS: HA-PDA@IQ NPs with high photothermal conversion efficiency (41.2%) and T1-relaxation efficiency were using HA as stabilizer by the one-pot oxidative polymerization. Then, HA-PDA@IQ loaded DOX via π-π stacking and mixed with thermal-sensitive hydrogel to form the HA-PDA@IQ/DOX HG. The hydrogel-confined delivery mode endowed HA-PDA@IQ/DOX NPs with multiple photothermal ablation performance once injection upon NIR irradiation due to the prolonged retention in tumor site. More importantly, this mode enabled HA-PDA@IQ/DOX NPs to promote the DC maturation, memory T cells in lymphatic node as well as cytotoxic T lymphocytes in spleen. CONCLUSION: Taken together, the HA-PDA@IQ/DOX HG could be served as a theranostic tumor vaccine for complete photothermal ablation to trigger robust antitumor immune responses.

Preoperative Noninvasive Evaluation of Tumor Budding in Rectal Cancer Using Multiparameter MRI Radiomics.

RATIONALE AND OBJECTIVES: To assess the value of a multiparametric magnetic resonance imaging (MRI)-based model integrating radiomics features with clinical and MRI semantic features for preoperative evaluation of tumor budding (TB) in rectal cancer. MATERIALS AND METHODS: A total of 120 patients with pathologically confirmed rectal cancer were retrospectively analyzed. The patients were randomized into training and validation cohorts in a 6:4 ratio. Radiomics features were extracted and selected from preoperative T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1-weighted imaging (T1CE) sequences, after which the corresponding radiomics score (RS) was calculated, and the radiomics models (T2WI model, DWI model, and T1CE model) were constructed. Logistic regression analysis was selected to develop a combined model integrated RST2WI, RSDWI, RST1CE, and clinical and MRI semantic features. The efficacy of each model in diagnosing TB grade was observed by the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was used to assess the clinical benefits of the models. RESULTS: Seven features were extracted and selected from each T2WI, DWI, and T1CE sequence to calculate the corresponding RS and construct the corresponding radiomics model. MRI reported N stage was an independent risk factor for TB. The area under the ROC curve of the combined model was 0.961 and 0.891 in the training and validation cohorts, respectively. The combined model showed better performance than the other models. DCA showed that the net benefit of the combined model was better than that of the other models in the vast majority of threshold probabilities. CONCLUSION: A combined model integrating radiomics features and MRI semantic features allows for noninvasive preoperative evaluation of TB grading in patients with rectal cancer.

Prognostic predictive value of urothelial carcinoma of the bladder after TURBT based on multiphase CT radiomics.

OBJECTIVE: To investigate multiphase computed tomography (CT) radiomics-based combined with clinical factors to predict overall survival (OS) in patients with bladder urothelial carcinoma (BLCA) who underwent transurethral resection of bladder tumor (TURBT). METHODS: Data were retrospectively collected from 114 patients with primary BLCA from February 2016 to February 2018. The regions of interest (ROIs) of the plain, arterial, and venous phase images were manually segmented. The Cox regression algorithm was used to establish 3 basic models for the plain phase (PP), arterial phase (AP), and venous phase (VP) and 2 combination models (AP + VP and PP + AP + VP). The highest-performing radiomics model was selected to calculate the radiomics score (Rad-score), and independent risk factors affecting patients' OS were analyzed using Cox regression. The Rad-score and clinical risk factors were combined to construct a joint model and draw a visualized nomogram. RESULTS: The combined model of PP + AP + VP showed the best performance with the Akaike Information Criterion (AIC) and Consistency Index (C-index) in the test group of 130.48 and 0.779, respectively. A combined model constructed with two independent risk factors (age and Ki-67 expression status) in combination with the Rad-score outperformed the radiomics model alone; AIC and C-index in the test group were 115.74 and 0.840, respectively. The calibration curves showed good agreement between the predicted probabilities of the joint model and the actual (p < 0.05). The decision curve showed that the joint model had good clinical application value within a large range of threshold probabilities. CONCLUSION: This new model can be used to predict the OS of patients with BLCA who underwent TURBT.

Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer.

PURPOSE: To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients. METHODS: A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions. RESULTS: Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value. CONCLUSION: The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.

Cascade biomimetic intelligent nanotheranostic agents for imaging-guided tumor synergistic therapy.

Aim: Achieving drug-targeting delivery and environment-responsive releasing to realize imaging-guided precise tumor therapy. Materials & methods: Graphene oxide (GO) was used as the drug-delivery system to load indocyanine green (ICG) and doxorubicin (DOX) to form a GO/ICG&DOX nanoplatform, in which GO can quench the fluorescence of ICG and DOX. MnO2 and folate acid-functionalized erythrocyte membrane were further coated into the surface of GO/ICG&DOX to obtain an FA-EM@MnO2-GO/ICG&DOX nanoplatform. Results: The FA-EM@MnO2-GO/ICG&DOX nanoplatform has longer blood circulation time, precise targeting delivery to tumor tissues and catalase-like activity. Both in vitro and in vivo results demonstrated that the FA-EM@MnO2-GO/ICG&DOX nanoplatform has better therapeutic efficacy. Conclusion: The authors successfully fabricated a glutathione-responsive FA-EM@MnO2-GO/ICG&DOX nanoplatform, which can achieve drug-targeting delivery and precise drug release.

Radiomics Analysis on Digital Breast Tomosynthesis: Preoperative Evaluation of Lymphovascular Invasion Status in Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: To develop a digital breast tomosynthesis (DBT)-based radiomics nomogram for preoperative evaluation of lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC). MATERIALS AND METHODS: A total of 135 patients with pathologically confirmed IBC who underwent preoperative DBT from July 2018 to May 2020 were retrospectively enrolled and randomized into the training and validation sets. Radiomics feature extraction was performed on the volume of interest (VOI) manually outlined. A four-step algorithmic was applied to screen the features with the highest predictive power in the training set for constructing the radiomics signature and calculating the correspondent radiomics score (Rad-score). Logistic regression analyses were utilized to develop a combined radiomics model that incorporated the DBT-reported clinicoradiological semantic features and Rad-score, which was visualized as a radiomics nomogram. RESULTS: The percentage of LVI-positive patients was 60.2% and 59.5% in the training and validation sets, respectively. The radiomics signature was constructed based on nine features selected from the 1218 radiomics features extracted. Higher Rad-score, maximum tumor diameter, and spiculate margin were independent risk factors for LVI. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity of the radiomics nomogram were 0.905, 72.7%, and 94.6% in the training set, and 0.835, 80.0%, and 76.5% in the validation set, respectively; this data was higher than models incorporating clinicoradiological semantic features alone or the radiomics signature in both sets. CONCLUSION: Preoperative DBT-based combined radiomic nomogram could be a potential biomarker for LVI in patients with IBC.

A Dual-Energy CT Radiomics of the Regional Largest Short-Axis Lymph Node Can Improve the Prediction of Lymph Node Metastasis in Patients With Rectal Cancer.

Objective: To explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer. Materials and Methods: One hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO). Results: Eight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P<0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income. Conclusion: Predictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.