Gene delivery to breast cancer by incorporated EpCAM targeted DARPins into AAV2.

OBJECTIVE: The aim of this study is to evaluate an AAV vector that can selectively target breast cancer cells and to investigate its specificity and anti-tumor effects on breast cancer cells both in vitro and in vivo, offering a new therapeutic approach for the treatment of EpCAM-positive breast cancer. METHODS: In this study, a modified AAV2 viral vector was used, in which EpCAM-specific DARPin EC1 was fused to the VP2 protein of AAV2, creating a viral vector that can target breast cancer cells. The targeting ability and anti-tumor effects of this viral vector were evaluated through in vitro and in vivo experiments. RESULTS: The experimental results showed that the AAV2MEC1 virus could specifically infect EpCAM-positive breast cancer cells and accurately deliver the suicide gene HSV-TK to tumor tissue in mice, significantly inhibiting tumor growth. Compared to the traditional AAV2 viral vector, the AAV2MEC1 virus exhibited reduced accumulation in liver tissue and had no impact on tumor growth. CONCLUSION: This study demonstrates that AAV2MEC1 is a gene delivery vector capable of targeting breast cancer cells and achieving selective targeting in mice. The findings offer a potential gene delivery system and strategies for gene therapy targeting EpCAM-positive breast cancer and other tumor types.

Real-Time Respiratory Tumor Motion Prediction Based on a Temporal Convolutional Neural Network: Prediction Model Development Study.

BACKGROUND: The dynamic tracking of tumors with radiation beams in radiation therapy requires the prediction of real-time target locations prior to beam delivery, as treatment involving radiation beams and gating tracking results in time latency. OBJECTIVE: In this study, a deep learning model that was based on a temporal convolutional neural network was developed to predict internal target locations by using multiple external markers. METHODS: Respiratory signals from 69 treatment fractions of 21 patients with cancer who were treated with the CyberKnife Synchrony device (Accuray Incorporated) were used to train and test the model. The reported model's performance was evaluated by comparing the model to a long short-term memory model in terms of the root mean square errors (RMSEs) of real and predicted respiratory signals. The effect of the number of external markers was also investigated. RESULTS: The average RMSEs of predicted (ahead time=400 ms) respiratory motion in the superior-inferior, anterior-posterior, and left-right directions and in 3D space were 0.49 mm, 0.28 mm, 0.25 mm, and 0.67 mm, respectively. CONCLUSIONS: The experiment results demonstrated that the temporal convolutional neural network-based respiratory prediction model could predict respiratory signals with submillimeter accuracy.

PCAF-mediated acetylation of Lin28B increases let-7 biogenesis in lung adenocarcinoma H1299 cells.

BACKGROUND: Lin28B and its paralog Lin28A are small RNA binding proteins that have similar inhibitory effects, although they target separate steps in the maturation of let-7 miRNAs in mammalian cells. Because Lin28B participates in the promotion and development of tumors mostly by blocking the let-7 tumor suppressor family members, we sought to explore the associated mechanisms to gain insights into how Lin28B might be decreased in human cancer cells to increase let-7 levels and reverse malignancy. RESULTS: We demonstrated that the histone acetyltransferase PCAF, via its cold shock domain, directly interacts with and subsequently acetylates Lin28B in lung adenocarcinoma-derived H1299 cells. RT-qPCR assays showed that both let-7a-1 and let-7g were increased in PCAF-transfected H1299 cells. Lin28B is acetylated by ectopic PCAF and translocates from the nucleus to the cytoplasm in H1299 cells. CONCLUSIONS: The effects of acetylated Lin28B on let-7a-1 and let-7g are similar to that of stable knockdown of Lin28B in H1299 cells. The new role of PCAF in mediating Lin28B acetylation and the specific release of its target microRNAs in H1299 cells may shed light on the potential application of let-7 in the clinical treatment of lung cancer patients.

Identification and characterization of IKKε gene from grass carp Ctenopharyngodon idella.

IKKε is an IKK-related kinase implicated in antiviral immune response in higher vertebrates. To elucidate the function of IKKε in teleost fish, grass carp IKKε (gcIKKε) has been cloned and characterized in this paper. The full-length cDNA of gcIKKε is composed of 2529 nucleotides and encodes a polypeptide of 723 amino acids. The mRNA transcription of gcIKKε was constitutively detected in all the selected tissues and the gcIKKε mRNA level increased at 36 h after GCRV infection. Western blot data of both HEK293T cells and EPC cells demonstrated that gcIKKε was around 80 KDa; and immunofluorescence staining data of both NIH3T3 cells and EPC cells determined gcIKKε was a cytosolic protein. The mRNA level of gcIKKε in CIK cells was increased more than 150 times right after poly(I:C) treatment and PMA treatment triggered gcIKKε mRNA transcription in CIK cells more than 100 times. Over-expression of gcIKKε in EPC cells activated the promoter activity of both zebrafish IFN and fathead minnow IFN. gcIKKε mRNA transcription level in CIK cells was increased from 48 h post GCRV infection with different MOIs. All the data support the idea that gcIKKε is a novel teleost IκB kinase recruited in the IFN-mediated antiviral immunity of grass carp.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

Purpose: To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. Methods: We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results: Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). Conclusion: The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

Modification of an oxyhalide solid-solution photocatalyst with an efficient O2-evolving cocatalyst and electron mediator for two-step photoexcitation overall water splitting.

Two-step photoexcitation overall water splitting based on particulate photocatalysts represents a promising approach for low-cost solar hydrogen production. The performance of an O2-evolution photocatalyst and electron mediator between two photocatalysts crucially influences the construction of an efficient two-step excitation water-splitting system. Bismuth-tantalum oxyhalides are emerging photocatalysts for O2 evolution reactions and can be applied in two-step water-splitting systems. In this study, a highly crystalline Bi4TaO8Cl0.9Br0.1 solid solution with microplatelet morphology was synthesized by the dual flux method. The light absorption intensity and charge transfer efficiency of the Bi4TaO8Cl0.9Br0.1 solid solution were higher than those of Bi4TaO8Cl and Bi4TaO8Br; thus, the sacrificial O2 evolution activity of Bi4TaO8Cl0.9Br0.1 photocatalyst was obviously enhanced. The two-step excitation water splitting with a solid-state electron mediator was successfully constructed using Bi4TaO8Cl0.9Br0.1 as the O2-evolution photocatalyst and Ru/SrTiO3:Rh as the H2-evolution photocatalyst. The CoOx cocatalyst and reduced graphene oxide decorations on the surface of Bi4TaO8Cl0.9Br0.1 promoted the catalytic O2 generation process on Bi4TaO8Cl0.9Br0.1 and electron transfer between CoOx/Bi4TaO8Cl0.9Br0.1 and Ru/SrTiO3:Rh photocatalysts, respectively. As a result, the apparent quantum yield for this overall water-splitting system was 1.26% at 420 nm, which surpassed the present performance of the two-step excitation water-splitting systems consisting of metal oxyhalide photocatalysts. This study demonstrates the validity of high-quality solid-solution photocatalysts with suitable surface modification for efficient solar hydrogen production from water splitting.

Comparison of initial learning algorithms for long short-term memory method on real-time respiratory signal prediction.

Aim: This study aimed to examine the effect of the weight initializers on the respiratory signal prediction performance using the long short-term memory (LSTM) model. Methods: Respiratory signals collected with the CyberKnife Synchrony device during 304 breathing motion traces were used in this study. The effectiveness of four weight initializers (Glorot, He, Orthogonal, and Narrow-normal) on the prediction performance of the LSTM model was investigated. The prediction performance was evaluated by the normalized root mean square error (NRMSE) between the ground truth and predicted respiratory signal. Results: Among the four initializers, the He initializer showed the best performance. The mean NRMSE with 385-ms ahead time using the He initializer was superior by 7.5%, 8.3%, and 11.3% as compared to that using the Glorot, Orthogonal, and Narrow-normal initializer, respectively. The confidence interval of NRMSE using Glorot, He, Orthogonal, and Narrow-normal initializer were [0.099, 0.175], [0.097, 0.147], [0.101, 0.176], and [0.107, 0.178], respectively. Conclusions: The experiment results in this study indicated that He could be a valuable initializer in the LSTM model for the respiratory signal prediction.

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort (n = 148) and a validation cohort (n = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted. Feature selection and radiomics score (Radscore) construction were performed using three different machine learning methods: least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM). The SVM-derived Radscore demonstrated a strong correlation with the pCR status, yielding area under the receiver operating characteristic curves (AUCs) of 0.880 and 0.830 in the training and validation cohorts, respectively, outperforming the RF and LASSO methods. Based on this, a nomogram was developed by combining the SVM-based Radscore with clinical indicators to predict pCR after neoadjuvant chemoradiotherapy. The nomogram exhibited superior predictive power, achieving AUCs of 0.910 and 0.866 in the training and validation cohorts, respectively. Calibration curves and decision curve analyses confirmed its appropriateness. The SVM-based Radscore demonstrated promising performance in predicting pCR for LARC patients. The machine learning-driven nomogram, which integrates the Radscore and clinical indicators, represents a valuable tool for predicting pCR in LARC patients.

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

BACKGROUND: Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator. METHODS: Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission. RESULTS: The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed. CONCLUSIONS: A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

Utility of Radiomics for Predicting Patient Survival in Hepatocellular Carcinoma With Portal Vein Tumor Thrombosis Treated With Stereotactic Body Radiotherapy.

Introduction: This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) treated with stereotactic body radiotherapy (SBRT). Materials and Methods: The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the model. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results: A total of four radiomic features and six clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [area under the curve (AUC) = 0.859 (CI: 0.770-0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641-0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion: This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to assess individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

Adaptive respiratory signal prediction using dual multi-layer perceptron neural networks.

PURPOSE: To improve the prediction accuracy of respiratory signals by adapting the multi-layer perceptron neural network (MLP-NN) model to changing respiratory signals. We have previously developed an MLP-NN to predict respiratory signals obtained from a real-time position management (RPM) device. Preliminary testing results indicated that poor prediction accuracy may be observed after several seconds for irregular breathing patterns as only a set of fixed data was used in one-time training. To improve the prediction accuracy, we introduced a continuous learning technique using the updated training data to replace one-time learning using the fixed training data. We carried on this new prediction using an adaptation approach with dual MLP-NNs rather than single MLP-NN. When one MLP-NN was performing prediction of the respiratory signals, another one was being trained using the updated data and vice versa. The predicted performance was evaluated by root-mean-square-error (RMSE) between the predicted and true signals from 202 patients' respiratory patterns each with 1 min recording length. The effects of adding an additional network, training parameter, and respiratory signal irregularity on the performance of the new predictor were investigated based on four different network configurations: a single MLP-NN, high-computation dual MLP-NNs (U1), two different combinations of high- and low-computation dual MLP-NNs (U2 and U3). The RMSEs using U1 method were reduced by 34%, 19%, and 10% compared to those using MLP-NN, U2 and U3 methods, respectively. Continuous training of an MLP-NN based on a dual-network configuration using updated respiratory signals improved prediction accuracy compared to one-time training of an MLP-NN using fixed signals.

Fully Automatic Sliding Motion Compensated and Simultaneous 4D-CBCT via Bilateral Filtering.

PURPOSE: To incorporate the bilateral filtering into the Deformable Vector Field (DVF) based 4D-CBCT reconstruction for realizing a fully automatic sliding motion compensated 4D-CBCT. MATERIALS AND METHODS: Initially, a motion compensated simultaneous algebraic reconstruction technique (mSART) is used to generate a high quality reference phase (e.g. 0% phase) by using all phase projections together with the initial 4D-DVFs. The initial 4D-DVF were generated via Demons registration between 0% phase and each other phase image. The 4D-DVF will then kept updating by matching the forward projection of the deformed high quality 0% phase with the measured projection of the target phase. The loss function during this optimization contains an projection intensity difference matching criterion plus a DVF smoothing constrain term. We introduce a bilateral filtering kernel into the DVF constrain term to estimate the sliding motion automatically. The bilateral filtering kernel contains three sub-kernels: 1) an spatial domain Guassian kernel; 2) an image intensity domain Guassian kernel; and 3) a DVF domain Guassian kernel. By choosing suitable kernel variances, the sliding motion can be extracted. A non-linear conjugate gradient optimizer was used. We validated the algorithm on a non-uniform rotational B-spline based cardiac-torso (NCAT) phantom and four anonymous patient data. For quantification, we used: 1) the Root-Mean-Square-Error (RMSE) together with the Maximum-Error (MaxE); 2) the Dice coefficient of the extracted lung contour from the final reconstructed images and 3) the relative reconstruction error (RE) to evaluate the algorithm's performance. RESULTS: For NCAT phantom, the motion trajectory's RMSE/MaxE are 0.796/1.02 mm for bilateral filtering reconstruction; and 2.704/4.08 mm for original reconstruction. For patient pilot study, the 4D-Dice coefficient obtained with bilateral filtering are consistently higher than that without bilateral filtering. Meantime several image content such as the rib position, the heart edge definition, the fibrous structures all has been better corrected with bilateral filtering. CONCLUSION: We developed a bilateral filtering based fully automatic sliding motion compensated 4D-CBCT scheme. Both digital phantom and initial patient pilot studies confirmed the improved motion estimation and image reconstruction ability. It can be used as a 4D-CBCT image guidance tool for lung SBRT treatment.

Predicting real-time 3D deformation field maps (DFM) based on volumetric cine MRI (VC-MRI) and artificial neural networks for on-board 4D target tracking: a feasibility study.

To predict real-time 3D deformation field maps (DFMs) using Volumetric Cine MRI (VC-MRI) and adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for 4D target tracking. One phase of a prior 4D-MRI is set as the prior phase, MRIprior. Principal component analysis (PCA) is used to extract three major respiratory deformation modes from the DFMs generated between the prior and remaining phases. VC-MRI at each time-step is considered a deformation of MRIprior, where the DFM is represented as a weighted linear combination of the PCA components. The PCA weightings are solved by minimizing the differences between on-board 2D cine MRI and its corresponding VC-MRI slice. The PCA weightings solved during the initial training period are used to train an ADMLP-NN to predict PCA weightings ahead of time during the prediction period. The predicted PCA weightings are used to build predicted 3D DFM and ultimately, predicted VC-MRIs for 4D target tracking. The method was evaluated using a 4D computerized phantom (XCAT) with patient breathing curves and MRI data from a real liver cancer patient. Effects of breathing amplitude change and ADMLP-NN parameter variations were assessed. The accuracy of the PCA curve prediction was evaluated. The predicted real-time 3D tumor was evaluated against the ground-truth using volume dice coefficient (VDC), center-of-mass-shift (COMS), and target tracking errors. For the XCAT study, the average VDC and COMS for the predicted tumor were 0.92 ± 0.02 and 1.06 ± 0.40 mm, respectively, across all predicted time-steps. The correlation coefficients between predicted and actual PCA curves generated through VC-MRI estimation for the 1st/2nd principal components were 0.98/0.89 and 0.99/0.57 in the SI and AP directions, respectively. The optimal number of input neurons, hidden neurons, and MLP-NN for ADMLP-NN PCA weighting coefficient prediction were determined to be 7, 4, and 10, respectively. The optimal cost function threshold was determined to be 0.05. PCA weighting coefficient and VC-MRI accuracy was reduced for increased prediction-step size. Accurate PCA weighting coefficient prediction correlated with accurate VC-MRI prediction. For the patient study, the predicted 4D tumor tracking errors in superior-inferior, anterior-posterior and lateral directions were 0.50 ± 0.47 mm, 0.40 ± 0.55 mm, and 0.28 ± 0.12 mm, respectively. Preliminary studies demonstrated the feasibility to use VC-MRI and artificial neural networks to predict real-time 3D DFMs of the tumor for 4D target tracking.

Methylation of C/EBPα by PRMT1 Inhibits Its Tumor-Suppressive Function in Breast Cancer.

C/EBPα is an essential transcription factor involved in regulating the expression or function of certain cell-cycle regulators, including in breast cancer cells. Although protein arginine methyltransferases have been shown to play oncogenic roles in a variety of cancers, little is known about the role of arginine methylation in regulating the antiproliferation activity of C/EBPα. Here, we report that the protein arginine methyltransferase 1 (PRMT1) is overexpressed in human breast cancer and that elevated PRMT1 correlates with cancer malignancy. RNA-sequencing analysis revealed that knockdown of PRMT1 in breast cancer cells is accompanied by a decrease in the expression of pro-proliferative genes, including cyclin D1. Furthermore, tandem affinity purification followed by mass spectrometry identified PRMT1 as a component of the C/EBPα complex. C/EBPα associated with and was methylated by PRMT1 at three arginine residues (R35, R156, and R165). PRMT1-dependent methylation of C/EBPα promoted the expression of cyclin D1 by blocking the interaction between C/EBPα and its corepressor HDAC3, which resulted in rapid growth of tumor cells during the pathogenesis of breast cancer. Inhibition of PRMT1 significantly impeded the growth of cancer cells from patients with triple-negative breast cancer. This evidence that PRMT1 mediates C/EBPα methylation sheds light on a novel pathway and potential therapeutic target in breast cancer. SIGNIFICANCE: This study provides novel mechanistic insight of the role of the arginine methyltransferase PRMT1 in breast cancer pathogenesis.

An investigation of machine learning methods in delta-radiomics feature analysis.

PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. METHODS: The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery (FLAIR) MRI were acquired. 61 radiomic features (intensity histogram-based, morphological, and texture features) were extracted from the gross tumor volume in each image. Delta-radiomics were calculated between the pre-treatment and post-treatment features. Univariate Cox regression and 3 multivariate machine learning methods (L1-regularized logistic regression [L1-LR], random forest [RF] or neural networks [NN]) were used to select a reduced number of features, and 7 machine learning methods (L1-LR, L2-LR, RF, NN, kernel support vector machine [KSVM], linear support vector machine [LSVM], or naïve bayes [NB]) was used to build classification models for predicting OS. The performances of the total 21 model combinations built based on single-time-point radiomics (pre-treatment, one-week post-treatment, and two-month post-treatment) and delta-radiomics were evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS: For a small cohort of 12 patients, delta-radiomics resulted in significantly higher AUC than pre-treatment radiomics (p-value<0.01). One-week/two-month delta-features resulted in significantly higher AUC (p-value<0.01) than the one-week/two-month post-treatment features, respectively. 18/21 model combinations were with higher AUC from one-week delta-features than two-month delta-features. With one-week delta-features, RF feature selector + KSVM classifier and RF feature selector + NN classifier showed the highest AUC of 0.889. CONCLUSIONS: The results indicated that delta-features could potentially provide better treatment assessment than single-time-point features. The treatment assessment is substantially affected by the time point for computing the delta-features and the combination of machine learning methods for feature selection and classification.

Assessment of concurrent stereotactic radiosurgery and bevacizumab treatment of recurrent malignant gliomas using multi-modality MRI imaging and radiomics analysis.

PURPOSE: To assess the response and predict the overall survival (OS) of recurrent malignant gliomas (MG) patients treated with concurrent BVZ/SRS using multi-modality MRI imaging and radiomics analysis.Methods and materials: SRS was delivered in a single fraction (18/24Gy) or 25Gy in 5 fractions. BVZ was administered immediately before SRS and 2 weeks after. MRI scans were performed before SRS, 1 week and 2 months after SRS. The MR protocol included 2 anatomical (T1w and T2w) and 2 functional (dynamic contrast-enhanced DCE-MRI and diffusion weighted DW-MRI) modalities. Functional biomarkers including apparent diffusion coefficient (ADC), micro-vascular transfer constant Ktrans, brain blood flow FB, and blood volume VB were analyzed. Radiomics analysis was performed to extract imaging features from anatomical MRI images and functional biomarker maps. Wicoxon signed rank tests were performed to evaluate treatment-induced changes, and Mann-Whitney U tests were performed to compare the differences of treatment-induced changes between different patient groups. Selected biomarkers and radiomics features were used to predict the OS after treatment using Support Vector Regression (SVR) with leave-one-out cross validation (LOOCV). RESULTS: Twelve patients with recurrent MG were studied. The median OS was 13.8 months post SRS. DCE results showed that Ktrans (p=0.035) and VB (p=0.035) showed significant decrease 2 months after SRS, and FB showed significant decrease as early as 1 week (p=0.017) after SRS. No functional parameters reflected statistically significant treatment response 1 week after SRS. A total of 888 radiomics features were extracted. 31/126 features demonstrated significant changes 1 week/2 months after SRS, respectively. 9 features' changes were significantly different between WHO Grade III vs IV patient groups, and 6 features' changes were found to be linearly correlated with OS. Using 5 selected features, 9 patients' survival time could be accurately predicted (Mean absolute error = 1.47 months, RMSE = 2.10 months). CONCLUSION: The results of this work demonstrate the potential of combined radiomics analysis and functional MR imaging in quantitatively identifying early treatment response of concurrent SRS/BVZ.

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.

BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis. METHODS: A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests. RESULTS: The gradient boosting linear models based on Cox's partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74). CONCLUSIONS: The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.

3D printing of thermochromic multilayer flexible film for multilevel information encryption.

Flexible thermal-responsive encryption devices are widely employed in information encryption and anti-counterfeiting due to their cost-effectiveness and dynamic data encryption and decryption capabilities. However, most current devices are limited to a single layer of encryption, resulting in restricted decryption methods and storage capacity, as well as reliance on external heating. In this study, we integrate multiple layers of encryption within a single device and introduce self-heating thermochromic technology along with infrared thermal imaging encryption to establish a novel concept of a multilayer flexible encryption system. By combining infrared encryption and thermochromic encryption in three-dimensional space enhances the difficulty level for decryption while achieving high storage capacity for information. The internally integrated conductive heating layer within the multilayer structure facilitates rapid and adjustable heating for thermochromic patterns, eliminating the need for external heat sources. Furthermore, we employ a low-cost customizable multi-material integrated 3D printing process for manufacturing multilayer flexible encryption devices. This research presents an innovative solution for designing and fabricating high-density multilevel flexible encryption devices.