Pseudo-medical image-guided technology based on 'CBCT-only' mode in esophageal cancer radiotherapy.

Purpose To minimize the various errors introduced by image-guided radiotherapy (IGRT) in the application of esophageal cancer treatment, this study proposes a novel technique based on the 'CBCT-only' mode of pseudo-medical image guidance. Methods The framework of this technology consists of two pseudo-medical image synthesis models in the CBCT→CT and the CT→PET direction. The former utilizes a dual-domain parallel deep learning model called AWM-PNet, which incorporates attention waning mechanisms. This model effectively suppresses artifacts in CBCT images in both the sinogram and spatial domains while efficiently capturing important image features and contextual information. The latter leverages tumor location and shape information provided by clinical experts. It introduces a PRAM-GAN model based on a prior region aware mechanism to establish a non-linear mapping relationship between CT and PET image domains.  As a result, it enables the generation of pseudo-PET images that meet the clinical requirements for radiotherapy. Results The NRMSE and multi-scale SSIM (MS-SSIM) were utilized to evaluate the test set, and the results were presented as median values with lower quartile and upper quartile ranges. For the AWM-PNet model, the NRMSE and MS-SSIM values were 0.0218 (0.0143, 0.0255) and 0.9325 (0.9141, 0.9410), respectively. The PRAM-GAN model produced NRMSE and MS-SSIM values of 0.0404 (0.0356, 0.0476) and 0.9154 (0.8971, 0.9294), respectively. Statistical analysis revealed significant differences (p < 0.05) between these models and others. The numerical results of dose metrics, including D98 %, Dmean, and D2 %, validated the accuracy of HU values in the pseudo-CT images synthesized by the AWM-PNet. Furthermore, the Dice coefficient results confirmed statistically significant differences (p < 0.05) in GTV delineation between the pseudo-PET images synthesized using the PRAM-GAN model and other compared methods. Conclusion The AWM-PNet and PRAM-GAN models have the capability to generate accurate pseudo-CT and pseudo-PET images, respectively. The pseudo-image-guided technique based on the 'CBCT-only' mode shows promising prospects for application in esophageal cancer radiotherapy.

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy. MATERIALS AND METHODS: PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. RESULTS: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature. CONCLUSIONS: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

Development and validation of bone-suppressed deep learning classification of COVID-19 presentation in chest radiographs.

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

Research on new treatment mode of radiotherapy based on pseudo-medical images.

BACKGROUND AND OBJECTIVE: Multi-modal medical images with multiple feature information are beneficial for radiotherapy. A new radiotherapy treatment mode based on triangle generative adversarial network (TGAN) model was proposed to synthesize pseudo-medical images between multi-modal datasets. METHODS: CBCT, MRI and CT images of 80 patients with nasopharyngeal carcinoma were selected. The TGAN model based on multi-scale discriminant network was used for data training between different image domains. The generator of the TGAN model refers to cGAN and CycleGAN, and only one generation network can establish the non-linear mapping relationship between multiple image domains. The discriminator used multi-scale discrimination network to guide the generator to synthesize pseudo-medical images that are similar to real images from both shallow and deep aspects. The accuracy of pseudo-medical images was verified in anatomy and dosimetry. RESULTS: In the three synthetic directions, namely, CBCT â†’ CT, CBCT â†’ MRI, and MRI â†’ CT, significant differences (p < 0.05) in the three-fold-cross validation results on PSNR and SSIM metrics between the pseudo-medical images obtained based on TGAN and the real images. In the testing stage, for TGAN, the MAE metric results in the three synthesis directions (CBCT â†’ CT, CBCT â†’ MRI, and MRI â†’ CT) were presented as mean (standard deviation), which were 68.67 (5.83), 83.14 (8.48), and 79.96 (7.59), and the NMI metric results were 0.8643 (0.0253), 0.8051 (0.0268), and 0.8146 (0.0267) respectively. In terms of dose verification, the differences in dose distribution between the pseudo-CT obtained by TGAN and the real CT were minimal. The H values of the measurement results of dose uncertainty in PGTV, PGTVnd, PTV1, and PTV2 were 42.510, 43.121, 17.054, and 7.795, respectively (P < 0.05). The differences were statistically significant. The gamma pass rate (2%/2 mm) of pseudo-CT obtained by the new model was 94.94% (0.73%), and the numerical results were better than those of the three other comparison models. CONCLUSIONS: The pseudo-medical images acquired based on TGAN were close to the real images in anatomy and dosimetry. The pseudo-medical images synthesized by the TGAN model have good application prospects in clinical adaptive radiotherapy.

Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy.

Objective.A multi-discriminator-based cycle generative adversarial network (MD-CycleGAN) model is proposed to synthesize higher-quality pseudo-CT from MRI images.Approach.MRI and CT images obtained at the simulation stage with cervical cancer were selected to train the model. The generator adopted DenseNet as the main architecture. The local and global discriminators based on a convolutional neural network jointly discriminated the authenticity of the input image data. In the testing phase, the model was verified by a fourfold cross-validation method. In the prediction stage, the data were selected to evaluate the accuracy of the pseudo-CT in anatomy and dosimetry, and they were compared with the pseudo-CT synthesized by GAN with the generator based on the architectures of ResNet, sUNet, and FCN.Main results.There are significant differences (P < 0.05) in the fourfold cross-validation results on the peak signal-to-noise ratio and structural similarity index metrics between the pseudo-CT obtained based on MD-CycleGAN and the ground truth CT (CTgt). The pseudo-CT synthesized by MD-CycleGAN had closer anatomical information to the CTgtwith a root mean square error of 47.83 ± 2.92 HU, a normalized mutual information value of 0.9014 ± 0.0212, and a mean absolute error value of 46.79 ± 2.76 HU. The differences in dose distribution between the pseudo-CT obtained by MD-CycleGAN and the CTgtwere minimal. The mean absolute dose errors of Dosemax, Dosemin, and Dosemeanbased on the planning target volume were used to evaluate the dose uncertainty of the four pseudo-CT. The u-values of the Wilcoxon test were 55.407, 41.82, and 56.208, and the differences were statistically significant. The 2%/2 mm-based gamma pass rate (%) of the proposed method was 95.45 ± 1.91, and the comparison methods (ResNet_GAN, sUnet_GAN, and FCN_GAN) were 93.33 ± 1.20, 89.64 ± 1.63, and 87.31 ± 1.94, respectively.Significance.The pseudo-CT images obtained based on MD-CycleGAN have higher imaging quality and are closer to the CTgtin terms of anatomy and dosimetry than other GAN models.

Research on obtaining pseudo CT images based on stacked generative adversarial network.

BACKGROUND: To investigate the feasibility of using a stacked generative adversarial network (sGAN) to synthesize pseudo computed tomography (CT) images based on ultrasound (US) images. METHODS: The pre-radiotherapy US and CT images of 75 patients with cervical cancer were selected for the training set of pseudo-image synthesis. In the first stage, labeled US images were used as the first conditional GAN input to obtain low-resolution pseudo CT images, and in the second stage, a super-resolution reconstruction GAN was used. The pseudo CT image obtained in the first stage was used as an input, following which a high-resolution pseudo CT image with clear texture and accurate grayscale information was obtained. Five cross validation tests were performed to verify our model. The mean absolute error (MAE) was used to compare each pseudo CT with the same patient's real CT image. Also, another 10 cases of patients with cervical cancer, before radiotherapy, were selected for testing, and the pseudo CT image obtained using the neural style transfer (NSF) and CycleGAN methods were compared with that obtained using the sGAN method proposed in this study. Finally, the dosimetric accuracy of pseudo CT images was verified by phantom experiments. RESULTS: The MAE metric values between the pseudo CT obtained based on sGAN, and the real CT in five-fold cross validation are 66.82±1.59 HU, 66.36±1.85 HU, 67.26±2.37 HU, 66.34±1.75 HU, and 67.22±1.30 HU, respectively. The results of the metrics, namely, normalized mutual information (NMI), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR), between the pseudo CT images obtained using the sGAN method and the ground truth CT (CTgt) images were compared with those of the other two methods via the paired t-test, and the differences were statistically significant. The dice similarity coefficient (DSC) measurement results showed that the pseudo CT images obtained using the sGAN method were more similar to the CTgt images of organs at risk. The dosimetric phantom experiments also showed that the dose distribution between the pseudo CT images synthesized by the new method was similar to that of the CTgt images. CONCLUSIONS: Compared with NSF and CycleGAN methods, the sGAN method can obtain more accurate pseudo CT images, thereby providing a new method for image guidance in radiotherapy for cervical cancer.

Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.

PURPOSE: To propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans. METHODS: The improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis. RESULTS: The MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm). CONCLUSION: The pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features.

Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT-Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images.Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT-Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT.The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT-Harris algorithms, and paired sample t tests were conducted (t = 6.142, t = 31.859, P < .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894.The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.

A study on the correlation between radiation field size and gamma index passing rate for MatriXX.

This study aimed to analyze the influence of the radiation field size on the passing rate of the treatment planning system using MatriXX if the field irradiated the circuit.Two sets of static fields which were 10 cm and 30 cm in the left-right direction (X), and was 31 cm to 40 cm in gun-target direction (Y) were designed. In these fields, the gantry was 0 and the monitor units were 200 MU. Two plans from an esophagus carcinoma patient with a planning target volume of 86.4 cm and a cervical carcinoma patient with a planning target volume (PTV) of 2094.1 cm were chosen. The passing rates of these plans were gained without and with protecting the circuit area from lead alloys. The gamma analysis was used and the standard was set to 3%/3 mm.The verification passing rate decreased from 95.0% to 69.2% when X was 10 cm while Y increased from 31 cm to 40 cm. With the protection from low melting point lead alloys, the passing rate was from 96.2% to 89.6%. The results of the second set of plans without lead alloys were similar but the passing rate decreased more sharply. The passing rates of the 2 patients were 99.5% and 57.1%. With the protection of the lead alloys, their passing rates were 99.8% and 72.1%, respectively.The results showed that with the increase of the radiation field size in the Y direction, more areas were irradiated in the circuit, and the passing rate gradually decreases and dropped sharply at a certain threshold. After putting lead alloys above the circuit, the passing rate was much better in the static field but was still less than 90% in the second patient volumetric modulated arc therapy (VMAT) because the circuit was irradiate in other directions. In daily QA, we should pay attention to these patients with long size tumor.

A comparative study of identical VMAT about two adjacent targets with and without fixed-jaw technique.

BACKGROUND: The radiation transmission through the multileaf collimators is undesired in modern techniques such as volumetric modulated arc therapy (VMAT). According to identical plans, in this study, we aim to investigate the dosimetric impact of jaw tracking on the VMAT plans on two adjacent targets. METHODS: Two treatment plans were designed for eight pelvic (cervical) patients with two targets using the same optimization parameters. The original plan (O-plan) used automatically selected jaw positions. In the new plan (F-plan), the jaws were fixed to block two targets in two beams. The dosimetric parameters of the two plans were compared to evaluate the improvement of dose sparing for the body volume between two targets (named interOAR) in F-VMAT. RESULTS: The mean dose of interOAR reduced significantly from 654.96 ± 113.38 cGy for O-VMAT, to 490.84 ± 80.26 cGy for F-VMAT (p = 0.018). The monitor units (MUs) in the F-plans were 1.49-fold higher than that in the O-plan. The F and O-plan performed similarly in target dose homogeneity. The differences in Dmax of spinal cord, Dmax of spinal cord planning organ at risk volume, and V20, V30, and V40 of the intestine were insignificant. CONCLUSIONS: VMAT plans with the fixed-jaw method can reduce the volume between two targets effectively. However, despite the plan quality, the method can only be used when the regular methods cannot reach the clinical requirements for critical organs because of additional MUs.

Imaging study of pseudo-CT images of superposed ultrasound deformation fields acquired in radiotherapy based on step-by-step local registration.

The purpose of this study is to create a new pseudo-computed tomography (CT) imaging approach under superposed ultrasound (US) deformation fields based on step-by-step local registration. Scanned CT and US 3D image datasets of three patients with postoperative cervical carcinoma were selected, including CT (CTsim) and US images (USsim) acquired during simulated positioning process and cone beam CT (CBCT) and US images for positioning verification (USpv) acquired after treatment for 10 times. Regions of interest such as urinary bladders were segmented out and accepted local registration to obtain different deformation fields. These deformation fields were successively performed according to their order and then applied to localized CT images to obtain pseudo-CT (CTps). After filtering, we obtained the final correct pseudo-CT (CTpsf). The pseudo-CT based on the mask of the whole imaging region of US images (WCTps) were acquired as control. Then, we compared CTpsf, CTps, WCTps, and CBCT in terms of their similarity in anatomical structure and differences in pseudo-CT and CTsim in terms of dosimetry. Structural similarity degree between CTpsf and CBCT was larger compared with that between CTps and WCTps. Target regions and dosages of endangered organs between CTpsf and CTsim were different under the same calculation conditions based on the Monte Carlo algorithm. Compared with the VMAT plan of CTsim, the pass rate of CTpsf in γ analysis under the standards of 2% dosage difference and 2-mm distance difference was 91.8%. The imaging quality of CTpsf was better compared with WCTps and CTps. It exhibited high similarity with CBCT in anatomical structure and had favorable application prospect in adaptive radiotherapy. Graphical abstract The local deformation registration is performed between the ultrasound images based on different regions of interest, and then stepwise applied to localized CT images to obtain pseudo-CT. After filtering, the corrected pseudo CT image is obtained.

Research on pseudo-CT imaging technique based on an ultrasound deformation field with binary mask in radiotherapy.

This study aimed to investigate the reliability of pseudo-computed tomography (pseudo-CT) imaging based on ultrasound (US) deformation fields under different binary masks in radiotherapy.We used 3-dimensional (3D) CT and US images, including those acquired during CT simulation positioning, and cone-beam CT (CBCT) and US images acquired 1 week after treating 3 patients with cervical cancer. Image data of 3 different layers were selected from the US images, and 3D CT images of each patient were selected. For US image registration, the following were created and applied: binary masks of the region of interest overlapping (ROIO) between the US image based on simulation positioning and US image for positioning verification, region of interest (ROI), whole overlapping (wholeO), and whole imaging region (whole). Accordingly, the deformation field was obtained and applied to CT images (CTsim), and different pseudo-CT images were acquired. Similarities between the pseudo-CT and CBCT images were compared, and registration accuracies between pseudo-CT images under different binary masks and CTsim were compared and discussed.A pair t test was conducted to normalized mutual information values of the registration accuracy between the pseudo-CT image based on ROIO binary mask and CTsim with other methods (P < .05), and the difference was statistically significant. A pair t test of normalized gray mean-squared errors was also performed (P < .05), and the difference was statistically significant. The similarity function means between pseudo-CT, that is, based on ROIO, ROI, wholeO, whole, and no binary mask, and CBCT were 0.9084, 0.8365, 0.7800, 0.6830, and 0.5518, respectively.Pseudo-CT based on ROIO binary mask best matched with CTsim and achieved the highest similarity with CBCT.

IMRT dose verification considering passing rate and respiratory motion.

The aim of the present study was to investigate the association between the dynamic intensity-modulated radiation therapy planned γ analysis passing rate and respiratory amplitude (A) and period (T) for different tumor volumes. A total of 30 patients with malignant lung tumors were divided into three groups: A; B; and C. The average tumor volumes (V) in the A, B and C groups were 635, 402 and 213 cm3, respectively. The simulated A values were set at 0, 5, 10, 15, 20 and 25 mm. The T values were set at 4, 5 and 6 sec. The γ analysis passing rate was calculated under different conditions (dose difference, 3%; distance difference, 3 mm). Compared with the γ analysis passing rate in the A group (A=0, static; T=4, 5, 6 sec), the γ analysis passing rate deviation (A=5 mm) was <3.3%. However, this difference was not statistically significant (P>0.05). With a gradual increase in A value, the passing rate decreased. The deviation between the 3 groups was <2.5% at the same A value (T=4, 5 and 6 sec). A descending trend of passing rate with increased A value was revealed. At the same A and T values, the passing rate decreased with decreased tumor volume. At the same tumor volume, the passing rate decreased when the A value increased. The respiratory cycle was not demonstrated to be associated with the passing rate. Overall, these results suggest that the A value should be controlled in clinical radiotherapy.

Effects of 16-bit CT imaging scanning conditions for metal implants on radiotherapy dose distribution.

Dose distribution was calculated and analyzed on the basis of 16-bit computed tomography (CT) images in order to investigate the effect of scanning conditions on CT for metal implants. Stainless steel and titanium rods were inserted into a phantom, and CT images were obtained by scanning the phantom under various scanning conditions: i) Fixed tube current of 230 mA and tube voltages of 100, 120, and 140 kV; and ii) fixed tube voltage of 120 kV and tube currents of 180, 230, and 280 mA. The CT value of the metal rod was examined and corrected. In a Varian treatment planning system, a treatment plan was designed on the basis of the CT images obtained under the set scanning conditions. The dose distributions in the phantom were then calculated and compared. The CT value of the metal area slightly changed upon tube current alteration. The dose distribution in the phantom was also similar. The maximum CT values of the stainless steel rod were 14,568, 14,127 and 13,295 HU when the tube voltages were modified to 100, 120, and 140 kV, respectively. The corresponding CT values of the titanium rod were 9,420, 8,140 and 7,310 HU. The dose distribution of the radiotherapy plan changed significantly as the tube voltage varied. Compared with the reference dose, the respective maximum dose differences of the stainless steel and titanium rods in the phantom were 5.70, and 6.62% when the tube voltage varied. The changes in tube currents resulted in a maximum dose error of <1% for stainless steel and titanium. In CT imaging, changes in tube voltages can significantly alter the CT values of metal implants. Thus, this can lead to large errors in radiotherapy dose distributions.

MicroRNA­214 targets Wnt3a to suppress liver cancer cell proliferation.

MicroRNAs (miRNAs/miRs) are crucial molecules that act as tumor suppressor genes or oncogenes in human cancer progression. The dysregulation of miRNA expression has been detected in liver cancer. The present study aimed to explore the molecular mechanisms by which miR­214 affects liver cancer cell proliferation. Reverse transcription­quantitative polymerase chain reaction was used to determine the expression of miR­214 in liver cancer cell lines and hepatocellular carcinoma (HCC) tissues. A luciferase reporter assay was performed to determine whether Wnt3a is a target gene of miR­214. Cell Counting kit­8 and cell cycle analysis were used to explore the effects of miR­214 on liver cancer cell proliferation. Immunohistochemistry was used to detect protein expression levels. Wnt3a knockdown was used to determine the function of Wnt3a in liver cancer cell proliferation. The results demonstrated that the expression levels of human miR­214 were reduced in HCC tissues and liver cancer cell lines compared with in control tissues and cells. Overexpression of miR­214 and Wnt3a silencing each inhibited liver cancer cell growth. Conversely, inhibition of miR­214 promoted liver cancer cell growth. The present study indicated that miR­214 acts as a tumor suppressor and may be considered a promising therapeutic target for the treatment of liver cancer.

MicroRNA-101 suppresses progression of lung cancer through the PTEN/AKT signaling pathway by targeting DNA methyltransferase 3A.

It is well established that transcriptional silencing of critical tumor suppressor genes by DNA methylation is a fundamental process in the initiation of lung cancer. However, the involvement of microRNAs (miRNAs) in restoring abnormal DNA methylation patterns in lung cancer is not well understood. Therefore, and since miRNA-101 is complementary to the 3'-untranslated region of DNA methyltransferase 3A (DNMT3A), we investigated whether miRNA-101 could restore normal DNA methylation patterns in lung cancer cell lines. Bioinformatics has indicated that DNMT3A is a major target of miR-101. In addition, the overexpression of miR-101 downregulates DNMT3A. Using a methylation-specific polymerase chain reaction assay, we demonstrated that methylation of the phosphatase and tensin homolog (PTEN) promoter was reduced in A549 cells transfected with miR-101, but not in the transfected control. Furthermore, overexpression of miR-101 and silencing of DNMT3A suppressed lung cell proliferation and S/G2 transition, and increased apoptosis through the PTEN/AKT pathway in vitro. Furthermore, we observed the opposite phenomenon in A549 cells transfected with a miR-101 inhibitor. Subsequent investigation revealed that overexpression of miR-101 significantly inhibited the tumorigenicity of A549 cells in a nude mouse xenograft model. These results demonstrate that miR-101 affects lung cancer progression through the PTEN/AKT signaling pathway by targeting DNMT3A in lung cells, suggesting that miR-101 may be a novel potential therapeutic strategy in lung cancer treatment.

EGR1 mediates miR-203a suppress the hepatocellular carcinoma cells progression by targeting HOXD3 through EGFR signaling pathway.

EGR1 plays a critical role in cancer progression. However, its precise role in hepatocellular carcinoma has not been elucidated. In this study, we found that the overexpression of EGR1 suppresses hepatocellular carcinoma cell proliferation and increases cell apoptosis by binding to the miR-203a promoter sequence. In addition, we investigated the function of miR-203a on progression of HCC cells. We verified that the effect of overexpression of miR-203a is consistent with that of EGR1 in regulation of cell progression. Through bioinformatic analysis and luciferase assays, we confirmed that miR-203a targets HOXD3. Silencing HOXD3 could block transition of the G2/M phase, increase cell apoptosis, decrease the expression of cell cycle and apoptosis-related proteins, EGFR, p-AKT, p-ERK, CCNB1, CDK1 and Bcl2 by targeting EGFR through EGFR/AKT and ERK cell signaling pathways. Likewise, restoration of HOXD3 counteracted the effects of miR-203a expression.In conclusion, our findings are the first to demonstrate that EGR1 is a key player in the transcriptional control of miR-203a, and that miR-203a acts as an anti-oncogene to suppress HCC tumorigenesis by targeting HOXD3 through EGFR-related cell signaling pathways.

MECP2 promotes the growth of gastric cancer cells by suppressing miR-338-mediated antiproliferative effect.

The methyl-CpG-binding protein 2 (MECP2), a transcriptional suppressor, is involved in gene regulation by binding to methylated promoters. We found that MECP2 is overexpressed in gastric cancer (GC), and that Mecp2 knockdown affects the growth of GC cells both in vitro and in vivo. MECP2 can directly bind to the methylated-CpG island of miR-338 promoter and suppress the expression of two mature microRNAs, namely, miR-338-3p and miR-338-5p. Furthermore, miR-338-5p can suppress GC cell growth by targeting BMI1 (B lymphoma Mo-MLV insertion region 1 homolog). We additionally found that decreased miR-338-5p expression in GC tissues, relative to normal tissues, was significantly negatively correlated with increased BMI1 expression. Silencing MECP2 can indirectly lead to reduced expression of P-REX2, which has been identified as the miR-338-3p target, as well as BMI1 and increasing expression of P16 or P21 both in vitro and in vivo. Altogether, our results indicate that MECP2 promote the proliferation of GC cells via miR-338 (miR-338-3p and miR-338-5p)-mediated antitumor and gene regulatory effect.