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Background: Migration of human aortic smooth muscle cells (HASMCs) contributes to the pathogenesis of atherosclerosis. This study aims to functionally characterize long noncoding RNA TPRG1-AS1 (tumor protein p63 regulated 1, antisense 1) in HASMCs and reveal the underlying mechanism of TPRG1-AS1 in HASMCs migration, neointima formation, and subsequent atherosclerosis. Methods: The expression of TPRG1-AS1 in atherosclerotic plaques was verified a series of in silico analysis and quantitative real-time polymerase chain reaction analysis. Northern blot, rapid amplification of cDNA ends and Sanger sequencing were used to determine its full length. In vitro transcription-translation assay was used to investigate the protein-coding capacity of TPRG1-AS1. RNA fluorescent in situ hybridization was used to confirm its subcellular localization. Loss- and gain-of-function studies were used to investigate the function of TPRG1-AS1. Furthermore, the effect of TPRG1-AS1 on the pathological response was evaluated in carotid balloon injury model, wire injury model, and atherosclerosis model, respectively. Results: TPRG1-AS1 was significantly increased in atherosclerotic plaques. TPRG1-AS1 did not encode any proteins and its full length was 1279nt, which was bona fide a long noncoding RNA. TPRG1-AS1 was mainly localized in cytoplasmic and perinuclear regions in HASMCs. TPRG1-AS1 directly interacted with MYH9 (myosin heavy chain 9) protein in HASMCs, promoted MYH9 protein degradation through the proteasome pathway, hindered F-actin stress fiber formation, and finally inhibited HASMCs migration. Vascular smooth muscle cell-specific transgenic overexpression of TPRG1-AS1 significantly reduced neointima formation, and attenuated atherosclerosis in apolipoprotein E knockout ( Apoe −/− ) mice. Conclusions: This study demonstrated that TPRG1-AS1 inhibited HASMCs migration through interacting with MYH9 protein and consequently suppressed neointima formation and atherosclerosis.

Several inverse planning algorithms have been developed for Gamma Knife (GK) radiosurgery to determine a large number of plan parameters by solving an optimization problem, which typically consists of multiple objectives. The priorities among these objectives need to be repetitively adjusted to achieve a clinically good plan for each patient. This study aimed to achieve automatic and intelligent priority tuning by developing a deep reinforcement learning (DRL)-based method to model the tuning behaviors of human planners.We built a priority-tuning policy network using deep convolutional neural networks. Its input was a vector composed of multiple plan metrics that were used in our institution for GK plan evaluation. The network can determine which tuning action to take based on the observed quality of the intermediate plan. We trained the network using an end-to-end DRL framework to approximate the optimal action-value function. A scoring function was designed to measure the plan quality to calculate the received reward of a tuning action.Vestibular schwannoma was chosen as the test bed in this study. The number of training, validation and testing cases were 5, 5, and 16, respectively. For these three datasets, the average scores of the initial plans obtained with the same initial priority set were 3.63 ± 1.34, 3.83 ± 0.86 and 4.20 ± 0.78, respectively, while they were improved to 5.28 ± 0.23, 4.97 ± 0.44 and 5.22 ± 0.26 through manual priority tuning by human expert planners. Our network achieved competitive results with 5.42 ± 0.11, 5.10 ± 0. 42, 5.28 ± 0.20, respectively.Our network can generate GK plans of comparable or slightly higher quality than the plans generated by human planners via manual priority tuning for vestibular schwannoma cases. The network can potentially be incorporated into the clinical workflow as planning assistance to improve GK planning efficiency and help to reduce plan quality variation caused by interplanner variability. We also hope that our method can reduce the workload of GK planners and allow them to spend more time on more challenging cases.

To assess groundwater nitrate contamination and its human health risks, 489 unconfined groundwater samples were collected and analyzed from Zhangjiakou, northern China. The spatial distribution of principle hydrogeochemical results showed that the average concentrations of ions in descend order was HCO3−, SO42−, Na+, Ca2+, Cl−, NO3−, Mg2+ and K+, among which the NO3− concentrations were between 0.25 and 536.73 mg/L with an average of 29.72 mg/L. In total, 167 out of 489 samples (~ 34%) exceeded the recommended concentration of 20 mg/L in Quality Standard for Groundwater of China. The high NO3− concentration groundwater mainly located in the northern part and near the boundary of the two geomorphic units. As revealed by statistical analysis, the groundwater chemistry was more significantly affected by anthropogenic sources than by the geogenic sources. Moreover, human health risks of groundwater nitrate through oral and dermal exposure pathways were assessed by model, the results showed that about 60%, 50%, 32% and 26% of the area exceeded the acceptable level (total health index>1) for infants, children, adult males and females, respectively. The health risks for different groups of people varied significantly, ranked: infants> children> adult males>adult females, suggesting that younger people are more susceptible to nitrate contamination, while females are more resistant to nitrate contamination than males. To ensure the drinking water safety in Zhangjiakou and its downstream areas, proper management and treatment of groundwater will be necessary to avoid the health risks associated with nitrate contamination.

Objective Isolated true subclavian artery aneurysm (SAA) without aberrant subclavian artery or coarctation of descending aorta is a rare peripheral aneurysm. Herein, the experience of our medical center in the treatment of this disease is presented. Methods The Division operative log was queried to identify cases of SAA repair between January 2012 and September 2019 that were not associated with coarctation of the aorta or the presence of an aberrant subclavian artery. A total of 22 cases were identified. The characteristics, treatment and clinical outcomes of these cases were assessed. Results The mean age of patients was 53.5 ± 14.3 years and 14 patients were male (63.6%). Half of the cases were attributed to atherosclerotic degeneration. The clinical symptoms of aneurysms were varied, including asymptomatic, pulsatile mass of supraclavicular fossa, local pain, upper limb embolism, Horner's syndrome and hoarseness. Aneurysms were located on the right in 17 cases, on the left in 3 cases and on both sides in 2 cases. Fifteen (68%) patients underwent an intervention, of which 11 (50%) underwent an open surgical repair, and 4 (18%) underwent endovascular repair. The mean diameter of the aneurysms was 39.5 ± 20.7 mm in the open surgery group, and 24.0 ± 4.7 mm in the endovascular group. The follow-up duration ranged from 2 months to 12 years. One patient died of cardiogenic disease in the untreated group. Patients undergoing open operative repair had 100% patency of the reconstruction. In the endovascular group, one patient had stent occlusion 2 years after the operation. Conclusions The most common cause of isolated subclavian aneurysm without aberrant subclavian artery or coarctation of descending aorta is atherosclerosis. The clinical symptoms of aneurysms are varied, and the aneurysms tend to occur on the right side. Based on the anatomical conditions of SAAs, open surgery and endovascular repair can be used for treatment.

Although commercial treatment planning systems (TPSs) can automatically solve the optimization problem for treatment planning, human planners need to define and adjust the planning objectives/constraints to obtain clinically acceptable plans. Such a process is labor-intensive and time-consuming. In this work, we show an end-to-end study to train a deep reinforcement learning (DRL) based virtual treatment planner (VTP) that can behave like a human to operate a dose-volume constrained treatment plan optimization engine following the parameters used in Eclipse TPS for high-quality treatment planning. We considered the prostate cancer IMRT treatment plan as the testbed. The VTP took the dose-volume histogram (DVH) of a plan as input and predicted the optimal strategy for constraint adjustment to improve the plan quality. The training of VTP followed the state-of-the-art Q-learning framework. Experience replay was implemented with epsilon-greedy search to explore the impacts of taking different actions on a large number of automatically generated plans, from which an optimal policy can be learned. Since a major computational cost in training was to solve the plan optimization problem repeatedly, we implemented a graphical processing unit (GPU)-based technique to improve the efficiency by 2-fold. Upon the completion of training, the established VTP was deployed to plan for an independent set of 50 testing patient cases. Connecting the established VTP with the Eclipse workstation via the application programming interface, we tested the performance the VTP in operating Eclipse TPS for automatic treatment planning with another two independent patient cases. Like a human planner, VTP kept adjusting the planning objectives/constraints to improve plan quality until the plan was acceptable or the maximum number of adjustment steps was reached under both scenarios. The generated plans were evaluated using the ProKnow scoring system. The mean plan score (± standard deviation) of the 50 testing cases were improved from 6.18 ± 1.75 to 8.14 ± 1.27 by the VTP, with 9 being the maximal score. As for the two cases under Eclipse dose optimization, the plan scores were improved from 8 to 8.4 and 8.7 respectively by the VTP. These results indicated that the proposed DRL-based VTP was able to operate the in-house dose-volume constrained TPS and Eclipse TPS to automatically generate high-quality treatment plans for prostate cancer IMRT.

Neointimal hyperplasia (NIH) plays a pivotal role in vascular restenosis after revascularization. We previously identified that chemokine‐like factor 1 (CKLF1) could aggravate NIH by arresting smooth muscle cells in G2/M phase and preventing apoptosis via phosphoinositide 3‐kinase/AKT/nuclear factor‐kappa B signaling. Here, we demonstrate that CKLF1 promotes monocyte adhesion and smooth muscle cell migration via VCAM‐1. Our work furthers our understanding of how CKLF1 contributes to NIH causality., Neointimal hyperplasia (NIH) is a complicated inflammatory process contributing to vascular restenosis. The present study aimed to explore whether chemokine‐like factor 1 (CKLF1) aggravates NIH via the nuclear factor‐kappa B (NF‐κB)/vascular cell adhesion molecule‐1 (VCAM‐1) pathway. We found the expression of CKLF1 and VCAM‐1 significantly increased in human carotid plaques compared to the control. In vivo, CKLF1 overexpression induced a thicker neointimal formation and VCAM‐1 expression was correspondingly upregulated. In vitro, CKLF1 activated NF‐κB and induced VCAM‐1 upregulation in human aortic smooth muscle cells (HASMCs). Functional experiments demonstrated that CKLF1 promoted monocyte adhesion and HASMC migration via VCAM‐1. These results suggest CKLF1 accelerates NIH by promoting monocyte adhesion and HASMC migration via the NF‐κB/VCAM‐1 pathway. Our findings contribute to a better understanding of the mechanisms underlying the causality of CKLF1 on NIH and could prove beneficial in designing therapeutic modalities with a focus on CKLF1.

Purpose Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U-net-based approach that synthesizes CT-like images with accurate numbers from planning CT, while keeping the same anatomical structure as on-treatment CBCT. Methods We formulated the CT synthesis problem under a deep learning framework, where a deep U-net architecture was used to take advantage of the anatomical structure of on-treatment CBCT and image intensity information of planning CT. U-net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U-net (sCTU-net), we include on-treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU-net, we use another seven independent patient cases (560 slices) for testing. Results We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same-day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU. Conclusions The proposed sCTU-net can synthesize CT-quality images with accurate CT numbers from on-treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time. To address this problem, we proposed to develop a deep learning-based system to automatically classify benign and malignant lung nodules. The proposed method automatically determines benignity or malignancy given the 3D CT image patch of a lung nodule to assist diagnosis process. Motivated by the fact that real structure among data is often embedded on a low-dimensional manifold, we developed a novel manifold regularized classification deep neural network (MRC-DNN) to perform classification directly based on the manifold representation of lung nodule images. The concise manifold representation revealing important data structure is expected to benefit the classification, while the manifold regularization enforces strong, but natural constraints on network training, preventing over-fitting. The proposed method achieves accurate manifold learning with reconstruction error of ~ 30 HU on real lung nodule CT image data. In addition, the classification accuracy on testing data is 0.90 with sensitivity of 0.81 and specificity of 0.95, which outperforms state-of-the-art deep learning methods. The proposed MRC-DNN facilitates an accurate manifold learning approach for lung nodule classification based on 3D CT images. More importantly, MRC-DNN suggests a new and effective idea of enforcing regularization for network training, possessing the potential impact to a board range of applications.

Purpose Applicator digitization is one of the most critical steps in 3D high-dose-rate brachytherapy (HDRBT) treatment planning. Motivated by recent advances in deep-learning, we propose a deep-learning–assisted applicator digitization method for 3D CT image-based HDRBT. This study demonstrates its feasibility and potential in gynecological cancer HDRBT. Methods and Materials Our method consisted of two steps. The first step used a U-net to segment applicator regions. We trained the U-net using two-dimensional CT images with a tandem-and-ovoid (T&O) applicator and corresponding applicator mask images. The second step applied a spectral clustering method and a polynomial curve fitting method to extract applicator central paths. We evaluated the accuracy, efficiency, and robustness of our method in different scenarios including other T&O cases that were not used in training, a T&O case scanned with cone-beam CT, and Y-tandem and cylinder-applicator cases. Results In test cases with a T&O applicator, average 3D Dice similarity coefficient between automatic and manual segmented applicator regions was 0.93. Average distance between tip positions and average Hausdorff distance between applicator channels determined by our method and manually were 0.64 mm and 0.68 mm, respectively. Although trained only using CT images of T&O cases, our tool can also digitize Y-tandem, cylinder applicator, and T&O applicator scanned in cone-beam CT with error of tip position and Hausdorff distance Conclusions We have developed a deep-learning–assisted applicator digitization tool for 3D CT image–based HDRBT of gynecological cancer. The achieved accuracy, efficiency, and robustness made our tool clinically attractive.

Purpose External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. There is a clinical need to identify patients at high risk for fistula formation such that treatment may be managed to prevent this toxic side effect. This work aims to develop a fistula prediction model framework using machine learning based on patient, tumor, and treatment features. Methods and Materials This retrospective study included 35 patients treated at our institution using interstitial brachytherapy for various gynecological malignancies. Five patients developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction framework. A nonlinear support vector machine was used to build the prediction model. Sequential backward feature selection and sequential floating backward feature selection methods were used to determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling technique was used to generate synthetic fistula cases for model training. Results Seven mixed data features were selected by both sequential backward selection and sequential floating backward selection methods. Our prediction model using these features achieved a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity. Conclusions A machine-learning–based prediction model of fistula formation has been developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy. This model may be clinically impactful pending refinement and validation in a larger series.

PURPOSE: We have previously proposed an intelligent automatic treatment planning (IATP) framework that builds a virtual treatment planner network (VTPN) to operate a treatment planning system (TPS) to generate high-quality radiation therapy (RT) treatment plans. While the potential of IATP in automating RT treatment planning has been demonstrated, its poor scalability caused by an almost linear growth of network size with the number of treatment planning parameters (TPPs) is a bottleneck, preventing its application in complicate, but clinically relevant treatment planning problems. The decision-making behavior of the trained network is hard to understand. Motivated by the decision-making process of a human planner, this study proposes a hierarchical IATP framework. METHODS AND MATERIALS: The hierarchical VTPN (HieVTPN) consists of three networks, i.e. Structure-Net, Parameter-Net, and Action-Net. When interacting with a TPS, the networks are employed in a sequential order in each step to decide the structure to adjust, the TPP to adjust for the selected structure, and the specific adjustment manner for the parameter, respectively. We developed an end-to-end hierarchical deep reinforcement learning (DRL) scheme to simultaneously train the three networks. We then evaluated the effectiveness of the proposed framework in the treatment planning problems for prostate cancer intensity modulated RT (IMRT) and stereotactic body RT (SBRT). We benchmarked the performance of our approach by comparing plans made by VTPN of a parallel architecture, and the human plans submitted for competition in the 2016 American Association of Medical Dosimetrist (AAMD)/Radiosurgery Society (RSS) Plan Study. We analyzed scalability of the network size with respect to the number of TPPs. Numerical experiments were also performed to understand the rationale of the decision-making behaviors of the trained HieVTPN. RESULTS: Both HieVTPNs for prostate IMRT and SBRT were trained successfully using 10 training patient cases and 5 validation cases. For IMRT, HieVTPN was able to generate high-quality plans for 59 testing patient cases that were not included in training process, achieving an average plan score of 8.62 (±0.83), with 9 being the maximal score. The score was comparable to that of the VTPN, 8.45 (±0.48). For SBRT planning, HieVTPN achieved an average plan score of 139.07 on five testing patient cases compared to the score of 132.21 averaged over the human plans summited for competition in AAMD/RSS plan study. Different from VTPN with network size linearly scaling with the number of TPPs, the network size of HieVTPN is almost independent of the number of TPPs. It was also observed that the decision-making behaviors of HieVTPN were understandable and generally agreed with the human experience. CONCLUSIONS: With the scalability and explainability, the hierarchical IATP framework is more favorable than the previous framework in terms of handling treatment planning problems involving a large number of TPPs.

MiR-520b belongs to the miR-373/520 family, is expressed only in human and nonhuman primates. Previous reports indicated that the expression of miR-520b was repressed in human atherosclerotic plaque tissue compared with healthy vessels. However, the role of miR-520b in coronary artery disease still remains to be uncovered. In this study, we demonstrated that endothelial cells (ECs) in human atherosclerotic plaques expressed miR-520b and aimed to elucidate the impact of miR-520b on EC activation and inflammatory response. To determine the potential targets of miR-520b, we performed RNA-seq analysis by transfecting miR-520b mimics in ECs. The quantitative real-time PCR (qPCR) validation suggested that miR-520b over-expression reduced pro-inflammatory gene expression (e.g. ICAM1, VCAM1, SELE) while the inhibition of miR-520b induced their expression. By combining bioinformatics prediction and functional assays, we identified that RELA (Nuclear Factor-κB (NF-κB) Transcription Factor P65) was a direct target of miR-520b. Moreover, miR-520b mimics attenuated monocyte adhesion and monocyte trans-endothelial migration (the initial steps of atherosclerotic formation) in response to lipopolysaccharides (LPS) stimulation. Re-expression of a non-miR-targetable version of p65 could rescue the reduced monocyte cell attachment, suggesting that this process is NF-κB p65 dependent. MiR-520b reduced the abundance of NF-κB p65 in cytoplasmic fractions without corresponding increase in nuclear fractions, indicating that this regulation is independent of p65 translocation process. MiR-520b mimics attenuated the activity of VCAM-1 promoter, whereas miR-520b inhibitor activated its activity. However, miR-520b inhibitor had no effect on promoter activity containing the mutated NF-κB p65 binding sites, strongly demonstrating that the impact of miR-520b on VCAM1 gene is mediated by NF-κB p65. Thus, we concluded that miR-520b suppressed EC inflammation and the cross-talk between monocytes and ECs by down-regulating NF-κB p65-ICAM1/VCAM1 axis and might serve as a potential therapeutic target for EC dysfunction and atherosclerosis.

Purpose. Needle catheter positions critically affect the quality of treatment plans in prostate cancer high-dose-rate (HDR) brachytherapy. The current standard needle positioning approach is based on human intuition, which cannot guarantee a high-quality plan. This study proposed a method to simultaneously select needle catheter positions and determine dwell time for preplanning of HDR brachytherapy of prostate cancer. Methods. We formulated the needle catheter selection problem and inverse dwell time optimization problem in a unified framework. In addition to the dose objectives of the planning target volume (PTV) and organs at risk (OARs), the objective function incorporated a group-sparsity term with a needle-specific adaptive weighting scheme to generate high-quality plans with the minimal number of needle catheters. The optimization problem was solved by a fast-iterative shrinkage-thresholding algorithm. For validation purposes, we tested the proposed algorithm on 10 patient cases previously treated at our institution and compared the resulting plans with plans generated using needle catheters selected manually. Results. Compared to the plan with manually selected needle catheters, when normalizing both plans to the same PTV coverage V 100% = 95%, the plans generated by the proposed algorithm reduced median V 125% from 65% to 64%, but increased median V 150% from 35% to 38%, and V 200% from 14% to 16%. All planning objectives were met. All clinically important dosimetric parameters of OARs were reduced. D 1cc of bladder and rectum were reduced from 8.57 Gy to 8.50 Gy and from 7.24 Gy to 6.80 Gy, respectively. D max of urethra was reduced from 15.85 Gy to 15.77 Gy. The median number of selected needle catheters was reduced by two. The computational time for solving the proposed optimization problem was ∼90 s using MATLAB. Conclusion. The proposed algorithm was able to generate plans for prostate cancer HDR brachytherapy preplanning with increased median conformity index (0.73–0.77) and slightly lower median homogeneity index (0.64–0.62) with the number of selected needles reduced by two compared to the manual needle selection approach.

Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups. The established DNN was able to predict malignancy rate of lung nodules based on CT images, achieving the area under the curve of 0.91 for the testing dataset in a tenfold cross validation as compared to radiologists’ prediction. We then evaluated its robustness against noise perturbations. We added to the input CT images noise signals generated randomly or via an optimization scheme using a realistic noise model based on a noise power spectrum for a given mAs level, and monitored the DNN’s output. The results showed that the CT noise was able to affect the prediction results of the established DNN model. With random noise perturbations at 100 mAs, DNN’s predictions for 11.2% of training data and 17.4% of testing data were successfully altered by at least once. The percentage increased to 23.4% and 34.3%, respectively, for optimization-based perturbations. We further evaluated robustness of models with different architectures, parameters, number of output labels, etc, and robustness concern was found in these models to different degrees. To improve model robustness, we empirically proposed an adaptive training scheme. It fine-tuned the DNN model by including perturbations in the training dataset that successfully altered the DNN’s perturbations. The adaptive scheme was repeatedly performed to gradually improve DNN’s robustness. The numbers of perturbations at 100 mAs affecting DNN’s predictions were reduced to 10.8% for training and 21.1% for testing by the adaptive training scheme after two iterations. Our study illustrated that robustness may potentially be a concern for an exemplary DL-based lung-nodule classification model for CT images, indicating the needs for evaluating and ensuring model robustness when developing similar models. The proposed adaptive training scheme may be able to improve model robustness.

Post-carotid endarterectomy hypertension is a well-recognized phenomenon closely related to surgical complications. This study aimed to determine whether different kinds of perioperative antihypertensive drugs had a protective effect on post-carotid endarterectomy hypertension and influence on intraoperative hemodynamics.We retrospectively investigated 102 carotid stenosis patients who underwent conventional endarterectomy with a perioperative baseline antihypertensive regimen. Post-carotid endarterectomy hypertension was defined as a postoperative peak systolic blood pressure ≥160 mmHg and/or a requirement for any additional antihypertensive therapies. We compared the clinical characteristics and types of baseline perioperative antihypertensive drugs between patients with and without post-carotid endarterectomy hypertension and then determined the significant independent effect of antihypertensive drugs on post-carotid endarterectomy hypertension through multivariate regression and detected their influence on intraoperative hypertension (induction-related systolic blood pressure and vasodilators consumption) and hemodynamic depression (intra-arterial systolic blood pressure ≤100 mmHg and/or heart rate ≤50 beats/min). We also investigated adverse events such as stroke, death, myocardial infarction, and cerebral hyperperfusion syndrome during the postoperative hospitalization.A total of 52/102 (51.0%) patients were defined as having post-carotid endarterectomy hypertension during the first three days postoperative, including eight patients with a postoperative systolic blood pressure that exceeded 160 mmHg at least once, 31 patients requiring postoperative antihypertensive treatment in addition to their baseline regimen, and 13 patients with both. The incidence of stroke/death/myocardial infarction and cerebral hyperperfusion syndrome after conventional endarterectomy during hospitalization were both 1.9%. A significantly increased risk of composite postoperative complications (including cerebral hyperperfusion syndrome, hyperperfusion-related symptoms, transient ischemic attacks, stroke, death, and cardiac complications) was observed in patients with post-carotid endarterectomy hypertension than without (15.4% versus 2.0%,The perioperative use of β-blockers is a protective factor for post-carotid endarterectomy hypertension and contributes to stabilizing the postoperative peak systolic blood pressure three days after conventional endarterectomy. However, β-blockers might also lead to intraoperative hemodynamic depression, especially for patients with a low baseline heart rate.

Motion management is a critical component of image guidance radiotherapy for lung cancer. We previously proposed a scheme using kV scattered x-ray photons for marker-less real-time image guidance in lung cancer radiotherapy. This study reports our recently progress using the photon counting detection technique to demonstrate potential feasibility of this method and using Monte Carlo (MC) simulations and ray-tracing calculations to characterize the performance. In our scheme, a thin slice of x-ray beam was directed to the target and we measured the outgoing scattered photons using a photon counting detector with a parallel-hole collimator to establish the correspondence between detector pixels and scatter positions. Image corrections of geometry, beam attenuation and scattering angle were performed to convert the raw image to the actual image of Compton attenuation coefficient. We set up a MC simulation system using an in-house developed GPU-based MC package modeling the image formation process. We also performed ray-tracing calculations to investigate the impacts of imaging system geometry on resulting image resolution. The experiment demonstrated feasibility of using a photon counting detector to measure scattered x-ray photons and generate the proposed scattered x-ray image. After correction, x-ray scattering image intensity and Compton scattering attenuation coefficient were linearly related, with R2=0.91. Contrast to Noise Ratios of different objects were improved and the values in experimental results and MC simulation results agreed with each other. Ray-tracing calculations revealed the dependence of image resolution on imaging geometry. The image resolution increases with reduced source to object distance and increased collimator height. The study demonstrated potential feasibility of using scattered x-ray imaging as a real-time image guidance method in radiation therapy., 16 pages, 10 figures

Driving pattern recognition based on features, such as GPS, gear, and speed information, is essential to develop intelligent transportation systems. However, it is usually expensive and labor intensive to collect a large amount of labeled driving data from real-world driving scenes. The lack of a labeled data problem in a driving scene substantially hinders the driving pattern recognition accuracy. To handle the scarcity of labeled data, we have developed a novel discriminative transfer learning method for driving pattern recognition to leverage knowledge from related scenes with labeled data to improve recognition performance in unlabeled scenes. Note that data from different scenes may have different distributions, which is a major bottleneck limiting the performance of transfer learning. To address this issue, the proposed method adopts a discriminative distribution matching scheme with the aid of pseudolabels in unlabeled scenes. It is able to reduce the intraclass distribution disagreement for the same driving pattern among labeled and unlabeled scenes while increasing the interclass distance among different patterns. Pseudolabels in unlabeled scenes are updated iteratively via an ensemble strategy that preserves the data structure while enhancing the model robustness. To evaluate the performance of the proposed method, we conducted comprehensive experiments on real-world parking lot datasets. The results show that the proposed method can substantially outperform state-of-the-art methods in driving pattern recognition.

Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuated, and CT contrast enhancement is not used, which further increase problem difficulty. Deep learning (DL) has demonstrated its power in many segmentation problems. However, standard 2-D approaches cannot handle the sigmoid segmentation problem due to incomplete geometry information and 3-D approaches often encounters the challenge of a limited training data size. Motivated by human's behavior that segments the sigmoid slice by slice while considering connectivity between adjacent slices, we proposed an iterative 2.5-D DL approach to solve this problem. We constructed a network that took an axial CT slice, the sigmoid mask in this slice, and an adjacent CT slice to segment as input and output the predicted mask on the adjacent slice. We also considered other organ masks as prior information. We trained the iterative network with 50 patient cases using five-fold cross validation. The trained network was repeatedly applied to generate masks slice by slice. The method achieved average Dice similarity coefficients of 0.82 0.06 and 0.88 0.02 in 10 test cases without and with using prior information.

PURPOSE In the treatment planning process of intensity-modulated radiation therapy (IMRT), a human planner operates the treatment planning system (TPS) to adjust treatment planning parameters, for example, dose volume histogram (DVH) constraints' locations and weights, to achieve a satisfactory plan for each patient. This process is usually time-consuming, and the plan quality depends on planer's experience and available planning time. In this study, we proposed to model the behaviors of human planners in treatment planning by a deep reinforcement learning (DRL)-based virtual treatment planner network (VTPN), such that it can operate the TPS in a human-like manner for treatment planning. METHODS AND MATERIALS Using prostate cancer IMRT as an example, we established the VTPN using a deep neural network developed. We considered an in-house optimization engine with a weighted quadratic objective function. Virtual treatment planner network was designed to observe an intermediate plan DVHs and decide the action to improve the plan by changing weights and threshold dose in the objective function. We trained the VTPN in an end-to-end DRL process in 10 patient cases. A plan score was used to measure plan quality. We demonstrated the feasibility and effectiveness of the trained VTPN in another 64 patient cases. RESULTS Virtual treatment planner network was trained to spontaneously learn how to adjust treatment planning parameters to generate high-quality treatment plans. In the 64 testing cases, with initialized parameters, quality score was 4.97 (±2.02), with 9.0 being the highest possible score. Using VTPN to perform treatment planning improved quality score to 8.44 (±0.48). CONCLUSIONS To our knowledge, this was the first time that intelligent treatment planning behaviors of human planner in external beam IMRT are autonomously encoded in an artificial intelligence system. The trained VTPN is capable of behaving in a human-like way to produce high-quality plans.

As one of the most popular approaches in artificial intelligence, deep learning (DL) has attracted a lot of attention in the medical physics field over the past few years. The goals of this topical review article are twofold. First, we will provide an overview of the method to medical physics researchers interested in DL to help them start the endeavor. Second, we will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions. As such, we divide the article into two major parts. The first part introduces general concepts and principles of DL and summarizes major research resources, such as computational tools and databases. The second part discusses challenges faced by DL, present available methods to mitigate some of these challenges, as well as our recommendations.

Objective. Treatment planning of radiation therapy is a time-consuming task. It is desirable to develop automatic planning approaches to generate plans favorable to physicians. The purpose of this study is to develop a deep learning based virtual physician network (VPN) that models physician’s preference on plan approval for prostate cancer stereotactic body radiation therapy (SBRT). Approach. VPN takes one planning target volume (PTV) and eight organs at risk structure images, as well as a dose distribution of a plan seeking approval as input. It outputs a probability of approving the plan, and a dose distribution indicating improvements to the input dose. Due to the lack of unapproved plans in our database, VPN is trained using an adversarial framework. 68 prostate cancer patients who received 45 Gy in 5-fraction SBRT were selected in this study, with 60 patients for training and cross validation, and 8 patients for independent testing. Main results. The trained VPN was able to differentiate approved and unapproved plans with Area under the curve 0.97 for testing data. For unapproved plans, after applying VPN’s suggested dose improvement, the improved dose agreed with ground truth with relative differences 2.03 ± 2.17 % for PTV D 98 % , 0.49 ± 0.29 % for PTV V 95 % , 3.08 ± 2.24 % for penile bulb D mean , 3.73 ± 2.20 % for rectum V 50 % , and 2.06 ± 1.73 % for bladder V 50 % . Significance. VPN was developed to accurately model a physician’s preference on plan approval and to provide suggestions on how to improve the dose distribution.

Intra-fractional motion is a concern during prostate radiation therapy, as it may cause deviations between planned and delivered radiation doses. Because accurate motion information during treatment delivery is critical to address dose deviation, we developed the projection marker matching method (PM3), a novel method for prostate motion reconstruction in volumetric modulated arc therapy. The purpose of this method is to reconstruct in-treatment prostate motion trajectory using projected positions of implanted fiducial markers measured in kV x-ray projection images acquired during treatment delivery. We formulated this task as a quadratic optimization problem. The objective function penalized the distance from the reconstructed 3D position of each fiducial marker to the corresponding straight line, defined by the x-ray projection of the marker. Rigid translational motion of the prostate and motion smoothness along the temporal dimension were assumed and incorporated into the optimization model. We tested the motion reconstruction method in both simulation and phantom experimental studies. We quantified the accuracy using 3D normalized root-mean-square (RMS) error defined as the norm of a vector containing ratios between the absolute RMS errors and corresponding motion ranges in three dimensions. In the simulation study with realistic prostate motion trajectories, the 3D normalized RMS error was on average [Formula: see text] (range from [Formula: see text] to [Formula: see text]). In an experimental study, a prostate phantom was driven to move along a realistic prostate motion trajectory. The 3D normalized RMS error was [Formula: see text]. We also examined the impact of the model parameters on reconstruction accuracy, and found that a single set of parameters can be used for all the tested cases to accurately reconstruct the motion trajectories. The motion trajectory derived by PM3 may be incorporated into novel strategies, including 4D dose reconstruction and adaptive treatment replanning to address motion-induced dose deviation.

Objective The endovascular approach has been widely used for aortoiliac occlusive disease (AIOD), especially for aortic bifurcation and iliac artery Trans-Atlantic Inter-Society Consensus-II (TASC-II) A and B lesions. However, the outcomes of self-expanding covered stents (SECSs) for extensive aortoiliac lesion remain unclear. This study aimed to assess the short-term patency of kissing covered stents for the revascularization of aortoiliac TASC-II C and D diseases. Materials and Methods Thirty-three patients with TASC-II C and D lesions of AIOD were treated with kissing covered stents. All patients were reviewed under a standard institutional review board protocol. Demographic variables, lesion location and characteristics, stenting configuration, and patency were analyzed. Results Thirty-one male and 2 female patients with a mean age of 65.1 ± 10.7 years underwent aortoiliac bifurcation reconstruction with kissing SECSs. Eight patients had TASC-II C lesions, 25 had TASC-II D lesions. Among them, 8 patients had total infrarenal aortoiliac occlusion, of which 5 had juxtarenal aortoiliac lesions. The mean lesion length was 11.6 ± 2.1 cm. Mean diameters of aorta and CIA were 18.3 ± 2.1 and 10.7 ± 1.5 mm, respectively. Among them, the abutting stent configuration was used in 11 patients with short or focal ostial lesions, whereas the crossing stent configuration was used in 22 patients with longer lesions extending into the distal aorta. The mean follow-up was 24.5 ± 7.8 months, the follow-up rate was 93.9% (31/33), and 29 patients had follow-up longer than 12 months. Primary patency rate at 12 months was 96.5%, and secondary patency rate was 100%. Conclusions The use of kissing SECSs for the revascularization of extensive AIOD is safe and effective. The short-term primary patency rates of endovascular treatment of TASC-II C and D lesions were favorable.

Purpose We previously proposed an intelligent automatic treatment planning framework for radiotherapy, in which a virtual treatment planner network (VTPN) is built using deep reinforcement learning (DRL) to operate a treatment planning system (TPS) by adjusting treatment planning parameters in it to generate high-quality plans. We demonstrated the potential feasibility of this idea in prostate cancer intensity-modulated radiation therapy (IMRT). Despite the success, the process to train a VTPN via the standard DRL approach with an e-greedy algorithm was time consuming. The required training time was expected to grow with the complexity of the treatment planning problem, preventing the development of VTPN for more complicated but clinically relevant scenarios. In this study, we proposed a novel knowledge-guided DRL (KgDRL) approach that incorporated knowledge from human planners to guide the training process to improve the efficiency of training a VTPN. Method Using prostate cancer IMRT as a testbed, we first summarized a number of rules in the actions of adjusting treatment planning parameters of our in-house TPS. During the training process of VTPN, in addition to randomly navigating the large state-action space, as in the standard DRL approach using the e-greedy algorithm, we also sampled actions defined by the rules. The priority of sampling actions from rules decreased over the training process to encourage VTPN to explore new policy on parameter adjustment that were not covered by the rules. To test this idea, we trained a VTPN using KgDRL and compared its performance with another VTPN trained using the standard DRL approach. Both networks were trained using 10 training patient cases and 5 additional cases for validation, while another 59 cases were employed for the evaluation purpose. Results It was found that both VTPNs trained via KgDRL and standard DRL spontaneously learned how to operate the in-house TPS to generate high-quality plans, achieving plan quality scores of 8.82 (±0.29) and 8.43 (±0.48), respectively. Both VTPNs outperformed treatment planning purely based on the rules, which had a plan score of 7.81 (±1.59). VTPN trained with eight episodes using KgDRL was able to perform similarly to that trained using DRL with 100 epochs. The training time was reduced from more than a week to ~13 hours. Conclusion The proposed KgDRL framework was effective in accelerating the training process of a VTPN by incorporating human knowledge, which will facilitate the development of VTPN for more complicated treatment planning scenarios.

Road dust from different sources directly contacts the human body and has potential effects on public health. In this study, a total number of 87 road dust samples were collected at 29 sampling sites from five different functional areas (commercial area (CA), residential area (RA), educational area (EA), industrial area (IA), and park area (PA)) in Zhengzhou to study the contamination status, distribution, source identification, ecological risk assessment, and spatial distribution of human health risks due to eight heavy elements. The geo-accumulation index (Igeo) and pollution index (PI) revealed that there was very high contamination with Cd and Hg caused by atmospheric deposition, which should be paid special attention. Additionally, the source identification indicated that Cr, Ni, Cu, Zn, Cd, and Pb originate from anthropogenic activities related to traffic, and Hg can originate from medical equipment and agricultural chemicals, while the extremely low level of pollution with As could be explained by geographic sources. Moreover, the calculated ecological risk index values were increased in the order of CA > RA > EA > IA > PA in different functional areas. According to the human health risks of the whole city, children exposed to Pb have the highest health risk, especially for CA and IA, as calculated by the noncarcinogenic hazard index (HI). For adults and children, health risks caused by Cu, Zn, and Pb were higher in the CA, RA, and PA of the downtown area, whereas Cr and Ni had the highest noncarcinogenic exposure risk in northwestern Zhengzhou due to point source pollution. Calculations of the carcinogenic risk (CR) values for Cr, Ni, As, and Cd indicate that the value of Cr is highest (1.17 × 10−7), especially inside the industrial area (8.55 × 10−7), which is close to the lower limit of the threshold values (10−6 to 10−4). These results can provide a theoretical basis and data support for air treatment, pollution control, and the implementation of public prevention in different functional areas of Zhengzhou.

Complex intra-fractional motion and deformation of the liver significantly impacts the accuracy of delivered dose in radiotherapy, and limits margin reduction, dose escalation and normal tissue sparing. A critical component of motion management is to accurately reconstruct tumor motion. In this study, we developed a Six Degree of Freedom Projection Marker Matching method (6-DoF PM3) to reconstruct translational and rotational liver tumor motion in a rotational treatment delivery, such as volumetric modulated arc therapy (VMAT). Specifically, we modeled the use of two gold markers implanted in a linear form. The four endpoints of the two gold linear markers were used as tracking surrogates. During delivery, kV x-ray projection images were acquired. A method was developed to automatically identify the 2D marker-endpoints on the projection images. 3D marker positions were determined by solving an optimization problem with the objective function penalizing the distance from the reconstructed 3D position of each fiducial marker endpoint to the corresponding straight line defined by the kV x-ray projection of the endpoints. We performed a series of tests to evaluate different components of the method. For 2D marker endpoints identification, 99.9% of the marker endpoints were identified with an error ≤0.338 mm (1 pixel) along both u and v directions. For 3D reconstruction of motion in simulation studies, error of rotational angle was (0.5 ± 6.5) × 10(−3) degree without considering the 2D marker identification error. The rotational angle error was relatively sensitive to the accuracy of 2D marker identification. When the error raised from 0.22mm to 0.776 mm, the error of 3D rotational angle increased from 0.5 degree to 2.5 degree. In the experimental end-to-end tests, the mean root-mean square error of the 3D reconstructed marker positions was 0.75 mm and the mean error of rotational angle was within 1.7°. Our method can accurately determine intra-fractional liver tumor motion in rotational radiotherapy using kV projections of only two linear fiducial markers.

Digitization of interstitial needles is a complicated and tedious process for the treatment planning of 3D CT image based interstitial high dose-rate brachytherapy (HDRBT) of gynecological cancer. We developed a deep-learning assisted auto-digitization method for interstitial needles. The digitization method consisted of two steps. The first step used a deep neural network with a U-net structure to segment all needles from CT images. The second step simultaneously clustered the segmented voxels into different needle groups and generated the needle central trajectories by solving an optimization problem. We evaluated the effectiveness of the developed method in ten interstitial HDRBT patient cases that were not used in the training of the U-net. Average number of needles per case was 20.7. For the segmentation step, average Dice similarity coefficient between automatic and manual segmentation was 0.93. For the digitization step, Hausdorff distance between needle trajectories determined by our method and manually by qualified medical physicists was ~0.71 mm on average and mean difference of tip positions was ~0.63 mm, which were considered acceptable for HDRBT treatment planning. It took ~5 min to complete the digitization process of an interstitial HDRBT case. The achieved accuracy and efficiency made our method clinically attractive.

PURPOSE: We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks. In this study, we trained a neural network for generating Pareto optimal dose distributions, and evaluate the effects of the domain specific loss on the model performance. METHODS: In this study, 3 loss functions—mean squared error (MSE) loss, dose volume histogram (DVH) loss, and adversarial (ADV) loss—were used to train and compare 4 instances of the neural network model: 1) MSE, 2) MSE+ADV, 3) MSE+DVH, and 4) MSE+DVH+ADV. The data for 70 prostate patients, including the planning target volume (PTV), and the organs-at-risk (OAR) were acquired as 96 × 96 × 24 dimension arrays at 5 mm(3) voxel size. The dose influence arrays were calculated for 70 prostate patients, using a 7 equidistant coplanar beam setup. Using a scalarized multicriteria optimization for intensity modulated radiation therapy, 1200 Pareto surface plans per patient were generated by pseudo-randomizing the PTV and OAR tradeoff weights. With 70 patients, the total number of plans generated was 84,000 plans. We divided the data into 54 training, 6 validation, and 10 testing patients. Each model was trained for a total of 100,000 iterations, with a batch size of 2. All models used the Adam optimizer, with a learning rate of 1 × 10(−3). RESULTS: Training for 100,000 iterations took 1.5 days (MSE), 3.5 days (MSE+ADV), 2.3 days (MSE+DVH), 3.8 days (MSE+DVH+ADV). After training, the prediction time of each model is 0.052 seconds. Quantitatively, the MSE+DVH+ADV model had the lowest prediction error of 0.038 (conformation), 0.026 (homogeneity), 0.298 (R50), 1.65% (D95), 2.14% (D98), 2.43% (D99). The MSE model had the worst prediction error of 0.134 (conformation), 0.041 (homogeneity), 0.520 (R50), 3.91% (D95), 4.33% (D98), 4.60% (D99). For both the mean dose PTV error and the max dose PTV, Body, Bladder and rectum error, the MSE+DVH+ADV outperformed all other models. Regardless of model, all predictions have an average mean and max dose error less than 2.8% and 4.2%, respectively. CONCLUSION: The MSE+DVH+ADV model performed the best in these categories, illustrating the importance of both human and learned domain knowledge. Expert human domain specific knowledge can be the largest driver in the performance improvement, and adversarial learning can be used to further capture nuanced attributes in the data. The real-time prediction capabilities allow for a physician to quickly navigate the tradeoff space for a patient, and produce a dose distribution as a tangible endpoint for the dosimetrist to use for planning. This is expected to considerably reduce the treatment planning time, allowing for clinicians to focus their efforts on the difficult and demanding cases.

An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and robustness because Bluetooth signal strength is subject to fluctuation. We developed a machine learning-based solution using a Long Short-Term Memory (LSTM) network followed by a Multilayer Perceptron classifier and a posterior constraint algorithm to improve RTLS performance. Training and validation datasets showed that most machine learning models perform well in classifying individual location zones, although LSTM was most reliable. However, when faced with data indicating cross-zone trajectories, all models showed erratic zone switching. Thus, we implemented a history-based posterior constraint algorithm to reduce the variability in exchange for a slight decrease in responsiveness. This network increases robustness at the expense of latency. When latency is less of a concern, we computed the latency-corrected accuracy which is 100% for our testing data, significantly improved from LSTM without constraint which is 96.2%. The balance between robustness and responsiveness can be considered and adjusted on a case-by-case basis, according to the specific needs of downstream clinical applications. This system was deployed and validated in an academic medical center. Industry best practices enabled system scaling without substantial compromises to performance or cost., Comment: 20 pages, 6 figures, submitted to Physics in Medicine & Biology

Cervical tumor segmentation on 3D (18)FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake (18)FDG tracers. This problem makes traditional segmentation based on intensity variation methods ineffective and reduces overall accuracy. Based on anatomy knowledge, including “roundness” of the cervical tumor and relative positioning between the bladder and cervix, we propose a supervised machine learning method that integrates convolutional neural network (CNN) with this prior information to segment cervical tumors. First, we constructed a spatial information embedded CNN model (S-CNN) that maps the PET image to its corresponding label map, in which bladder, other normal tissue, and cervical tumor pixels are labeled as −1, 0, and 1, respectively. Then, we obtained the final segmentation from the output of the network by a prior information constrained (PIC) thresholding method. We evaluated the performance of the PIC-S-CNN method on PET images from 50 cervical cancer patients. The PIC-S-CNN method achieved a mean Dice similarity coefficient (DSC) of 0.84 while region-growing, Chan-Vese, graph-cut, fully convolutional neural networks (FCN) based FCN-8 stride, and FCN-2 stride, and U-net achieved 0.55, 0.64, 0.67, 0.71, 0.77, and 0.80 mean DSC, respectively. The proposed PIC-S-CNN provides a more accurate way for segmenting cervical tumors on 3D PET images. Our results suggest that combining deep learning and anatomic prior information may improve segmentation accuracy for cervical tumors.

Adaptive-radiation-therapy (ART) is applied to account for anatomical variations observed over the treatment course. Daily or weekly cone-beam computed tomography (CBCT) is commonly used in clinic for patient positioning, but CBCT’s inaccuracy in Hounsfield units (HU) prevents its application to dose calculation and treatment planning. Adaptive re-planning can be performed by deformably registering planning CT (pCT) to CBCT. However, scattering artifacts and noise in CBCT decrease the accuracy of deformable registration and induce uncertainty in treatment plan. Hence, generating from CBCT a synthetic CT (sCT) that has the same anatomical structure as CBCT but accurate HU values is desirable for ART. We proposed an unsupervised style-transfer-based approach to generate sCT based on CBCT and pCT. Unsupervised learning was desired because exactly matched CBCT and CT are rarely available, even when they are taken a few minutes apart. In the proposed model, CBCT and pCT are two inputs that provide anatomical structure and accurate HU information, respectively. The training objective function is designed to simultaneously minimize (1) contextual loss between sCT and CBCT to maintain the content and structure of CBCT in sCT and (2) style loss between sCT and pCT to achieve pCT-like image quality in sCT. We used CBCT and pCT images of 114 patients to train and validate the designed model, and another 29 independent patient cases to test the model’s effectiveness. We quantitatively compared the resulting sCT with the original CBCT using the deformed same-day pCT as reference. Structure-similarity-index, peak-signal-to-noise-ratio, and mean-absolute-error in HU of sCT were 0.9723, 33.68, and 28.52, respectively, while those of CBCT were 0.9182, 29.67, and 49.90, respectively. We have demonstrated the effectiveness of the proposed model in using CBCT and pCT to synthesize CT-quality images. This model may permit using CBCT for advanced applications such as adaptive treatment planning.

ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.

Because in PET imaging cervical tumors are close to the bladder with high capacity for the secreted (18)FDG tracer, conventional intensity-based segmentation methods often misclassify the bladder as a tumor. Based on the observation that tumor position and area do not change dramatically from slice to slice, we propose a two-stage scheme that facilitates segmentation. In the first stage, we used a graph-cut based algorithm to obtain initial contouring of the tumor based on local similarity information between voxels; this was achieved through manual contouring of the cervical tumor on one slice. In the second stage, initial tumor contours were fine-tuned to more accurate segmentation by incorporating similarity information on tumor shape and position among adjacent slices, according to an intensity-spatial-distance map. Experimental results illustrate that the proposed two-stage algorithm provides a more effective approach to segmenting cervical tumors in 3D (18)FDG PET images than the benchmarks used for comparison.

The dual-energy CT-based (DECT) approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation as compared to the conventional stoichiometric calibration approach. The objective of this study was to analyze the factors contributing to uncertainty in SPR estimation using the DECT-based approach and to derive a comprehensive estimate of the range uncertainty associated with SPR estimation in treatment planning. Two state-of-the-art DECT-based methods, the Hünemohr-Saito method (2014, 2012) and the Bourque method (2014), were selected and implemented on a Siemens SOMATOM Force DECT scanner. The uncertainties were first divided into five independent categories. The uncertainty associated with each category was estimated for lung, soft and bone tissues separately. A single composite uncertainty estimate was eventually determined for three tumor sites (lung, prostate and head-and-neck) by weighting the relative proportion of each tissue group for that specific site. The uncertainties associated with the two selected DECT methods were found to be similar, therefore the following results applied to both methods. The overall uncertainty (1σ) in SPR estimation with the DECT-based approach was estimated to be 3.8%, 1.2% and 2.0% for lung, soft and bone tissues, respectively. The dominant factor contributing to uncertainty in the DECT approach was the imaging uncertainties, followed by the DECT modeling uncertainties. Our study showed that the DECT approach can reduce the overall range uncertainty to approximately 2.2% (2σ) in clinical scenarios, in contrast to the previously reported 1%.

Inverse treatment planning in radiation therapy is formulated as solving optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem. While a treatment planning optimization engine can solve the optimization problem with given weights, adjusting the weights to yield a high-quality plan is typically performed by a human planner. Yet the weight-tuning task is labor intensive, time consuming, and it critically affects the final plan quality. An automatic weight-tuning approach is strongly desired. The procedure of weight adjustment to improve the plan quality is essentially a decision-making problem. Motivated by the tremendous success in deep learning for decision making with human-level intelligence, we propose a novel framework to adjust the weights in a human-like manner. This study used inverse treatment planning in high-dose-rate brachytherapy (HDRBT) for cervical cancer as an example. We developed a weight-tuning policy network (WTPN) that observes dose volume histograms of a plan and outputs an action to adjust organ weighting factors, similar to the behaviors of a human planner. We trained the WTPN via end-to-end deep reinforcement learning. Experience replay was performed with the epsilon greedy algorithm. After training was completed, we applied the trained WTPN to guide treatment planning of five testing patient cases. It was found that the trained WTPN successfully learnt the treatment planning goals and was able to guide the weight tuning process. On average, the quality score of plans generated under the WTPN's guidance was improved by ~8.5% compared to the initial plan with arbitrarily set weights, and by 10.7% compared to the plans generated by human planners. To our knowledge, this was the first time that a tool was developed to adjust organ weights for the treatment planning optimization problem in a human-like fashion based on intelligence learnt from a training process, which was different from existing strategies based on pre-defined rules. The study demonstrated potential feasibility to develop intelligent treatment planning approaches via deep reinforcement learning.

A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative weights among them. It is of critical importance to tune these parameters, as quality of the solution depends on their values. Tuning parameter is a relatively straightforward task for a human, as one can intelligently determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative CT reconstruction with a pixel-wise total-variation regularization term. We set up a parameter tuning policy network (PTPN), which maps an CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN for parameter tuning at each pixel, reconstructed CT images attain quality similar or better than in those reconstructed with manually tuned parameters., Comment: 8 pages, 8 figures, 2 tables

Atherosclerotic occlusive disease of the lower extremities (ASO-LE) has the third highest rate among systematic atherosclerosis obliterans, ranking after coronary heart disease and stoke, and the disease burden of ASO-LE has been continuously increasing. Invasive revascularizations, which is presented by endovascular therapy technique, has undergone a dramatic development in the past couples of decades. However, controversy concerned about the surgical management and operative indications has heated up in the meanwhile. Thus Society for Vascular Surgery (SVS) published the practice guidelines for ASO-LE with asymptomatic disease and claudication in March, 2015. At the first time the guideline definitely opposed the aggressive invasive revascularization for ASO-LE patients with asymptomatic disease or claudication under satisfied tolerance. Instead, it posed the extreme emphasis on the pharmacotherapy with risk reduction of atherosclerosis at the core and the exercise therapy with supervised or home-based exercise program at the core for ASO-LE patients with asymptomatic disease and claudication.

To evaluate the clinical safety and efficacy of percutaneous transluminal atherectomy for in-stent restenosis (ISR) in patients with low extremity peripheral arterial diseases (PAD).PubMed, Elsevier, EBSCO, Spring databases and Cochrane Library were searched for relevant articles. Based on the different mechanisms of atherectomy, the patients were divided into mechanic atherectomy group and laser atherectomy group. The safety end points included the rate of distal embolism and severe arterial wall injuries. And the efficacy end points included primary patency rate and freedom from target vessel revascularization (TVR-free) 6 months and 12 months after surgery.A total of 9 studies and 620 patients (published between 2006 and 2014) were accepted. The rate of distal embolism was 4.2% (95% confidence interval (CI): 1.7%-6.7%), while that of severe arterial wall injuries was 1.9% (95%CI: 0.9%-3.0%), respectively. Laser atherectomy was responsible for more distal embolism (6.8%) compared to mechanic atherectomy (2.0%), which was significantly different (Q=21.66, P=0.010). At 6-month follow-up, primary patency rate and rate of TVR-free were 63.0% (95% CI: 55.5%-70.6%) and 80.4% (95% CI: 70.5%-90.3%), while at 12-month follow-up were 43.5% (95%CI: 32.2%-54.9%) and 58.0% (95% CI: 52.1%-63.9%), respectively. The free-TVR rate at 6 months follow-up in mechanical atherectomy group was 77.9%, and was inferior to that in laser atherectomy group (80.8%, Q=13.49, P=0.009). Published bias was discovered at the analysis of 12-month TVR-free rate by means of Begg Test (P=0.039). Meta analysis concerned about the 3 randomized controlled trials demonstrated that there was no significant improvement using atherectomy for ISR comparing to standard balloon at 6-month TVR-free rate (OR=1.34, 95% CI: 0.86-2.07, P=0.196).To treat ISR lesion in lower extremities, laser atherectomy has a lower free-TVR rate in the middle term follow-up.A higher rate of distal embolism is noted though. On balance, percutaneous transluminal atherectomy demonstrates no significant improvement compared to plain balloon angioplasty for ISR lesions.

To study the treatment strategy and survival of patients with primary leiomyosarcoma of inferior vena cava (PIVCLMS).Clinical data of 12 cases with PIVCLMS admitted in Peking University People's Hospital from January 2006 to September 2014 were reviewed retrospectively. All cases were confirmed by pathology examination. Among them, there were 4 male and 8 female patients with a mean age of (54 ± 9) years old. Tumors arose from the inferior vena cava (IVC) upper segment in 5 patients, from the middle in other 7 patients. Cardiac extension was observed in 4 cases. Tumor resection was undertaken in 8 patients, the other 4 patients were inoperable. The series was analyzed to identify clinical outcome of surgical strategy and protective factors for patient survival.In tumor resection group, 6 patients had radical resection and 2 underwent palliative resection. As for IVC reconstruction, caval wall resection with a direct suture was carried out in 6 patients or with prosthetic patch in 1 patient. The other 1 patient underwent a segment caval resection and prosthetic graft replacement in situ. In 4 cases of suprahepatic PIVCLMS cardiopulmonary bypass or perfusion by right atrial intubation was performed to assist bleeding control and maintain circulation stabilization, among them 1 patient survived for more than 101 months with no tumor recurrence or metastasis. Among the patients submitted to tumor resection 2 early postoperative deaths occurred, and another 2 patients had complications. All 4 patients submitted to non-resective operation (only neoplasm biopsy) died of PIVCLMS within 8 months. Except for 2 cases of early death, mean survival after tumor resection was (54 ± 40) months. Two patients presented local recurrence and hepatic metastasis at follow-up of 16 months and 68 months.Tumor resection is the only therapy for PIVCLMS with an expectation for long-term survival. The applicant of cardiopulmonary bypass makes some inoperable indicated to tumor resection.

Computational fluid dynamics (CFD) technology has the potential to simulate normal or pathologic aortic blood flow changes of mechanical properties and flow field, thereby helping researchers understand and reveal the occurrence, development and prognosis of aortic disease. In aortic diseases research, the initial conditions of CFD numerical simulation has experienced a developed process from idealization (forward engineering), rigid vessel wall, uniform cross-sections, laminar flow and stable blood flow towards personalization (reverse engineering), elastic vessel wall (fluid-solid coupling technique), cone-shaped diminishing cross-sections, turbulent flow, pulsatile blood flow. In this review, the research status, the technical superiority and application prospect of CFD technology were discussed with examples in following three major application areas: (1) dynamics characteristic and mechanical properties in normal thoracic aorta; (2) occurrence, advance and disruptive risk predicting in thoracic aortic aneurysm; (3) therapeutic effect and aneurysmal dilatation simulation in thoracic aortic dissection. For the future, the CFD technology may profoundly put an influence on the awareness to aortic diseases and treatment strategies.

Objective: To clone and identify genes differentially expressed in human lung squamous cell carcinoma (LSCC). Methods: A subtracted cDNA library of human LSCC was constructed by using suppression subtractive hybridization (SSH) method. Through screening, the subtracted library clones, representing mRNAs that are truly differentially expressed in LSCC but not in normal lung tissues, were selected out to identify by semi-quantitative RT-PCR in 12 patients of LSCC and performed DNA sequencing. Nucleic acid homology searches were performed using the BLAST program. Partial novel genes were detected by Northern blot. Results: By this technique, we obtained 10 differentially expressed gene cDNA fragments of LSCC. Among them six were already known genes; two sequences were already identified but their functions were still unknown (hypothetical protein); two were novel (GenBank accession number were AF363068 and AY032661, respectively). The results from semi-quantitative RT-PCR showed that the transcription expression level of these clones including PPP1CB, caluminin, S100A2, HSNOV1, OCIA and AY032661 was down-regulated in 12 cases of LSCC, while the transcription of HSP90, ferritin, gp96 and AF363068 was up-regulated in same cases. Conclusion: SSH is a powerful technique of high sensitivity for the detection of differential gene expression in LSCC and an effective method to clone novel genes. Six already known genes identified by SSH technique have been already implicated in the pathogenesis of lung carcinogenesis, or they are involved in immunological defense mechanism in human body. Two hypothetical proteins probably also play an important role in lung cancer pathogenesis. The function of two novel genes in lung carcinogenesis is under research.