Effects of sterilization and disinfection methods on digitally designed surgical implant guide accuracy: An in vitro study.

INTRODUCTION: Surgical guides are commonly used to assist with dental implant placement. This study investigated the effects of five sterilization and disinfection methods on the accuracy of implant guides. METHODS: Thirty surgical guides (five in each group) were designed and printed (with digital light processing technology) using different sterilization or disinfection methods categorized into six groups: hydrogen peroxide sterilization (group one); glutaraldehyde sterilization (group two); autoclaving (group three); plasma sterilization (group four); iodophor disinfection (group five); and blank group (group six). Verification was determined using three methods: distance and angle between the cross-shaped marks, deformation after superimposing the guides, and displacement and axial changes in the virtual implant. RESULTS: After disinfection and sterilization, the guides in the autoclaving and iodophor groups showed a more pronounced color change and the guide in the autoclaving group had visible cracks. More significant changes were observed in the H2O2, glutaraldehyde, autoclaving, and iodophor groups regarding deformation after superimposing the guides and the distance and angle between the cross-shaped marks. The average labial deformation values (mm) of the first through fifth groups of guides were 0.283, 0.172, 0.289, 0.153, and 0.188, respectively. All groups were statistically different from the blank group for displacement and axial changes of the virtual implant (p < 0.05). CONCLUSION: The sizes of almost all surgical guides changed after sterilization and disinfection treatments, with between-group differences. Plasma sterilization was more suitable for surgical guide sterilization because of the smaller deformations after treatment.

A Hybrid Model for Fetal Growth Restriction Assessment by Automatic Placental Radiomics on T2-Weighted MRI and Multifeature Fusion.

BACKGROUND: MRI-based placental analyses have been used to improve fetal growth restriction (FGR) assessment by complementing ultrasound-based measurements. However, these are still limited by time-consuming manual annotation in MRI data and the lack of mother-based information. PURPOSE: To develop and validate a hybrid model for accurate FGR assessment by automatic placental radiomics on T2-weighted imaging (T2WI) and multifeature fusion. STUDY TYPE: Retrospective. POPULATION: 274 pregnant women (29.5 ± $$ \pm $$ 4.0 years) from two centers were included and randomly divided into training (N = 119), internal test (N = 40), time-independent validation (N = 43), and external validation (N = 72) sets. FIELD STRENGTH/SEQUENCE: 1.5-T, T2WI half-Fourier acquisition single-shot turbo spin-echo pulse sequence. ASSESSMENT: First, the placentas on T2WI were manually annotated, and a deep learning model was developed to automatically segment the placentas. Then, the radiomic features were extracted from the placentas and selected by three-step feature selection. In addition, fetus-based measurement features and mother-based clinical features were obtained from ultrasound examinations and medical records, respectively. Finally, a hybrid model based on random forest was constructed by fusing these features, and further compared with models based on other machine learning methods and different feature combinations. STATISTICAL TESTS: The performances of placenta segmentation and FGR assessment were evaluated by Dice similarity coefficient (DSC) and the area under the receiver operating characteristic curve (AUROC), respectively. A P-value <0.05 was considered statistically significant. RESULTS: The placentas were automatically segmented with an average DSC of 90.0%. The hybrid model achieved an AUROC of 0.923, 0.931, and 0.880 on the internal test, time-independent validation, and external validation sets, respectively. The mother-based clinical features resulted in significant performance improvements for FGR assessment. DATA CONCLUSION: The proposed hybrid model may be able to assess FGR with high accuracy. Furthermore, information complementation based on placental, fetal, and maternal features could also lead to better FGR assessment performance. TECHNICAL EFFICACY: Stage 2.

Graph structure learning layer and its graph convolution clustering application.

To learn the embedding representation of graph structure data corrupted by noise and outliers, existing graph structure learning networks usually follow the two-step paradigm, i.e., constructing a "good" graph structure and achieving the message passing for signals supported on the learned graph. However, the data corrupted by noise may make the learned graph structure unreliable. In this paper, we propose an adaptive graph convolutional clustering network that alternatively adjusts the graph structure and node representation layer-by-layer with back-propagation. Specifically, we design a Graph Structure Learning layer before each Graph Convolutional layer to learn the sparse graph structure from the node representations, where the graph structure is implicitly determined by the solution to the optimal self-expression problem. This is one of the first works that uses an optimization process as a Graph Network layer, which is obviously different from the function operation in traditional deep learning layers. An efficient iterative optimization algorithm is given to solve the optimal self-expression problem in the Graph Structure Learning layer. Experimental results show that the proposed method can effectively defend the negative effects of inaccurate graph structures. The code is available at https://github.com/HeXiax/SSGNN.

A heuristic method for rapid and automatic radiofrequency ablation planning of liver tumors.

PURPOSE: Preprocedural planning is a key step in radiofrequency ablation (RFA) treatment for liver tumors, which is a complex task with multiple constraints and relies heavily on the personal experience of interventional radiologists, and existing optimization-based automatic RFA planning methods are very time-consuming. In this paper, we aim to develop a heuristic RFA planning method to rapidly and automatically make a clinically acceptable RFA plan. METHODS: First, the insertion direction is heuristically initialized based on tumor long axis. Then, the 3D RFA planning is divided into insertion path planning and ablation position planning, which are further simplified into 2D by projections along two orthogonal directions. Here, a heuristic algorithm based on regular arrangement and step-wise adjustment is proposed to implement the 2D planning tasks. Experiments are conducted on patients with liver tumors of different sizes and shapes from multicenter to evaluate the proposed method. RESULTS: The proposed method automatically generated clinically acceptable RFA plans within 3 min for all cases in the test set and the clinical validation set. All RFA plans of our method achieve 100% treatment zone coverage without damaging the vital organs. Compared with the optimization-based method, the proposed method reduces the planning time by dozens of times while generating RFA plans with similar ablation efficiency. CONCLUSION: The proposed method demonstrates a new way to rapidly and automatically generate clinically acceptable RFA plans with multiple clinical constraints. The plans of our method are consistent with the clinical actual plans on almost all cases, which demonstrates the effectiveness of the proposed method and can help reduce the burden on clinicians.

Predicting the risk of fetal growth restriction by radiomics analysis of the placenta on T2WI: A retrospective case-control study.

INTRODUCTION: Fetal growth restriction (FGR) is associated with placental abnormalities, and its precise diagnosis is challenging. This study aimed to explore the role of radiomics based on placental MRI in predicting FGR. METHODS: A retrospective study using T2-weighted placental MRI data were conducted. A total of 960 radiomic features were automatically extracted. Features were selected using three-step machine learning methods. A combined model was constructed by combining MRI-based radiomic features and ultrasound-based fetal measurements. The receiver operating characteristic curves (ROC) were conducted to assess model performance. Additionally, decision curves and calibration curves were performed to evaluate prediction consistency of different models. RESULTS: Among the study participants, pregnant women who delivered from January 2015 to June 2021 were randomly divided into training (n = 119) and test (n = 40) sets. Forty-three other pregnant women who delivered from July 2021 to December 2021 were used as the time-independent validation set. After training and testing, three radiomic features that were strongly correlated with FGR were selected. The area under the ROC curves (AUCs) of the MRI-based radiomics model reached 0.87 (95% confidence interval [CI]: 0.74-0.96) and 0.87 (95% CI: 0.76-0.97) in the test and validation sets, respectively. Moreover, the AUCs for the model comprising MRI-based radiomic features and ultrasound-based measurements were 0.91 (95% CI: 0.83-0.97) and 0.94 (95% CI: 0.86-0.99) in the test and validation sets, respectively. DISCUSSION: MRI-based placental radiomics could accurately predict FGR. Moreover, combining placental MRI-based radiomic features with ultrasound indicators of the fetus could improve the diagnostic accuracy of FGR.

MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.

OBJECTIVES: To develop and validate a two-stage deep learning model for automatic detection and segmentation of brain metastases (BMs) in MRI images. METHODS: In this retrospective study, T1-weighted (T1) and T1-weighted contrast-enhanced (T1ce) MRI images of 649 patients who underwent radiotherapy from August 2019 to January 2022 were included. A total of 5163 metastases were manually annotated by neuroradiologists. A two-stage deep learning model was developed for automatic detection and segmentation of BMs, which consisted of a lightweight segmentation network for generating metastases proposals and a multi-scale classification network for false-positive suppression. Its performance was evaluated by sensitivity, precision, F1-score, dice, and relative volume difference (RVD). RESULTS: Six hundred forty-nine patients were randomly divided into training (n = 295), validation (n = 99), and testing (n = 255) sets. The proposed two-stage model achieved a sensitivity of 90% (1463/1632) and a precision of 56% (1463/2629) on the testing set, outperforming one-stage methods based on a single-shot detector, 3D U-Net, and nnU-Net, whose sensitivities were 78% (1276/1632), 79% (1290/1632), and 87% (1426/1632), and the precisions were 40% (1276/3222), 51% (1290/2507), and 53% (1426/2688), respectively. Particularly for BMs smaller than 5 mm, the proposed model achieved a sensitivity of 66% (116/177), far superior to one-stage models (21% (37/177), 36% (64/177), and 53% (93/177)). Furthermore, it also achieved high segmentation performance with an average dice of 81% and an average RVD of 20%. CONCLUSION: A two-stage deep learning model can detect and segment BMs with high sensitivity and low volume error. KEY POINTS: • A two-stage deep learning model based on triple-channel MRI images identified brain metastases with 90% sensitivity and 56% precision. • For brain metastases smaller than 5 mm, the proposed two-stage model achieved 66% sensitivity and 22% precision. • For segmentation of brain metastases, the proposed two-stage model achieved a dice of 81% and a relative volume difference (RVD) of 20%.

Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.

Purposes and Objectives: The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT). Methods: A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively. Then, the prognostic and robust features were selected by machine learning methods, such as (1) univariate analysis based on a Cox proportional hazard (CPH) model, (2) redundancy removing using the variance inflation factor (VIF), and (3) multivariate importance analysis based on random survival forests (RSF). Finally, PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic area under the curve (C/D AUC). Results: In total, 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964). Conclusion: The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict the PFS for patients with SCLC by a high accuracy, which could be used as a useful tool to support the personalized clinical decision for the diagnosis and patient management of patients with SCLC.

Radiomics model of 18F-FDG PET/CT imaging for predicting disease-free survival of early-stage uterine cervical squamous cancer.

BACKGROUND: To explore an effective predictive model based on PET/CT radiomics for the prognosis of early-stage uterine cervical squamous cancer. METHODS: Preoperative PET/CT data were collected from 201 uterine cervical squamous cancer patients with stage IB-IIA disease (FIGO 2009) who underwent radical surgery between 2010 and 2015. The tumor regions were manually segmented, and 1318 radiomic features were extracted. First, model-based univariate analysis was performed to exclude features with small correlations. Then, the redundant features were further removed by feature collinearity. Finally, the random survival forest (RSF) was used to assess feature importance for multivariate analysis. The prognostic models were established based on RSF, and their predictive performances were measured by the C-index and the time-dependent cumulative/dynamics AUC (C/D AUC). RESULTS: In total, 6 radiomic features (5 for CT and 1 for PET) and 6 clinicopathologic features were selected. The radiomic, clinicopathologic and combination prognostic models yielded C-indexes of 0.9338, 0.9019 and 0.9527, and the mean values of the C/D AUC (mC/D AUC) were 0.9146, 0.8645 and 0.9199, respectively. CONCLUSIONS: PET/CT radiomics could achieve approval power in predicting DFS in early-stage uterine cervical squamous cancer.

3D Graph-Connectivity Constrained Network for Hepatic Vessel Segmentation.

Segmentation of hepatic vessels from 3D CT images is necessary for accurate diagnosis and preoperative planning for liver cancer. However, due to the low contrast and high noises of CT images, automatic hepatic vessel segmentation is a challenging task. Hepatic vessels are connected branches containing thick and thin blood vessels, showing an important structural characteristic or a prior: the connectivity of blood vessels. However, this is rarely applied in existing methods. In this paper, we segment hepatic vessels from 3D CT images by utilizing the connectivity prior. To this end, a graph neural network (GNN) used to describe the connectivity prior of hepatic vessels is integrated into a general convolutional neural network (CNN). Specifically, a graph attention network (GAT) is first used to model the graphical connectivity information of hepatic vessels, which can be trained with the vascular connectivity graph constructed directly from the ground truths. Second, the GAT is integrated with a lightweight 3D U-Net by an efficient mechanism called the plug-in mode, in which the GAT is incorporated into the U-Net as a multi-task branch and is only used to supervise the training procedure of the U-Net with the connectivity prior. The GAT will not be used in the inference stage, and thus will not increase the hardware and time costs of the inference stage compared with the U-Net. Therefore, hepatic vessel segmentation can be well improved in an efficient mode. Extensive experiments on two public datasets show that the proposed method is superior to related works in accuracy and connectivity of hepatic vessel segmentation.

Synthesis of Sulfonamide-Based Ynamides and Ynamines in Water.

Ynamides, though relatively more stable than ynamines, are still moisture-sensitive and prone to hydration especially under acidic and heating conditions. Here we report an environmentally benign, robust protocol to synthesize sulfonamide-based ynamides and arylynamines via Sonogashira coupling reactions in water, using a readily available quaternary ammonium salt as the surfactant.

Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints.

Segmenting the bladder wall from MRI images is of great significance for the early detection and auxiliary diagnosis of bladder tumors. However, automatic bladder wall segmentation is challenging due to weak boundaries and diverse shapes of bladders. Level-set-based methods have been applied to this task by utilizing the shape prior of bladders. However, it is a complex operation to adjust multiple parameters manually, and to select suitable hand-crafted features. In this paper, we propose an automatic method for the task based on deep learning and anatomical constraints. First, the autoencoder is used to model anatomical and semantic information of bladder walls by extracting their low dimensional feature representations from both MRI images and label images. Then as the constraint, such priors are incorporated into the modified residual network so as to generate more plausible segmentation results. Experiments on 1092 MRI images shows that the proposed method can generate more accurate and reliable results comparing with related works, with a dice similarity coefficient (DSC) of 85.48%.

A nordehydroabietyl amide-containing chiral diene for rhodium-catalysed asymmetric arylation to nitroolefins.

A highly enantioselective rhodium catalysed asymmetric arylation (RCAA) of nitroolefins with arylboronic acids is presented using a newly developed, C1-symmetric, non-covalent interacted, phellandrene derived, nordehydroabietyl amide-containing chiral diene under mild conditions. Stereoelectronic effects were studied, suggesting an activation of the bound substrate through the secondary amide as a hydrogen-bond donor.