Resonance-Induced Dynamic Triplet Exciton Population for Photoactivated Organic Ultralong Room Temperature Phosphorescence.

Dynamically populating triplet excitons under external stimuli is desired to develop smart optoelectronic materials, but it remains a formidable challenge. Herein, we report a resonance-induced excited state regulation strategy to dynamically modulate the triplet exciton population by introducing a self-adaptive N-C═O structure to phosphors. The developed phosphors activated under high-power ultraviolet irradiation exhibited enhanced photoactivated organic ultralong room temperature phosphorescence (PA-OURTP) with lifetimes of up to ∼500 ms. The enhanced PA-OURTP was ascribed to activated N-C═O resonance variation-induced intersystem crossing to generate excess triplet excitons. The excellent PA-OURTP performance and ultralong deactivation time under ambient conditions of the developed materials could function as a reusable recorded medium for time-sensitive information encryption through optical printing. This study provides an effective approach to dynamically regulating triplet excitons and offers valuable guidance to develop high-performance PA-OURTP materials for security printing applications.

Ultralong Room-Temperature Phosphorescence in Ca(II) Metal-Organic Frameworks Based on Nicotinic Acid Ligands.

In recent years, metal-organic framework (MOF) materials with long persistent luminescence (LPL) have inspired extensive attention and presented various applications in security systems, information anticounterfeiting, and biological imaging fields. However, obtaining LPL materials with ultralong lifetime remains challenging. Halogen atoms, as nonmetallic elements existing in the frameworks, can not only induce the heavy-atom effect, effectively enhancing spin-orbit coupling and promoting intersystem crossing (ISC) processes, but also suppress non-radiative transition of the triplet states through the intra- and intermolecular interactions. Specifically, fluorine atoms with the strongest electronegativity may form intermolecular aggregate interlockings through halogen-bonding interactions that restrict molecular motions and vibrations, thereby improving phosphorescent lifetime. With the aforementioned considerations, two distinct types of MOFs with/without fluorine atoms (namely, Ca-MOF and 5FCa-MOF) were synthesized. Notably, by introducing fluorine atoms into MOFs, fluorine-induced intermolecular aggregate interlockings effectively enhanced the phosphorescent lifetime of 5FCa-MOF exceeding 264 ms compared to that of Ca-MOF (103.94 ms). Remarkably, both MOFs displayed bright LPL to the naked eye after removal of the irradiation source, especially 5FCa-MOF which can last for about 2 s. By introducing fluorine atoms, 5FCa-MOF exhibits greatly enhanced ISC with a rate constant up to 4.1 × 106 s-1 and suppressed non-radiative decay down to 3.73 s-1, thereby extending the LPL time. The thus obtained LPL provides potential in information encryption, security systems, optical anticounterfeiting, and so on.

Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.

PURPOSE: This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT). METHODS: A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison. RESULTS: The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category. CONCLUSION: The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow.

PURPOSE: Obvious inconsistencies in auto-segmentations exist among various AI software. In this study, we have developed a novel convolutional neural network (CNN) fine-tuning workflow to achieve precise and robust localized segmentation. METHODS: The datasets include Hubei Cancer Hospital dataset, Cetuximab Head and Neck Public Dataset, and Québec Public Dataset. Seven organs-at-risks (OARs), including brain stem, left parotid gland, esophagus, left optic nerve, optic chiasm, mandible, and pharyngeal constrictor, were selected. The auto-segmentation results from four commercial AI software were first compared with the manual delineations. Then a new multi-scale lightweight residual CNN model with an attention module (named as HN-Net) was trained and tested on 40 samples and 10 samples from Hubei Cancer Hospital, respectively. To enhance the network's accuracy and generalization ability, the fine-tuning workflow utilized an uncertainty estimation method for automatic selection of candidate samples of worthiness from Cetuximab Head and Neck Public Dataset for further training. The segmentation performances were evaluated on the Hubei Cancer Hospital dataset and/or the entire Québec Public Dataset. RESULTS: A maximum difference of 0.13 and 0.7 mm in average Dice value and Hausdorff distance value for the seven OARs were observed by four AI software. The proposed HN-Net achieved an average Dice value of 0.14 higher than that of the AI software, and it also outperformed other popular CNN models (HN-Net: 0.79, U-Net: 0.78, U-Net++: 0.78, U-Net-Multi-scale: 0.77, AI software: 0.65). Additionally, the HN-Net fine-tuning workflow by using the local datasets and external public datasets further improved the automatic segmentation with the average Dice value by 0.02. CONCLUSION: The delineations of commercial AI software need to be carefully reviewed, and localized further training is necessary for clinical practice. The proposed fine-tuning workflow could be feasibly adopted to implement an accurate and robust auto-segmentation model by using local datasets and external public datasets.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

PURPOSE: To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. METHODS: The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2 mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. RESULTS: The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. CONCLUSIONS: The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

Achieving Stimuli-Responsive Amorphous Organic Afterglow in Single-Component Copolymer through Self-Doping.

The development of stimuli-responsive materials with afterglow emission is highly desirable but remains a formidable challenge in a single-component material system. Herein, we propose a strategy to achieve photoactivated afterglow emission in a variety of amorphous copolymers through self-doping, endowed by the synergetic effect of self-host-induced guest sensitization and thermal-processed polymer rigidification for boosting the generation and stabilization of triplet excitons. Upon continuous ultraviolet illumination for regulating the oxygen concentration, a photoactivated afterglow showing increased lifetimes from 0.34 to 867.4 ms is realized. These afterglow emissions can be naturally or quickly deactivated to the pristine state under ambient conditions or heating treatment. Interestingly, programmable and reusable afterglow patterns, conceptual pulse-width indicators, and "excitation-time lock" Morse code are successfully established using stimuli-responsive afterglow polymers as recorded media. These findings offer an avenue to construct a single-component polymeric system with photoactivated organic afterglow features and demonstrate the superiority of stimuli-responsive materials for remarkable applications.

Resonance-Induced Stimuli-Responsive Capacity Modulation of Organic Ultralong Room Temperature Phosphorescence.

Organic ultralong room temperature phosphorescence (OURTP) materials having stimuli-responsive attributes have attracted great attention due to their great potential in a wide variety of advanced applications. It is of fundamental importance but challengeable to develop stimuli-responsive OURTP materials, especially such materials with modulated optoelectronic properties in a controlled manner probably due to the lack of an authentic construction approach. Here, we propose an effective strategy for OURTP materials with controllably regulated stimuli-responsive properties by engineering the resonance linkage between flexible chain and phosphor units. A quantitative parameter to demonstrate the stimuli-responsive capacity is also established by the responsivity rate constant. The designed OURTP materials demonstrate efficient photoactivated OURTP with lifetimes up to 724 ms and tunable responsivity rate constants ranging from 0.132 to 0.308 min-1 upon continuous UV irradiation. Moreover, the applications of stimuli-responsive resonance OURTP materials have been illustrated by the rewritable paper for snapshot and Morse code for multiple information encryption. Our works, which enable the accomplishment of OURTP materials capable of on-demand manipulated optical properties, demonstrate a viable design to explore smart OURTP materials, giving deep insights into the dynamically stimuli-responsive process.

Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images.

PURPOSE: Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three-dimensional V-net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area. MATERIAL AND METHODS: A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V-net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto-segmentation by V-net was compared to auto-segmentation by U-net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD). RESULTS: The V-net and U-net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V-net model in the colon was significantly better than the U-net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U-net model. For prediction of low-dose areas, the average DSC of the patients' 5 Gy dose area in the test set were 0.88 and 0.83, for V-net and U-net, respectively. CONCLUSIONS: It is feasible to use the V-Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V-net is better than U-net. It also offers advantages with its feature of predicting the low-dose area prospectively before radiation therapy (RT).

Phosphine Sulfide-Based Bipolar Host Materials for Blue Phosphorescent Organic Light-Emitting Diodes.

Three phosphine sulfide-based bipolar host materials, vizCzPhPS, DCzPhPS, and TCzPhPS, were facilely prepared through a one-pot synthesis in excellent yields. The developed hosts exhibit superior thermal stabilities with the decomposition temperatures (Td) all exceeding 350 °C and the melting temperatures (Tm) over 200 °C. In addition, their triplet energy (ET) levels are estimated to be higher than 3.0 eV, illustrating that they are applicable to serve as hosts for blue phosphorescent organic light-emitting diodes (PhOLEDs). The maxima luminance, current efficiency (CE), power efficiency (PE), and external quantum efficiency (EQE) of 17,223 cd m-2, 36.7 cd A-1, 37.5 lm W-1, and 17.5% are achieved for the blue PhOLEDs hosted by CzPhPS. The PhOLEDs based on DCzPhPS and TCzPhPS show inferior device performance than that of CzPhPS, which might be ascribed to the deteriorated charge transporting balance as the increased number of the constructed carbazole units in DCzPhPS and TCzPhPS molecules would enhance the hole-transporting ability of the devices to a large extent. Our study demonstrates that the bipolar hosts derived from phosphine sulfide have enormous potential applications in blue PhOLEDs, and the quantity of donors should be well controlled to exploit highly efficient phosphine sulfide-based hosts.

Primary Nucleation-Dominated Chemical Vapor Deposition Growth for Uniform Graphene Monolayers on Dielectric Substrate.

Direct chemical vapor deposition growth of high quality graphene on dielectric substrates holds great promise for practical applications in electronics and optoelectronics. However, graphene growth on dielectrics always suffers from the issues of inhomogeneity and/or poor quality. Here, we first reveal that a novel precursor-modification strategy can successfully suppress the secondary nucleation of graphene to evolve ultrauniform graphene monolayer film on dielectric substrates. A mechanistic study indicates that the hydroxylation of silica substrate weakens the binding between graphene edges and substrate, thus realizing the primary nucleation-dominated growth. Field-effect transistors based on the graphene films show exceptional electrical performance with the charge carrier mobility up to 3800 cm2 V-1 s-1 in air, which is much higher than those reported results of graphene films grown on dielectrics.

Attention Connect Network for Liver Tumor Segmentation from CT and MRI Images.

Introduction: Currently, the incidence of liver cancer is on the rise annually. Precise identification of liver tumors is crucial for clinicians to strategize the treatment and combat liver cancer. Thus far, liver tumor contours have been derived through labor-intensive and subjective manual labeling. Computers have gained widespread application in the realm of liver tumor segmentation. Nonetheless, liver tumor segmentation remains a formidable challenge owing to the diverse range of volumes, shapes, and image intensities encountered. Methods: In this article, we introduce an innovative solution called the attention connect network (AC-Net) designed for automated liver tumor segmentation. Building upon the U-shaped network architecture, our approach incorporates 2 critical attention modules: the axial attention module (AAM) and the vision transformer module (VTM), which replace conventional skip-connections to seamlessly integrate spatial features. The AAM facilitates feature fusion by computing axial attention across feature maps, while the VTM operates on the lowest resolution feature maps, employing multihead self-attention, and reshaping the output into a feature map for subsequent concatenation. Furthermore, we employ a specialized loss function tailored to our approach. Our methodology begins with pretraining AC-Net using the LiTS2017 dataset and subsequently fine-tunes it using computed tomography (CT) and magnetic resonance imaging (MRI) data sourced from Hubei Cancer Hospital. Results: The performance metrics for AC-Net on CT data are as follows: dice similarity coefficient (DSC) of 0.90, Jaccard coefficient (JC) of 0.82, recall of 0.92, average symmetric surface distance (ASSD) of 4.59, Hausdorff distance (HD) of 11.96, and precision of 0.89. For AC-Net on MRI data, the metrics are DSC of 0.80, JC of 0.70, recall of 0.82, ASSD of 7.58, HD of 30.26, and precision of 0.84. Conclusion: The comparative experiments highlight that AC-Net exhibits exceptional tumor recognition accuracy when tested on the Hubei Cancer Hospital dataset, demonstrating highly competitive performance for practical clinical applications. Furthermore, the ablation experiments provide conclusive evidence of the efficacy of each module proposed in this article. For those interested, the code for this research article can be accessed at the following GitHub repository: https://github.com/killian-zero/py_tumor-segmentation.git.

A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization.

Objective.Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize 'incorrect' auto-segmentations.Approach.A total of 586 CT images and labels from nine institutions were used. The OARs included the brainstem, parotid, and mandible. The deep learning generated contours were compared with the manual ground truth delineations. In this study, we proposed a novel contour quality assurance and adaptive optimization (CQA-AO) strategy, which consists of the following three main components: (1) the contour QA module classified the deep learning generated contours as either accepted or unaccepted; (2) the unacceptable contour categories analysis module provided the potential error reasons (five unacceptable category) and locations (attention heatmaps); (3) the adaptive correction of unacceptable contours module integrate vision-language representations and utilize convex optimization algorithms to achieve adaptive correction of 'incorrect' contours.Main results. In the contour QA tasks, the sensitivity (accuracy, precision) of CQA-AO strategy reached 0.940 (0.945, 0.948), 0.962 (0.937, 0.913), and 0.967 (0.962, 0.957) for brainstem, parotid and mandible, respectively. The unacceptable contour category analysis, the(FI,AccI,Fmicro,Fmacro)of CQA-AO strategy reached (0.901, 0.763, 0.862, 0.822), (0.855, 0.737, 0.837, 0.784), and (0.907, 0.762, 0.858, 0.821) for brainstem, parotid and mandible, respectively. After adaptive optimization correction, the DSC values of brainstem, parotid and mandible have been improved by 9.4%, 25.9%, and 13.5%, and Hausdorff distance values decreased by 62%, 70.6%, and 81.6%, respectively.Significance. The proposed CQA-AO strategy, which combines QA of contour and adaptive optimization correction for OARs contouring, demonstrated superior performance compare to conventional methods. This method can be implemented in the clinical contouring procedures and improve the efficiency of delineating and reviewing workflow.

PRT-Net: a progressive refinement transformer for dose prediction to guide ovarian transposition.

Introduction: Young cervical cancer patients who require ovarian transposition usually have their ovaries moved away from the pelvic radiotherapy (RT) field before radiotherapy. The dose of ovaries during radiotherapy is closely related to the location of the ovaries. To protect ovarian function and avoid ovarian dose exceeding the limits, a safe location of transposed ovary must be determined prior to surgery. Methods: For this purpose, we input the patient's preoperative CT into a neural network model to predict the dose distribution. Surgeons were able to quickly locate low-dose regions based on the dose distribution before surgery, thus determining the safe location of the transposed ovary. In this work, we proposed a new progressive refinement transformer model PRT-Net that can generate dose prediction at multiple scale resolutions in one forward propagation, and refine the dose prediction using prediction details from low to high resolution based on a deep supervision strategy. A multi-loss function fusion algorithm was also built to fit the prediction results under different loss dimensions. The clinical feasibility of the method was verified through an actual cases. Results and discussion: Therefore, using PRT-Net to predict the dose distribution by preoperative CT in cervical cancer patients can assist clinicians to perform ovarian transposition surgery and prevent patients' ovaries from exceeding the prescribed dose limit in postoperative radiotherapy.

Rethinking automatic segmentation of gross target volume from a decoupling perspective.

Accurate and reliable segmentation of Gross Target Volume (GTV) is critical in cancer Radiation Therapy (RT) planning, but manual delineation is time-consuming and subject to inter-observer variations. Recently, deep learning methods have achieved remarkable success in medical image segmentation. However, due to the low image contrast and extreme pixel imbalance between GTV and adjacent tissues, most existing methods usually obtained limited performance on automatic GTV segmentation. In this paper, we propose a Heterogeneous Cascade Framework (HCF) from a decoupling perspective, which decomposes the GTV segmentation into independent recognition and segmentation subtasks. The former aims to screen out the abnormal slices containing GTV, while the latter performs pixel-wise segmentation of these slices. With the decoupled two-stage framework, we can efficiently filter normal slices to reduce false positives. To further improve the segmentation performance, we design a multi-level Spatial Alignment Network (SANet) based on the feature pyramid structure, which introduces a spatial alignment module into the decoder to compensate for the information loss caused by downsampling. Moreover, we propose a Combined Regularization (CR) loss and Balance-Sampling Strategy (BSS) to alleviate the pixel imbalance problem and improve network convergence. Extensive experiments on two public datasets of StructSeg2019 challenge demonstrate that our method outperforms state-of-the-art methods, especially with significant advantages in reducing false positives and accurately segmenting small objects. The code is available at https://github.com/shijun18/GTV_AutoSeg.

Deep learning promoted target volumes delineation of total marrow and total lymphoid irradiation for accelerated radiotherapy: A multi-institutional study.

BACKGROUND AND PURPOSE: One of the current roadblocks to the widespread use of Total Marrow Irradiation (TMI) and Total Marrow and Lymphoid Irradiation (TMLI) is the challenging difficulties in tumor target contouring workflow. This study aims to develop a hybrid neural network model that promotes accurate, automatic, and rapid segmentation of multi-class clinical target volumes. MATERIALS AND METHODS: Patients who underwent TMI and TMLI from January 2018 to May 2022 were included. Two independent oncologists manually contoured eight target volumes for patients on CT images. A novel Dual-Encoder Alignment Network (DEA-Net) was developed and trained using 46 patients from one internal institution and independently evaluated on a total of 39 internal and external patients. Performance was evaluated on accuracy metrics and delineation time. RESULTS: The DEA-Net achieved a mean dice similarity coefficient of 90.1 % ± 1.8 % for internal testing dataset (23 patients) and 91.1 % ± 2.5 % for external testing dataset (16 patients). The 95 % Hausdorff distance and average symmetric surface distance were 2.04 ± 0.62 mm and 0.57 ± 0.11 mm for internal testing dataset, and 2.17 ± 0.68 mm, and 0.57 ± 0.20 mm for external testing dataset, respectively, outperforming most of existing state-of-the-art methods. In addition, the automatic segmentation workflow reduced delineation time by 98 % compared to the conventional manual contouring process (mean 173 ± 29 s vs. 12168 ± 1690 s; P < 0.001). Ablation study validate the effectiveness of hybrid structures. CONCLUSION: The proposed deep learning framework achieved comparable or superior target volume delineation accuracy, significantly accelerating the radiotherapy planning process.

Deep learning for head and neck semi-supervised semantic segmentation.

Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias.Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer.Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms.Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.

In Situ Growth of High-Quality Single-Crystal Twisted Bilayer Graphene on Liquid Copper.

Twisted bilayer graphene (TBG) generates significant attention in the fundamental research of 2D materials due to its distinct twist-angle-dependent properties. Exploring the efficient production of TBG with a wide range of twist angles stands as one of the major frontiers in moiré materials. Here, the local space-confined chemical vapor deposition growth technique for high-quality single-crystal TBG with twist angles ranging from 0° to 30° on liquid copper substrates is reported. The clean surface, pristine interface, high crystallinity, and thermal stability of TBG are verified by using comprehensive characterization techniques including optical microscopy, electron microscopy, and secondary-ion mass spectrometry. The proportion of TBG in bilayer graphene reaches as high as 89%. In addition, the stacking structure and growth mechanism of TBG are investigated, revealing that the second graphene layer develops beneath the first one. A series of comparative experiments illustrates that the liquid copper surface, with its excellent fluidity, promotes the growth of TBG. Electrical measurements show the twist-angle-dependent electronic properties of as-grown TBG, achieving a room-temperature carrier mobility of 26640 cm2 V-1 s-1 . This work provides an approach for the in situ preparation of 2D twisted materials and facilitates the application of TBG in the fields of electronics.

Integration of dosimetric parameters, clinical factors, and radiomics to predict symptomatic radiation pneumonitis in lung cancer patients undergoing combined immunotherapy and radiotherapy.

PURPOSE: This study aimed to combine clinical/dosimetric factors and handcrafted/deep learning radiomic features to establish a predictive model for symptomatic (grade ≥ 2) radiation pneumonitis (RP) in lung cancer patients who received immunotherapy followed by radiotherapy. MATERIALS AND METHODS: This study retrospectively collected data of 73 lung cancer patients with prior receipt of ICIs who underwent thoracic radiotherapy (TRT). Of these 73 patients, 41 (56.2 %) developed symptomatic grade ≥ 2 RP. RP was defined per multidisciplinary clinician consensus using CTCAE v5.0. Regions of interest (ROIs) (from radiotherapy planning CT images) utilized herein were gross tumor volume (GTV), planning tumor volume (PTV), and PTV-GTV. Clinical/dosimetric (mean lung dose and V5-V30) parameters were collected, and 107 handcrafted radiomic (HCR) features were extracted from each ROI. Deep learning-based radiomic (DLR) features were also extracted based on pre-trained 3D residual network models. HCR models, Fusion HCR model, Fusion HCR + ResNet models, and Fusion HCR + ResNet + Clinical models were built and compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC). Five-fold cross-validation was performed to avoid model overfitting. RESULTS: HCR models across various ROIs and the Fusion HCR model showed good predictive ability with AUCs from 0.740 to 0.808 and 0.740-0.802 in the training and testing cohorts, respectively. The addition of DLR features improved the effectiveness of HCR models (AUCs from 0.826 to 0.898 and 0.821-0.898 in both respective cohorts). The best performing prediction model (HCR + ResNet + Clinical) combined HCR & DLR features with 7 clinical/dosimetric characteristics and achieved an average AUC of 0.936 and 0.946 in both respective cohorts. CONCLUSIONS: In patients undergoing combined immunotherapy/RT for lung cancer, integrating clinical/dosimetric factors and handcrafted/deep learning radiomic features can offer a high predictive capacity for RP, and merits further prospective validation.

Dosimetric predictors of radiation pneumonitis in patients with prior immunotherapy exposure: A multi-institutional analysis.

BACKGROUND AND PURPOSE: Combining immune checkpoint inhibitors (ICIs) and thoracic radiotherapy (TRT) may magnify the radiation pneumonitis (RP) risk. Dosimetric parameters can predict RP, but dosimetric data in context of immunotherapy are very scarce. To address this knowledge gap, we performed a large multicenter investigation to identify dosimetric predictors of RP in this under-studied population. MATERIALS AND METHODS: All lung cancer patients from five institutions who underwent conventionally-fractionated thoracic intensity-modulated radiotherapy with prior ICI receipt were retrospectively compiled. RP was defined per CTCAE v5.0. Statistics utilized logistic regression modeling and receiver operating characteristic (ROC) analysis. RESULTS: The vast majority of the 192 patients (median follow-up 14.7 months) had non-small cell lung cancer, received PD-1 inhibitors, and did not receive concurrent systemic therapy with TRT. Grades 1-5 RP occurred in 21.9%, 25.0%, 8.3%, 1.6%, and 1.0%, respectively. The mean MLD for patients with grades 1-5 RP was 10.7, 11.6, 12.6, 14.7, and 12.8 Gy, respectively. On multivariable analysis, tumor location and mean lung dose (MLD) significantly predicted for any-grade and grade ≥ 2 pneumonitis. Only MLD significantly predicted for grade ≥ 3 RP. ROC analysis was able to pictorially model RP risk probabilities for a variety of MLD thresholds, which can be an assistive tool during TRT treatment planning. CONCLUSION: This study, by far the largest to date of dosimetric predictors of RP in the immunotherapy era, illustrates that MLD is the most critical dose-volume parameter influencing RP risk. These data may provide a basis for revising lung dose constraints in efforts to better prevent RP in this rapidly expanding ICI/TRT population.

Controllable Synthesis and Growth Mechanism of Interlayer-Coupled Multilayer Graphene.

The potential applications of multilayer graphene in many fields, such as superconductivity and thermal conductivity, continue to emerge. However, there are still many problems in the growth mechanism of multilayer graphene. In this paper, a simple control strategy for the preparation of interlayer-coupled multilayer graphene on a liquid Cu substrate was developed. By adjusting the flow rate of a carrier gas in the CVD system, the effect for finely controlling the carbon source supply was achieved. Therefore, the carbon could diffuse from the edge of the single-layer graphene to underneath the layer of graphene and then interlayer-coupled multilayer graphene with different shapes were prepared. Through a variety of characterization methods, it was determined that the stacked mode of interlayer-coupled multilayer graphene conformed to AB-stacking structure. The small multilayer graphene domains stacked under single-layer graphene was first found, and the growth process and growth mechanism of interlayer-coupled multilayer graphene with winged and umbrella shapes were studied, respectively. This study reveals the growth mechanism of multilayer graphene grown by using a carbon source through edge diffusion, paving the way for the controllable preparation of multilayer graphene on a liquid Cu surface.