Search

Dissolved organic matter (DOM) is a widely occurring substance in rivers that can strongly complex with heavy metal ions (HMIs), severely interfering with the electrochemical signal of anodic stripping voltammetry (ASV) and reducing the detection accuracy of HMIs in water. In this study, we investigated a novel advanced oxidation process (AOP) that involves the activation of peroxymonosulfate (PMS) using low-pressure ultraviolet (LPUV) radiation and CoFe 2 O 4 photocatalysis. This novel AOP was used for the first time as an effective pretreatment method to break or weaken the complexation between HMIs and DOM, thereby restoring the electrochemical signals of HMIs. The key parameters, including the PMS concentration, CoFe 2 O 4 concentration, and photolysis time, were optimized to be 6 mg/L, 12 mg/L, and 30 s for eliminating DOM interference during the electrochemical analysis of HMIs via LPUV/CoFe 2 O 4 -based photolysis. Investigations of the microstructure, surface morphology, specific surface area, and pore volume of CoFe 2 O 4 were conducted to reveal the exceptional signal recovery capability of LPUV/CoFe 2 O 4 /PMS-based photolysis in mitigating interference from DOM during HMIs analysis. The PMS activation mechanism, which is critical to the signal recovery process, was elucidated by analyzing the reactive oxygen species (ROS) and the surface elemental composition of CoFe 2 O 4 . Additionally, the degradation and transformation behavior of humus-HMIs complexes were analyzed to study the mechanism of ASV signal recovery further. Notably, the detection results of HMIs in actual water samples obtained using the proposed pretreatment method were compared with those obtained from ICP-MS, yielding an RMSE less than 0.04 μg/L, which indicated the satisfactory performance of the proposed pretreatment method for the ASV detection of HMIs in complex actual samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: This study aims to identify radiomic features extracted from contrast-enhanced CT scans that differentiate osteoradionecrosis (ORN) from normal mandibular bone in patients with head and neck cancer (HNC) treated with radiotherapy (RT)., Materials and Methods: Contrast-enhanced CT (CECT) images were collected for 150 patients (80% train, 20% test) with confirmed ORN diagnosis at The University of Texas MD Anderson Cancer Center between 2008 and 2018. Using PyRadiomics, radiomic features were extracted from manually segmented ORN regions and the corresponding automated control regions, the later defined as the contralateral healthy mandible region. A subset of pre-selected features was obtained based on correlation analysis (r > 0.95) and used to train a Random Forest (RF) classifier with Recursive Feature Elimination. Model explainability SHapley Additive exPlanations (SHAP) analysis was performed on the 20 most important features identified by the trained RF classifier., Results: From a total of 1316 radiomic features extracted, 810 features were excluded due to high collinearity. From a set of 506 pre-selected radiomic features, the optimal subset resulting on the best discriminative accuracy of the RF classifier consisted of 67 features. The RF classifier was well calibrated (Log Loss 0.296, ECE 0.125) and achieved an accuracy of 88% and a ROC AUC of 0.96. The SHAP analysis revealed that higher values of Wavelet-LLH First-order Mean and Median were associated with ORN of the jaw (ORNJ). Conversely, higher Exponential GLDM Dependence Entropy and lower Square First-order Kurtosis were more characteristic of normal mandibular tissue., Conclusion: This study successfully developed a CECT-based radiomics model for differentiating ORNJ from healthy mandibular tissue in HNC patients after RT. Future work will focus on the detection of subclinical ORNJ regions to guide earlier interventions.

Gallium nitride offers an ideal material platform for next-generation high-power electronics devices, which enable a spectrum of applications. The thermal management of the ever-growing power density has become a major bottleneck in the performance, reliability, and lifetime of the devices. GaN/diamond heterostructures are usually adopted to facilitate heat dissipation, given the extraordinary thermal conduction properties of diamonds. However, thermal transport is limited by the interfacial conductance at the material interface between GaN and diamond, which is associated with significant mechanical stress at the atomic level. In this work, we investigate the effect of mechanical strain perpendicular to the GaN/diamond interface on the interfacial thermal conductance of heterostructures using full-atom non-equilibrium molecular dynamics simulations. We found that the heterostructure exhibits severe mechanical stress at the interface in the absence of loading, which is due to lattice mismatch. Upon tensile/compressive loading, the interfacial stress is more pronounced, and the strain is not identical across the interface owing to the contrasting elastic moduli of GaN and diamond. In addition, the interfacial thermal conductance can be notably enhanced and suppressed by tensile and compressive strains, respectively, leading to a 400% variation in thermal conductance. More detailed analyses reveal that the change in interfacial thermal conductance is related to the surface roughness and interfacial bonding strength, as described by a generalized relationship. Moreover, phonon analyses suggest that the unequal mechanical deformation under compressive strain in GaN and diamond induces different frequency shifts in the phonon spectra, leading to an enhancement in phonon overlapping energy, which promotes phonon transport at the interface and elevates the thermal conductance and vice versa for tensile strain. The effect of strain on interface thermal conductance was investigated at various temperatures. Based on the mechanical tunability of thermal conductance, we propose a conceptual design for a mechanical thermal switch that regulates thermal conductance with excellent sensitivity and high responsiveness. This study offers a fundamental understanding of how mechanical strain can adjust interface thermal conductance in GaN/diamond heterostructures with respect to mechanical stress, deformation, and phonon properties. These results and findings lay the theoretical foundation for designing thermal management devices in a strain environment and shed light on developing intelligent thermal devices by leveraging the interplay between mechanics and thermal transport.

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer., Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines., Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%)., Conclusion: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Soil organic matter (SOM) significantly impacts the detection accuracy of Cd 2+ and Pb 2+ using square wave anodic stripping voltammetry (SWASV) due to the complexation of SOM to heavy metal ions (HMIs), thereby attenuating SWASV signals. This study explored an effective pretreatment method that combined low-pressure ultraviolet (LPUV) photolysis with the ZnO/g-C 3 N 4 photocatalyst, activating the photocatalyst to generate highly oxidative •OH radicals and O 2 •- radicals, which effectively disrupted this complexation, consequently restoring the electroactivity of HMIs and achieving high-fidelity SWASV signals. The parameters of the LPUV-ZnO/g-C 3 N 4 photocatalytic system were meticulously optimized, including the pH of photolysis, duration of photolysis, g-C 3 N 4 mass fraction, and concentration of the photocatalyst. Furthermore, the ZnO/g-C 3 N 4 photocatalyst was thoroughly characterized, with an in-depth investigation on the synergistic interaction between ZnO and g-C 3 N 4 and the mechanisms contributing to the restoration of SWASV signals. This synergistic interaction effectively separated charge carriers and reduced charge transfer resistance, enabling photogenerated electrons (e - ) from the conduction band of g-C 3 N 4 to be quickly transferred to the conduction band of ZnO, preventing the recombination of e - and hole (h + ) and generating more radicals to disrupt complexation and restore the SWASV signals. Finally, the analysis of HMIs in real soil extracts using the proposed pretreatment method demonstrated high detection accuracy of 94.9% for Cd 2+ and 99.8% for Pb 2+ , which validated the feasibility and effectiveness of the proposed method in environmental applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The increasing demand for large-scale energy storage propels the development of lithium-ion batteries with high energy and high power density. Low tortuosity electrodes with aligned straight channels have proved to be effective in building such batteries. However, manufacturing such low tortuosity electrodes in large scale remains extremely challenging. In contrast, high-performance electrodes with customized gradients of materials and porosity are possible to be made by industrial roll-to-roll coating process. Yet, the desired design of gradients combining materials and porosity is unclear for high-performance gradient electrodes. Here, triple gradient LiFePO 4 electrodes (TGE) are fabricated featuring distribution modulation of active material, conductive agent, and porosity by combining suction filtration with the phase inversion method. The effects and mechanism of active material, conductive agent, and porosity distribution on electrode performance are analyzed by experiments. It is found that the electrode with a gradual increase of active material content from current collector to separator coupled with the distribution of conductive agent and porosity in the opposite direction, demonstrates the best rate capability, the fastest electrochemical reaction kinetics, and the highest utilization of active material. This work provides valuable insights into the design of gradient electrodes with high performance and high potential in application., (© 2024 Wiley‐VCH GmbH.)

Background: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical., Methods: Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach., Results: We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail., Conclusions: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation., (© 2024. The Author(s).)

Importance: Extranodal extension (pENE) is a critical prognostic factor in oropharyngeal cancer (OPC) that drives therapeutic disposition. Determination of pENE from radiological imaging has been associated with high inter-observer variability. However, the impact of clinician specialty on human observer performance of imaging-detected extranodal extension (iENE) remains poorly understood., Objective: To characterize the impact of clinician specialty on the accuracy of pre-operative iENE in human papillomavirus-positive (HPV+) OPC using computed tomography (CT) images., Design Setting and Participants: This prospective observational human performance study analyzed pre-therapy CT images from 24 HPV+ OPC patients, with duplication of 6 scans (n=30) of which 21 were pathologically confirmed pENE. Thirty-four expert observers, including 11 radiologists, 12 surgeons, and 11 radiation oncologists, independently assessed these scans for iENE and reported human-detected radiologic criteria and observer confidence., Main Outcomes and Measures: The primary outcomes included accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and Brier score for each physician, compared to ground-truth pENE. The significance of radiographic signs for prediction of pENE were determined through logistic regression analysis. Fleiss' kappa measured interobserver agreement, and Hanley-MacNeil AUC discrimination testing., Results: Median accuracy across all specialties was 0.57 (95%CI 0.39 to 0.73), with no specialty showing discriminate performance greater than random estimation (median AUC 0.64, 95%CI 0.44 to 0.83). Significant differences between radiologists and surgeons in Brier scores (0.33 vs. 0.26, p < 0.01), radiation oncologists and surgeons in sensitivity (0.48 vs. 0.69, p > 0.1), and radiation oncologists and radiologists/surgeons in specificity (0.89 vs. 0.56, p > 0.1). Indistinct capsular contour and nodal necrosis were significant predictors of correct pENE status among all specialties. Interobserver agreement was weak for all the radiographic criteria, regardless of specialty ( κ <0.6)., Conclusions and Relevance: Multiobserver testing shows physician discrimination of HPV+OPC pENE on pre-operative CT remains non-different than blind guessing, with high interrater variability and low diagnostic accuracy, regardless of clinician specialty. While minor differences in diagnostic performance among specialties are noted, they do not significantly affect the overall poor agreement and discrimination rates observed. The findings underscore the need for further research into automated detection systems or enhanced imaging techniques to improve the accuracy and reliability of iENE assessments in clinical practice., Competing Interests: Conflict of Interest Statement: Dr. Fuller has received unrelated direct industry grant/in-kind support, honoraria, and travel funding from Elekta AB; honoraria, and travel funding from Philips Medical Systems; and honoraria, and travel funding from Varian/Siemens Healthineers. Dr. Fuller has unrelated licensing/royalties from Kallisio, Inc. Dr. Sandulache is a consultant for, and equity holder in, Femtovox Inc (unrelated to current work).

Purpose: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors., Methods: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure., Results: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations., Conclusion: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC., (© 2024. The Author(s).)

Abstract: A 67-year-old man underwent 18 F-FDG PET/CT for lung cancer staging. Interestingly, the PET scan revealed strip-shaped FDG uptake in the right inguinal contoured area, which was later confirmed as a right varicocele through ultrasound imaging., Competing Interests: Conflicts of interest and sources of funding: This study was supported in part by research foundation projects from Luzhou Science & Technology Department (20107) and (2022-JYJ-118) and The Affiliated Hospital of Southwest Medical University (20087). Conflicts of interest and sources of funding: The authors declare that they have no competing interests., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Competing Interests: K.A.W. serves as an Editorial Board Member for Physics and Imaging in Radiation Oncology. C.D.F. has received travel, speaker honoraria, and/or registration fee waivers unrelated to this project from The American Association for Physicists in Medicine, the University of Alabama-Birmingham, The American Society for Clinical Oncology, The Royal Australian and New Zealand College of Radiologists, The American Society for Radiation Oncology, The Radiologic Society of North America, and The European Society for Radiation Oncology. The other authors have no interests to disclose. During the preparation of this work, the authors used ChatGPT (GPT-4 architecture; ChatGPT September 25 Version) to improve the grammatical accuracy and semantic structure of portions of the text. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Background: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction., Purpose: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose., Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP&#95;RF&#95;4], IPP with 1 fraction [IPP&#95;1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL&#95;ST&#95;5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics., Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP&#95;1, and IPP&#95;RF&#95;4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL&#95;ST&#95;5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314)., Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP&#95;1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow., (© 2023 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Competing Interests: Clifton D. Fuller has received unrelated direct industry grant/in-kind support, honoraria, and travel funding from Elekta AB.

Humic acid (HA), the primary composition of natural organic matter (NOM) widely distributed in water and soil, can complex with heavy metal ions (HMIs), i.e., Cd(II) and Pb(II) in this study, which deters the accurate detection of HMIs using square wave anodic stripping voltammetry (SWASV). Hence, in this study, an efficient pretreatment method was proposed to restore the electrochemical signal of Cd(II) and Pb(II) by breaking the complexation based on AgNPs-doped SnO 2 photocatalyst combined with LP/UV irradiation. Optimization of the key parameters for electrochemical signal restoration including pH for photolysis, AgNPs doping rate, photocatalyst dosage and photolysis time were performed to further elevating the accuracy in the proposed pretreatment method over 96.9% for Cd(II) and Pb(II) in 15 min. The effect of different HA concentrations on SWASV signal of Cd(II) and Pb(II) was also investigated adopting the optimal parameters. Then, the UV-vis absorption spectra, crystal structure, and the morphology of AgNPs-doped SnO 2 photocatalyst were investigated to excavate the reasons behind the most excellent AgNPs doping rate to SnO 2 in signal restoration. Moreover, the behavior of HA degradation and transformation under LP/UV irradiation was studied to investigate the mechanism of electrochemical signal restoration. Finally, the feasibility of the proposed method was testified by comparing detection results with ICP-MS results using real water samples extracted from aquaculture water., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Purpose: To improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized., Approach: After initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics., Results: Sequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning., Conclusions: Our study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk., (© 2023 The Authors.)

In stereo matching, various learning-based approaches have shown impressive performance in solving traditional difficulties on multiple datasets. While most progress is obtained on a specific dataset with a dataset-specific network design, the performance on the single dataset and cross dataset affected by training strategy is often ignored. In this article, we analyze the relationship between different training strategies and performance by retraining some representative state-of-the-art methods (e.g., geometry and context network (GC-Net), pyramid stereo matching network (PSM-Net), and guided aggregation network (GA-Net), etc.). According to our research, it is surprising that the performance of networks on single or cross datasets is significantly improved by pre-training and data augmentation without any particular structure acquirement. Based on this discovery, we improve our previous non-local context attention network (NLCA-Net) to NLCA-Net v2 and train it with the novel strategy and rethink the training strategy of stereo matching concurrently. The quantitative experiments demonstrate that: 1) our model is capable of reaching top performance on both the single dataset and the multiple datasets with the same parameters in this study, which also won the 2nd place in the stereo task of the ECCV Robust vision Challenge 2020 (RVC 2020); and 2) on small datasets (e.g., KITTI, ETH3D, and Middlebury), the model's generalization and robustness are significantly affected by pre-training and data augmentation, even exceeding the network structure's influence in some cases. These observations present a challenge to the conventional wisdom of network architectures in this stage. We expect these discoveries to encourage researchers to rethink the current paradigm of "excessive attention on the performance of a single small dataset" in stereo matching.

Objective: To clarify the intervention guidance of return to sports WeChat applet and evaluate the rehabilitation effectiveness after anterior cruciate ligament (ACL) reconstruction., Methods: Between September 2020 and September 2022, 80 patients who met the selection criteria and underwent ACL anatomical single bundle reconstruction were selected as the research objects. According to the double-blind random method, they were divided into the applet group and the regular group, with 40 cases in each group. Patients in the applet group were rehabilitated under the guidance of the return to sports WeChat applet, and the patients were asked to perform the test once a month after operation, including patients' subjective scores [Tegner score, knee injury and osteoarthritis outcome score (KOOS), International Knee Documentation Committee (IKDC) score, American Hospital for Special Surgery (HSS) score], psychological assessment [ACL recovery sports injury scale (ACL-RSI) score], jumping test, balance test, bending angle test. Patients in the regular group were followed up by doctors and nurses regularly by telephone every month. All the patients were reexamined at 3, 6, 9, and 12 months after operation, and the range of motion of the knee joint with 6 degrees of freedom (flexion and extension angle, varus and valgus angle, internal and external rotation angle, anteroposterior displacement, superior and inferior displacement, and internal and external displacement) recorded by Opti&#95;Knee three-dimensional knee joint motion measurement gait analysis system was observed. The anterior tibial translation difference (ATTD) was measured by Ligs knee measuring instrument when a forward thrust of 120 N was applied to the posterior part of the proximal tibia. Tegner score, IKDC score, KOOS score (including KOOS-Pain score, KOOS-Symptoms score, KOOS-Activities of daily living score, KOOS-Sport score, and KOOS-Quality of life score), HSS score, ACL-RSI score, jumping ability, balance ability, patients' satisfaction with the rehabilitation process, and ACL healing grading according to ACL continuity and signal intensity shown by MRI., Results: There were significant differences in various indicators between different time points after operation in the two groups ( P <0.05). At 3 months after operation, except that the ACL-RSI score of the applet group was significantly higher than that of the regular group ( P <0.05), there was no significant difference in the other indicators between the two groups ( P >0.05). At 6 months after operation, the ACL-RSI score, IKDC score, Tegner score, KOOS scores of different items, HSS score, balance and jumping ability of the applet group were significantly higher than those of the regular group ( P <0.05), and there was no significant difference in the other indicators between the two groups ( P >0.05). At 9 months after operation, there was no significant difference in all indicators between the two groups ( P >0.05). At 12 months after operation, 27 cases (67.5%) in the applet group and 21 cases (52.5%) in the regular group returned to sport, with a significant difference of the return to sports incidence between the two groups [ RR (95% CI )=1.50 (1.00, 2.25), P =0.049]. In the applet group, 27 cases were very satisfied with the rehabilitation process, 10 cases were satisfied, 2 cases were basically satisfied, and 1 case was not satisfied, while 19, 13, 5, and 3 cases in the regular group, respectively. The satisfaction degree of the applet group was significantly better than that of the regular group ( P =0.049). MRI examination of the two groups showed that the ACL was continuous without secondary rupture or necrosis. The ACL healing grade of the applet group was 31 cases of grade 1 and 9 cases of grade 2, and that of the regular group was 28 cases of grade 1 and 12 cases of grade 2, there was no significant difference in ACL healing grade between the two groups ( P =0.449)., Conclusion: The application of return to sports WeChat applet in the rehabilitation of patients after ACL reconstruction can significantly reduce the fear of return to sports and improve the rate of return to sports. The return to sports WeChat applet is convenient to operate, with high utilization rate and high patient compliance, which significantly improves the satisfaction.

Objectives: Pain during Radiation Therapy (RT) for oral cavity/oropharyngeal cancer (OC/OPC) is a clinical challenge due to its multifactorial etiology and variable management. The objective of this study was to define complex pain profiles through temporal characterization of pain descriptors, physiologic state, and RT-induced toxicities for pain trajectories understanding., Materials and Methods: Using an electronic health record registry, 351 OC/OPC patients treated with RT from 2013 to 2021 were included. Weekly numeric scale pain scores, pain descriptors, vital signs, physician-reported toxicities, and analgesics were analyzed using linear mixed effect models and Spearman's correlation. Area under the pain curve (AUC pain ) was calculated to measure pain burden over time., Results: Median pain scores increased from 0 during the weekly visit (WSV)-1 to 5 during WSV-7. By WSV-7, 60% and 74% of patients reported mouth and throat pain, respectively, with a median pain score of 5. Soreness and burning pain peaked during WSV-6/7 (51%). Median AUC pain was 16% (IQR (9.3-23)), and AUC pain significantly varied based on gender, tumor site, surgery, drug use history, and pre-RT pain. A temporal increase in mucositis and dermatitis, declining mean bodyweight (-7.1%; P < 0.001) and mean arterial pressure (MAP) 6.8 mmHg; P < 0.001 were detected. Pulse rate was positively associated while weight and MAP were negatively associated with pain over time ( P < 0.001)., Conclusion: This study provides insight on in-depth characterization and associations between dynamic pain, physiologic, and toxicity kinetics. Our findings support further needs of optimized pain control through temporal data-driven clinical decision support systems for acute pain management.

Introduction: Diffusion-weighted imaging (DWI) on MRI-linear accelerator (MR-linac) systems can potentially be used for monitoring treatment response and adaptive radiotherapy in head and neck cancers (HNC) but requires extensive validation. We performed technical validation to compare six total DWI sequences on an MR-linac and MR simulator (MR sim) in patients, volunteers, and phantoms., Methods: Ten human papillomavirus-positive oropharyngeal cancer patients and ten healthy volunteers underwent DWI on a 1.5 T MR-linac with three DWI sequences: echo planar imaging (EPI), split acquisition of fast spin echo signals (SPLICE), and turbo spin echo (TSE). Volunteers were also imaged on a 1.5 T MR sim with three sequences: EPI, BLADE (vendor tradename), and readout segmentation of long variable echo trains (RESOLVE). Participants underwent two scan sessions per device and two repeats of each sequence per session. Repeatability and reproducibility within-subject coefficient of variation (wCV) of mean ADC were calculated for tumors and lymph nodes (patients) and parotid glands (volunteers). ADC bias, repeatability/reproducibility metrics, SNR, and geometric distortion were quantified using a phantom., Results: In vivo repeatability/reproducibility wCV for parotids were 5.41%/6.72%, 3.83%/8.80%, 5.66%/10.03%, 3.44%/5.70%, 5.04%/5.66%, 4.23%/7.36% for EPI MR-linac , SPLICE, TSE, EPI MR sim , BLADE, RESOLVE. Repeatability/reproducibility wCV for EPI MR-linac , SPLICE, TSE were 9.64%/10.28%, 7.84%/8.96%, 7.60%/11.68% for tumors and 7.80%/9.95%, 7.23%/8.48%, 10.82%/10.44% for nodes. All sequences except TSE had phantom ADC biases within ± 0.1x10 -3 mm 2 /s for most vials (EPI MR-linac , SPLICE, and BLADE had 2, 3, and 1 vials out of 13 with larger biases, respectively). SNR of b = 0 images was 87.3, 180.5, 161.3, 171.0, 171.9, 130.2 for EPI MR-linac , SPLICE, TSE, EPI MR sim , BLADE, RESOLVE., Conclusion: MR-linac DWI sequences demonstrated near-comparable performance to MR sim sequences and warrant further clinical validation for treatment response assessment in HNC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To investigate the effectiveness of anterior cruciate ligament (ACL) reconstruction assisted by personalized femoral locator based on the apex of deep cartilage (ADC) combined with patient imaging data., Methods: Between January 2021 and January 2022, a total of 40 patients with primary ACL rupture were selected and randomly divided into study group (ACL reconstruction assisted by personalized femoral locator based on ADC) and control group (ACL reconstruction assisted by intraoperative fluoroscopy and traditional femoral locator), with 20 cases in each group. There was no significant difference in gender, age, body mass index, affected side, cause of injury, and preoperative International Knee Documentation Committee (IKDC) score, Lyshlom score, and Tegner score between the two groups ( P >0.05). IKDC score, Lyshlom score, and Tegner score were used to evaluate the functional recovery of the affected knee before operation and at 3, 6, and 12 months after operation. CT scan and three-dimensional reconstruction were performed before and after operation to measure the horizontal distance from ADC to the anterior cartilage margin (L) and the horizontal distance from ADC to the center of the femoral canal (I), and the anteroposterior position of the bone canal (R) was calculated by I/L; the distance from the center to the distal cartilage margin (D) was measured on the two-dimensional cross section; the R value and D value were compared between the two groups., Results: The operation time of the study group was significantly less than that of the control group [ MD =-6.90 (-8.78, -5.03), P <0.001]. The incisions of the two groups healed by first intention, and no complication such as intra-articular infection, nerve injury, and deep vein thrombosis of lower limbs occurred. There was no significant difference in the R value and D value between the preoperative simulated positioning and the actual intraoperative positioning in the study group [ MD =0.52 (-2.85, 3.88), P =0.758; MD =0.36 (-0.39, 1.11), P =0.351]. There was no significant difference in the actual intraoperative positioning R value and D value between the study group and the control group [ MD =1.01 (-2.57, 4.58), P =0.573; MD =0.24 (-0.34, 0.82), P =0.411]. The patients in both groups were followed up 12-13 months (mean, 12.4 months). The IKDC score, Lysholm score, and Tegner score of the two groups increased gradually with time, and there were significant differences between pre- and post-operation ( P <0.05). There was no significant difference in the scores between the two groups at each time point after operation ( P >0.05)., Conclusion: The personalized femoral locator based on ADC can accurately assist the femoral tunnel positioning in ACL reconstruction, which can shorten the operation time when compared with traditional surgical methods, and achieve satisfactory early effectiveness.

Objective: To investigate the changes of knee joint kinematics after anterior cruciate ligament (ACL) reconstruction assisted by personalized femoral positioner based on the apex of deep cartilage (ADC)., Methods: Between January 2021 and January 2022, a total of 40 patients with initial ACL rupture who met the selection criteria were randomly divided into the study group (using the personalized femoral positioner based on ADC design to assist ACL reconstruction) and the control group (not using the personalized femoral positioner to assist ACL reconstruction), with 20 patients in each group. Another 20 volunteers with normal knee were collected as a healthy group. There was no significant difference in gender, age, body mass index, and affected side between groups ( P >0.05). Gait analysis was performed at 3, 6, and 12 months after operation using Opti &#95; Knee three-dimensional knee joint motion measurement and analysis system, and the 6 degrees of freedom (flexion and extension angle, varus and valgus angle, internal and external rotation angle, anteroposterior displacement, superior and inferior displacement, internal and external displacement) and motion cycle (maximum step length, minimum step length, and step frequency) of the knee joint were recorded. The patients' data was compared to the data of healthy group., Results: In the healthy group, the flexion and extension angle was (57.80±3.45)°, the varus and valgus angle was (10.54±1.05)°, the internal and external rotation angle was (13.02±1.66)°, and the anteroposterior displacement was (1.44±0.39) cm, the superior and inferior displacement was (0.86±0.20) cm, and the internal and external displacement was (1.38±0.39) cm. The maximum step length was (51.24±1.29) cm, the minimum step length was (45.69±2.28) cm, and the step frequency was (12.45±0.47) step/minute. Compared with the healthy group, the flexion and extension angles and internal and external rotation angles of the patients in the study group and the control group decreased at 3 months after operation, and the flexion and extension angles of the patients in the control group decreased at 6 months after operation, and the differences were significant ( P <0.05); there was no significant difference in the other time points and other indicators when compared with healthy group ( P >0.05). In the study group, the flexion and extension angles and internal and external rotation angles at 6 and 12 months after operation were significantly greater than those at 3 months after operation ( P <0.05), while there was no significant difference in the other indicators at other time points ( P >0.05). There was a significant difference in flexion and extension angle between the study group and the control group at 6 months after operation ( P <0.05), but there was no significant difference of the indicators between the two groups at other time points ( P >0.05)., Conclusion: Compared with conventional surgery, ACL reconstruction assisted by personalized femoral positioner based on ADC design can help patients achieve more satisfactory early postoperative kinematic results, and three-dimensional kinematic analysis can more objectively and dynamically evaluate the postoperative recovery of knee joint.

Purpose: To determine DWI parameters associated with tumor response and oncologic outcomes in head and neck (HNC) patients treated with radiotherapy (RT)., Methods: HNC patients in a prospective study were included. Patients had MRIs pre-, mid-, and post-RT completion. We used T2-weighted sequences for tumor segmentation which were co-registered to respective DWIs for extraction of apparent diffusion coefficient (ADC) measurements. Treatment response was assessed at mid- and post-RT and was defined as: complete response (CR) vs. non-complete response (non-CR). The Mann-Whitney U test was used to compare ADC between CR and non-CR. Recursive partitioning analysis (RPA) was performed to identify ADC threshold associated with relapse. Cox proportional hazards models were done for clinical vs. clinical and imaging parameters and internal validation was done using bootstrapping technique., Results: Eighty-one patients were included. Median follow-up was 31 months. For patients with post-RT CR, there was a significant increase in mean ADC at mid-RT compared to baseline ((1.8 ± 0.29) × 10 -3 mm 2 /s vs. (1.37 ± 0.22) × 10 -3 mm 2 /s, p < 0.0001), while patients with non-CR had no significant increase (p > 0.05). RPA identified GTV-P delta (Δ)ADC mean  < 7% at mid-RT as the most significant parameter associated with worse LC and RFS (p = 0.01). Uni- and multi-variable analysis showed that GTV-P ΔADC mean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADC mean significantly improved the c-indices of LC and RFS models compared with standard clinical variables (0.85 vs. 0.77 and 0.74 vs. 0.68 for LC and RFS, respectively, p < 0.0001 for both)., Conclusion: ΔADC mean at mid-RT is a strong predictor of oncologic outcomes in HNC. Patients with no significant increase of primary tumor ADC at mid-RT are at high risk of disease relapse., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Objective: To summarize the evaluation methods of return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR) in recent years, in order to provide reference for clinical practice., Methods: The literature related to the RTS after ACLR was searched from CNKI, Wanfang, PubMed, and Foreign Medical Information Resources Retrieval Platform (FMRS) databases. The retrieval range was from 2010 to 2023, and 66 papers were finally included for review. The relevant literature was summarized and analyzed from the aspects of RTS time, objective evaluation indicators, and psychological evaluation., Results: RTS is the common desire of patients with ACL injury and doctors, as well as the initial intention of selecting surgery. A reasonable and perfect evaluation method of RTS can not only help patients recover to preoperative exercise level, but also protect patients from re-injury. At present, the main criterion for clinical judgement of RTS is time. It is basically agreed that RTS after 9 months can reduce the re-injury. In addition to time, it is also necessary to test the lower limb muscle strength, jumping, balance, and other aspects of the patient, comprehensively assess the degree of functional recovery and determine the different time of RTS according to the type of exercise. Psychological assessment plays an important role in RTS and has a good clinical predictive effect., Conclusion: RTS is one of the research hotspots after ACLR. At present, there are many related evaluation methods, which need to be further optimized by more research to build a comprehensive and standardized evaluation system.

Despite wide-temperature tolerance and high-voltage compatibility, employing propylene carbonate (PC) as electrolyte in lithium-ion batteries (LIBs) is hampered by solvent co-intercalation and graphite exfoliation due to incompetent solvent-derived solid electrolyte interphase (SEI). Herein, trifluoromethylbenzene (PhCF 3 ), featuring both specific adsorption and anion attraction, is utilized to regulate the interfacial behaviors and construct anion-induced SEI at low Li salts' concentration (<1 m). The adsorbed PhCF 3 , showing surfactant effect on graphite surface, induces preferential accumulation and facilitated decomposition of bis(fluorosulfonyl)imide anions (FSI - ) based on the adsorption-attraction-reduction mechanism. As a result, PhCF 3 successfully ameliorates graphite exfoliation-induced cell failure in PC-based electrolyte and enables the practical operation of NCM613/graphite pouch cell with high reversibility at 4.35 V (96% capacity retention over 300 cycles at 0.5 C). This work constructs stable anion-derived SEI at low concentration of Li salt by regulating anions-co-solvents interaction and electrode/electrolyte interfacial chemistries., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Background/purpose: Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images., Materials/methods: We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance., Results: 19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods., Conclusions: The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy., (© 2022 American Association of Physicists in Medicine.)

Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications., (© 2023. The Author(s).)

Background: Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs)., Methods: A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni&#95;refacer, DeepDefacer, defacer, fsl&#95;deface, mask&#95;face, mri&#95;deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC)., Results: Most defacing methods were unable to produce any usable images for evaluation, while mask&#95;face, fsl&#95;deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask&#95;face, fsl&#95;deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask&#95;face, fsl&#95;deface, and pydeface, respectively., Conclusion: Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sahlsten, Wahid, Glerean, Jaskari, Naser, He, Kann, Mäkitie, Fuller and Kaski.)

Purpose: Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a crowdsourced challenge engaging radiation oncologists across various expertise levels in segmentation. An obstacle to artificial intelligence (AI) development is the paucity of multiexpert datasets; consequently, we sought to characterize whether aggregate segmentations generated from multiple nonexperts could meet or exceed recognized expert agreement., Approach: Participants who contoured ≥ 1 region of interest (ROI) for the breast, sarcoma, head and neck (H&N), gynecologic (GYN), or gastrointestinal (GI) cases were identified as a nonexpert or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. STAPLE nonexpert ROIs were evaluated against STAPLE expert contours using Dice similarity coefficient (DSC). The expert interobserver DSC ( IODSC expert ) was calculated as an acceptability threshold between STAPLE nonexpert and STAPLE expert . To determine the number of nonexperts required to match the IODSC expert for each ROI, a single consensus contour was generated using variable numbers of nonexperts and then compared to the IODSC expert ., Results: For all cases, the DSC values for STAPLE nonexpert versus STAPLE expert were higher than comparator expert IODSC expert for most ROIs. The minimum number of nonexpert segmentations needed for a consensus ROI to achieve IODSC expert acceptability criteria ranged between 2 and 4 for breast, 3 and 5 for sarcoma, 3 and 5 for H&N, 3 and 5 for GYN, and 3 for GI., Conclusions: Multiple nonexpert-generated consensus ROIs met or exceeded expert-derived acceptability thresholds. Five nonexperts could potentially generate consensus segmentations for most ROIs with performance approximating experts, suggesting nonexpert segmentations as feasible cost-effective AI inputs., (© 2023 The Authors.)

Background: Quick magnetic resonance imaging (MRI) scans with low contrast-to-noise ratio are typically acquired for daily MRI-guided radiotherapy setup. However, for patients with head and neck (HN) cancer, these images are often insufficient for discriminating target volumes and organs at risk (OARs). In this study, we investigated a deep learning (DL) approach to generate high-quality synthetic images from low-quality images., Methods: We used 108 unique HN image sets of paired 2-minute T2-weighted scans (2mMRI) and 6-minute T2-weighted scans (6mMRI). 90 image sets (~20,000 slices) were used to train a 2-dimensional generative adversarial DL model that utilized 2mMRI as input and 6mMRI as output. Eighteen image sets were used to test model performance. Similarity metrics, including the mean squared error (MSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) were calculated between normalized synthetic 6mMRI and ground-truth 6mMRI for all test cases. In addition, a previously trained OAR DL auto-segmentation model was used to segment the right parotid gland, left parotid gland, and mandible on all test case images. Dice similarity coefficients (DSC) were calculated between 2mMRI and either ground-truth 6mMRI or synthetic 6mMRI for each OAR; two one-sided t-tests were applied between the ground-truth and synthetic 6mMRI to determine equivalence. Finally, a visual Turing test using paired ground-truth and synthetic 6mMRI was performed using three clinician observers; the percentage of images that were correctly identified was compared to random chance using proportion equivalence tests., Results: The median similarity metrics across the whole images were 0.19, 0.93, and 33.14 for MSE, SSIM, and PSNR, respectively. The median of DSCs comparing ground-truth vs. synthetic 6mMRI auto-segmented OARs were 0.86 vs. 0.85, 0.84 vs. 0.84, and 0.82 vs. 0.85 for the right parotid gland, left parotid gland, and mandible, respectively (equivalence p<0.05 for all OARs). The percent of images correctly identified was equivalent to chance (p<0.05 for all observers)., Conclusions: Using 2mMRI inputs, we demonstrate that DL-generated synthetic 6mMRI outputs have high similarity to ground-truth 6mMRI, but further improvements can be made. Our study facilitates the clinical incorporation of synthetic MRI in MRI-guided radiotherapy., Competing Interests: CF has received direct industry grant support, speaking honoraria, and travel funding from Elekta AB. JX, NO’C, DT, NC, and JC are employees of Elekta AB. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wahid, Xu, El-Habashy, Khamis, Abobakr, McDonald, O’ Connell, Thill, Ahmed, Sharafi, Preston, Salzillo, Mohamed, He, Cho, Christodouleas, Fuller and Naser.)

Artificial intelligence (AI) has exceptional potential to positively impact the field of radiation oncology. However, large curated datasets - often involving imaging data and corresponding annotations - are required to develop radiation oncology AI models. Importantly, the recent establishment of Findable, Accessible, Interoperable, Reusable (FAIR) principles for scientific data management have enabled an increasing number of radiation oncology related datasets to be disseminated through data repositories, thereby acting as a rich source of data for AI model building. This manuscript reviews the current and future state of radiation oncology data dissemination, with a particular emphasis on published imaging datasets, AI data challenges, and associated infrastructure. Moreover, we provide historical context of FAIR data dissemination protocols, difficulties in the current distribution of radiation oncology data, and recommendations regarding data dissemination for eventual utilization in AI models. Through FAIR principles and standardized approaches to data dissemination, radiation oncology AI research has nothing to lose and everything to gain., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

The accurate determination of sarcopenia is critical for disease management in patients with head and neck cancer (HNC). Quantitative determination of sarcopenia is currently dependent on manually-generated segmentations of skeletal muscle derived from computed tomography (CT) cross-sectional imaging. This has prompted the increasing utilization of machine learning models for automated sarcopenia determination. However, extant datasets currently do not provide the necessary manually-generated skeletal muscle segmentations at the C3 vertebral level needed for building these models. In this data descriptor, a set of 394 HNC patients were selected from The Cancer Imaging Archive, and their skeletal muscle and adipose tissue was manually segmented at the C3 vertebral level using sliceOmatic. Subsequently, using publicly disseminated Python scripts, we generated corresponding segmentations files in Neuroimaging Informatics Technology Initiative format. In addition to segmentation data, additional clinical demographic data germane to body composition analysis have been retrospectively collected for these patients. These data are a valuable resource for studying sarcopenia and body composition analysis in patients with HNC., (© 2022. The Author(s).)

Background/purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM., Materials/methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the section was auto-selected, and a 2D ResUNet auto-segmented the auto-selected slice. Both the 3D and 2D approaches trained five sub-models (5-fold cross-validation) and combined sub-model predictions on the test set using majority vote ensembling. Model performance was primarily determined using the Dice similarity coefficient (DSC). Predicted SMI was calculated using the auto-segmented SM cross-sectional area. Finally, using established SMI cutoffs, we performed a Kaplan-Meier analysis to determine associations with overall survival., Results: Mean test set DSC of the 3D and 2D models were 0.96 and 0.95, respectively. Predicted SMI had high correlation to the ground-truth SMI in males and females (r>0.96). Predicted SMI stratified patients for overall survival in males (log-rank p = 0.01) but not females (log-rank p = 0.07), consistent with ground-truth SMI., Conclusion: We developed a high-performance, multi-stage, fully-automated approach to segment cervical vertebra SM. Our study is an essential step towards fully-automated sarcopenia-related decision-making in patients with HNC., Competing Interests: CF has received direct industry grant support, speaking honoraria, and travel funding from Elekta AB. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Naser, Wahid, Grossberg, Olson, Jain, El-Habashy, Dede, Salama, Abobakr, Mohamed, He, Jaskari, Sahlsten, Kaski and Fuller.)

Lithium cobalt oxide (LCO) is a widely used cathode material for lithium-ion batteries. However, it suffers from irreversible phase transition during cycling because of high cutoff voltage or huge concentration polarization in thick electrode, resulting in deteriorated cyclability. Here, we design a low tortuous LiCoO 2 (LCO-LT) electrode by ice-templating method and investigate the reversibility of LCO phase transition. LCO-LT thick electrode shows accelerated lithium-ion transport and reduced concentration polarization, achieving excellent rate capability and homogeneous actual operating voltage. Moreover, LCO-LT thick electrode exhibits a durable phase transition between O2 and H1-3, mitigated volume expansion, and suppressed microcrack formation. LCO-LT electrode (25 mg cm -2 ) delivers improved capacity retentions of 94.4% after 200 cycles and 93.3% after 150 cycles at cutoff voltages of 4.3 and 4.5 V, respectively. This strategy provides a new concept to improve the reversibility of LCO phase transition in thick electrode by low tortuosity design.

Background: To analyze the influence of radiation dose on late radiation-associated taste impairment in oropharyngeal cancer (OPC) patients treated with intensity-modulated radiotherapy (IMRT) using the taste bud bearing tongue mucosa as organ at risk., Material and Methods: This study is part of an ongoing, prospective observational study. Cancer-free OPC survivors with at least 24 months from IMRT were included in this analysis. Scores for taste impairment and dry mouth were extracted from the MD Anderson Symptom Inventory Head and Neck module (MDASI-HN) with scores of ≥5 considered as moderate-to-severe symptoms. The mean dose, minimum and maximum dose to the taste bud bearing tongue mucosa, the ipsi- and contralateral parotid and submandibular glands were extracted and analyzed for correlation with moderate-to-severe taste impairment., Results: One hundred sixteen T1-4 OPC patients were included (81% males, median age: 55). The primary tumor was in the tonsil in 92 cases (79%) and in the base of tongue in 21 cases (18%). Patients were treated with 64.2-72.0 Gy; 37 patients (32%) received concurrent chemotherapy and 22 (19%) concurrent targeted therapy. After a median of 58 months from RT (IQR: 43-68) 38 patients (33%) suffered from moderate-to-severe long-term radiation-associated taste impairment. No dose volume parameter of the taste bud bearing tongue mucosa and the salivary glands was significantly associated with moderate-to-severe taste impairment for the whole patient cohort. For patients without concurrent chemotherapy, the minimum and mean dose to the ipsilateral parotid gland, and the maximum dose to the submandibular gland was significantly associated with late taste impairment (all p  < 0.05). A significant correlation was found between taste impairment and dry mouth ( p  < 0.001)., Conclusion: The dose to the ipsilateral parotid gland seems to play an important role in the development of late taste impairment. The influence of dose to the taste bud bearing tongue mucosa remains unclear and needs further investigation.

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Our DSC and 95% HD test results are within 0.01 and 0.06 mm of the top ranked model in the competition, respectively. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation. Future investigations should target the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.

PET/CT images provide a rich data source for clinical prediction models in head and neck squamous cell carcinoma (HNSCC). Deep learning models often use images in an end-to-end fashion with clinical data or no additional input for predictions. However, in the context of HNSCC, the tumor region of interest may be an informative prior in the generation of improved prediction performance. In this study, we utilize a deep learning framework based on a DenseNet architecture to combine PET images, CT images, primary tumor segmentation masks, and clinical data as separate channels to predict progression-free survival (PFS) in days for HNSCC patients. Through internal validation (10-fold cross-validation) based on a large set of training data provided by the 2021 HECKTOR Challenge, we achieve a mean C-index of 0.855 ± 0.060 and 0.650 ± 0.074 when observed events are and are not included in the C-index calculation, respectively. Ensemble approaches applied to cross-validation folds yield C-index values up to 0.698 in the independent test set (external validation), leading to a 1 st place ranking on the competition leaderboard. Importantly, the value of the added segmentation mask is underscored in both internal and external validation by an improvement of the C-index when compared to models that do not utilize the segmentation mask. These promising results highlight the utility of including segmentation masks as additional input channels in deep learning pipelines for clinical outcome prediction in HNSCC.

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2 nd in the competition. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed.

Background and Purpose: Conventional magnetic resonance imaging (MRI) poses challenges in quantitative analysis because voxel intensity values lack physical meaning. While intensity standardization methods exist, their effects on head and neck MRI have not been investigated. We developed a workflow based on healthy tissue region of interest (ROI) analysis to determine intensity consistency within a patient cohort. Through this workflow, we systematically evaluated intensity standardization methods for MRI of head and neck cancer (HNC) patients., Materials and Methods: Two HNC cohorts (30 patients total) were retrospectively analyzed. One cohort was imaged with heterogenous acquisition parameters (HET cohort), whereas the other was imaged with homogenous acquisition parameters (HOM cohort). The standard deviation of cohort-level normalized mean intensity (SD NMI c ), a metric of intensity consistency, was calculated across ROIs to determine the effect of five intensity standardization methods on T2-weighted images. For each cohort, a Friedman test followed by a post-hoc Bonferroni-corrected Wilcoxon signed-rank test was conducted to compare SD NMI c among methods., Results: Consistency (SD NMI c across ROIs) between unstandardized images was substantially more impaired in the HET cohort (0.29 ± 0.08) than in the HOM cohort (0.15 ± 0.03). Consequently, corrected p-values for intensity standardization methods with lower SD NMI c compared to unstandardized images were significant in the HET cohort (p < 0.05) but not significant in the HOM cohort (p > 0.05). In both cohorts, differences between methods were often minimal and nonsignificant., Conclusions: Our findings stress the importance of intensity standardization, either through the utilization of uniform acquisition parameters or specific intensity standardization methods, and the need for testing intensity consistency before performing quantitative analysis of HNC MRI., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Analyzing the influence of emotion on false memory through electroencephalogram is helpful to further explore the cognition function of brain. In this study, material emotion and participant emotion were introduced into Deese-Roediger-McDermott (DRM) paradigm experiment at the same time, which made the experiment process closer to real life. Different music was used to induce the emotion of 28 participants, and they were divided into positive group and negative group. Then, we analyzed the difference between the two groups from the behavior data, source location and cortex functional network of event related potential. The results of difference analysis show that the false memory rate of positive group (85 ± 8.6%) is significantly higher than that of negative group (72 ± 9.7%), and the activation degree and voxel number of the positive group are significantly higher than those of the negative group in the brain regions related to semantic coding (BA24 and BA45), and the compactness, the speed of information transmission and the small-world property of the brain network in positive group are significantly higher than those in negative group. The above results show that in the positive group, more brain resources are used for semantic association when they judge keywords, which confuse the keywords and learning words, and result in more false memories., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT., Methods and Materials: For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LET d ) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve., Results: An overall statistically significant RAIC association with dose and LET d was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LET d , respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LET d effect demonstrated increased risk of RAIC with increasing LET d for the majority of patients. Estimated probability of RAIC with LET d  = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD 15 were estimated to be 63.6 and 50.1 Gy with LET d equal to 2 and 5 keV/µm, respectively., Conclusions: Our results suggest that the LET d effect could be of clinical significance for some patients; LET d assessment in clinical treatment plans should therefore be taken into consideration., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background/purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance., Materials/methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers., Results: Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p < 0.05) and for FND for the T2 + Ve and ALL models (p < 0.05). No model demonstrated significant correlations between tumor size and DSC (p > 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p < 0.05), except those that included ADC or Ve as input channels (p > 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05)., Conclusion: DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

Objective: To investigate the effectiveness of the posterior-posterior triangulation technique for arthroscopic posterior cruciate ligament (PCL) reconstruction by comparing with the anteroposterior approach., Methods: Retrospective analysis was performed on 40 patients who underwent arthroscopic PCL reconstruction between February 2016 and February 2020. The PCLs were reconstructed via anteroposterior approach in 20 patients (anteroposterior approach group) and posterior-posterior triangulation technique in 20 patients (posterior-posterior triangulation technique group). There was no significant difference in gender, age, cause of injury, injury side, disease duration, preoperative International Knee Documentary Committee (IKDC) score, and Lysholm score between the two groups ( P >0.05). The operation time, surgical complications, and postoperative posterior drawer test, Lysholm score, and IKDC score were recorded and compared between the two groups., Results: The operation time was (65.25±10.05) minutes in the anteroposterior approach group and (56.15±8.15) minutes in the posterior-posterior triangulation technique group, and the difference was significant ( t =3.145, P =0.003). All incisions healed by first intention, and there was no complication such as vascular and nerve injuries or infection. Patients were followed up (27.05±11.95) months in the anteroposterior approach group and (21.40±7.82) months in the posterior-posterior triangulation technique group, with no significant difference ( t =1.770, P =0.085). At last follow-up, the posterior drawer tests were positive in 4 cases (3 cases of stageⅠand 1 case of stage Ⅱ) of the anteroposterior approach group and in 1 case (stageⅠ) of the posterior-posterior triangulation technique group, showing no significant difference between the two groups ( P =0.342). At last follow-up, Lysholm score and IKDC score in both groups were significantly higher than those before operation ( P <0.05). The above functional scores in the posterior-posterior triangulation technique group were significantly higher than those in the anteroposterior approach group ( P <0.05). Imaging reexamination showed that the position, shape, and tension of the grafts were well in both groups, and the grafts were covered with the synovium in the posterior-posterior triangulation technique group, the meniscofemoral ligaments were well preserved. There was no re-rupture of the reconstructed ligament during follow-up., Conclusion: Compared to the anteroposterior approach, the posterior-posterior triangulation technique provides a clearer view under arthroscopy, no blind spot, sufficient operating space, and relative safety. Moreover, it is easier to retain the remnant and the meniscofemoral ligaments, and can obtain good short-term effectiveness.

Purpose: To introduce a contouring guideline for the taste bud bearing tongue mucosa for head and neck cancer patients receiving radiotherapy., Methods and Materials: CT simulation images of oropharyngeal cancer patients were used to delineate both the whole tongue (extrinsic/intrinsic tongue muscles, floor of mouth) and the taste bud bearing tongue mucosa (method A: adaptation of the whole tongue structure; method B: axial adaptation of a mid-sagittal contour). Volumetric and dosimetric parameters of the whole tongue and the two methods of mucosal delineation, spatial overlap between methods A and B, and inter-observer variability for method B were calculated., Results: The study cohort was comprised of 70 patients with T1-4 N0-1 tonsillar (83%) and base of tongue (17%) cancers. Most of the comparative parameters between the whole tongue and mucosa (method A) significantly differed (mean, minimum, and maximum dose, V5-V70, D40-D90). The mean dose calculated for the whole tongue deviated on average 3.77 Gy compared to method A. No significant differences were found between methods A and B of the taste bud bearing tongue mucosa structure, and none of the dosimetric parameters differed more than 1.03 Gy on average. The mean Dice similarity coefficient for both mucosal structures was 0.79 ± 0.05, and 0.63 ± 0.12 for the inter-observer analysis of method B., Conclusions: We defined two methods for delineating the taste bud bearing mucosa and both are equally satisfactory procedures. Either method is preferable over delineation of the whole tongue as organ at risk for taste impairment., (Copyright © 2020. Published by Elsevier B.V.)