ZnO/g-C3N4 photocatalyst activated by low-pressure ultraviolet for restoring the SWASV signals: A fast pretreatment method for electrochemically detecting Cd2+ and Pb2+ in soil extracts.

Soil organic matter (SOM) significantly impacts the detection accuracy of Cd2+ and Pb2+ 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-C3N4 photocatalyst, activating the photocatalyst to generate highly oxidative •OH radicals and O2•- 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-C3N4 photocatalytic system were meticulously optimized, including the pH of photolysis, duration of photolysis, g-C3N4 mass fraction, and concentration of the photocatalyst. Furthermore, the ZnO/g-C3N4 photocatalyst was thoroughly characterized, with an in-depth investigation on the synergistic interaction between ZnO and g-C3N4 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-C3N4 to be quickly transferred to the conduction band of ZnO, preventing the recombination of e- and 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 Cd2+ and 99.8% for Pb2+, which validated the feasibility and effectiveness of the proposed method in environmental applications.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

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.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

FDG Uptake Caused by Right Varicocele.

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.

Enhancing Electrode Performance through Triple Distribution Modulation of Active Material, Conductive Agent, and Porosity.

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 LiFePO4 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.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

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_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_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_1, and IPP_RF_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_ST_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_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.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review.

Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). 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 of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer.

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.

Determining The Role Of Radiation Oncologist Demographic Factors On Segmentation Quality: Insights From A Crowd-Sourced Challenge Using Bayesian Estimation.

BACKGROUND: Medical image auto-segmentation is poised to revolutionize radiotherapy workflows. The quality of auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of these clinician-derived segmentations have yet to be fully understood or quantified. Therefore, the purpose of this study was to determine the role of common observer demographic variables on quantitative segmentation performance. METHODS: Organ at risk (OAR) and tumor volume segmentations provided by radiation oncologist observers from the Contouring Collaborative for Consensus in Radiation Oncology public dataset were utilized for this study. Segmentations were derived from five separate disease sites comprised of one patient case each: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and gastrointestinal (GI). Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus gold standard primarily using the Dice Similarity Coefficient (DSC); surface DSC was investigated as a secondary metric. Metrics were stratified into binary groups based on previously established structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Markov chain Monte Carlo Bayesian estimation were used to investigate the association between demographic variables and the binarized segmentation quality for each disease site separately. Variables with a highest density interval excluding zero - loosely analogous to frequentist significance - were considered to substantially impact the outcome measure. RESULTS: After filtering by practicing radiation oncologists, 574, 110, 452, 112, and 48 structure observations remained for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of observations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumor volumes, respectively. Bayesian regression analysis revealed tumor category had a substantial negative impact on binarized DSC for the breast (coefficient mean ± standard deviation: -0.97 ± 0.20), sarcoma (-1.04 ± 0.54), H&N (-1.00 ± 0.24), and GI (-2.95 ± 0.98) cases. There were no clear recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations and wide highest density intervals. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality. Future studies should investigate additional demographic variables, more patients and imaging modalities, and alternative metrics of segmentation acceptability.

An efficient pretreatment method based on AgNPs-doped SnO2 photocatalyst for the accurate detection of heavy metals in organic-rich water samples.

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 SnO2 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 SnO2 photocatalyst were investigated to excavate the reasons behind the most excellent AgNPs doping rate to SnO2 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.

[Return to sports WeChat applet for evaluating the rehabilitation effects after anterior cruciate ligament reconstruction].

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_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.

[Effectiveness of anterior cruciate ligament reconstruction with personalized femoral locator based on apex of deep cartilage].

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.

[Three-dimensional kinematic analysis of knee joint after anterior cruciate ligament reconstruction with personalized femoral positioner based on apex of deep cartilage].

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 _ 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.

Longitudinal diffusion and volumetric kinetics of head and neck cancer magnetic resonance on a 1.5T MR-Linear accelerator hybrid system: A prospective R-IDEAL Stage 2a imaging biomarker characterization/ pre-qualification study.

Objectives: We aim to characterize the serial quantitative apparent diffusion coefficient (ADC) changes of the target disease volume using diffusion-weighted imaging (DWI) acquired weekly during radiation therapy (RT) on a 1.5T MR-Linac and correlate these changes with tumor response and oncologic outcomes for head and neck squamous cell carcinoma (HNSCC) patients as part of a programmatic R-IDEAL biomarker characterization effort. Methods: Thirty patients with pathologically confirmed HNSCC who received curative-intent RT at the University of Texas MD Anderson Cancer Center, were included in this prospective study. Baseline and weekly Magnetic resonance imaging (MRI) (weeks 1-6) were obtained, and various ADC parameters (mean, 5 th , 10 th , 20 th , 30 th , 40 th , 50 th , 60 th , 70 th , 80 th , 90 th and 95 th percentile) were extracted from the target regions of interest (ROIs). Baseline and weekly ADC parameters were correlated with response during RT, loco-regional control, and the development of recurrence using the Mann-Whitney U test. The Wilcoxon signed-rank test was used to compare the weekly ADC versus baseline values. Weekly volumetric changes (Δvolume) for each ROI were correlated with ΔADC using Spearman's Rho test. Recursive partitioning analysis (RPA) was performed to identify the optimal ΔADC threshold associated with different oncologic outcomes. Results: There was an overall significant rise in all ADC parameters during different time points of RT compared to baseline values for both gross primary disease volume (GTV-P) and gross nodal disease volumes (GTV-N). The increased ADC values for GTV-P were statistically significant only for primary tumors achieving complete remission (CR) during RT. RPA identified GTV-P ΔADC 5 th percentile >13% at the 3 rd week of RT as the most significant parameter associated with CR for primary tumor during RT (p <0.001). Baseline ADC parameters for GTV-P and GTV-N didn't significantly correlate with response to RT or other oncologic outcomes. There was a significant decrease in residual volume of both GTV-P & GTV-N throughout the course of RT. Additionally, a significant negative correlation between mean ΔADC and Δvolume for GTV-P at the 3 rd and 4 th week of RT was detected (r = -0.39, p = 0.044 & r = -0.45, p = 0.019, respectively). Conclusion: Assessment of ADC kinetics at regular intervals throughout RT seems to be correlated with RT response. Further studies with larger cohorts and multi-institutional data are needed for validation of ΔADC as a model for prediction of response to RT.

Prospective evaluation of in vivo and phantom repeatability and reproducibility of diffusion-weighted MRI sequences on 1.5 T MRI-linear accelerator (MR-Linac) and MR simulator devices for head and neck cancers.

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 EPIMR-linac, SPLICE, TSE, EPIMR sim, BLADE, RESOLVE. Repeatability/reproducibility wCV for EPIMR-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 mm2/s for most vials (EPIMR-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 EPIMR-linac, SPLICE, TSE, EPIMR 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.

[Progress in evaluation of return to sports after anterior cruciate ligament reconstruction].

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.

Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.

Background: Oropharyngeal cancer (OPC) is a widespread disease, with radiotherapy being a core treatment modality. Manual segmentation of the primary gross tumor volume (GTVp) is currently employed for OPC radiotherapy planning, but is subject to significant interobserver variability. Deep learning (DL) approaches have shown promise in automating GTVp segmentation, but comparative (auto)confidence metrics of these models predictions has not been well-explored. Quantifying instance-specific DL model uncertainty is crucial to improving clinician trust and facilitating broad clinical implementation. Therefore, in this study, probabilistic DL models for GTVp auto-segmentation were developed using large-scale PET/CT datasets, and various uncertainty auto-estimation methods were systematically investigated and benchmarked. Methods: We utilized the publicly available 2021 HECKTOR Challenge training dataset with 224 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations as a development set. A separate set of 67 co-registered PET/CT scans of OPC patients with corresponding GTVp segmentations was used for external validation. Two approximate Bayesian deep learning methods, the MC Dropout Ensemble and Deep Ensemble, both with five submodels, were evaluated for GTVp segmentation and uncertainty performance. The segmentation performance was evaluated using the volumetric Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance at 95% (95HD). The uncertainty was evaluated using four measures from literature: coefficient of variation (CV), structure expected entropy, structure predictive entropy, and structure mutual information, and additionally with our novel Dice-risk measure. The utility of uncertainty information was evaluated with the accuracy of uncertainty-based segmentation performance prediction using the Accuracy vs Uncertainty (AvU) metric, and by examining the linear correlation between uncertainty estimates and DSC. In addition, batch-based and instance-based referral processes were examined, where the patients with high uncertainty were rejected from the set. In the batch referral process, the area under the referral curve with DSC (R-DSC AUC) was used for evaluation, whereas in the instance referral process, the DSC at various uncertainty thresholds were examined. Results: Both models behaved similarly in terms of the segmentation performance and uncertainty estimation. Specifically, the MC Dropout Ensemble had 0.776 DSC, 1.703 mm MSD, and 5.385 mm 95HD. The Deep Ensemble had 0.767 DSC, 1.717 mm MSD, and 5.477 mm 95HD. The uncertainty measure with the highest DSC correlation was structure predictive entropy with correlation coefficients of 0.699 and 0.692 for the MC Dropout Ensemble and the Deep Ensemble, respectively. The highest AvU value was 0.866 for both models. The best performing uncertainty measure for both models was the CV which had R-DSC AUC of 0.783 and 0.782 for the MC Dropout Ensemble and Deep Ensemble, respectively. With referring patients based on uncertainty thresholds from 0.85 validation DSC for all uncertainty measures, on average the DSC improved from the full dataset by 4.7% and 5.0% while referring 21.8% and 22% patients for MC Dropout Ensemble and Deep Ensemble, respectively. Conclusion: We found that many of the investigated methods provide overall similar but distinct utility in terms of predicting segmentation quality and referral performance. These findings are a critical first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Prospective validation of diffusion-weighted MRI as a biomarker of tumor response and oncologic outcomes in head and neck cancer: Results from an observational biomarker pre-qualification study.

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 mm2/s vs. (1.37 ± 0.22) × 10-3 mm2/s, p < 0.0001), while patients with non-CR had no significant increase (p > 0.05). RPA identified GTV-P delta (Δ)ADCmean < 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 ΔADCmean at mid-RT ≥ 7% was significantly associated with better LC and RFS. The addition of ΔADCmean 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: ΔADCmean 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.