Evaluation Of Treatment Efficacy Research Articles

Evaluation of teplizumab's efficacy and safety in treatment of type 1 diabetes mellitus: A systematic review and meta-analysis

BACKGROUND Islets of Langerhans beta cells diminish in autoimmune type 1 diabetes mellitus (T1DM). Teplizumab, a humanized anti-CD3 monoclonal antibody, may help T1DM. Its long-term implications on clinical T1DM development, safety, and efficacy are unknown. AIM To assess the effectiveness and safety of teplizumab as a therapeutic intervention for individuals with T1DM. METHODS A systematic search was conducted using four electronic databases (PubMed, Embase, Scopus, and Cochrane Library) to select publications published in peer-reviewed journals written in English. The odds ratio (OR) and risk ratio (RR) were calculated, along with their 95%CI. We assessed heterogeneity using Cochrane Q and I 2 statistics and the appropriate P value. RESULTS There were 8 randomized controlled trials (RCTs) in the current meta-analysis with a total of 1908 T1DM patients from diverse age cohorts, with 1361 patients receiving Teplizumab and 547 patients receiving a placebo. Teplizumab was found to have a substantial link with a decrease in insulin consumption, with an OR of 4.13 (95%CI: 1.72 to 9.90). Teplizumab is associated with an improved C-peptide response (OR 2.49; 95%CI: 1.62 to 3.81) and a significant change in Glycated haemoglobin A1c (HbA1c) levels in people with type 1 diabetes [OR 1.75 (95%CI: 1.03 to 2.98)], and it has a RR of 0.71 (95%CI: 0.53 to 0.95). CONCLUSION In type 1 diabetics, teplizumab decreased insulin consumption, improved C-peptide response, and significantly changed HbA1c levels with negligible side effects. Teplizumab appears to improve glycaemic control and diabetes management with good safety and efficacy.

Meaningful changes in motor function in Duchenne muscular dystrophy (DMD): A multi-center study.

Evaluations of treatment efficacy in Duchenne muscular dystrophy (DMD), a rare genetic disease that results in progressive muscle wasting, require an understanding of the 'meaningfulness' of changes in functional measures. We estimated the minimal detectable change (MDC) for selected motor function measures in ambulatory DMD, i.e., the minimal degree of measured change needed to be confident that true underlying change has occurred rather than transient variation or measurement error. MDC estimates were compared across multiple data sources, representing >1000 DMD patients in clinical trials and real-world clinical practice settings. Included patients were ambulatory, aged ≥4 to <18 years and receiving steroids. Minimal clinically important differences (MCIDs) for worsening were also estimated. Estimated MDC thresholds for >80% confidence in true change were 2.8 units for the North Star Ambulatory Assessment (NSAA) total score, 1.3 seconds for the 4-stair climb (4SC) completion time, 0.36 stairs/second for 4SC velocity and 36.3 meters for the 6-minute walk distance (6MWD). MDC estimates were similar across clinical trial and real-world data sources, and tended to be slightly larger than MCIDs for these measures. The identified thresholds can be used to inform endpoint definitions, or as benchmarks for monitoring individual changes in motor function in ambulatory DMD.

Heart Rate Variability Biofeedback Efficacy on Fatigue and Energy Levels in Fibromyalgia: A Secondary Analysis of RCT NCT0412183

Background: Fibromyalgia syndrome (FMs) is a chronic condition characterized by widespread musculoskeletal pain and a range of complex symptoms, with chronic fatigue being a central feature significantly impacting daily life. The aim of this study was to analyze the secondary outcomes, specifically those related to perceived energy and fatigue symptoms in a randomized controlled trial (RCT) assessing the efficacy of heart rate variability biofeedback (HRV-BF) as an adjunctive treatment for FMs. Methods: Sixty-four FMs patients were randomly assigned to either receive 10 HRV-BF training sessions alongside standard pharmacological therapy (experimental group) or standard therapy alone for 10 weeks (control group). For this secondary analysis, potential improvements in specific items were evaluated regarding perceived energy (Item 10 of the Short-Form Health Survey), the ability to walk and climb stairs (Item 7 and Item 11 of the Fibromyalgia Impact Questionnaire, respectively), and the impact of pain on movement ability (Item 17 of the Bodily and Emotional Perception of Pain). Results: The experimental group demonstrated an improvement in the perception of energy, the ability to walk, and the impact of pain on movement ability. However, the same improvement was not observed in the ability to climb stairs. Conclusions: Fatigue assessment has emerged as a crucial factor for evaluating treatment efficacy in FMs and related conditions linked to altered energy levels, such as bipolar depression, and can offer valuable insights for precisely guiding HRV-BF treatments. ClinicalTrials.gov with code: NCT04121832.

Heart Rate Variability Biofeedback Efficacy on Fatigue and Energy Levels in Fibromyalgia: A Secondary Analysis of RCT NCT0412183

Background: Fibromyalgia syndrome (FMs) is a chronic condition characterized by widespread musculoskeletal pain and a range of complex symptoms, with chronic fatigue being a central feature significantly impacting daily life. The aim of this study was to analyze the secondary outcomes, specifically those related to perceived energy and fatigue symptoms in a randomized controlled trial (RCT) assessing the efficacy of heart rate variability biofeedback (HRV-BF) as an adjunctive treatment for FMs. Methods: Sixty-four FMs patients were randomly assigned to either receive 10 HRV-BF training sessions alongside standard pharmacological therapy (experimental group) or standard therapy alone for 10 weeks (control group). For this secondary analysis, potential improvements in specific items were evaluated regarding perceived energy (Item 10 of the Short-Form Health Survey), the ability to walk and climb stairs (Item 7 and Item 11 of the Fibromyalgia Impact Questionnaire, respectively), and the impact of pain on movement ability (Item 17 of the Bodily and Emotional Perception of Pain). Results: The experimental group demonstrated an improvement in the perception of energy, the ability to walk, and the impact of pain on movement ability. However, the same improvement was not observed in the ability to climb stairs. Conclusions: Fatigue assessment has emerged as a crucial factor for evaluating treatment efficacy in FMs and related conditions linked to altered energy levels, such as bipolar depression, and can offer valuable insights for precisely guiding HRV-BF treatments. ClinicalTrials.gov with code: NCT04121832.

Intention to treat and per protocol analyses: differences and similarities

Randomized trials can take more explanatory or more pragmatic approaches. Pragmatic studies, conducted closer to real-world conditions, assess treatment effectiveness while considering factors like protocol adherence. In these studies, intention-to-treat (ITT) analysis is fundamental, comparing outcomes regardless of the actual treatment received. Explanatory trials, conducted closer to optimal conditions, evaluate treatment efficacy, commonly with a per protocol (PP) analysis, which includes only outcomes from adherent participants.ITT and PP are strategies used in the conception, design, conduct (protocol execution), analysis, and interpretation of trials. Each serves distinct objectives. While both can be valid, when bias is controlled, and complementary, each has its own limitations. By excluding non-adherent participants, PP analyses can lose the benefits of randomization, resulting in group differences in factors (influencing adherence and outcomes) that were present at baseline. Additionally, clinical and social factors affecting adherence can also operate during follow-up, i.e., after randomization. Therefore, incomplete adherence may introduce postrandomization confounding. Conversely, ITT analysis, including all participants regardless of adherence, may dilute treatment effects. Moreover, varying adherence levels could limit the applicability of ITT findings in settings with diverse adherence patterns. Both ITT and PP analyses can be affected by selection bias due to differential losses and nonresponse (i.e., missing data) during follow-up.Combining high quality and comprehensive data with advanced statistical methods, known as g-methods, like inverse probability weighting, may help address postrandomization confounding in PP analysis, as well as selection bias in both ITT and PP analyses.

A systematic review of outcome measures evaluating treatment efficacy in vulval lichen sclerosus and evaluation of patients' priorities

A systematic review of outcome measures evaluating treatment efficacy in vulval lichen sclerosus and evaluation of patients' priorities

Futility Interim Analysis Based on Probability of Success Using a Surrogate Endpoint.

In clinical trials with time-to-event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long-term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision-making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical "final" endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility-stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior-data conflicts while maintaining acceptable performance even in the presence of significant prior-data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression-free survival as the surrogate endpoint.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

Endoscopic band ligation or endoscopic tissue adhesive injection in the treatment of gastric varices: Which is better?

The combination of endoscopic ultrasound with endoscopic treatment of type 1 gastric variceal hemorrhage may improve the robustness and generalizability of the findings in future studies. Moreover, the esophageal varices should also be included in the evaluation of treatment efficacy in subsequent studies to reach a more convincing conclusion.

Comparing a Low-Fluence Picosecond 1064 nm Nd:YAG Laser with a 532 nm Nd:YAG Laser for the Treatment of Pigmented Lesions in Chinese Patients: A Retrospective Analysis

The use of low-fluence picosecond (LFPS) 1064 nm neodymium-doped yttrium aluminum garnet (Nd:YAG) lasers, referred to as laser toning, is increasingly acknowledged as an effective treatment for pigmentation disorders in the Asian skin phenotype. This study aimed to conduct a comparative analysis on the effectiveness and safety of utilizing LFPS 1064 nm Nd:YAG lasers against picosecond 532 nm Nd:YAG lasers in treating pigmented lesions among Chinese patients. A retrospective photographic analysis and chart reviews were performed on 31 subjects exhibiting Fitzpatrick skin types III–VI who underwent LFPS 1064 nm Nd:YAG or picosecond 532 nm Nd:YAG treatments at a single tertiary center. Utilizing VISIA Complexion Analysis, comparative photographs were taken. Two independent physicians evaluated treatment efficacy using a visual analog scale (VAS) to assess the percentage of pigmentary clearance in standard photographs. Solar lentigines were the most prevalent pigmentary disorder, followed by post-inflammatory hyperpigmentation (PIH), nevus zygomaticus, melasma, freckles, and nevus of Ota. The clinical effectiveness of picosecond 532 nm and LFPS 1064 nm laser treatments proved comparable for lesions on the face, with mean VAS scores of 2.2 ± 1.1 and 1.8 ± 0.8, respectively. There were two cases of PIH in the picosecond 532 nm group, which resolved within one month. Overall, the LFPS 1064 nm laser demonstrates promise as a safe and efficient therapeutic modality for managing pigmented lesions in Chinese patients.

Autologous osteoperiosteal transplantation is effective in the treatment of single cystic osteochondral lesions of the talus and the prognostic impact of age should be emphasized.

To investigate the clinical efficacy and prognostic factors associated with autologous osteoperiosteal transplantation for the treatment of single cystic osteochondral lesions of the talus (OLT). The clinical data of patients with single cystic OLT undergoing autologous osteoperiosteal transplantation at the Department of Foot and Ankle Surgery of our hospital between 2018 and 2022, including complete follow-up, were retrospectively analyzed. Imaging data from each patient were imported into Mimics software to measure the surface area, volumeand depth of the lesions. Then, the talus nine-compartment partitioning method was used to partition the injury site. Preoperative and final follow-up assessments were performed using the American Orthopaedic Foot and Ankle Society (AOFAS) score, visual analogue scale (VAS) for painand 36-item Short-Form Health Survey (SF-36) to evaluate treatment efficacy and analyze prognostic factors. Of the 31 patients with single cystic OLT with a complete set of follow-up data, there were 17 males and 14 females, with a mean age of 43.3 ± 13.6 years, a mean follow-up time of 30.1 ± 14.0 monthsand a mean illness duration of 30.4 ± 20.0 months. The postoperative final follow-up AOFAS score was 90.7 ± 5.5; this represented significant improvement when compared to the preoperative score of 57.0 ± 8.5 (P < 0.001). The final postoperative follow-up VAS score was 18.5 ± 8.3; this was significantly better than the preoperative score of 57.8 ± 8.7 (P < 0.001). The physical component summary (PCS)score and mental component summary (MCS)score on the SF-36 scale showed significant improvement at the final postoperative follow-up when compared to preoperative scores (p < 0.001). No other complications were observed during follow-up, such as wound infection or pain at the donor site. One of the patients showed less improvement, which may be related to premature weight-bearing or re-sprained ankle after surgery. There was no significant correlation between the duration of illness, genderand the location, depth, surface areaand volume of the OLTand the postoperative scores. However, patient age showed a significant negative correlation with the postoperative SF-36 PCS and MCS scores. Autologous osteoperiosteal transplantation for single cystic OLT demonstrated good clinical efficacy with a low incidence of complications. Furthermore, age represents an important factor influencing prognosis. Level II.

Inflammatory and Cardiovascular Biomarkers to Monitor Fabry Disease Progression.

Fabry disease is an invalidating multisystemic disorder affecting α-Galactosidase, a rate-limiting hydrolase dedicated to lipid catabolism. Non-metabolized substrates, such as Globotriaosylceramide and its derivatives trigger the direct or indirect activation of inflammatory events and endothelial dysfunction. In spite of the efficacy demonstrated by enzyme replacement therapy or pharmacological chaperones in delaying disease progression, few studies have analyzed whether these treatments can improve the pro-inflammatory state of FD patients. Therefore, the aim of this work was to assess cytokines and cardiovascular risk-related proteins detectable in plasma from FD patients, whether treated or not with ERT, to evaluate the reliability of these markers in monitoring disease stage and treatment effects. We identified inflammatory and endothelial dysfunction markers (ADAMTS-13, TNF-α, GDF-15, MIP-1β, VEGFA, MPO, and MIC-1) that cooperate in a common pathway and are increased in FD patients' plasma samples. As shown by the assessment of these proteins over time, they can help to evaluate the risk of higher severity in FD, as well as ERT effects. Even though the analyzed proteins cannot be considered as proper biomarkers due to their non-specificity to FD, taken together they can provide a signature of reference molecules with prognostic value for early diagnosis, and evaluation of disease progression and treatment efficacy, using blood samples.

Responsiveness of the condition-specific OVAMA questionnaire to measure symptoms and appearance in patients with vascular malformations.

Evidence-based guidelines for the treatment of vascular malformations are not readily available, possibly due to the diversity in methods used to evaluate treatment efficacy in clinical research, complicating the aggregation and comparison of study results. The OVAMA (Outcome Measures for VAscular Malformations) questionnaire was developed to uniformly measure symptoms and appearance, i.e., condition-specific core outcome domains, in patients with vascular malformations. However, the OVAMA questionnaire needs to be responsive to changes in these constructs in order to assess whether the disease status has altered since treatment. To assess the responsiveness of the OVAMA questionnaire in patients with vascular malformations. In a prospective longitudinal study, patients completed the OVAMA questionnaire at baseline and eight weeks follow-up since treatment or watchful waiting policy. Additionally, patients completed the Global rating of change (GRC) scales at follow-up. Responsiveness was evaluated following the criterion approach of testing predefined hypotheses about expected relationships between the OVAMA questionnaire and GRC scales, measuring the same constructs. The OVAMA questionnaire was considered responsive if ≥ 75% of the hypotheses were confirmed. Between July 2020 and September 2022, 89 patients were recruited in a vascular anomaly centre in the Netherlands, of which 63 patients completed the questionnaires at baseline and follow-up. In total, fifteen constructs of the OVAMA questionnaire were assessed for five hypotheses. Of these 75 hypotheses, 63 (84%) hypotheses were confirmed and thereby providing evidence that the OVAMA questionnaire is responsive to change. Our study found convincing evidence that the OVAMA questionnaire is responsive to changes in symptoms and appearance in patients with vascular malformations. In addition to determining a baseline of symptoms and appearance, the OVAMA questionnaire can now be used to evaluate the effect of treatment from the patient's perspective. The responsive OVAMA questionnaire allows for uniform evaluation and comparison of the effects of treatment on the condition-specific core outcome domains, tackling heterogeneity in outcome measurement and improving the clinical research of vascular malformations.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications.

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies

Metastatic brain cancer is a condition characterized by the migration of cancer cells to the brain from extracranial sites. Notably, metastatic brain tumors surpass primary brain tumors in prevalence by a significant factor, they exhibit an aggressive growth potential and have the capacity to spread across diverse cerebral locations simultaneously. Magnetic resonance imaging (MRI) scans of individuals afflicted with metastatic brain tumors unveil a wide spectrum of characteristics. These lesions vary in size and quantity, spanning from tiny nodules to substantial masses captured within MRI. Patients may present with a limited number of lesions or an extensive burden of hundreds of them. Moreover, longitudinal studies may depict surgical resection cavities, as well as areas of necrosis or edema. Thus, the manual analysis of such MRI scans is difficult, user-dependent and cost-inefficient, and – importantly – it lacks reproducibility. We address these challenges and propose a pipeline for detecting and analyzing brain metastases in longitudinal studies, which benefits from an ensemble of various deep learning architectures originally designed for different downstream tasks (detection and segmentation). The experiments, performed over 275 multi-modal MRI scans of 87 patients acquired in 53 sites, coupled with rigorously validated manual annotations, revealed that our pipeline, built upon open-source tools to ensure its reproducibility, offers high-quality detection, and allows for precisely tracking the disease progression. To objectively quantify the generalizability of models, we introduce a new data stratification approach that accommodates the heterogeneity of the dataset and is used to elaborate training-test splits in a data-robust manner, alongside a new set of quality metrics to objectively assess algorithms. Our system provides a fully automatic and quantitative approach that may support physicians in a laborious process of disease progression tracking and evaluation of treatment efficacy.

Determining meaningful thresholds for evaluating treatment efficacy in patients with alopecia areata

AbstractBackgroundThe Severity of Alopecia Tool (SALT) is a clinician‐reported outcome measure of scalp hair loss in alopecia areata (AA).ObjectivesTo characterise the magnitudes of change in SALT scores corresponding to meaningful treatment benefits from the patient's perspective.MethodsAnchor‐based methods for the estimation of meaningful within‐patient change thresholds were applied to pooled data from a randomised, double‐blind trial of ritlecitinib. Anchors included a patient‐reported measure of change in AA severity, the Patient Global Impression of Change (PGI‐C) and three items comprising the Patient Satisfaction with Hair Growth (P‐Sat) questionnaire. After reviewing Pearson correlations between change‐from‐baseline SALT scores and each anchor to confirm adequate association, potential thresholds were computed as mean change‐from‐baseline SALT scores among patients who reported moderate improvement on the PGI‐C and/or moderate satisfaction on each of three P‐Sat items at week 24.ResultsSix hundred and fifty participants (86% adults, 14% adolescents) had mean (standard deviation) SALT scores of 90.6 (14.3) at baseline, suggesting a sample with primarily severe AA. Correlations between SALT change‐from‐baseline scores and the patient‐reported items supported their use as anchors. Estimates based on patients reporting moderate improvement in AA (n = 102) on the PGI‐C and those reporting moderate satisfaction on the P‐Sat item related to the amount of hair growth at week 24 (n = 122) were −42.2 (26.1) and −43.1 (26.8), respectively. Supportive estimates based on the remaining P‐Sat items were similar in magnitude.ConclusionsAmong patients with severe AA, SALT change‐from‐baseline scores of 42 or 43 represent meaningful improvements. While the achievement of low SALT scores of ≤10–≤20 have been used to characterise efficacy in clinical trials, the amount of change required to meet this endpoint far exceeds the estimates in this study. The treatment goals of individual patients must be considered when evaluating benefit in both clinical trials and clinical practice.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes.

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

Baseline trait mindfulness moderates the efficacy of mindfulness interventions and active controls: A meta‐analysis of 177 randomised controlled trials

AbstractPrevious evidence suggests that benefits of mindfulness‐based interventions (MBIs) may depend on individuals' baseline trait mindfulness (BTM) levels. This meta‐analysis investigated moderating effects of BTM on changes in mental health outcomes and trait mindfulness in randomised controlled trials (RCTs) of MBIs. A total of 177 primary studies (total N = 13,486), comparing the treatment effects of MBIs against active, treatment‐as‐usual (TAU) and waitlist control groups, were synthesised via three‐level meta‐analysis. Lower BTM was associated with larger changes in mental health outcomes (B = −0.14, 95% CI [−0.21, −0.06], p &lt; .001) and trait mindfulness (B = −0.09 [−0.16, −0.02], p = .009) in MBIs and active controls combined. These associations were significantly larger than from those in TAU and waitlist controls. Moderating effects were less tangible for changes in trait mindfulness in MBIs and active controls individually and less tangible in various sensitivity analyses which, however, were confounded by client type across the RCTs. Individuals low in BTM may, by a small effect size, benefit more from MBIs and active control interventions. BTM may thus affect the evaluation of treatment efficacy. MBIs and active interventions could be offered specifically to persons low in BTM.

Application of MR images in radiotherapy planning for brain tumor based on deep learning

Purpose Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods. Methods 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint. Results Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and Dmean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1 mm: 93.96%±6.75%, 2%/2 mm: 99.87%±0.30%, 3%/3 mm: 100.00%±0.00%; and 1%/1 mm: 99.14%±0.80%, 2%/2 mm: 99.92%±0.08%, 3%/3 mm: 99.99%±0.01%. Conclusion MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.

Automatic height measurement of central serous chorioretinopathy lesion using a deep learning and adaptive gradient threshold based cascading strategy

Accurately quantifying the height of central serous chorioretinopathy (CSCR) lesion is of great significance for assisting ophthalmologists in diagnosing CSCR and evaluating treatment efficacy. The manual measurement results dominated by single optical coherence tomography (OCT) B-scan image in clinical practice face the dilemma of weak reference, poor reproducibility, and experience dependence. In this context, this paper constructs two schemes: Scheme Ⅰ draws on the idea of ensemble learning, namely, integrating multiple models for locating starting key point in the height direction of lesion in the inference stage, which appropriately improves the performance of a single model. Scheme Ⅱ designs an adaptive gradient threshold (AGT) technique, followed by the construction of cascading strategy, which involves preliminary location of starting key point through deep learning, and then employs AGT for precise adjustment. This strategy not only achieves effective location for starting key point, but also significantly reduces the large appetite of deep learning model for training samples. Subsequently, AGT continues to play a crucial role in locating the terminal key point in the height direction of lesion, further demonstrating its feasibility and effectiveness. Quantitative and qualitative key point location experiments in the height direction of lesion on 1152 samples, as well as the final height measurement display, consistently conveys the superiority of the constructed schemes, especially the cascading strategy, expanding another potential tool for the comprehensive analysis of CSCR.