Automated Lugano Metabolic Response Assessment in 18F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on 18F-Fluorodeoxyglucose-Positron Emission Tomography.

PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For 18F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic. METHODS: Here, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers: NCT02257567, NCT02500407, 20% holdout in ClinicalTrials.gov identifier: NCT01287741), outputs the detected lesions at baseline and follow-up to enable focused radiologist review. RESULTS: The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses. CONCLUSION: These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.

Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B-cell non-Hodgkin lymphoma.

Mosunetuzumab (Mosun) is a CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step-up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and maximize efficacy in early cycles. A population pharmacokinetic (popPK) model was developed from 439 patients with relapsed/refractory B-Cell Non-Hodgkin lymphoma receiving Mosun IV monotherapy, including fixed dosing (0.05-2.8 mg IV every 3 weeks (q3w)) and Cycle 1 step-up dosing groups (0.4/1/2.8-1/2/60/30 mg IV q3w). Prior to Mosun treatment, ~50% of patients had residual levels of anti-CD20 drugs (e.g., rituximab or obinutuzumab) from prior treatment. CD20 receptor binding dynamics and rituximab/obinutuzumab PK were incorporated into the model to calculate the Mosun CD20 receptor occupancy percentage (RO%) over time. A two-compartment model with time-dependent clearance (CL) best described the data. The typical patient had an initial CL of 1.08 L/day, transitioning to a steady-state CL of 0.584 L/day. Statistically relevant covariates on PK parameters included body weight, albumin, sex, tumor burden, and baseline anti-CD20 drug concentration; no covariate was found to have a clinically relevant impact on exposure at the approved dose. Mosun CD20 RO% was highly variable, attributed to the large variability in residual baseline anti-CD20 drug concentration (median = 10 µg/mL). The 60 mg loading doses increased Mosun CD20 RO% in Cycle 1, providing efficacious exposures in the presence of the competing anti-CD20 drugs. PopPK model simulations, investigating Mosun dose delays, informed treatment resumption protocols to ensure CRS mitigation.

A comprehensive laser image dataset for real-time measurement of wheelset geometric parameters.

Railway transportation has experienced significant growth worldwide, offering numerous benefits to society. Most railway accidents are caused by wheelset faults so it's significant to monitor wheelset conditions. Therefore, we need to collect wheelset images, repaint them, extract laser stripe centerlines, construct 3D contour, and measure their geometric parameters to judge the wheelset's conditions. Deep learning can fulfill the tasks satisfyingly because it's adaptable, robust, and generalize compared with traditional methods. To train the deep learning models effectively, we need rich and high-quality wheelset datasets. So far, there are no applicable public train wheelset datasets available, which greatly hinders the research on train wheelsets. Thus we construct a publicly available Wheelset Laser Image Dataset (WLI-Set). WLI-Set consists of four sub-datasets, Original, Inpainting, Segmentation, and Centerline. The dataset contains abundant annotated multiline laser stripe images that can facilitate the research on train wheelsets effectively.

Durable Responses With Mosunetuzumab in Relapsed/Refractory Indolent and Aggressive B-Cell Non-Hodgkin Lymphomas: Extended Follow-Up of a Phase I/II Study.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.Mosunetuzumab is a CD20xCD3 T-cell-engaging bispecific antibody administered as an off-the-shelf, fixed-duration treatment in an outpatient setting. We report an updated analysis of the durability of response, by investigator assessment, after an overall median follow-up of 3.5 years in patients with relapsed/refractory indolent or aggressive B-cell non-Hodgkin lymphoma (iNHL/aNHL) from the dose-escalation stage of a phase I/II study of mosunetuzumab (ClinicalTrials.gov identifier: NCT02500407). Across dose levels, 65.7% of patients with iNHL and 36.4% with aNHL achieved a complete or partial response to mosunetuzumab. Median duration of response (DoR) in patients with iNHL for all responders was 23.2 months (95% CI, 13.8 to not estimable [NE]), but was not reached in complete responders (95% CI, 21.0 to NE). After a median time on study of 38.9 months, no relapses were observed beyond 26 months in complete responders. In patients with aNHL, median DoR for all responders was 7.8 months (95% CI, 4.6 to 22.8). Among 12 complete responders who progressed postmosunetuzumab treatment and were retreated with mosunetuzumab, 83.3% had an objective response and 58.3% achieved a second complete response. Our study reports the longest follow-up using bispecific antibodies in patients with B-cell non-Hodgkin lymphoma and demonstrates that mosunetuzumab can mediate durable remissions with time-limited treatment.

Loss of CD20 expression as a mechanism of resistance to mosunetuzumab in relapsed/refractory B-cell lymphomas.

ABSTRACT: CD20 is an established therapeutic target in B-cell malignancies. The CD20 × CD3 bispecific antibody mosunetuzumab has significant efficacy in B-cell non-Hodgkin lymphomas (NHLs). Because target antigen loss is a recognized mechanism of resistance, we evaluated CD20 expression relative to clinical response in patients with relapsed and/or refractory NHL in the phase 1/2 GO29781 trial investigating mosunetuzumab monotherapy. CD20 was studied using immunohistochemistry (IHC), RNA sequencing, and whole-exome sequencing performed centrally in biopsy specimens collected before treatment at predose, during treatment, or upon progression. Before treatment, most patients exhibited a high proportion of tumor cells expressing CD20; however, in 16 of 293 patients (5.5%) the proportion was <10%. Analyses of paired biopsy specimens from patients on treatment revealed that CD20 levels were maintained in 29 of 30 patients (97%) vs at progression, where CD20 loss was observed in 11 of 32 patients (34%). Reduced transcription or acquisition of truncating mutations explained most but not all cases of CD20 loss. In vitro modeling confirmed the effects of CD20 variants identified in clinical samples on reduction of CD20 expression and missense mutations in the extracellular domain that could block mosunetuzumab binding. This study expands the knowledge about the occurrence of target antigen loss after anti-CD20 therapeutics to include CD20-targeting bispecific antibodies and elucidates mechanisms of reduced CD20 expression at disease progression that may be generalizable to other anti-CD20 targeting agents. These results also confirm the utility of readily available IHC staining for CD20 as a tool to inform clinical decisions. This trial was registered at www.ClinicalTrials.gov as #NCT02500407.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images.

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

Observer-Based Fault-Tolerant Finite-Time Control of Nonlinear Multiagent Systems.

In this article, an adaptive neural containment control for a class of nonlinear multiagent systems considering actuator faults is introduced. By using the general approximation property of neural networks, a neuro-adaptive observer is designed to estimate unmeasured states. In addition, in order to reduce the computational burden, a novel event-triggered control law is designed. Furthermore, the finite-time performance function is presented to improve the transient and steady-state performance of the synchronization error. Utilizing the Lyapunov stability theory, it will be shown that the closed-loop system is cooperatively semiglobally uniformly ultimately bounded (CSGUUB), and the followers' outputs reach the convex hull constructed by the leaders. Moreover, it is shown that the containment errors are limited to the prescribed level in a finite time. Eventually, a simulation example is presented to corroborate the capability of the proposed scheme.

SDMT: Spatial Dependence Multi-Task Transformer Network for 3D Knee MRI Segmentation and Landmark Localization.

Knee segmentation and landmark localization from 3D MRI are two significant tasks for diagnosis and treatment of knee diseases. With the development of deep learning, Convolutional Neural Network (CNN) based methods have become the mainstream. However, the existing CNN methods are mostly single-task methods. Due to the complex structure of bone, cartilage and ligament in the knee, it is challenging to complete the segmentation or landmark localization alone. And establishing independent models for all tasks will bring difficulties for surgeon's clinical using. In this paper, a Spatial Dependence Multi-task Transformer (SDMT) network is proposed for 3D knee MRI segmentation and landmark localization. We use a shared encoder for feature extraction, then SDMT utilizes the spatial dependence of segmentation results and landmark position to mutually promote the two tasks. Specifically, SDMT adds spatial encoding to the features, and a task hybrided multi-head attention mechanism is designed, in which the attention heads are divided into the inter-task attention head and the intra-task attention head. The two attention head deal with the spatial dependence between two tasks and correlation within the single task, respectively. Finally, we design a dynamic weight multi-task loss function to balance the training process of two task. The proposed method is validated on our 3D knee MRI multi-task datasets. Dice can reach 83.91% in the segmentation task, and MRE can reach 2.12 mm in the landmark localization task, it is competitive and superior over other state-of-the-art single-task methods.

Mosunetuzumab monotherapy is active and tolerable in patients with relapsed/refractory diffuse large B-cell lymphoma.

As part of a phase 1 or 2 study, this single-arm expansion cohort established the efficacy and safety of mosunetuzumab monotherapy in patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) (received ≥2 previous lines of therapy). Intravenous mosunetuzumab was administered with cycle (C) 1 step-up dosing for cytokine release syndrome (CRS) mitigation: C1 day (D) 1: 1 mg; C1D8 2 mg; C1D15 and C2D1: 60 mg; C3 + D1: 30 mg. Hospitalization was not mandatory. Patients with complete response (CR) completed treatment after C8; those with partial response or stable disease continued treatment for a total of 17 cycles. The primary end point was CR rate (best response), assessed against a historical control CR rate (20%) by independent review facility. Eighty-eight patients (73.9% de novo DLBCL; 26.1% transformed follicular lymphoma) were enrolled; all had received previous anthracycline and anti-CD20 therapy. Overall response and CR rates were 42.0% (95% confidence interval [CI], 31.6-53.1) and 23.9% (95% CI, 15.4-34.1), respectively; CR rate did not reach statistical significance vs the historical control (P = .36). Median time to first response was 1.4 months. Median progression-free survival was 3.2 months (95% CI, 2.2-5.3). The CR rate in 26 patients who received previous chimeric antigen receptor T-cell (CAR-T) therapy was 12%. CRS was one of the most common adverse events (26.1% of patients); predominantly grade 1 to 2 and primarily in C1. Four patients (4.5%) discontinued mosunetuzumab owing to adverse events. Mosunetuzumab demonstrated notable efficacy and a manageable safety profile in patients with R/R DLBCL, including those previously treated with CAR-Ts. This trial was registered at www.clinicaltrials.gov as #NCT02500407.

An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition.

Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced. Furthermore, there are commonalities between the bearing fault detection, classification, and identification tasks. Based on these observations, this article proposes a novel integrated multitasking intelligent bearing fault diagnosis scheme with the aid of representation learning under imbalanced sample condition, which realizes bearing fault detection, classification, and unknown fault identification. Specifically, in the unsupervised condition, a bearing fault detection approach based on modified denoising autoencoder (DAE) with self-attention mechanism for bottleneck layer (MDAE-SAMB) is proposed in the integrated scheme, which only uses the healthy data for training. The self-attention mechanism is introduced into the neurons in the bottleneck layer, which can assign different weights to the neurons in the bottleneck layer. Moreover, the transfer learning based on representation learning is proposed for few-shot fault classification. Only a few fault samples are used for offline training, and high-accuracy online bearing fault classification is achieved. Finally, according to the known fault data, the unknown bearing faults can be effectively identified. A bearing dataset generated by rotor dynamics experiment rig (RDER) and a public bearing dataset demonstrates the applicability of the proposed integrated fault diagnosis scheme.

Analysis of factors affecting testicular spermatogenesis capacity by using the tissue transcriptome data from GTEx.

In human, endo- or exogeneous factors might alter the cellular composition, the endocrine and inflammatory micro-environments and the metabolic balance in testis. These factors will further impair the testicular spermatogenesis capacity and alter the transcriptome of testis. Conversely, it should be possible that the alteration of the transcriptomes in testes be used as an indicator to evaluate the testicular spermatogenesis capacity and to predict the causing factors. In this study, using the transcriptome data of human testes and whole blood which were collected by the genotype-tissue expression project (GTEx), we analyzed the transcriptome differences in human testes and explored those factors that affecting spermatogenesis. As a result, testes were clustered into five clusters according to their transcriptomic features, and each cluster of testes was evaluated as having different spermatogenesis capacity. High rank genes of each cluster and the differentially expressed genes in lower functional testes were analyzed. Transcripts in whole blood which may be associated with testis function were also analyzed by the correlation test. As a result, factors such as immune response, oxygen transport, thyrotropin, prostaglandin and tridecapeptide neurotensin were found associated with spermatogenesis. These results revealed multiple clues about the spermatogenesis regulation in testis and provided potential targets to improve the fertility of men in clinic.

Systems-based digital twins to help characterize clinical dose-response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL.

Phase I oncology clinical trials often comprise a limited number of patients representing different disease subtypes who are divided into cohorts receiving treatment(s) at different dosing levels and schedules. Here, we leverage a previously developed quantitative systems pharmacology model of the anti-CD20/CD3 T-cell engaging bispecific antibody, mosunetuzumab, to account for different dosing regimens and patient heterogeneity in the phase I study to inform clinical dose/exposure-response relationships and to identify biological determinants of clinical response. We developed a novel workflow to generate digital twins for each patient, which together form a virtual population (VPOP) that represented variability in biological, pharmacological, and tumor-related parameters from the phase I trial. Simulations based on the VPOP predict that an increase in mosunetuzumab exposure increases the proportion of digital twins with at least a 50% reduction in tumor size by day 42. Simulations also predict a left-shift of the exposure-response in patients diagnosed with indolent compared to aggressive non-Hodgkin's lymphoma (NHL) subtype; this increased sensitivity in indolent NHL was attributed to the lower inferred values of tumor proliferation rate and baseline T-cell infiltration in the corresponding digital twins. Notably, the inferred digital twin parameters from clinical responders and nonresponders show that the potential biological difference that can influence response include tumor parameters (tumor size, proliferation rate, and baseline T-cell infiltration) and parameters defining the effect of mosunetuzumab on T-cell activation and B-cell killing. Finally, the model simulations suggest intratumor expansion of pre-existing T-cells, rather than an influx of systemically expanded T-cells, underlies the antitumor activity of mosunetuzumab.

MsgeCNN: Multiscale geometric embedded convolutional neural network for ONFH segmentation and grading.

BACKGROUND: The incidence of osteonecrosis of the femoral head (ONFH) is increasing gradually, rapid and accurate grading of ONFH is critical. The existing Steinberg staging criteria grades ONFH according to the proportion of necrosis area to femoral head area. PURPOSE: In the clinical practice, the necrosis region and femoral head region are mainly estimated by the observation and experience of doctor. This paper proposes a two-stage segmentation and grading framework, which can be used to segment the femoral head and necrosis, as well as to diagnosis. METHODS: The core of the proposed two-stage framework is the multiscale geometric embedded convolutional neural network (MsgeCNN), which integrates geometric information into the training process and accurately segments the femoral head region. Then, the necrosis regions are segmented by the adaptive threshold method taking femoral head as the background. The area and proportion of the two are calculated to determine the grade. RESULTS: The accuracy of the proposed MsgeCNN for femoral head segmentation is 97.73%, sensitivity is 91.17%, specificity is 99.40%, dice score is 93.34%. And the segmentation performance is better than the existing five segmentation algorithms. The diagnostic accuracy of the overall framework is 90.80%. CONCLUSIONS: The proposed framework can accurately segment the femoral head region and the necrosis region. The area, proportion, and other pathological information of the framework output provide auxiliary strategies for subsequent clinical treatment.

Inheritance of perturbed methylation and metabolism caused by uterine malnutrition via oocytes.

BACKGROUND: Undernourishment in utero has deleterious effects on the metabolism of offspring, but the mechanism of the transgenerational transmission of metabolic disorders is not well known. In the present study, we found that undernourishment in utero resulted in metabolic disorders of female F1 and F2 in mouse model. RESULTS: Undernutrition in utero induced metabolic disorders of F1 females, which was transmitted to F2 females. The global methylation in oocytes of F1 exposed to undernutrition in utero was decreased compared with the control. KEGG analysis showed that genes with differential methylation regions (DMRs) in promoters were significantly enriched in metabolic pathways. The altered methylation of some DMRs in F1 oocytes located at the promoters of metabolic-related genes were partially observed in F2 tissues, and the expressions of these genes were also changed. Meanwhile, the abnormal DNA methylation of the validated DMRs in F1 oocytes was also observed in F2 oocytes. CONCLUSIONS: These results indicate that DNA methylation may mediate the transgenerational inheritance of metabolic disorders induced by undernourishment in utero via female germline.

Breast Surgical Oncology Epidemiologic Research: A Guide and Comparison of Four National Databases.

BACKGROUND: National databases are a rich source of epidemiologic data for breast surgical oncology research. However, these databases differ in the demographic, surgical, and oncologic variables provided. This study aimed to compare the strengths and limitations of four national databases in the context of breast surgical oncology research. METHODS: The study comprised a descriptive analysis of four national databases (the National Surgical Quality Improvement Program [NSQIP], the Nationwide Inpatient Sample [NIS], the Surveillance, Epidemiology and End Results [SEER] program, and the National Cancer Database [NCDB]) to assess their strengths and limitations in the context of breast surgical oncology. The study assessed the data available in each database for female patients with a breast cancer diagnosis between 2007 and 2017, and compared patient age, ethnicity, and race distributions. RESULTS: Data from 3.9 million female patients were examined, with most patients being between 60 and 69 years of age, non-Hispanic, and white. Age, ethnicity, and race distributions were similar in the databases. The NSQIP includes data on operative details, comorbidities, and postoperative outcomes. The NIS provides health services and inpatient utilization information, but does not evaluate outpatient procedures. The SEER program provides population-based oncologic detail including stage, histology, and neoadjuvant/adjuvant treatment. The NCDB offers hospital-based oncologic information and the largest population in the study period, with approximately 2.5 million breast cancer patients. CONCLUSION: Epidemiologic datasets offer tremendous potential for the examination of oncologic breast surgery, with each database providing unique data useful for addressing different epidemiologic questions. Understanding the strengths and limitations of each database creates a more efficient and productive research environment.

Epigallocatechin gallate improves the quality of diabetic oocytes.

BACKGROUND: Maternal diabetes compromises the quality and developmental potential of oocytes. Therefore, it is important to study how to ameliorate the adverse effects of diabetes on oocyte quality. Epigallocatechin gallate (EGCG) has a variety of physiological activities, including anti-inflammatory, antioxidant, and anti-diabetes. In the present study, we evaluated the effect of EGCG on the maturation of diabetic oocytes in vitro. OBJECTIVE: Investigating the role of EGCG in restoring the adverse effects of diabetes on oocyte quality. METHODS: Diabetes mouse model was established by a single injection of streptozotocin (STZ). Oocytes were collected and matured in vitro with/without EGCG in M16 medium. RESULTS: Compared with control, diabetic oocytes have a higher frequency of spindle defects and chromosome misalignment, but EGCG effectively reduces the incidence of oocytes with abnormal spindle assembly and chromosome mismatches. Moreover, the abnormal mitochondrial membrane potential (MMP) of diabetic oocytes is significantly alleviated by EGCG, and the reduced expression of genes regulating mitochondrial fusion (Mfn1 and Mfn2) and fission (Drp1) in diabetic oocytes is significantly increased while EGCG is added. EGCG also decreases the higher level of reactive oxygen species (ROS) in diabetic oocytes that may be regulated by the increased expression of superoxide dismutase 1 (Sod1) and superoxide dismutase 2 (Sod2). EGCG can also reduce the DNA damage of diabetic oocytes. CONCLUSIONS: Our results suggest that EGCG, at least partially, improve the quality of diabetic oocytes.

Performance Optimization and Fault-Tolerance of Highly Dynamic Systems Via Q-Learning With an Incrementally Attached Controller Gain System.

High-performance and reliable control of systems that are highly dynamic and open-loop unstable is challenging but of considerable practical interest. Thus, this article investigates the performance optimization and fault tolerance of highly dynamic systems. First, an incremental control structure is proposed, where a controller gain system is attached to the predesigned controller, and by reconfiguring the controller gain system, the performance can be equivalently optimized as configuring the predesigned one. The incremental attachment of the controller gain system does not modify the existing control system, and it can be easily attached via various communication channels. Second, a structure integrating fault-tolerance strategy and hardware redundancy is proposed. Under this structure, command fusion and fault-tolerance strategies are developed where the control commands from different control units are optimally fused, and each control unit can be reconfigured w.r.t. the performance of the other ones. Furthermore, Q -learning algorithms are developed to realize the proposed structures and strategies in real-time model-freely. As such, varying operational conditions of the highly dynamic system can be tackled. Finally, the proposed structures and algorithms are validated case by case to show their effectiveness.

Distributed Adaptive-Neural Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems Subject to Saturated Inputs.

In this article, the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems (MASs) (with directed graph communication) in the presence of unknown dynamics of agents, stochastic perturbations, external disturbances (mismatched and matched), and input saturation nonlinearities is addressed and studied. By combining the backstepping control method, the command filter technique, a finite-time auxiliary system, and artificial neural networks, innovative control inputs are designed and proposed such that outputs of follower agents converge to the output of the leader agent within a finite time. Radial-basis function neural networks (RBFNNs) are employed to approximate unknown dynamics, stochastic perturbations, and external disturbances. To overcome the complexity explosion problem of the conventional backstepping method, a novel finite-time command filter approach is proposed. Then, to deal with the destructive effects of input saturation nonlinearities, the finite-time auxiliary system is designed and developed. By mathematical analysis, it is proven that the mentioned MAS (injected by the proposed control inputs) is semiglobally finite-time stable in probability (SGFSP) and all consensus tracking errors converge to a small neighborhood of the zero during a finite time. Finally, a numerical simulation onto a group of four single-link robot manipulators is carried out to illustrate the effectiveness of the suggested control scheme.